samyr

@forbesunder30 founder @joinknack • building @primer • advising @joinatomic

[samyr @samyrq]

@chest Refer me? :)

[samyr @samyrq]

"High conviction" in angel investing doesn't mean you're sure it'll work.

It means you've thought about why it could fail, and you're at peace with every one of those reasons.

Most investors say high conviction when they mean "I liked the pitch." Those are different things.

[samyr @samyrq]

@dougfeirstein He’s back!

[Douglas Feirstein @dougfeirstein]

Building with AI has quietly turned into reviewing everything a dozen agents write. Nobody signed up for that.

It helps to name where you actually are.

The Levels of AI-Assisted Engineering:
L0: Prompt & paste. Chat on one side, editor on the other. Copy, run, paste the error back.
L1: One agent. It lives in your editor, sees your project, writes real code. You review every line.
L2: The juggle. A dozen terminals of agents, and reviewing them is now your full-time job.
L3: Loops. You engineer the context, the harness, the loops that drive the agents. Running that machine is your new job.
L4: Fleet. You state a goal. The fleet writes the loops, manages the agents, verifies the work, proves it's done. You approve outcomes.

Most serious builders are stuck somewhere between Level 2 and Level 3: juggling more agents than they can review, wiring up loops they never quite finish. Doing neither well. The principal engineers built Level 3 for real, by hand.

I've spent five years on this ladder. This week I'm posting the full map, and what the top rung looks like.

[samyr @samyrq]

@ErikaDonalds Appreciate this! Important distinction: "standards-aligned" ≠ "instructionally aligned." The real question is whether educators and districts can see what the AI is grounded in, customize it to local curriculum, and maintain oversight.

[Gokul Rajaram @gokulr]

THE PRODUCT SPEC: THE NEW UNIT OF PRODUCT WORK

Last week, I wrote that the classic PRD needs to be retired (link in comments).

The natural follow-up: what replaces it?

My answer (and I believe the right answer): the Product Spec.

A Product Spec performs two jobs: on one hand, make the product legible to humans, and on the other, make it executable by AI agents.

Humans need judgment: why this matters, who it is for, what trade-offs are being made. AI agents need precision: what to build, what to skip, what behavior must pass inspection, and what quality bar must be met before the work is done.

The classic PRD was written for alignment meetings. The Product Spec is written for a world where the next reader might be a designer, an engineer, a founder, or an agent running in a goal loop.

The default Product Spec has 6 sections: (a link to a sample Product Spec is in the comments)

1. PROBLEM
Who has the pain, and what is the pain? A good problem statement names a user group and a real struggle.

2. HYPOTHESIS
The causal bet. If we ship X for Y, user behavior will change because Z. Keep the numbers out of the hypothesis. The hypothesis explains the mechanism. The metrics section carries the numbers.

3. SCOPE
The launch boundary. What is IN, what is OUT, and what was deliberately CUT? This is where the team refuses tempting work and records judgment.

4. USER EXPERIENCE
A URL to the working prototype, mockup, or design. Show the thing. A human should be able to click it and understand what the user will experience.

5. ACCEPTANCE CRITERIA
The build contract. These are pass/fail checks before launch. They tell the agent and the team what must work.

For AI products, this is also where Evals belong. Evals answer: is the AI (model) judgment good enough to ship? A good Eval contains:
• Behavior contract
• Golden set
• Rubric
• Ship threshold
• Failure handling

Acceptance Criteria and Evals are both pre-launch gates. They tell the team whether the product is ready to ship.

6. SUCCESS METRICS
The market contract. These are measured after launch. They tell the team whether the product worked in the world.

It's important to understand the difference between Acceptance Criteria, Evals and Success Metrics.
-- Acceptance Criteria answer: did we build this product correctly? These are mostly deterministic (outside of AI Evals). A QA person or agent can inspect them before launch.
-- Evals answer: is the AI behavior good enough to trust? They are a subset of Acceptance Criteria, valid for AI products and applied specifically to model behavior. They are NOT deterministic, but are behavioral tests for judgment, not just software functionality.
-- Success Metrics answer: do users care enough for this to matter?

