Jam

[Nick Mehta @nrmehta]

Some of the most coherent and thought-provoking predictions I've read in a long time

[Paul Graham @paulg]

If you want to start a startup, don't learn "entrepreneurship." Learn how to build things. The hard part of startups is not "entrepreneurship" but product: to know what to build, and to be able to build it.

[Tony Dang @dangtony98]

Every forward-thinking company, startup or not, should be investing time into building out internal agents capabilities and the infrastructure needed for it.

We run an equivalent solution to what Clay describes as an MCP gateway called Agent Vault that we’ve actually open sourced at @infisical.

Agent Vault inventories all of our internal agents and brokers them granular access to any service, agnostic of the interface be it MCP, SDK, CLI, or raw API call. It’s deployed within a private network within close proximity to internal agents to reduce latency amid hundreds and thousands of inference calls passing through it.

We also have an internal template that folks can use to build custom agents that are readily compatible with Agent Vault.

We’ll be publishing more on how to build an agent workforce, especially the security infrastructure portion of it, soon but for now I’d recommend peeking Agent Vault. This is basically the starting point.

[Garry Tan @garrytan]

The founder in their 40s with taste and discernment is the new gentleman unicorn founder

Because there can be 100x to 1000x of them working at their beck and call via agents and software factories all the time

[Phil Chen @philhchen]

https://t.co/oMoEAYkqGl

[Lenny Rachitsky @lennysan]

Really good

[Paul Graham @paulg]

AI companies are making a lot of money. Apparently the revenues from this wave of technology are growing "roughly three times more rapidly than the mobile or Internet waves."

[Jon Erlichman @JonErlichman]

Jeff Bezos on why the United States has more entrepreneurial success than other countries:

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Marc Andreessen 🇺🇸 @pmarca]

Interesting.

[Pierre-Eliott Lallemant @pierreeliottlal]

We went from 0 to 2,200 paying customers in under a year by following @ycombinator's 15 rules:

1/ Do things that don't scale. Get your first 10 customers by hand.

2/ Launch now, not when it's "ready". A mediocre product in front of real users teaches you more in a week than 6 months of polishing in the dark.

3/ Charge from day one. If nobody will pay, you don't have a startup, you have a hobby.

4/ Talk to users every single day. The roadmap you need is sitting in your customers' heads, and they'll hand it to you for free

5/ Always hunt the 90/10 solution. For almost any feature there's a way to capture 90% of the value with 10% of the effort.

6/ There are only two real jobs: write code and talk to users. Everything else (conferences, press, VC coffees, corp dev calls) is fake work.

7/ You pick your customers as much as they pick you. 10 users who love you beat 1,000 who kind of like you.

8/ Growth is an output, not a strategy. Grow before product market fit and all you're buying is churn.

9/ Do less, really well. Pick one or two metrics and judge every task against them.

10/ Know if you're default alive. Paul Graham's question: on current growth and current burn, do you reach profitability before the money runs out?

11/ Don't hire until it hurts. Headcount is not progress, it's burn. Every great startup was embarrassingly small for embarrassingly long.

12/ Momentum is the only real moat in year one. Ship something every week, even something tiny.

13/ Every great startup is badly broken at some point. The game isn't avoiding fires, it's how fast you put them out. Again. And again

14/ Ignore your competitors. Startups die of suicide, not murder. In year one, the only company that can kill yours is your own

15/ Startups rarely die from running out of money. They die because the founders fall out. Brutal honesty with your cofounder is the cheapest insurance you'll ever buy

Good luck !

[Dmitry Shapiro @dmitry]

@garrytan @connorking @NousResearch This is the EXACT stack that I covered in yesterday's 1 hour workshop. https://t.co/9xB2GdqfFk + @youdotcom

[Shann³ @shannholmberg]

how I’m building an agent company inside my agency.

the structure looks like this:

Agency gBrain
→ Orchestrator Hermes Agent
→ Department verticals
→ Specialist agents
→ Scoped sub-agents

gBrain is the company brain.

