Better yet… What if you can pop open the hood of ChatGPT, Claude, Perplexity, etc. and actually KNOW what they know about YOU?

That’s what Epistemic Me is positioned to solve for, and these are some of the questions we tackle in this week’s episode of ABCs for Building the Future.

Welcome to Episode 5 of The ABCs for Building The Future

In Episode 5, the Epistemic Me team dives deep into how we’re creating the foundational architecture for personalizing AI-driven experiences.

Whether you're an entrepreneur, developer, or tech enthusiast, this episode is packed with groundbreaking ideas about belief modeling, predictive processing, and the future of AI personalization.

We get pretty nerdy in this episode and talk a LOT about our data model, amongst other project updates that we are excited about.

Here’s What You’ll Learn:

* The Power of Belief Modeling: Deen Aariff introduces the concept of belief systems and why mapping a user’s beliefs is key to building more effective, human-centered AI. As he puts it, “Users have different selves that operate in various contexts. Modeling this gives us the ability to personalize recommendations in ways that were previously unimaginable.”

* From Philosophy to Code: Jonathan explains how the team bridged the gap between abstract epistemology (what we know and how we know it) and concrete technical implementation. The result? An SDK that allows developers to integrate belief systems into their apps seamlessly. (well, it will be more seamless as time goes on!)

“We started by defining five or six key terms that matter most. That clarity allowed us to design data models before writing a single line of code.”—Deen

* Real-World Application: AI Health Coaching: The team discusses how Epistemic Me could be used to build something like “Robert’s Health Coach,” an AI that scales personal interaction while staying true to a creator’s philosophy. Imagine an app that doesn’t just give you generic advice but evolves its recommendations based on your specific beliefs, preferences, and context.

* Quantifying Subjectivity: How do you measure something as fluid as belief systems? The team introduces their predictive processing framework, a way to analyze and update beliefs dynamically. This approach enables applications to mirror a user’s worldview, provide tailored insights, and challenge outdated perspectives.

Highlight Moments from the Podcast

* Jonathan on Predictive Processing: “The system generates a belief statement, deconstructs it into sub-questions, and validates it with evidence—allowing users to evolve their understanding step by step.”

* Deen on Personalization: “A belief isn’t static; it’s a living, evolving thing. Our framework ensures that apps can grow alongside their users.”

* Robert’s Reflection: “One user might be influenced by data and research, while another prefers storytelling. Personalizing how we present information is just as important as the information itself.”

Why It Matters

Personalization isn’t just a buzzword; it’s the future of human-AI interaction. By modeling belief systems, we can create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

“If we don’t understand ourselves, how can AI understand us?”

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you.

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

He used a landing page, a few cold emails, and a clear vision—and convinced Rover, a billion-dollar company, to work with him. No code. No team. Just hustle, story, and a deep understanding of what people really need.

In this episode of ABCs for Building the Future, host Robert Ta sits down with David Stepania, founder of ThirstySprout, one of the fastest-growing companies in the U.S., to unpack:

* How to build with nothing but belief and a landing page

* Why community and content are underrated superpowers

* The truths (and myths) about hiring, VCs, and scaling

* What every founder should really focus on

1. Why You Don’t Need a Team to Get Started

David launched ThirstySprout with no engineers, no product, and no funding. His first move? A WordPress landing page and some outbound emails.

“I literally had nothing. No product. Just a landing page and some good copy—and I landed Rover.” – David Stepania

That first “yes” changed everything. David built a team for Rover by tapping friends in Eastern Europe and learning everything as he went.

Takeaway: Start small, sell the vision, and build after you have proof someone cares. Don’t wait until it’s perfect—validate first.

2. Lessons From Failure: How Losing $100K Built a Better Business

Before ThirstySprout, David lost $100,000 outsourcing a software idea that never took off. But it wasn’t wasted.

“We didn’t know how to build. And that’s when I realized—what if I could create a team people trust?” – David Stepania

That failure became fuel. He turned his pain into a business model: building trustworthy engineering teams for startups that don’t have time to waste money like he did.

Takeaway: Failures are tuition. If you learn the right lesson, they can lead to your best ideas.

3. The Fastest Path to PMF Isn’t Always a Product

David shares the hard truth: too many founders waste time coding instead of testing ideas. He’s seen companies die trying to build before selling.

“If you do it right, an MVP shouldn’t take more than 30 to 90 days.” – David Stepania

Instead of code, he focused on solving a problem: great startups need great developers fast. He solved it manually first, then scaled it.

Takeaway: Customer conversations are your MVP. Build what people want, not what you think they want.

4. What Founders Get Wrong About Hiring and VCs

One of David’s boldest takes is about hiring and fundraising:

“You don’t need a FANG engineer to build your MVP. You need someone who gets stuff done.” – David Stepania

He also warns about blindly following VC advice—especially when investors push startups to hire from their own networks.

“It’s not about control. It’s about solving real problems. Startups forget that.” – David Stepania

Takeaway: Hire for grit, not prestige. And always follow the problem, not the politics.

5. Why Content is the Founder Superpower of This Era

David is prolific on LinkedIn, with posts reaching millions—and it wasn’t luck. It was craft.

“English isn’t even my first language. But I study what makes people care—and speak from the heart.” – David Stepania

He treats LinkedIn like a product: experimenting with angles, writing with honesty, and using feedback to refine.

His tip for early founders? Start building your voice now. It's the cheapest way to win trust and lower CAC.

Takeaway: Your personal brand is your distribution engine. Use it to connect, not just promote.

Check Out David’s Links

Connect with him on LinkedIn

Build your A-team with ThirstySprout

Check out his AI Newsletter and community, Homebase

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

That’s the provocative question guiding Episode 23 of ABCs for Building the Future. In a sweeping conversation that blends cognitive science, product design, and philosophical rigor, hosts Robert and Jonathan introduce a bold new framing for personalizing AI: Epistemic Evals.

This blog distills the episode’s most compelling insights for founders, developers, and health-tech innovators who want to move beyond token-level tuning and toward true understanding—at scale.

🎙️ Context: Building AI That Understands People—Not Just Prompts

This week’s episode is a build-in-public deep dive into Robert and Jonathan’s latest developments on their open-source SDK, Epistemic Me. Their mission? Make belief systems a first-class citizen in AI alignment and personalization.

Core themes include:

* The emerging category of “epistemic evals”

* The three layers of memory in personalized AI agents

* How beliefs determine alignment and behavior change

* A live product demo of their self-modeling evaluation system

* Mapping philosophical complexity to practical AI architecture

🧭 1. Why AI Needs to Start With Belief Systems

“We don’t think you can solve hyper-personalization or AI alignment without modeling a user’s belief system.” — Robert

The team starts with a powerful claim: personalization begins at the belief level. If an AI doesn’t understand what you believe—about health, money, relationships, or yourself—it cannot make meaningful recommendations. And without alignment on beliefs, there can be no trustworthy AI.

Robert and Jonathan argue that the most effective AI agents will be belief-adaptive, not just data-reactive. This goes beyond tone and formatting preferences; it’s about modeling how a user sees the world and shaping responses accordingly.

🔍 Application: AI agents in health, education, or coaching domains should model user belief systems over time—not just answer questions on demand.

🧠 2. Introducing Epistemic Evals: The Top Layer of Alignment

“Epistemic evals happen at the belief and self-model level. They’re how we measure if the AI actually understands the user.” — Jonathan

Traditional application-level evaluations (e.g., “Did the agent return the right recipe?”) aren’t enough for deeply personal domains. Enter epistemic evals—a new category for evaluating how well an AI models a user’s worldview, belief system, and internal logic.

Inspired by user-centric evaluation papers and grounded in neuroscience (e.g., Friston’s free energy principle), epistemic evals look at:

* The agent’s representation of a user’s belief states

* How beliefs affect perceived recommendation efficacy

* Whether the agent's suggestions align with the user's internal model of causality

📊 Application: Use epistemic evals to unlock truly personalized agents for longevity, mental health, or financial coaching.

🧱 3. Building With Memory: Working, Episodic, Semantic

“You can’t personalize without memory—and not just one kind of memory.” — Jonathan

In a standout section, Jonathan lays out their AI architecture, adapted from human cognition:

* Working Memory: Recent chat turns, current session inputs.

* Episodic Memory: Personal user events and past experience (e.g., “last time I felt like this”).

* Semantic Memory: General knowledge + structured belief systems (e.g., “I believe fasting boosts clarity”).

By treating belief systems as dynamic, timestamped objects within this memory stack, they’re able to surface beliefs that are relevant to the current user query. The result: responses that feel less canned, more attuned.

🧠 Application: Personalization doesn’t scale unless you have a structured memory framework. Start there.

🎯 4. From Evaluation to Recommendation: A New Loop for Personalization

“It all comes down to recommendations. And those have to match the user’s causal worldview.” — Jonathan

Using a live demo of their self-management agent, the team shows how epistemic evals map directly to behavior change. They’ve built a layered feedback loop:

* Input (user query)

* Context (beliefs + past states)

* Output (response + recommendation)

* Evaluation (did it fit the user’s belief model?)

This evaluation loop isn’t just for accuracy—it’s for empathy. By understanding what users value and how they think change happens, agents can recommend plans users are more likely to follow.

⚙️ Application: Whether you're building a coaching bot or customer success agent, measure success by belief-congruent recommendations, not just click-through rates.

🔗 Resources & Further Reading

* User-Centric Evals Paper

* Friston’s Free Energy Principle (Wikipedia)

* Inside Out (Pixar) as an agentic metaphor

* LLM AI Evaluation Course

* Open Source SDK: Epistemic Me

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

Hamel shares his evolution from pioneering code understanding models at GitHub to breaking free from corporate America — and makes a compelling case that we’re measuring AI products all wrong. We don’t need more automated eval vendors or hallucination scores. We need teams who know how to look at their data, count their failures, and iterate like investigative journalists.

If you’re a founder, engineer, or product leader drowning in eval tooling demos and wondering why your AI product still feels broken — this conversation is a masterclass in cutting through the hype, escaping the matrix of engineering elitism, and building products that actually work.

1. Error Analysis Before Automation: The 30-Minute Practice That Beats Every Vendor Tool

“The first step in doing eval is first understand what is wrong with your system. So doing some data analysis of your system to figure out what is broken.”