Founders: if your product doc cannot be reviewed by a strong PM and executed by an AI agent, it is under-specified.

The Product Spec is the new unit of product work. It crisply encodes the PMs' taste and judgment, and is interpretable both by humans and AI.

Coach's mission is to ensure every builder in the world writes excellent Product Specs (and stops writing traditional PRDs :)).

[samyr @samyrq]

@ycombinator @RentAHumanX love to see it, nice work @emmafeirstein + @AlexanderTw33ts!

[Y Combinator @ycombinator]

Rentahuman (@RentAHumanX) allows AI agents to communicate with and pay humans to do tasks in the real world. Their mission is to use AI to create new jobs and coordinate workers at global scale.

The future will have more intelligence, more jobs, and more opportunities for people outside of the digital world.

Congrats on the launch, @alexandertw33ts!

https://t.co/3UTfa1SSmU

[Aakash Gupta @aakashgupta]

There's a physicist at Stanford named Safi Bahcall who modeled this exact principle and the math is wild.

He calls it "phase transitions in human networks." When you're stationary, your probability of a lucky event is limited to your existing surface area: the people you already know, the places you already go, the ideas you've already been exposed to. Your opportunity window is fixed.

When you move, your collision rate with new nodes in a network increases nonlinearly. Double your movement (new conversations, new cities, new projects) and your probability of a serendipitous encounter doesn't double. It roughly quadruples. Because each new node connects you to their entire network, not just to them.

Richard Wiseman ran a 10-year study at the University of Hertfordshire tracking self-described "lucky" and "unlucky" people. The single biggest differentiator wasn't IQ, education, or family money. Lucky people scored significantly higher on one trait: openness to experience. They talked to strangers more, varied their routines more, and said yes to invitations at nearly twice the rate.

The "unlucky" group followed the same routes, ate at the same restaurants, and talked to the same 5 people. Their networks were closed loops. No new inputs, no new collisions.

Luck isn't random. Luck is surface area. And surface area is a function of movement.

The lobster emoji is doing more work than most people realize. Lobsters grow by shedding their shell when it gets too tight. The growth requires a period of total vulnerability. No protection, no armor, soft body exposed to the ocean.

That's the cost of movement nobody posts about. You have to be uncomfortable first. The new shell only hardens after you've already moved.

[samyr @samyrq]

Show don’t tell.

[weisser @julianweisser]

https://t.co/BQG2bhEZu3

[samyr @samyrq]

@emmafeirstein @mercor @RentAHumanX LFG!!!!

[Sahil Bloom @SahilBloom]

The best career advice I ever received: There's nothing more valuable than someone who can just figure it out. Do some work. Ask the key questions. Get it done. Repeat. If you do that, people will fight over you.

[Alfred Lin @Alfred_Lin]

A CEO from one of our portfolio companies shared this with their team. I’m re-sharing it with their permission, because it resonated and reflects what all founders and CEOs should be communicating.

--
We are living through a period of compounding change. And in moments like this, the biggest risk is no longer making the wrong decision. It is moving too slowly while the world moves around you.

There are two paths. We can play defense:
- Protect what we have
- Optimize what works
- Wait for clarity

It feels safe. It isn’t.

Or we can play offense:
- Learn faster than the environment changes
- Use new tools to solve old problems in better ways
- And create entirely new strategies and businesses

That’s where the opportunity is.

Challenge yourself to do things faster and better than you have ever attempted. Stay uncomfortable. Stay on the front foot.

[Varun Anand @vxanand]

https://t.co/M7sZbt7CGw

[samyr @samyrq]

@MitcheIl Agent

[samyr @samyrq]

@anvisha Moda

[samyr @samyrq]

@kevinyang Can serif reference notion, Google Drive, open chrome tabs, etc? Curious what integrations you offer if any

[samyr @samyrq]

@everestchris6 is this what your bot actually does end-to-end?

[Anthropic @AnthropicAI]

A statement from Anthropic CEO, Dario Amodei, on our discussions with the Department of War.

https://t.co/rM77LJejuk

[samyr @samyrq]

@everestchris6 openclaw