Sachin

Present

[Sachin @imsdolta]

❤️❤️❤️❤️❤️

[Sachin @imsdolta]

Let me sleep, Mira!

[Mira Murati @miramurati]

Our first model, Inkling. Trained from scratch, weights are open, fine-tunable on Tinker today.

[Sachin @imsdolta]

@myprasanna @vorfluxai @Rippling Competing with @chamath ?

[Sachin @imsdolta]

@ohshinbhat How come you never keep your feet on the chair

[Sachin @imsdolta]

@neembu_paani31 Read Geek to freak by @tferriss

[Sachin @imsdolta]

@midjourney Kudos to the team

[Midjourney @midjourney]

Announcing a new division of Midjourney called "Midjourney Medical"

[Sachin @imsdolta]

@ThePulseSPN @0xMortyx Yes, it would be fun to have unlimited tokens. I'd write loops all day :)

[AnonTeacher @teacherhottakes]

@Freyy_is It's not x, but y.

Not this partial thought. Not this fragment. Not this half idea.

It's the "quietly" doing any action.

It's the double space between each line of a post.

Not this. Not that. Not the other.

It's all of it. And the worst part? It's EVERYWHERE.

[Sachin @imsdolta]

Absolutely. In complex systems that require multiple engineers to understand different parts, you still need domain experts who can ultimately make the call e.g. on networking, distributed systems, security, or tricky business logic.

[Lee Robinson @leerob]

You might believe you should spend less time thinking about code because of AI.

I strongly disagree! We’re watching this play out live where tons of AI generated code becomes a liability.

At the end of the day, an engineer needs to be responsible / on call for code that gets shipped to production. If you don’t understand the system you’re trying to debug, you’re probably going to have a bad time.

Yes, AI can help with all of this, if you set up the proper systems. You can have agents triage prod logs, look at errors, etc. You can speed up parts of the investigation, but an engineer needs to make the call. There might be serious customer or financial implications from that change.

I expect the trend continue for trimming dependencies, vendoring code so you can modify it directly, preferring simpler systems with fewer abstractions, and spending waaaay more time thinking about system design and code maintenance.

I’ve said this before, but it’s a great time to get familiar with CS fundamentals and some of the history behind what great software looks like. Many parts will be different in the coming years as AI progresses, but also a lot more than people realize will stay the same.

[Naval Podcast @navalpodcast]

Trying something new. There’s a bunch of podcasts I’ve been meaning to listen to, so I’m turning our Executive Brief format on them. LMK what you think. — @nivi

—

Executive Brief

Burn Tokens, Not Headcount
Tom Blomfield at Y Combinator @t_blom

Summary: AI does not make the company 20% faster; it changes the shape of the company. The Roman-legion hierarchy gives way to a company brain: record everything, make knowledge legible, turn repeated work into self-improving loops, burn tokens instead of headcount, and keep humans at the edge where judgment, emotion, ethics, and reality matter.

1.⁠ The old company was a Roman legion. Information moved up and down the hierarchy, and human beings were the conduit. AI breaks the assumption that this is the natural shape of a company.

2.⁠ Copilots are the small idea. Making engineers 20% more productive is just putting a bigger engine on the old machine. The bigger idea is to reimagine what a company is and how it acts.

3.⁠ The company’s real operating system is its domain knowledge. It lives in people’s heads, Slack messages, emails, Notion, office hours, support tickets, product telemetry, and code changes. Make that knowledge legible, and the company can start becoming intelligent.

4.⁠ AI is not something you bolt onto the side of a company. The AI-native company is a set of recursive, self-improving loops: sense the world, make a decision, use tools, pass quality gates, learn from the result, and loop again.

5.⁠ The goal is a company that improves while you sleep. If the system can see where it failed, ask why, update the tool, change the skill file, open the pull request, review it, merge it, and deploy it, the company is no longer waiting for a manager to notice.

6.⁠ The holy-shit moment is not an agent answering a question. It is a monitoring agent watching every failed query and making the next version of the system better. That is not AI making one person 20% more effective; that is AI learning how to improve the company.

7.⁠ Product can become a self-optimizing loop. An agent finds the highest-friction part of the sales funnel, researches best practices, launches an A/B test, runs it for a week, picks the winner, deploys it, and does it again.

8.⁠ Customer support can become a product loop. Suggestions come in; agents triage them like a CPO and CTO, discard what does not fit the roadmap, and ship what does. Overnight, without waiting for a meeting.

9.⁠ Burn tokens, not headcount. The next constraint is not how many people you can hire; it is how much intelligence you can apply to repeated work. Token usage is dumb and gameable, but it still points at who is actually experimenting.

10.⁠ Middle management was a coordination layer. If AI can summarize, route, monitor, escalate, and improve workflows, the coordination problem changes. Everyone becomes an IC: a builder, an operator, a directly responsible individual.

11.⁠ Committees are the wrong primitive. For anything important, you need a named human, not a group, not a committee, not a vague owner. AI can coordinate more work, but accountability still needs a person.

12.⁠ Record everything. If it was recorded, it happened to the AI; if it was not recorded, it did not happen to your intelligence. The company brain cannot learn from conversations that disappear into the air.

13.⁠ Raw recordings are not enough. You cannot shove 100,000 hours of meetings into a context window. You have to aggregate, synthesize, categorize, and leave breadcrumbs the intelligence can actually use.

14.⁠ The user manual should become a living brain. YC took thousands of hours of recorded office hours, synthesized them into a new manual, and can now update it every month. Every new piece of advice is compared with the old manual and either incorporated or thrown away.

15.⁠ Preserve the data; throw away the software. Store the emails, DMs, skills, context, and know-how with care. Dashboards and internal tools are ephemeral: generate them, use them, discard them, and regenerate them when the models improve.

16.⁠ Humans move to the edge of the company brain. They handle the places where intelligence touches reality: novel situations, ethical calls, high-stakes moments, co-founder breakups, emotional conversations, and sales rooms where a human still matters.

[Aesthetics 𝕏 @aestheticsguyy]

I dare you to beat my wallpaper ☁️

[Brian Armstrong @brian_armstrong]

People are capable of far more than they think, on far shorter timelines. Problems expand to fill the time you give them.

[Sachin @imsdolta]

Deepseek v4 is finally here

[Sachin @imsdolta]

"To excel in innovation, entrepreneurship, art, caring, hospitality, science and discovery, humans must try things that don’t work, embrace failures, encourage small talk and playfulness - all inefficient. Efficiency is for robots" -
@kevin2kelly

[Sachin @imsdolta]

Here are the JS concepts infographic using latest ChatGPT Images 2.0 model

1. Prototypes

[Cole Jaczko @colejaczko]

Positivity rant:

Why deciding to fail MORE is the best decision you’ll ever make

[Sachin @imsdolta]

Agreed

[Naval @naval]

Vibe coding is more addictive than any video game ever made (if you know what you want to build).

[Sachin @imsdolta]

@a16z Elisha Long made it to X - wild times

[Sachin @imsdolta]

@gaxrav Hmm

[Derek Sivers @sivers]

When I’m feeling troubled, it helps to look around at reality. Am I in physical danger? No. I’m in a room. I’m safe. It’s a reminder that the trouble is in my head.

So I get away from all people and media, to avoid all viewpoints, opinions, and drama.