Hamel’s career-defining insight didn’t come from a research paper or a vendor pitch. It came from watching client after client get stuck in the same place: building AI products that “work” but don’t work well.

The pattern was identical every time. Teams would glue together RAG pipelines, add tool calling, get excited about the demo—then hit a wall. “How do we make it actually good?” they’d ask. And Hamel would ask back: “How are you measuring what’s improving?”

The breakthrough: Most teams jump straight to evals (or worse, eval vendors) without understanding their actual failure modes. They’re solving the wrong problem.

What actually works:

* Pull 100 traces from your production system

* Take notes on what breaks (be specific: “interrupts user mid-thought” not “bad UX”)

* Categorize your notes into failure types

* Count them

That’s it. No LLM judges, no automated hallucination scores, no vendor dashboards.

Reflection: Hamel shared a story about auditing a recruiting email platform. The AI-generated messages were generic LinkedIn spam: “Based on your background at Epistemic Me...” When he pointed out he’d immediately delete these, the team said “everything works.” But they weren’t actually trying to recruit anyone. They weren’t measuring conversion. They had convinced themselves the product worked because they hadn’t looked at the data.

If you’ve ever felt like your AI product is “almost there” but can’t articulate why it’s not—this 30-minute practice will tell you more than three months of tooling evaluation.

2. The Dogfooding Delusion: Why Most Teams Are Just Testing (Not Using)

“Are you dog fooding on that level? Where you are the expert. You are using it in anger all the time... Its output affects your livelihood.”

When Anthropic revealed that Claude Code’s success came partly from intensive internal use, Twitter exploded: “See, you don’t need evals!” But Hamel caught what everyone missed—a crucial distinction between real dogfooding and performance theater.

Real dogfooding (Anthropic engineers with Claude Code):

* They ship production code using it daily

* When it breaks, their work stops

* The output affects their livelihood

* They’re both the domain expert AND the user

Fake dogfooding (most teams):

* “Using” the product occasionally to “test it”

* Not actually trying to achieve the goal

* Wouldn’t pay for it themselves

* Building an AI health coach but not trying to lose weight with it

The gap is everything. In fake dogfooding, you convince yourself the product works because you’re not feeling the pain of it failing.

Reflection: Hamel’s litmus test is disarmingly simple: Would you pay for this with your own money? Does using it save or cost you real time? If not, you’re not dogfooding—you’re testing. And that’s a fundamentally different feedback loop that lets broken products feel functional far longer than they should.

The dangerous corollary: Everyone thinks they’re dogfooding. The teams building recruiting tools, fitness coaches, coding assistants—they all believe they’re using their products “in anger.” But ask them about conversion rates, about whether they’d recommend it to a friend, about whether it’s actually solving their problem—and the story changes.

3. Data Science as Investigative Journalism: The Mindset That Changes Everything

“Good data science in big businesses looks like investigative journalism. You’re figuring out, teasing out the story of what exactly happened here and what can I learn.”

This reframe completely shifted how I think about AI product work. It’s not about running queries or computing metrics—it’s about investigation.

The parallel:

Investigative journalist:

* Conducts interviews (qualitative insight)

* Analyzes public records and data (quantitative patterns)

* Connects dots others miss

* Synthesizes into a narrative that drives action

Effective AI product developer:

* Examines traces and user conversations (qualitative)

* Measures failure patterns and metrics (quantitative)

* Identifies root causes, not symptoms

* Communicates what’s broken and why in ways that mobilize teams

Why this matters: Most engineers are trained to dismiss storytelling as “soft skills” or “non-technical fluff.” But when you’re working with stochastic systems that can’t be reduced to deterministic tests, your ability to investigate, synthesize, and communicate becomes the core competency.

You can’t fix what you can’t explain. And you can’t explain what you haven’t investigated.

Reflection: Hamel describes how at companies experiencing margin leakage or churn spikes, the best data scientists operate exactly like reporters: they gather evidence from multiple sources (qualitative interviews, quantitative metrics), look for patterns, rule out false leads, and ultimately tell a coherent story about what happened and why it matters.

The person who can explain why your AI health coach sounds paternalistic is worth infinitely more than someone who can compute its “tone score.”

If you’re hiring for AI product roles, look for people who can tell stories with data—not just run statistical analyses.

4. Silicon Valley’s Status Games: Why Engineers Defend the Matrix (And How to Escape)

“Engineers will defend the matrix. They will defend the matrix to the death... You kind of have to decide, like you have to see that. If you want to get outside the matrix.”

This might be the most uncomfortable truth Hamel shared and the most liberating.

The Silicon Valley hierarchy (unspoken but real):

* VC-backed unicorn founder (peak status)

* Bootstrapped product founder

* Senior engineer at FAANG

* Course creator / educator (low status, sometimes openly mocked as “course bro”)

The absurd reality: Hamel’s first course on fine-tuning LLMs generated 5x his consulting income while he slept. More critically, it gave him leverage and freedom—the very things most engineers claim to want but are trained to dismiss.

“I would go to sleep and then wake up in the morning and be like, all these sales while I was sleeping. That totally rewired my brain. I can never go back to a job.”

Why this matters: Engineers are systematically taught to mock anything “non-technical”—marketing, communication, teaching, writing. If you’re good at explaining concepts or building community, you’re dismissed as “not a real engineer” or “the marketing guy.”

But these are exactly the skills that enable you to escape trading time for money.

The trap Hamel identifies: At companies, if you’re a strong developer who’s also good at writing, communication, or devrel—you’ll often get lower pay, hit career ceilings faster, and face subtle elitism from peers. The message is clear: spike on coding only, or you’re not serious.

But if you want to run your own business? Those “soft skills” are suddenly the highest leverage activities you can do.

Reflection: Hamel traces this back to his consulting days at Accenture, where consultants would laugh at their clients over dinner: “Can you believe they don’t know what they’re doing?” It’s a different matrix—the consulting matrix—where you’re convinced you could never work at “those companies.”

He only broke free after going to law school (which he hated), resetting his brain, and realizing: there are multiple matrices. And most engineers defend theirs without even seeing it.

The litmus test: If you’re good at writing, teaching, or explaining—that’s not weakness. The question isn’t whether it has “social status” in your team’s Slack. The question is: does it give you freedom?

5. When Evals Actually Matter (And When They’re Just Theater)

“You shouldn’t do evals unless you’re getting some value out of it. If you do an eval you get some immediate value out of it. Otherwise you shouldn’t do it.”

After all the discussion about what evals aren’t, Hamel is crystal clear about when they become genuinely valuable—and when they’re just performance.

Use evals when:

* You’ve identified a specific, recurring failure through error analysis

* The fix isn’t obvious (you’ll need to iterate)

* You have enough examples to validate your measurement approach

* The eval provides a signal you trust to guide rapid iteration

Skip evals when:

* You found a simple bug you can just fix (wrong tool in API call, syntax error)

* You’re trying to automate away understanding your product

* You’re chasing generic metrics without product context (hallucination scores, toxicity)

* You haven’t looked at your actual data yet

The spectrum of eval cost:

Code-based evals (cheap):

* Does the output contain a user ID?

* Are code blocks properly formatted?

* Uses regex or simple assertions

* Runs like a unit test

LLM-as-judge evals (expensive):

* Is the tone appropriate?

* Is advice personalized enough?

* Requires making an LLM call

* Requires evaluating the evaluator (meta-eval)

The critical nuance: If you’re going to use an LLM to judge something, you have to know if it’s judging correctly. This means labeling examples, checking agreement rates, iterating on the judge prompt. Even your evals need evals.

Reflection: The discourse around evals has become tribal: “Evals are everything!” vs “Evals are dead!” Both are wrong. Hamel’s position is pragmatic: evals are a tool. Use them when they accelerate product improvement. Skip them when they don’t.

The real enemy isn’t evals or no-evals—it’s “eval theater.” Teams running automated vendor tools to generate scores no one understands, trusts, or acts on. Dashboards that look impressive in investor decks but don’t change what gets shipped.

What matters: Are you getting better, faster? That’s the only eval that counts.

🎧 Resources and Further Listening

Connect with Hamel:

* Follow Hamel on LinkedIn

* Follow Hamel on X/Twitter

* Hamel on YouTube

* Hamel’s Substack

Tools & Platforms Mentioned:

* Get 35% off Hamel’s LLM Evals Course – Next cohort starts October 6th.

* Code Search Net – Hamel’s pioneering work on code understanding at GitHub, creating a benchmark used by early coding models like the original Codex

* Delphi AI – The platform Hamel uses to create AI assistants (course students get exclusive access to one trained on all evals materials)

Referenced Ideas:

* Peter Thiel’s Competition Philosophy – “Competition is for losers” - discussed in the context of avoiding crowded markets like evals tooling

This is a big Build In Public update + talk on Consciousness.

Key Takeaways:

* Epistemic Me’s positioning—“cohorts by belief, not by click”—resonated deeply with product leaders. It reframes personalization by focusing on the why behind user actions, not just the what.

* Robert and Jonathan used Sidebar and Vitalist Bay as live learning labs to refine positioning, messaging, and collect real-time feedback. Conferences weren’t just networking—they were epistemic wind tunnels.

* The idea of belief systems as UX primitives bridges deep philosophy with product design. Epistemic Me offers a toolkit for self-modeling, which can be applied to health, AI coaching, product growth, and more.

* Robert shares the powerful origin story of wanting to model his brother’s belief system to help treat his mental illness. This human angle grounds the tech in empathy and urgency.

* If AI is to understand us, we must first understand ourselves. Epistemic Me sees belief mapping as foundational to building aligned, ethical AI—and it’s inviting a community of builders to co-create that future.

Resources and Links

epistemicme.ai

https://patrickhouse.com/

https://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenstern_utility_theorem

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

Every founder right now is asking the same question:

“How do I build a hyper-personalized AI product… that actually works?”

Not just a chatbot with nice vibes.

But an AI that understands your user’s beliefs, learns from their choices, and adapts like a real coach would.

An AI that gets your user to their target outcomes, step by step through every interaction.

In this week’s ABCs for Building the Future episode, we give you raw build in public updates on what we’ve learned and what we’ve done.

No script. No prep. Just real-time product review and philosophy in motion.

What we built:

The SDK Playground — a tool for developers building AI coaches that personalize in real-time.What we uncovered:

To create AI that actually resonates, you need to model the user’s beliefs and track how they evolve. Period.