It gets ingested with the data and experience we already have:

> transcripts
> chats
> previous campaigns
> client learnings
> strategy docs
> internal workflows
> examples of what good looks like

That brain is maintained by a human champion plus an orchestrator Hermes Agent.

Under the orchestrator, we have different department verticals inside the agency.

Each vertical has its own specialist agents.

Some of those specialist agents have even narrower scoped agents underneath them.

I’ve found that narrow scope improves output quality and reduces drift.

> a general “marketing agent” is too vague.

> a lifecycle email agent with access to the right campaigns, voice rules, approval gates, and examples can get very good.

> a technical SEO agent with its own tools, checklists, and source standards can get very good.

> a content research agent with narrow inputs and a clear definition of done can get very good.

The narrower the job, the easier it is to improve the agent.

I use different harnesses for this.

Mostly Hermes Agent, but also CLI harnesses like Codex and Claude Code depending on the job.

I’m still looking for a good bare-bones harness for model routers to run on.

To keep track, I maintain an org chart inside the company gBrain.

The org chart shows:

> top-level orchestrator
> department verticals
> specialist agents
> scoped sub-agents
> which brain each agent reads from
> which tools each agent is allowed to use
> where human approval is required

For clients, I do downstream pods.

Think of them as new agent companies that are isolated from the agency brain, but can still communicate with our agency agents when needed.

A client pod has its own:

> client gBrain
> client orchestrator
> client specialist agents
> client-specific workflows
> client-specific approvals
> client-specific memory

This is important.

You do not want client context bleeding across accounts.

You do not want one agent with every client’s data, every tool, and every permission.

Scope is what keeps the system useful.

The powerful part is that once you build one vertical agent well, you can fork it.

Not copy-paste blindly.

You still need to customize the context, examples, approvals, voice, tools, and workflows.

But you are not starting from zero.

You might have 75% of the agent already done.

That changes the agency model.

You no longer need a full traditional department for every function before you can deliver a well-rounded marketing service.

One or two strong marketing engineers can run an output surface that used to require a much larger team.

But this only works if the agents are actually good.

It takes iteration, taste, source material, QA, workflow design, and real marketing experience.

Bad agents do not become good because you connected more tools.

Vague agents just create vague output faster.

TLDR:
> turn the agency’s knowledge into a brain
> turn repeated work into scoped agents
> turn each client into an isolated pod
> let skilled operators run the system

[Garry Tan @garrytan]

So close to product market fit is still not product market fit

[Garry Tan @garrytan]

I like this essay and hope we can both work hard on things we care about

AND

live good lives where we enjoy good and beautiful things: food, experiences, being real with one another

It’s not grindslop vs aristocratic malaise. This is a false dichotomy.

[Elon Musk @elonmusk]

True

[Paul Graham @paulg]

There is nothing more powerful than well-informed optimism. It has to be well-informed though. The "everything will be fine" type of optimism may also be somewhat useful, but it's not as useful as the "Hmm, what if we tried x?" kind.

[Rahul @sairahul1]

Karpathy just described what hiring looks like in 2026:

"Build a large project with Claude Code — like a Twitter clone. Make it secure. Have real agents using the platform doing stuff. The interviewer uses parallel agents trying to break in to verify security."

One person. Multiple agents. Shipping and defending production code simultaneously.

This is not a future job description.

This is happening right now.

The founders who get there first are not the smartest ones in the room. They are the ones who stopped doing everything themselves and built agents to do it for them.

Here is the complete playbook — 13 agents, exact prompts, 90-day build plan ↓

Read this before your competition does.

[Aditya Agarwal @adityaag]

https://t.co/AoyPtuI9jx

[Deedy @deedydas]

The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen.

Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation).

Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there.

Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI.

As a result,
1. The corporate ladder looks like the wrong building to climb.
Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more.

2. There’s a deep malaise about work (and its future).
Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire"

3. The mid to late middle managers feel paralyzed.
Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies.

4. The rich aren’t particularly happy either.
No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money."

I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here.

Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success".

Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.