I still remember the exact room, watching @stevenbjohnson break down how he writes books. That was the spark for a little project we never imagined would grow this big.

Today, over 30 million people and 600,000 organizations use it, and it keeps growing. We all feel like it's just getting started.

For a while, we’ve just called it "Notebook" internally. Today, we're making it official externally. :)

Spurious correlation or causation? https://t.co/9XVcUBfT74

Look at this beauty https://t.co/arLnM9smr9

Evening! We’ve gotten lots of great feedback on the new ChatGPT desktop app (which we didn't get totally quite right on the first try), and as a result, we've made some changes.

1/ ChatGPT conversation history and projects are now visible in the sidebar. Also, your Chat and Work history now sync across web, mobile, and desktop. Local tasks still stay on your computer.
2/ You can now easily switch between Chat and Work modes inside ChatGPT on desktop, which is now also consistent with how it shows on web and mobile.
3/ Nothing is changing for users on Codex mode. It's still the OG and best at what it does.

And overall we're continuing to fix paper cuts and improve performance, reliability, and efficiency.

Keep up the feedback, hope you like the updates!

Underrated reply 🤣 https://t.co/TkpX22Be1e

I'm surprised Claude Code doesn't have any Google Workspace connectors beyond Drive while ChatGPT does

smh trying to use Claude Code browser use

Hope they hire some good folks to fix this https://t.co/3fOppcdeK0

You know your workflows best. Show our team how you use Cowork so we can make it work better for you!

If you're in marketing, sales, finance, legal, or any non-engineering role, sign up here for a 30 min screen share with our team: https://t.co/kvc8uYzcNL

$NVDA should be pumping on the K3 news. Instead it’s down.

Apparently the distillation model can outperform the teacher model 😂 https://t.co/fFp5UnrBPR

If you want to play my chess engine (WIP): https://t.co/4WJ9SYmr1o

It already seems to perform better than frontier models on chess.

It’s fine-tuned on 2M stockfish-labeled positions, then short GRPO RL pass.

Documentation/tutorial with all the experiments and annotated code. https://t.co/INdb7rojHU

Open-weight models like Kimi and GLM will cause a complete rethink of the enterprise AI stack.
If you are running an enterprise, you need to maximimize model optionality.
Here are 3 things you should be doing:

1. Evals - build rigorous evals representative of your use case
(a) Regression evals - tablestakes features that should always work with high reliability
(b) Aspirational / hill-climbing evals - harder use cases that the best model for your price point struggles to solve. So you scaffold it with prompting, context management and other techniques to overcome weaknesses or wait for a better model
These evals should be easy and quick to run. Eval velocity is a competitive advantage.

2. Model routing - Model selection is about tradiing off quality/cost/latency for your use case. If you have good evals, you know which models to route traffic to for different use cases.
This is something you ideally build yourself cos nobody understands your business and users like you do.
There are off the shelf routers, but I haven't seen one I would recommend yet.

3. Model-agnostic harness - your system should never know which model is behind the API call.
This means your harness normalizes prompt structure, context management, tool definitions, and output parsing across models, making it easy to switch... once your evals pass.

The bigger payoff comes when fixing and maintaining happens in the background and your teams can focus on building. That's when you start doing things that weren't even in range before.

Anthropic is on step 3 and pushing toward 4. Personally, I just hit level 4.

Curious where you are -- what step is your team on?

Once your teams are bought in, how do you track it? Usage is worth watching (e.g. a dashboard), but it measures activity, not return. A better question: would you have spent engineering effort on this anyway? If yes, how much and what would it have cost in manual eng-hours? That's your return.

In practice that means giving Claude ways to verify its own work end to end. It means enabling auto mode for permissions, defaulting on automated code review and security review, and using interfaces that let you manage multiple agents at once (Agent view in CLI, Desktop app, iOS and Android apps, Tag).

To get to higher levels it means /loop, /batch, dynamic workflows, and worktree isolation for subagents. It's not about a single feature, but rather using the right features with the right guardrails that enable Claude to automate entire classes of work in a way that your team can trust the output.

So many interesting hardware ideas from China

Like this face mask that doubles as a mic so that you can use voice dictation in public areas without being overheard https://t.co/k7v80LfVvJ

Just be a YC startup

Problem solved https://t.co/UWByP04Uio

Please join @garryslist

We are trying to organize the common sense builders

It’s going to be the defining fight for California and America https://t.co/IpYLoSaU5s

this is who runs this account https://t.co/5xGlSlZwTl https://t.co/ot04FHFqoq

HOLY FUCKING SHIT the react avengers have assembled https://t.co/8hqGSTOM16

we make sure some of the top YC AI companies are featured at AIE every single year.

this year, we were graced by @garrytan and @eve_bouff to cap off our startups and design engineer focused audiences respectively. Really enjoyed these raw high value perspectives! https://t.co/AqIFQlf0jI

@mattpocockuk @trq212 @xdg's session/tree based grill me looks cool but havent tried https://t.co/7hFZr3S5F7 https://t.co/Pc4J4ZE4Q5

This great conversation with @sk7037 of @OpenAI is also available on Spotify, Apple Podcasts and here on YouTube

(note: we had sound qualities in this episode, apologies, but the conversation is well worth a listen)

https://t.co/83BkKKQa6K

"We can't build fast enough": my conversation with Sachin Katti (@sk7037), Head of Industrial Compute at @OpenAI about Stargate, Jalapeno, data center financing, and all things compute.

00:00 — Cold open: “One of the largest things humanity has ever built”

00:30 — Welcome: Sachin Katti, Head of Industrial Compute at OpenAI

01:44 — Is this the biggest infrastructure buildout in history? 03:41 — Why OpenAI is building a new industrial muscle

04:54 — What an AI data center actually is

05:27 — “Factories turning electrons into tokens”

06:35 — Why AI data centers need liquid cooling everywhere

08:10 — The power problem: grids, generation, transmission, substations

10:43 — Behind-the-meter power and gas turbines

11:02 — Why nuclear “can’t come soon enough”

11:49 — Jalapeño: why OpenAI is designing its own AI chips

13:19 — Tokens per watt: the new metric that matters

13:38 — Why inference may now dominate AI compute

14:58 — Is OpenAI overbuilding compute?

16:47 — Why OpenAI thinks the bigger risk is not building fast enough

17:55 — Communities, jobs, water, and the local data-center debate

21:16 — How OpenAI chooses data-center sites

22:25 — What “industrial compute” means inside OpenAI

25:59 — Sachin’s path: Stanford, startups, Intel, OpenAI

28:05 — OpenAI’s compute portfolio: Microsoft, hyperscalers, neoclouds

29:37 — Stargate explained

31:21 — Abilene, Oracle, and the next wave of AI data centers

32:48 — How massive AI compute gets financed

34:05 — How OpenAI designed Jalapeño so quickly

35:59 — AI is starting to help design AI chips

36:20 — MRC: the networking problem behind 100,000 GPUs

38:47 — Bottlenecks: transformers, turbines, electricians, supply chains

40:29 — Guaranteed capacity: intelligence as a supply unit

42:08 — Will AI data centers move to space?