“If we don’t understand ourselves, how can AI understand us?” — Robert Ta

Jonathan walks us through the new belief evaluation loop using synthetic conversations, cohort filters, and deep eval integrations — all while answering the key design question:

How do we teach an AI coach to learn what matters about a user?

Have fun (:

Key Takeaways:

* A belief system is the new user model. Track beliefs, not just behavior.

* AI that recommends the right thing at the right time must understand the user’s reasoning.

* Epistemic emotions like confusion, belief confirmation, and curiosity are the real alignment metrics.

* Developers need tools that help them build coaches, not just bots.

Resources and Links

epistemicme.ai

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

What do you learn about yourself when you're bedridden, staring at a white ceiling, unable to hold up your own head?

For Rahul Chaudhari, that moment became the inflection point for a lifetime of introspection, innovation, and impact.

This episode of ABCs for Building the Future dives deep into the human behind the product—his painful experiences, his transformation, and his tenacious vision for building tech that matters.

The Episode at a Glance

* Host: Robert Ta

* Guest: Rahul Chaudhari, former Amazon product leader, now Head of Marketing & E-commerce Tech at Kohl’s

* Theme: Navigating trauma, identity, and curiosity to lead with purpose in tech

This podcast unpacks the most potent moments from the conversation—and why they matter to entrepreneurs, executives, and builders everywhere.

1. Trauma as a Catalyst: The Accident That Changed Everything

Early in university, Rahul was hit head-on in a bicycle accident. His neck fractured. His body immobilized. His dreams on hold.

“I couldn’t even hold up my own head. Six months of staring at the ceiling gave me a new lens on life.”

This wasn’t just a physical recovery—it was a mental rewiring. He began to confront the uncomfortable truth: he was blindly following the herd, not choosing his path. That insight became his first lesson in intentional living—a mindset that would later shape every product and team he led.

Takeaway for Listeners:Your defining moments may feel like devastation in the short-term—but they often create the depth that later defines your leadership.

2. Breaking Free from the Herd Mentality

Before the accident, Rahul was on the well-trodden path: a pharmaceutical undergrad planning to pursue a PhD abroad—because that’s what everyone around him was doing.

“I realized I was chasing something I didn’t choose. That accident gave me the pause to ask, what do I want?”

This awakening propelled him toward product and business—where curiosity, not conformity, would drive his career. In a world obsessed with optimization, his story is a bold reminder: sometimes, the real innovation is in questioning your default programming.

Takeaway for Listeners:Entrepreneurs and execs: Your biggest breakthroughs might come from zooming out, not doubling down.

3. Curiosity as Craft: Building Across Domains

Rahul’s curiosity took him from retail banking to telecom, from CPG to e-commerce giants like Amazon. What connected all those roles? A relentless pursuit of meaningful problems to solve.

“I found my true home in product—because it lets you play at the intersection of people, systems, and possibilities.”

From launching products at Amazon to transforming martech at Kohl’s, Rahul has mastered the art of navigating complexity without losing humanity.

Takeaway for Listeners:Versatility isn’t a lack of focus—it’s a superpower, especially when it’s fueled by mission and curiosity.

4. Philosophy, Not Just Frameworks

In today’s AI-driven world, Rahul reminds us: the best products aren’t just built—they’re believed in. This episode isn’t just a biography; it’s a reflection on the role of belief in innovation, tying beautifully into the broader goals of the Epistemic Me movement.

“It’s not just about scaling products—it’s about scaling insight, integrity, and intentionality.”

Takeaway for Listeners:Great leaders don’t just ship features. They shape futures—through the stories they tell, the systems they build, and the beliefs they challenge.

Quotes to Remember

“Staring at the ceiling gave me the space to realize I wasn’t living my life—I was living someone else’s.”

“I used to chase what others wanted. Now I chase what matters.”

“Curiosity is the compass. Product is the playground.”

“We talk about innovation—but the real innovation is in how we choose to live.”

“I don't just build tech. I build tools that change how people experience the world.”

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

What if the key to high-impact leadership in the AI age isn’t in your credentials — but in your ability to listen to your body, trust your joy, and design your own definition of success?

🎧 Episode Context:

In this episode of ABCs for Building the Future, host Robert Ta sits down with Dr. Serena Huang — former Data Scientist turned founder, global speaker, and passionate advocate for inclusive, data-driven well-being at work.

Serena’s journey, from a 13-year-old immigrant to a corporate leader and entrepreneur, is a masterclass in growth, self-awareness, and courageous change.

If you’re an entrepreneur, tech leader, or visionary navigating reinvention in the age of AI, this episode is packed with tools, reflections, and frameworks to help you thrive.

🔍 Key Themes & Insights:

1. Leaving the Ladder: Why Joy Beats Job Titles

After climbing the corporate ranks, Serena found herself spending her days “stakeholder-managing every single minute” — far from the data work and public speaking she loved. A reflective moment in a 1:1 meeting with the CHRO marked her unexpected pivot.

“I wondered… could I make a living doing just things that bring me joy?” – Dr. Serena Huang

2. Disciplined Freedom: Rebuilding Structure as a Founder

Serena traded corporate discipline for entrepreneurial chaos — and then returned to structure on her own terms.

“I needed that initial freedom… but at the core, I’m a structured person. I block time now for workouts, reflection, and uninterrupted lunches.”

Freedom isn’t the absence of structure. It’s the ability to create your own — based on what energizes and grounds you.

3. The Myth of Overnight Success

Serena’s post-corporate career took off fast — 30 cities, keynotes, global clients — but it wasn’t luck. It was momentum built over years of unpaid speaking, content creation, and behind-the-scenes resilience.

“There’s no such thing as an overnight success. People didn’t see the 10 years of work before I made my first sale.”

What slow, invisible work are you investing in today that will compound later?

4. AI, Inclusion, and Wellbeing: The Missing Link

Through her research and new book, Serena explores how AI and data can be used to measure — and improve — inclusion and wellbeing at work. Spoiler: they’re deeply connected.

“You can’t have wellbeing without inclusion.”

Using AI to analyze meeting invites, calendar patterns, and Slack sentiment can reveal exclusion risks and burnout signals long before surveys do.

🔗 Explore the intersection of DEI, wellbeing, and AI

5. Rewriting Worth: From Achievement to Authenticity

Serena shares how years of therapy helped her separate identity from output — and choose self-compassion over self-optimization.

“You are worthy simply as you are. You don’t need to achieve a thing.”

Success is not a substitute for self-worth. Build your life around your values — not just your goals.

🔗 Resources and Links:

* Serena’s company: Data with Serena

* Serena’s book: The Inclusion Equation

* Pie of Life framework

* Sidebar Summit: sidebar.com

In this episode, the founders of Epistemic Me discuss the inception and development of their project—a tool designed to deeply understand users' beliefs and optimize personalized recommendations. They delve into philosophical concepts like the self and interconnectedness, the role of an open-source approach, and their vision for the future. Key highlights include the project's potential applications in health, mental well-being, and science, and the importance of epistemology in evolving AI systems. The conversation also touches on the next steps for launching the open-source GitHub repository and community involvement.

00:00 Are We Living in a Simulation?

01:20 Introducing the Podcast and Hosts

01:59 What is Epistemic Me?

04:51 The Origin Story of Epistemic Me

01:09 Explaining Epistemic Me to a 5-Year-Old

21:22 Personal Journeys and Philosophical Insights

40:43 Purpose and Identity

46:36 The Process of Epistemology

47:12 Operating System Analogy

47:29 Maslow's Hierarchy and Self-Discovery

48:13 Future of Epistemic Me: 5 to 10 Years

49:01 Personal Recommendation Engines

50:32 Data-Driven Science

52:36 Success in 5 to 10 Years

57:38 Open Source and Ethical Considerations

01:12:59 Long-Term Vision: 500 Years Ahead

01:26:33 Community and Contributor Call

01:33:28 Closing Thoughts and Future Plans

Is there a clear boundary between you and the rest of the world?

Is it your skin? Your thoughts? The limits of your awareness?

In our latest episode of Epistemic Me, we unravel one of the biggest philosophical and scientific questions of our time: Where does consciousness end, and where does the external world begin? And more importantly — what if the thing you call “you” is just a beautifully useful lie?

This is a big Build In Public update + talk on Consciousness.

Key Takeaways:

* Consciousness Has No Clear Boundary: The line between you and not-you — between mind and world — is more blurred than we think. Consciousness may extend beyond the brain and body.

* Your Brain Lies — By Design: From the moment you opened your eyes, your brain has been constructing a version of reality optimized for survival, not truth. This is not malfunction — it's evolution.

* You Are Not One — You Are Many: Your body hosts trillions of microorganisms that influence your behavior, emotions, and decisions. “You” might be a collective, not an individual.

* AI Can’t Understand Us Until We Understand Ourselves: If we’re building machines to align with human values and thought — but we don’t understand our own beliefs or how they’re formed — we risk programming misunderstanding at scale.

Resources and Links

epistemicme.ai

https://patrickhouse.com/

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

That’s the core of what we explore in this week’s podcast.

WE ARE LIVE: https://github.com/Epistemic-Me

My teammates, Deen and Jonathan, have been instrumental in getting our GitHub ready for open source contribution. You can read everything in documentation in the GitHub and here in our intro docs. Thanks to them we’re LIVE! Woohoo.

As I reflect on this milestone, one of the biggest reasons we’re here is decision-making throughput. Because there were many decisions that we had to make to get to this point. Read the accompanying newsletter to find out more about how the Epistemic Me team thinks through optimizing decision-making throughput.

Welcome to Episode 2 of the ABCs for Building The Future—a podcast about the big ideas shaping our world, brought to you by the team behind Epistemic Me.

In this episode, we go deep into what it means to understand ourselves, evolve our beliefs, and why all of this matters for building better AI.

Here’s What You’ll Hear

1. Why Suffering Is Different From PainDeen shared a perspective that stuck with me: suffering isn’t just about the pain itself—it’s about how we react to it. “Attachment is the root of suffering,” he said, and that hit home. How much of our suffering comes from holding too tightly to what we think is true?

2. Beliefs Are the Core of Reality (and AI)We dug into what it means to model beliefs—not just for ourselves, but for technology. If beliefs shape how we see the world, and there are 8 billion versions of reality walking around, how do we build AI that understands us without losing the nuance? Jonathan broke this down with some fascinating examples from philosophy and AI development.

3. A Tool to See Yourself ClearlyWhat if you had a mirror for your mind—a way to map your beliefs, challenge them, and grow? That’s what we’re building with Epistemic Me. We want to make it easier for people to reflect, adapt, and thrive by understanding the core of who they are. And we believe this isn’t just a personal growth tool; it’s foundational to making AI that aligns with human values.

Highlight Moments

* Jonathan on AI Evolution: “I was wrong about how fast language models could deliver utility, but I still believe the next leap will come from understanding beliefs at a deeper, sensory level.”

* Deen on Attachment and Freedom: “Attachment creates suffering because we cling to abstractions. Letting go of those opens up clarity and connection.”

* Robert’s Why: I shared a personal story about my brother’s mental health struggles. “If we could map beliefs—even paranoid delusions for those with schizophrenia—and give that data to a doctor, it could be life-changing. That’s one of the dreams I have for this project.”

Thank You

Liked this article?

💚 Click the like button.

Feedback or addition?

💬 Add a comment.

Know someone that would find this helpful?

🔁 Share this post.

FAQ: What You’re Probably Wondering

Q: What’s the big deal about beliefs?A: Everything starts with beliefs. They shape how we see the world, how we make decisions, and how we connect with others. By modeling beliefs, we’re giving people (and technology) a better way to understand and navigate reality.

Q: Who is this podcast for?A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: What’s Epistemic Me, exactly?A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned, and allow hyper-personalization for application use cases requiring understanding your users better (main first use cases are around health). Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?A: Glad you asked! We’re gearing up to open-source the project, and we need contributors, testers, and advocates. If you’re interested, sign up here.

Q: Who can join?

A: Developers, philosophers, and anyone passionate about the underpinnings of human decision-making.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

Valuable supply of something is created for demand of value for solving a problem: people need cleanliness, people sell soap.

In a typical fashion, people create businesses to capitalize on this value.

They do some form of “need-finding”—making sense of the world through the lens of some population of people, their Ideal Customer Profile (ICP).

And those ICPs, do the same thing.

They watch commercials, they see products and services, they read reviews, they try things out to solve their problems.

This is match making of supply and demand. That is a frame in which we view the hyper-personalization problem: how can we eliminate friction to that “need-finding”?

Could reshaping how we model human beliefs revolutionize our approach to AI and longevity?

Could we help people get healthier faster, hyper-personalized to them? (supply meets demand)

The answers to the above depend on accurately modeling belief systems.

Because it is our beliefs that shape our perceptions, that are a function of that hyper-personalization problem.

In this episode of "ABCs for Building The Future," join hosts Robert, Jonathan, and Deen as they explore the intricate dance between human belief systems and artificial intelligence.

Fresh from the "Don't Die" summit hosted by Bryan Johnson, they dive into transformative insights at the intersection of philosophy and cutting-edge technology.

Here’s What You Can Expect..

The Power of Philosophical Alignment: The episode explores how philosophical beliefs can serve as a foundation for AI systems. During a pivotal interaction with Bryan Johnson, known for his "Don't Die" philosophy focused on longevity, the team highlights the alignment of his views with the mission of Epistemic Me. "We're modeling human belief systems in a way that mirrors philosophical insights, aiming to create AI that truly understands human values," as Robert shares.

Hyper-Personalization in AI: Jonathan, Deen, and Robert discuss the potential of hyper-personalization, emphasizing its importance in creating AI that not only meets but anticipates user needs. Hyper-personalization is not just about utility but about deeply understanding belief systems to provide nuanced interactions across various industries, from health to finance.

Building a Collaborative Future: A key theme throughout the podcast is the vision of making Epistemic Me a global open-source project that empowers users and developers alike. The team envisions an ecosystem where AI development is transparent and driven by diverse, collaborative efforts, much needed in today's ever-evolving tech landscape.

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you.

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

This week’s podcast concludes our intro 3 part series on “Meet The Founders”, and we dive into how predictive processing, epistemology, and the quest for shared understanding are not only philosophical but foundational to creating aligned AI systems.

UPDATE:

We’re thrilled to share that Epistemic Me is now live on GitHub! Thanks to the incredible work of Deen and Jonathan, our documentation is up and ready for open-source contributions. 🎉 Explore our resources and join the movement to build belief-driven AI systems.

Check it out here: Epistemic Me GitHub

Here’s What You’ll Hear

Predictive Processing: Generating Reality in Real-Time

Jonathan shared a paradigm-shifting concept:

"There’s no common world model in predictive processing. Each of us generates our reality based on internal beliefs, creating the world onto the self in real-time."

This concept lays the foundation for designing AI systems that better understand human behavior by mimicking this generative process.

* Predictive processing enables AI to adapt to the nuances of individual beliefs.

* It opens possibilities for personalizing AI tools for health, communication, and decision-making.

Beliefs: The Framework of Reality

Deen posed a critical question:

"How do we form new beliefs, know they’re true, and use them to create models of self?"

By treating beliefs as the architecture of self-knowledge, we unlock the potential to bridge gaps in understanding between individuals, organizations, and even AI.

Imagine organizations using belief models to enhance collaboration, reduce friction, and solve problems with shared understanding.

Why AI Needs to Model Belief Systems

Robert emphasized:"The problem of human problems is miscommunication and lack of shared understanding."

By modeling beliefs, AI can bridge these gaps, creating scalable ways to foster empathy and solve global challenges.

Epistemic Me’s SDK allows developers to design tools that map belief systems, enabling breakthroughs in mental health, team dynamics, and societal cooperation.

Thank You

Liked this article?

💚 Click the like button.

Feedback or addition?

💬 Add a comment.

Know someone that would find this helpful?

🔁 Share this post.

FAQ: What You’re Probably Wondering

FAQs

Q: What is predictive processing?A: framework suggesting that our brains actively generate reality by predicting sensory inputs, updating beliefs as new information arises.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: Who is this podcast for?A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How is this different from other AI tools?A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?A: Glad you asked! We’re gearing up to open-source the project, and we need contributors, testers, and advocates. If you’re interested, sign up here.

Q: Who can join?

A: Developers, philosophers, and anyone passionate about the underpinnings of human decision-making.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this mind-expanding episode of The ABCs for Building the Future, Robert Ta and Jonathan McCoy explore how belief systems shape behavior, health, and even consciousness itself.

Fresh from user testing at the Don't Die Summit in Miami, the team shares real-world feedback on product in the wild, discusses belief modeling as the foundation for AI coaching, and takes us on a speculative journey into how quantum computing might make robots truly come alive.

Belief Systems Are the Interface for Growth

Robert shares insights from the field after onboarding users into the Don't Die app. The hypothesis was simple but powerful: starting with someone's beliefs, rather than just behaviors or data, enables a smarter AI to recommend the next best action toward self-actualization.

“Between knowing and doing is a lot of uncertainty. We believe starting with beliefs closes that gap.”

By modeling the user's self-concept—how they see themselves now and who they want to become—AI can deliver far more personalized coaching. This isn’t about generic advice. It’s about real-time, belief-aligned feedback.

Consciousness and the Case for Quantum AI

In the episode’s second act, the team dives deep into the history of consciousness—from Plato to phenomenology—and introduces theories where consciousness might be fundamental to reality, not emergent.

“Maybe consciousness didn’t emerge from intelligence—maybe it created it.”

They explore Suzanne Gildert’s work on quantum computing and humanoid robotics, the free energy principle from neuroscience, and whether machines might ever feel gut instinct or emotion. This conversation bridges product theory, AI ethics, and metaphysics.

Resources and Links

suzannegildert.com

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this episode of ABCs for Building the Future, hosts Robert Ta, and Jonathan McCoy, talk about how intelligence has changed over time, from fire to AI. They ask big questions like:

* Who is really in charge—humans or machines?

* How can we make sure AI makes good choices?

* What happens when computers start thinking for themselves?

If you are curious about the future and how AI might change everything, this episode is for you.

Big Ideas from This Episode

1. The Evolution of Intelligence: From Fire to AI

Long ago, humans figured out how to make fire by hitting rocks together. Now, we put intelligence inside rocks (which is what computers and chips are made of).

"Apes made fire from rock. Now we put intelligence into rocks. Now what?" – Robert

We have always used tools to help us do things. But now, those tools are starting to think for themselves. The big question is: Are we guiding AI, or is AI starting to change us?

2. How Technology Changes Over Time

There is a way to think about the future called the Three Horizons Framework:

* Horizon 1 – The way things work today

* Horizon 2 – The messy middle, where old and new ideas mix

* Horizon 3 – The future, where new ideas take over

"Horizon 1 is the present, Horizon 3 is the future, and Horizon 2 is the chaotic transition. The question is—what role does humanity play in Horizon 3?" – Jonathan

The problem is, we don’t know if AI will help us or replace us. Right now, we are in Horizon 2, where AI is changing fast.

3. AI and Human Decisions: Who Should Be in Charge?

Some people think humans should stay in control of big decisions, like health and medicine. Others think AI should decide for us because it can process more information.

There are two ways to think about this:

* Aubrey de Grey believes humans should make decisions and use science to live longer.

* Bryan Johnson believes AI should tell us what to do to keep us healthy.

"Aubrey is betting on biology. Bryan is betting on AI. The question is—how much agency should we give AI over human health?" – Robert

Should we trust AI to make life-and-death decisions? Or should humans always have the final say?

4. Are We Losing Control of Our Choices?

Humans make choices every day, but AI is starting to make more choices for us. Think about how we used to memorize driving directions, but now we just follow Google Maps.

"Remember when we had to memorize routes before GPS? Now we just follow Google Maps. That’s a small loss of agency—but multiply that across every decision, and what do we become?" – Jonathan

Now, AI is choosing:

* What we watch

* What news we read

* What products we buy

If we let AI decide everything, what happens to our own thinking?

5. How Do We Know AI Is Safe?

AI is getting smarter, but how do we know if it’s giving good answers? AI companies are working on evaluations—ways to test AI before it makes big decisions.

"We need to evaluate AI the way we evaluate software—before we deploy it into the world. Otherwise, we're just unleashing black boxes and hoping for the best." – Jonathan

The goal is to make sure AI is reliable and safe, especially for health and medicine.

6. Measuring Intelligence: The Consciousness Complexity Index

Jonathan introduces an idea called the Consciousness Complexity Index (CCI). This idea tries to measure intelligence across different species and AI.

"What if we could quantify consciousness? If we had a Complexity Index for intelligence, could we track how AI and humans evolve together?" – Jonathan

If we understand intelligence better, maybe we can make AI that works with us instead of replacing us.

Links and Resources

3 Horizons Framework: https://en.wikipedia.org/wiki/Three_Horizons

Theory of Change: https://en.wikipedia.org/wiki/Theory_of_Change

Free Energy Principle: https://en.wikipedia.org/wiki/Free_energy_principle

Three Body Problem: https://en.wikipedia.org/wiki/Euler%27s_three-body_problem

Michael Levin: https://en.wikipedia.org/wiki/Michael_Levin_(biologist)

Karl Friston: https://en.wikipedia.org/wiki/Karl_J._Friston

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this episode of ABCs for Building the Future, hosts Robert Ta, Jonathan McCoy, and Deen Aariff deconstruct a groundbreaking talk by Benjamin Bratton on planetary computation, AI alignment, and epistemic institutions.

From the critique of modern knowledge systems to the idea that computation itself is an evolutionary force, this discussion explores the biggest questions in AI, epistemology, and the future of intelligence.

This is a must-listen for entrepreneurs, researchers, and technologists at the frontier of AI alignment, philosophy, and synthetic intelligence.

Key Themes from the Episode

1. The Rise of Planetary Computation: AI as Evolutionary Scaffolding

Bratton introduces the idea that planetary computation is inevitable—an unstoppable force in the evolution of intelligence. He compares it to Gaia theory, where computation is simply the next phase of planetary self-organization.

Bratton argues that AI isn’t just a tool—it’s part of a larger planetary system of intelligence formation. In this view, computation itself is an organizing principle, much like biological evolution.

Takeaway: AI may not be something we control, but a process we are being shaped by. If so, how do we ensure human values don’t get lost in the process?

2. Why We Need New Epistemic Institutions

One of Bratton’s strongest critiques is that modern epistemic institutions—universities, research labs, grant-funded science—are broken. They are not structured to answer the biggest questions we now face in AI.

Bratton’s Antikythera Foundation is an attempt to create a new kind of epistemic institution—one designed for the emerging reality of AI-driven intelligence.

Takeaway: The future of AI alignment isn’t just technical—it’s institutional. The way we fund and structure knowledge creation needs a complete rethink.

3. Are We Artificializing Ourselves?

Bratton’s talk introduces a radical idea: that computation is not just a tool—it’s an agent of artificialization.

In other words: Just as we shape AI, AI is reshaping us.

Artificialization is the process by which technology reshapes intelligence itself. It means our very way of thinking, perceiving, and understanding reality is being transformed by AI.

Takeaway: If computation is reshaping human intelligence, the key question is: who or what is in control of that process?

4. AI and the Failure of Modern Philosophy

One of Bratton’s biggest claims is that our philosophical tools are outdated. The analytic philosophy that shaped modern AI lacks the conceptual tools to handle evolution, intelligence, and subjective reality.

“The CEO of DeepMind literally said: ‘We need philosophy to catch up to AI.’” – Jonathan

Bratton argues that we need entirely new categories of thought—new nouns, metaphors, and conceptual models—to make sense of what AI is becoming.

Takeaway: If philosophy doesn’t evolve, we risk building AI systems that outpace our ability to understand them.

5. The Feedback Loop Between AI and Human Intelligence

One of the most powerful ideas in this discussion is the recursive relationship between AI and human intelligence. AI is not just learning from us—it is teaching us new ways to think.

In a world where AI is a co-evolving system, alignment isn’t just about controlling AI—it’s about making sure that humans remain part of the intelligence feedback loop.

Takeaway: AI alignment must be bidirectional—it’s not just about shaping AI, but about ensuring AI helps humans evolve on our own terms.

Links and Resources

Benjamin Bratton | A Philosophy of Planetary Computation: From Antikythera to Synthetic Intelligence

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this episode, the founders of Epistemic Me discuss the inception and development of their project—a tool designed to deeply understand users' beliefs and optimize personalized recommendations. They delve into philosophical concepts like the self and interconnectedness, the role of an open-source approach, and their vision for the future. Key highlights include the project's potential applications in health, mental well-being, and science, and the importance of epistemology in evolving AI systems. The conversation also touches on the next steps for launching the open-source GitHub repository and community involvement.

00:00 Are We Living in a Simulation?

01:20 Introducing the Podcast and Hosts

01:59 What is Epistemic Me?

04:51 The Origin Story of Epistemic Me

01:09 Explaining Epistemic Me to a 5-Year-Old

21:22 Personal Journeys and Philosophical Insights

40:43 Purpose and Identity

46:36 The Process of Epistemology

47:12 Operating System Analogy

47:29 Maslow's Hierarchy and Self-Discovery

48:13 Future of Epistemic Me: 5 to 10 Years

49:01 Personal Recommendation Engines

50:32 Data-Driven Science

52:36 Success in 5 to 10 Years

57:38 Open Source and Ethical Considerations

01:12:59 Long-Term Vision: 500 Years Ahead

01:26:33 Community and Contributor Call

01:33:28 Closing Thoughts and Future Plans

In this episode, the founders of Epistemic Me discuss the inception and development of their project—a tool designed to deeply understand users' beliefs and optimize personalized recommendations. They delve into philosophical concepts like the self and interconnectedness, the role of an open-source approach, and their vision for the future. Key highlights include the project's potential applications in health, mental well-being, and science, and the importance of epistemology in evolving AI systems. The conversation also touches on the next steps for launching the open-source GitHub repository and community involvement.

00:00 Are We Living in a Simulation?

01:20 Introducing the Podcast and Hosts

01:59 What is Epistemic Me?

04:51 The Origin Story of Epistemic Me

01:09 Explaining Epistemic Me to a 5-Year-Old

21:22 Personal Journeys and Philosophical Insights

40:43 Purpose and Identity

46:36 The Process of Epistemology

47:12 Operating System Analogy

47:29 Maslow's Hierarchy and Self-Discovery

48:13 Future of Epistemic Me: 5 to 10 Years

49:01 Personal Recommendation Engines

50:32 Data-Driven Science

52:36 Success in 5 to 10 Years

57:38 Open Source and Ethical Considerations

01:12:59 Long-Term Vision: 500 Years Ahead

01:26:33 Community and Contributor Call

01:33:28 Closing Thoughts and Future Plans

In this episode, the founders of Epistemic Me discuss the inception and development of their project—a tool designed to deeply understand users' beliefs and optimize personalized recommendations. They delve into philosophical concepts like the self and interconnectedness, the role of an open-source approach, and their vision for the future. Key highlights include the project's potential applications in health, mental well-being, and science, and the importance of epistemology in evolving AI systems. The conversation also touches on the next steps for launching the open-source GitHub repository and community involvement.

00:00 Are We Living in a Simulation?

01:20 Introducing the Podcast and Hosts

01:59 What is Epistemic Me?

04:51 The Origin Story of Epistemic Me

01:09 Explaining Epistemic Me to a 5-Year-Old

21:22 Personal Journeys and Philosophical Insights

40:43 Purpose and Identity

46:36 The Process of Epistemology

47:12 Operating System Analogy

47:29 Maslow's Hierarchy and Self-Discovery

48:13 Future of Epistemic Me: 5 to 10 Years

49:01 Personal Recommendation Engines

50:32 Data-Driven Science

52:36 Success in 5 to 10 Years

57:38 Open Source and Ethical Considerations

01:12:59 Long-Term Vision: 500 Years Ahead

01:26:33 Community and Contributor Call

01:33:28 Closing Thoughts and Future Plans

What if our survival depends not on smarter machines, but smarter belief systems?

As we enter the era of superintelligence, one question looms large: What belief systems are equipped to guide us through the next chapter of human evolution?

In this episode of ABCs for Building the Future, Robert Ta and Jonathan McCoy unpack how religion has historically evolved to ensure human survival—and why we may need a new kind of belief system to align ourselves, and our AI, for the future.

1. The Utility of Dogma: Why We Need Useful Beliefs

“Dogma gets a bad rap, but it’s a powerful tool when we admit what we can’t know.” – Jonathan McCoy

Dogma—often dismissed as rigid—is a mental model that can help us function in the face of uncertainty. Religion has historically offered dogmas that enabled people to act coherently when answers were out of reach.

In the age of AI, uncertainty is multiplying. From black-box models to geopolitical instability, we need belief systems that provide clarity, cohesion, and ethical grounding—especially for things we can't fully understand or predict.

Why it matters:If we don't agree on what matters, we can't align the tools we're building. And misaligned tools at scale create existential risk.

2. The Axial Age: A Precedent for Systemic Transformation

“All the major religions emerged at the same time. That’s not an accident—it’s an evolutionary moment.” – Jonathan McCoy

Between 800 and 300 BCE, nearly every major philosophical and religious tradition arose independently—Confucianism, Buddhism, Greek philosophy, monotheism. This period, known as the Axial Age, was marked by civilizational upheaval, new technologies (like the chariot), and a need for coherence in chaotic times.

These belief systems unified fragmented societies. They created shared values, norms, and narratives that made it possible for civilizations to grow, stabilize, and survive.

Why it matters:If we’re now entering a similar moment—this time driven by AI—we may need a modern Axial response: new belief systems that match the complexity and scale of today’s challenges.

3. Don’t Die: A Universal Operating System?

“Everything in nature plays the game of ‘Don’t Die.’ What if that became our universal principle?” – Robert Ta

The “Don’t Die” philosophy, proposed by Bryan Johnson, suggests that longevity—avoiding death—is the one universal value shared by all living systems. It cuts across culture, religion, and politics.

It’s being framed as more than just a health protocol. It’s a belief system. A full-stack ideology. A possible candidate for a “religion of the AI age” that is actionable, measurable, and highly aligned with individual and collective survival.

Why it matters:If AI alignment is the problem, perhaps longevity-based alignment is the simplest and most unifying solution. It provides a shared fitness function: don’t die.

4. The Modern Threat Landscape: More Than Just Algorithms

“We don’t have real defenses against nuclear missiles. That should be part of the AI alignment conversation too.” – Jonathan McCoy

Beyond philosophical questions, the conversation turns practical: AI is already intersecting with warfare, misinformation, and national security. If we don’t define a coherent moral framework, AI may amplify our worst tendencies—or accelerate our self-destruction.

The hosts explore how existential risk from nuclear war or autonomous weapons may demand not just technical safeguards—but societal alignment.

Why it matters:Alignment isn't just about preventing AI from going rogue. It's about ensuring the humans guiding AI are operating from shared, reality-aligned values.

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is the Axial Age?A: A period from 800–300 BCE during which the world's major philosophical and religious systems independently emerged across different civilizations.

Q: What is “Don’t Die”?A: A belief system focused on health, longevity, and existential risk reduction—proposed by Bryan Johnson as a candidate for a modern philosophy (or religion) aligned with survival.

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: Can AI become a source of dogma?A: Potentially. As people ask AI questions they can’t answer themselves, they may start to treat its output as belief-worthy—even when it’s probabilistic or uncertain.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

I sat down with Rich—early at Atlassian, who spent years in the trenches building products used by millions—and we went deep on something most founders don’t want to hear. Your users don’t want more features. They want clarity. They want tools that solve real problems without drowning them in options they’ll never touch.

This conversation hit me hard because I’m building Clarity right now, and Rich’s insights forced me to question everything. Are we building for users, or are we building to feel productive? Are we creating value, or are we creating noise?

If you’re a founder, product leader, or engineer who’s ever felt the pressure to “just ship one more thing,” this is your intervention. Let’s get into it.

1. The Feature Bloat Trap: How Success Becomes Your Burden

Every product team faces this paradox. You build something people love. They start using it. Then come the requests: “Can you add this? What about that?” Before you know it, your elegant solution has morphed into a Swiss Army knife that nobody knows how to use anymore.

Rich lived this at Atlassian.

“You’re not building for your existing customers anymore. You’re building for the imagined customer that might come in the future,” he told me. “And that’s when you lose focus.

The psychology here is brutal, and I think about it every single day while building. Adding features feels like progress. It feels like growth. It gives your team something to do, your sales team something to pitch, and your investors something to point to in their decks. But here’s the truth: each new feature is a cognitive tax on every user who has to navigate around it just to get to what they actually need.

I keep asking myself: Are we building this because users need it, or because we need to feel like we’re moving forward?

Application for builders: Before adding your next feature, pause. Ask yourself that question. The best product decisions often involve saying no, not yes. And saying no is f*****g hard when everyone around you is screaming for more.

2. The Oracle Years: When Engineering Excellence Meets Corporate Bloat

Rich spent fourteen years at Oracle and PeopleSoft. Fourteen years watching what happens when feature accumulation becomes institutional religion. Large enterprise software companies don’t just add features—they acquire them, bolt them on, create labyrinths of functionality that require armies of consultants to navigate.

What struck me most was this moment of reflection:

“I tended to be in the back seat most of the time,” Rich said. “I would probably suggest moving up to the front, maybe even getting towards the driver’s seat and figuring out how to control and do something more meaningful with your career.”

This wasn’t just career advice. This was about ownership. When you’re deep in a large organization, it’s easy to become a feature factory worker—executing someone else’s roadmap, optimizing someone else’s metrics, building toward someone else’s vision. You lose sight of the why behind what you’re building.

And here’s the irony that kills me: many of these organizations are drowning in talented engineers who know exactly what needs to be cut, simplified, and reimagined. But the incentive structures reward addition, not subtraction.

Application for builders: If you find yourself building features you don’t believe in, it’s time to either influence the roadmap or find a different seat. Your best work happens when you care about the outcome, not just the output. I learned this the hard way at my first startup in crypto gaming when I was just executing without conviction. Never again.

3. The Clarity Insight: What Journaling Teaches Us About Product Design

One of the most validating moments in our conversation came when Rich reflected on what I’m building with Clarity—my tool for personal performance management through journaling and self-reflection.

“The thing that appealed to me when you were talking about it is like, it’s really like a personal performance management app,” Rich said. “If you write in a journal, the only time a journal is really useful is if you’re deliberate about it. You have discipline in writing in it regularly, and then your mind will clarify.”

But here’s the brutal truth about most journals: they become write-only systems. You pour your thoughts onto pages, close the book, and never look back. All that valuable reflection becomes historical noise. Gone. Useless.

Rich compared Clarity to using Granola for meeting notes—having the ability to search through past conversations, find patterns, extract insights months or years later. That transforms raw data into actionable knowledge. That’s the whole f*****g point.

This is the opposite of feature bloat. Instead of adding more ways to capture information, we’re focused on making existing information more valuable. Instead of building forty different input methods, we’re building one great way to retrieve insight when you need it most.

Application for builders: Before you write a single line of code for that next feature, stop. Ask yourself one question: “Does this help users find clarity, or does it add to their cognitive burden?”

The best products reduce mental overhead. They don’t increase it. And if we’re honest with ourselves, most features we ship increase it. We add complexity in the name of capability, and our users pay the price in confusion.

4. The Jobs-to-be-Done Lens: What Are You Really Solving?

Midway through our conversation, I pushed Rich to articulate the core job that Clarity is solving. I love using Bob Moesta and Teresa Torres’s frameworks for this. What’s the fundamental outcome users are hiring the product to deliver?

His answer stopped me in my tracks:

“The promise of AI is about truly being your assistant over your life. Things fail—our memories are going to fail. We forget things over time. We’re not perfect. If you have some history of everything you’ve done or things you’ve talked about, and if you could make use of that somehow... it’s like a living autobiography.”

A living autobiography. Holy s**t.

This is what great product thinking looks like. Not “we’re building a journaling app” or “we’re adding AI features.” But rather: “We’re solving the fundamental problem that human memory is unreliable, and valuable experiences disappear into the void unless we have systems to preserve and surface them.”

When you understand the job at this level, feature decisions become obvious. Does this help create a living autobiography? Does it surface forgotten insights? Does it reduce the burden of remembering? If not, it doesn’t belong in the product.

What I’m taking from this: I wrote down our one job on a sticky note and put it above my desk: Create a living autobiography of who you’re becoming. Every feature request now gets evaluated against that job. If it doesn’t serve that core purpose, it’s noise. It’s a distraction.

5. The Experience Over Output Paradox: Why We Build the Wrong Things

The most philosophical moment came when Rich talked about his relationship with hobbies, particularly photography. He confessed something I deeply relate to: he often feels the need to see progress, validation, recognition for the things he creates—even when he knows he should just enjoy the process.

“I feel like I’m wired that way where like, I need to see some sort of progress, some validation, some recognition for the things that you do,” he admitted. “I know every human wants to feel confident. They want to feel like they belong.”

F**k, I felt that. This is the trap that destroys products. We build for validation—from users, investors, the market, ourselves—rather than building for genuine utility. We add features because we want to show progress. We complicate interfaces because simplicity doesn’t feel like enough work.

But the best products respect that value comes from experience, not from feature count. A journal is valuable because of the clarity you gain while writing, not because of the pages you fill. A camera is valuable because of how it changes what you see, not because of megapixel counts.

Rich’s self-awareness here is the mark of a mature product thinker: recognizing the impulse to optimize for the wrong metrics and constantly pulling yourself back to what actually matters.

I’ve been thinking about this a lot lately. How much of what I build is for me to feel like I’m making progress versus what actually serves the user? How often do I add complexity because I need external validation that I’m “doing something”?

Application for builders: We need to build cultures where “we decided not to build that” is celebrated as much as “we shipped this new feature.” The best product teams are disciplined about what they say no to. That’s the real flex.

Key Takeaways: The Anti-Feature Manifesto

Your product doesn’t need more features. It needs more focus.

1. Question Every Feature Request

Most come from imagined future customers, not actual needs. Ask: “Who is this really for?”

2. Value Subtraction Over Addition

Removing confusion beats adding functionality. Every feature taxes user attention.

3. Design for Retrieval, Not Capture

Information without insight is noise. Make the past useful, not just stored.

4. Understand the Core Job

If you can’t say it in one sentence, you’re building on shaky ground. Ours: living autobiography.

5. Build for Experience, Not Validation

Stop building for your ego. Build for user value.

6. Take the Driver’s Seat

Own the direction. Don’t be a passenger in your own life.

The pressure to add more never stops. The market always demands more features. Competitors always tout expanding capabilities.

Resist.

The products that endure stay focused on solving one problem extraordinarily well. Rich’s journey from Oracle to Atlassian to entrepreneurship proves it: clarity beats complexity every single time.

Maybe the best thing we can build isn’t more. Maybe it’s better.

🎧 Resources and Further Listening

* Jobs to be Done Framework by Bob Moesta

* Continuous Discovery Habits by Teresa Torres

* The Lean Product Playbook by Dan Olsen

* Atlassian Product Development Philosophy

* Granola - AI Meeting Notes Tool

Resources and Links

Patrick House

Nineteen Ways of Looking At Consciousness

Official page for "Lights On" by Annaka Harris

Michael Levin Lab at Tufts University

Donald Hoffman Wikipedia

Conway’s Law Wikipedia

Jean Piaget Wikipedia

BBC Earth – Ayumu chimpanzee memory test (YouTube)

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

He’s also a filmmaker turned product builder, full-time dad, part-time philosopher, and creator of a deck of mental model cards designed to help leaders think clearly in the chaos.

In this episode of The ABCs for Building the Future, we go deep into how Juan Carlos rebuilt his sense of self—from the inside out. It starts with one word:

Clarity.

“Clarity of thought leads to a purposeful life. And that’s true for me—and for others.”

This is more than a podcast about productivity hacks or mental models. It’s a conversation about identity, purpose, the illusion of free will, and how we can architect our own mental infrastructure to make better decisions—at work and in life.

What You’ll Learn:

* How Juan Carlos went from auteur filmmaker to tech exec

* The one mental model he applies in almost every business scenario

* Why he moved to Joshua Tree to escape the noise—and find his own thoughts again

* The design principles behind his card deck and upcoming mental model app

* A vulnerable reflection on ego, failure, and reinventing yourself mid-career

“I realized the film part didn’t matter—it was the impact that did.”

Juan’s side project, Remind, is a beautifully designed system of flashcards (and a companion app) that turns frameworks like “circle of competence,” “inversion,” and “first principles” into real-time leadership tools. You don’t just read these ideas—you use them.

Key Takeaways:

* A belief system is the new user model. Track beliefs, not just behavior.

* AI that recommends the right thing at the right time must understand the user’s reasoning.

* Epistemic emotions like confusion, belief confirmation, and curiosity are the real alignment metrics.

* Developers need tools that help them build coaches, not just bots.

Resources and Links

Check out Juan on LinkedIn

Check out his work on mental models with Remind

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

Like a real coach?

In this week’s episode of the ABCs for Building the Future podcast, Robert and Jonathan dive deep into the practical journey of bringing an AI health coach to life. They debrief their latest build sprint, demo progress, and debate what it really means to personalize AI for health and longevity.

From modeling belief systems to designing end-to-end user experiences, they discuss the architecture, user goals, and feedback loops that shape their vision.

It’s a real-time look at product innovation at the intersection of AI, health, and hyper-personalization—built out in the open.

Whether you’re an AI developer, a product strategist, or a founder working on the future of wellness tech, this is your inside look at how to actually build something that matters.

Quotes

“You can’t personalize without beliefs.”

“You train the evals to train the prompts.”

“What if AI Bryan helped you find your Don't Die tribe?”

“Your bio-age isn't just data—it’s a dialogue.”

“Personalization isn’t just nice to have. It’s necessary for trust.”

Resources

https://hamel.dev/blog/posts/field-guide/#empower-domain-experts-to-write-prompts

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is the Axial Age?A: A period from 800–300 BCE during which the world's major philosophical and religious systems independently emerged across different civilizations.

Q: What is “Don’t Die”?A: A belief system focused on health, longevity, and existential risk reduction—proposed by Bryan Johnson as a candidate for a modern philosophy (or religion) aligned with survival.

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: Can AI become a source of dogma?A: Potentially. As people ask AI questions they can’t answer themselves, they may start to treat its output as belief-worthy—even when it’s probabilistic or uncertain.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

From discussions on how to map human beliefs to ensuring AI coaches don’t hallucinate their way into irrelevance, this episode is a must-listen for entrepreneurs, product builders, and technologists pushing the boundaries of AI.

1. The Problem with Health AI Today: Uncertainty and Personalization

Most AI health solutions today operate on generic, one-size-fits-all advice. But the biggest hurdle for health behavior change isn't knowledge—it’s uncertainty.

🗣 “People know they should sleep more, eat better, and exercise, but what stops them is uncertainty—when will I actually see the benefit?” – Robert

🔍 Takeaway: Hyper-personalization is key. By modeling belief systems, Epistemic Me can tailor health AI responses to an individual's mental models and motivations.

2. The Roadmap for a Hyper-Personalized AI Health Coach

The team outlines four major areas of development for AI-powered health coaching:

* Self-Modeling AI: The AI builds a belief map of the user based on previous interactions.

* Predictive Processing: Instead of static rules, AI can dynamically update and predict how user beliefs evolve over time.

* Dialectic Design: AI is trained to ask the next best question to guide the user’s learning journey.

* Trust and AI Alignment: AI must avoid hallucination by following structured question trees and validated responses.

🗣 “Our goal is to make sure AI doesn’t just throw random advice at you. Instead, it understands who you are and what actually matters to you.” – Jonathan

🔍 Takeaway: The future of health AI isn’t just giving advice—it’s understanding belief systems and adjusting recommendations dynamically.

3. Mapping Beliefs: The Key to AI That Feels Human

The team is developing a "belief mirror chat", a feature that reflects a user’s core health beliefs back at them, helping them recognize patterns they may not even be aware of.

🗣 "We could give people a mirror of themselves and their beliefs about the world. That could literally help people self-actualize." – Robert

🔍 Takeaway: AI that helps users see their own limiting beliefs could be the key to real, lasting behavioral change.

4. AI Alignment and Trust: Preventing AI from Going Off the Rails

AI health coaches need to be reliable, trustworthy, and predictable. The team is using LLM-powered reinforcement learning to train AI not to hallucinate while still allowing for organic, human-like interactions.

🗣 “We don't want AI to be a black box that suddenly decides you should eat only walnuts for three weeks. We need structured learning objectives that ensure predictability.” – Jonathan

🔍 Takeaway: The biggest risk in health AI is unpredictable, misleading recommendations. Epistemic Me is solving this by keeping AI responses anchored in validated belief models.

5. How This Could Change Healthcare

Beyond consumer health, this AI model could be used for private medical practices, helping doctors understand patient psychology and compliance patterns.

🗣 "Imagine a doctor being able to instantly see a patient’s belief systems around health—where they are resistant, where they’re motivated. That’s game-changing." – Robert

🔍 Takeaway: AI-powered belief mapping could help doctors, therapists, and coaches provide more effective, personalized care.

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you.

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

Surprise extra podcast this week, which is the premiere of our guest interview series for ABCs for Building The Future.

I’m so excited to bring on Darren Fanton, CEO of ScreenSpace, who helps GTM teams shine with interactive storytelling to bring prospects fully into your product experience.

His differentiating experience working on music videos for the likes of Britney Spears and Kanye West sets him apart from the competition—and it’s clear in ScreenSpace’s product design.

We had a great time with this podcast, and I know you will too.

Here’s What You’ll Hear

In this episode of the ABCs for Building the Future podcast, we dive deep into the world of storytelling with Darren Fanton, a seasoned expert with roots in Hollywood and a passion for innovating in the tech startup space.

Join us as Darren shares his journey from directing music videos for top stars like Kanye West and Britney Spears to creating storytelling software that empowers B2B marketing teams.

Discover the art of crafting transformative moments, the importance of thinking outside the box, and how to achieve an outlier level of success.

Plus, gain insights into balancing personal well-being with the demanding life of a founder, the future of storytelling, and leveraging AI and unique POV to stand out in today's content-rich world.

This episode is packed with actionable tips and inspiring stories for entrepreneurs, marketers, and creatives looking to elevate their storytelling game.

Check out Darren’s links here!

Darren on LinkedIn

ScreenSpace

ScreenSpace on LinkedIn

Thank You

Liked this?

💚 Click the like button.

Feedback or addition?

💬 Add a comment.

Know someone that would find this helpful?

🔁 Share this post.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this transformative episode of ABCs for Building the Future, host Robert sits down with Louisa Henry — executive coach, former product leader at Gusto and Airtable, and creator of the podcast Any Job Can Be a Climate Job.

Louisa shares her personal evolution from Fortune 100 product roles to climate activism — and makes a compelling case that we don’t need every job to be in climate tech. We need every worker, in every role, to think like a climate changemaker.

If you’re a founder, technologist, or executive searching for purpose in your work — this conversation is a masterclass in reimagining your role, your rituals, and your ripple effect.

1. From Chase to Change: Louisa’s Climate Awakening

“I was looking at my annual plan and realized — this isn’t aligned with my values. I’m not doing what matters most to me.”

Louisa's career was a blueprint of tech success: Airtable, Gusto, JPMorgan Chase. But the pandemic, a family tragedy, and a growing internal dissonance pulled her in a new direction.

After walking away from a promising health tech job offer, Louisa leaned into uncertainty and grief — and emerged with clarity. Climate wasn’t just a cause. It was her cause.

Reflection: If you’ve ever felt misaligned with your work, Louisa’s pivot is a reminder: discontent can be a compass, not a curse.

2. Why Any Job Can Be a Climate Job

“You don’t have to work at a climate startup to be a climate leader. Every company has an impact — and every employee has influence.”

The central thesis of Louisa’s new podcast — and mission — is disarmingly simple: climate change isn’t someone else’s job.

Whether you’re in finance, marketing, product, or HR, there are always leverage points: policies, vendors, product energy consumption, company culture.

She shares a powerful story of one ad tech employee who asked, “Where is our biggest energy waste?” The answer led to a new, greener, faster product — and a shift in company direction.

Reflection: Don’t underestimate your domain knowledge. Climate action isn’t just about values — it’s about understanding systems, incentives, and change from within.

3. The Power of Presence: Rituals That Rewire the Mind

“If you can be present in every moment — that’s the secret to life.”

Louisa’s leadership transformation is inseparable from her spiritual one.

A key turning point came when she embraced mindfulness practices — daily journaling, 5:15 AM meditations, walking meetings, and "stop and be" tattoo-level clarity.

Meditation gave her something the corporate world rarely does: meta-attention. The ability to notice what she was paying attention to — and decide where it should go.

Reflection: Founders and execs often optimize everything but their own mind. Presence is a performance enhancer — and an ethical compass.

4. Building Climate-Conscious Community from the Inside Out

“We don’t need every company to be a climate company. But we do need every company to care.”

Through her podcast and in-person events, Louisa is building a growing ecosystem of climate-conscious professionals — not activists outside the system, but intrapreneurs reshaping it from within.

At SF Climate Week, her founder circles were oversubscribed within hours — a sign that leaders are hungry for connection, clarity, and courage.

She also calls out the tension between capitalism and sustainability — and how meaningful change often begins with "non-obvious" allies inside the system.

Reflection: The climate movement isn’t just about information. It’s about belonging. If you’re lonely in your convictions, find your circle — or create one.

5. Your Micro-Choices Create Macro Change

“The world is soft clay. You can mold it.”

Louisa offers practical pathways to climate action:

* Audit your team’s resource consumption

* Advocate for greener vendor policies

* Influence product energy usage

* Shift budgets — personal or professional — toward regenerative systems

* Practice intentional consumption (wait 24 hours before every online purchase)

* Remember: eco-anxiety is real. Be kind to yourself.

You don’t need to be perfect. You just need to start.

Reflection: Real impact isn’t one heroic act. It’s consistent, aligned micro-decisions that compound into culture.

🎧 Resources and Further Listening

* Follow Louisa on LinkedIn

* Follow Louisa on Substack

* Louisa’s Podcast: Any Job Can Be a Climate Job

* Listen on Apple

* Listen on Spotify

* Are you a senior leader or founder navigating pressure, growth, and culture strain? Louisa can help!

* Her episode on climate action from within ad tech (with Gabe): Listen here

* 30 Day Free Trial for the Meditation app she recommends: Waking Up

* Book on attention and meditation: Emotional Intelligence by Daniel Goleman

* Foundational environmental insight: Braiding Sweetgrass by Robin Wall Kimmerer

What if artificial intelligence isn’t just a tool, but a product of evolution itself?

Could AI follow the same trajectory as domesticated animals, mitochondria, or even humanity itself?

In this episode of ABC’s for Building the Future, hosts Robert Ta and Jonathan McCoy, Co-Founders of Epistemic Me, dive deep into a mind-bending conversation about artificialization—the process by which intelligence reshapes itself and the world around it.

With insights drawn from Benjamin Bratton’s theories, the domestication of animals, and the history of life itself, this conversation is a must-listen for entrepreneurs, technologists, and AI thinkers looking to understand where we’re headed in the AI age.

Get an additional behind the scenes “Build in Public” style roadmap update, where they actually work through feature prioritization.

Key Themes from the Episode

1. The Evolution of Artificial Intelligence: Are We the Wolves of the Future?

Jonathan introduces a fascinating concept from Benjamin Bratton’s talk: agency preceded subjectivity—meaning that intelligence has always shaped its environment before truly understanding itself.

He compares AI’s current trajectory to the way wolves evolved into domesticated dogs and how wild cattle became modern cows:

"The cows that exist today are just artificial cows—they can’t survive outside of farms anymore."

Could AI be undergoing the same process? As humans create AI to serve economic value, are we, in turn, being reshaped by our own creations?

2. AI as an Economic Replicator: What Happens When We’re No Longer Needed?

Robert and Jonathan discuss how AI today is not just a tool—it’s a replicator of economic value. AI systems learn from human intelligence and then automate tasks at a fraction of the cost.

Jonathan explains:

"What AGI does is replicate our economic value—at one-millionth of the cost. Once that happens, humans may no longer be needed to perform those tasks."

What happens to society when AI artificializes human labor? Will we become obsolete? Or will we adapt and evolve into something new?

3. The Mitochondria Analogy: Will Humans Merge with AI?

One of the most fascinating analogies from the episode compares AI to mitochondria—the ancient bacteria that became an essential part of our cells.

Jonathan explains:

"Hundreds of millions of years ago, cells absorbed mitochondria, and instead of being separate entities, they became part of the same system."

If mitochondria were once independent life forms that merged with their hosts, could AI merge with human intelligence in a similar way?

This brings up the biggest existential question of all:

* Will AI always remain separate from humans?

* Or will we artificialize AI in a way that fuses it into our very biology?

4. The Simulated Reality of AI and Humans

As AI becomes more embedded in our lives, Jonathan suggests that we might be living in an artificial world without realizing it—much like how animals bred for domestication don’t recognize their natural origins.

"We're independent acting agents, but we live within a simulated environment. And that simulated reality is only going to get more purposeful."

From social media algorithms that manipulate our attention to future AI systems that determine our choices, the lines between reality and simulation are becoming blurred.

5. AI and the Future of Human Agency

Robert brings up Victor Frankl’s famous quote:

“Between stimulus and response, there is a space. And in that space lies our freedom to choose our response.”

As AI takes on more decision-making roles, how much of that space is left for us? Will we still be in control, or will AI systems reduce our choices without us realizing it?

Links and Resources

Benjamin Bratton | A Philosophy of Planetary Computation: From Antikythera to Synthetic Intelligence

Why It Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

Can’t get enough? Check out the companion newsletter to this podcast.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. Check out the companion newsletter to this podcast, ABCs for Building The Future, where I also share my own written perspective of building in the open and entrepreneurial lessons learned.

And if you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

In this thought-provoking episode of The ABCs for Building the Future, Robert Ta and Jonathan McCoy sit down with Dr. Bishoy Goubran, an Assistant Professor of Psychiatry, to explore a groundbreaking frontier: how our belief systems shape mental health and how decoding them could align future AI systems with human values.

Together, they dive deep into cognitive distortions, psychosis, belief formation — and how creating a "conceptual GPS" of the human psyche could be the missing link in both medicine and machine learning.

This blog captures the core insights and reflections from this rich conversation — your map to understanding a future where AI helps heal, not harm.

Belief Systems: The Hidden GPS of Mental Health

Dr. Goubran shares how virtually all psychiatric illnesses — from depression to psychosis — show disruptions in automatic beliefs and associations. Understanding these disruptions could offer a predictive "map" of mental health states, allowing interventions before symptoms worsen.

“You could have a conceptual map, like a GPS, where the therapist or AI maneuvers through onion layers of beliefs.” — Dr. Bishoy Goubran

Imagine therapy sessions (or AI-assisted coaching) where underlying negative beliefs are mapped and adjusted, much like navigating detours on a map. This could revolutionize how mental health care is delivered — faster, more accurate, more compassionate.

AI as a Belief Cartographer: A New Role for Technology

Jonathan expands on how Epistemic Me’s framework could give AI the tools to understand belief structures — enabling systems to predict human responses and personalize interactions with high fidelity.

“The next best question to ask someone — that heuristic — could be systematically learned and optimized by AI.” — Jonathan McCoy

Rather than cold, mechanical bots, future AIs could become deeply empathetic guides. Startups, healthcare systems, and even governments could use these models to better serve diverse populations without flattening human nuance.

Healing Through Belief Confirmation and Clarification

The discussion introduces the idea that aligning a patient’s belief patterns — or clarifying distorted ones — can restore mental health baselines. By systematically tracking and understanding belief shifts, recovery could be accelerated.

“From a psychiatric point of view, belief confirmation means the patient is on the expected healing trajectory.” — Dr. Bishoy Goubran

This insight suggests new clinical tools: belief tracking dashboards, cognitive healing maps, and personalized recovery plans powered by AI and human collaboration.

Resources and Links

Find Bishoy on LinkedIn

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: What is the relationship between belief systems and mental health?A: Dr. Goubran explains that disruptions in belief patterns often signal psychiatric conditions. Mapping these belief disruptions could provide early warning signs, enabling more accurate diagnosis, intervention, and long-term support.

Q: How does Epistemic Me fit into this conversation?A: Epistemic Me is developing tools to map human beliefs with precision and empathy, creating a conceptual framework (similar to a GPS) that could assist therapists, AI systems, and individuals in navigating complex mental and emotional landscapes.

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn

If you’re not building your personal brand today, you may not even exist tomorrow.

That’s the clear, hard-hitting message from Joe Shalaby — founder and CEO of EMortgage Capital — on this week’s episode of The ABCs for Building the Future.

Joe built the largest non-depository mortgage bank in California, the #5 broker nationwide, and a personal brand that reaches millions across TikTok, Instagram, and beyond.

And he’s just getting started.

Here’s the biggest insight from our conversation:

In a world dominated by AI, the only differentiator left will be the trust and credibility behind your name.

Joe didn’t just stumble onto this. His story is a relentless climb through adversity: from immigrating to the U.S. from Cairo, to starting over in a one-bedroom apartment, to facing bullying and poverty, to eventually building an empire — one resilient step at a time.

“Challenges don’t impact me like they do others. They’re just another walk in the park.”

Joe believes entrepreneurs today must master two things simultaneously:

* Build businesses that automate the basics

* Build personal brands that establish human connection

Because soon, the cold efficiency of AI will dominate every commoditized task. And the only thing left standing will be who you are — and what people believe about you.

What You’ll Learn from This Episode:

* Why personal brand is now your #1 startup investment

* How Joe coaches 900+ entrepreneurs through adversity and brand building

* The secret behind his viral growth across every major social media platform

* Why resilience — real, gritty resilience — is the unteachable superpower

* How Joe connects faith, entrepreneurship, and service into a business philosophy that wins long-term

* Predictions for 2050: What happens to the mortgage and finance industry when AI runs everything

Resources and Links

Find him on Instagram

Check out Emortgage Capital

Why Epistemic Me Matters

“How can AI understand us if we don’t fully understand ourselves?”

We solve for this by create programmatic models of self, modeling belief systems, which we believe are the basis of defense against existential risk.

In the longevity tech space, we create tools that meet users where they are, helping them make better decisions, form healthier habits, and align with their deepest values.

ABCs for Building The Future is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get Involved

Epistemic Me is building the foundational tools to make this vision a reality—and we’re doing it in the open. Here’s how you can join the movement:

* Check out the GitHub repo to explore our open-source SDK and start contributing.

* Subscribe to the podcast for weekly insights on technology, philosophy, and the future.

* Join the community. Whether you’re a developer, researcher, or someone passionate about the intersection of AI and humanity, we want to hear from you. Email me anytime!

FAQs

Q: Why does this matter for AI?A: Because without shared values, we can’t align AI. Belief systems that scale and unify are essential to building tools that serve humanity, not destroy it.

Q: What is Epistemic Me?

A: It’s an open-source SDK designed to model belief systems and make AI more human-aligned.

Q: Who is this podcast for?

A: Entrepreneurs, builders, developers, researchers, and anyone who’s curious about the intersection of technology, philosophy, and personal growth. If you’ve ever wondered how to align AI with human values—or just how to understand yourself better—this is for you.

Q: How can I contribute?

A: Visit epistemicme.ai or check out our GitHub to start contributing today.

Q: Why open source? A: Transparency and collaboration are key to building tools that truly benefit humanity.

Q: Why focus on beliefs in AI?A: Beliefs shape our understanding of the world. Modeling them enables AI to adapt to human nuances and foster shared understanding.

Q: How does Epistemic Me work?

A: Our open-source SDK uses predictive models to help developers create belief-driven, hyper-personalized solutions for applications in health, collaboration, and personal growth. Think of it as a toolkit for understanding how people think and making better tools, apps, or decisions because of it.

Q: How is this different from other AI tools?

A: Most AI tools are about predictions and automation. Epistemic Me is about understanding—building models that reflect the nuances of human thought and behavior. And it’s open source!

Q: How can I get involved?

A: Glad you asked! Check out our GitHub.

Q: Who can join?

A: Developers, philosophers, researchers, scientists and anyone passionate about the underpinnings of human beliefs, and interested in solving for AI Alignment.

Q: How to start?

A: Visit our GitHub repository, explore our documentation, and become part of a project that envisions a new frontier in belief modeling.

Q: Why open-source?

A: It’s about harnessing collective intelligence for innovation, transparency, and global community involvement in shaping belief-driven solutions.

P.S. If you haven’t already checked out my other newsletter, ABCs for Growth—that’s where I have personal reflections on personal growth related to applied emotional intelligence, leadership and influence concepts, etc.

P.S.S. Want reminders on entrepreneurship, growth, leadership, empathy, and product?

Follow me on..

YouTube

Threads

Twitter

LinkedIn