Kathleen Chaykowski

Comms @OpenAI. Formerly @ScaleAI @robinhoodapp @wsj. Compulsive list maker, word nerd

[Alex Xu @alexxubyte]

https://t.co/z3rXDKYo04

[Greg Brockman @gdb]

Introducing GPT-6 Astra: https://t.co/0eb3Ek6Gl8

[Greg Brockman @gdb]

inference numbers published for jalapeno, team did an amazing job https://t.co/WNJIA2dESm

[OpenAI @OpenAI]

GPT-Live can listen while it speaks.

To make that feel natural at ChatGPT scale, we rebuilt the voice stack from client to model.

This new architecture keeps audio flowing continuously, so deeper reasoning and tool use don't interrupt the conversation.

[Tibo @thsottiaux]

We kept ourselves busy with GPT-5.6 Sol. It's pretty good at things.

Rolling out
- 80% lower prices on Luna
- 20% lower prices on Terra
- faster speeds on GPT-5.6 Sol in /fast mode
- ~ 10X cheaper auto-approve mode ("review for me" in the app), which now uses Luna to prevent many high risk actions from the main agent

Towards intelligence too cheap to meter and many more breakthroughs ahead.

[Tibo @thsottiaux]

Efficiency!

In two steps
a) Train fantastic model
b) Use fantastic model to make everything better, including its own infrastructure, inference stack, kernels, etc, etc

https://t.co/kGtmX3LAow

[OpenAI @OpenAI]

After deployment, we applied GPT-5.6 Sol to advance the frontier of efficiency by making itself more efficient to run.

The results:
- 20% lower serving costs from production GPU kernel improvements.
- 15%+ better token-generation efficiency from improved speculative decoding.

[OpenAI @OpenAI]

10,000 reasons people love GPT-5.6 Sol
https://t.co/Mu6PBPkrop

[Ronnie Chatterji @RonnieChatterji]

As we reach 7M, consider that it took just a few months for the rest of OpenAI employees to catch up to research & engineering in terms of Codex usage. Interesting to see how quickly this happens in other orgs moving from technical to less technical roles.

[OpenAI Developers @OpenAIDevs]

Your favorite Codex shortcuts are getting an upgrade.

July 15th.

[OpenAI Developers @OpenAIDevs]

Applications to attend OpenAI DevDay 2026 are open.

Join us in San Francisco on September 29 to:

• Tinker with what’s new
• Swap build notes with other builders
• Go deep in technical sessions
• Bring your sharpest questions

Apply by July 10:

https://t.co/mda0bFx2Hp

[dominik kundel @dkundel]

🖲️ We love our community! To celebrate getting together with many of you we brought @thsottiaux's reset button to AIE World's Fair.

Congrats Melanie for pushing the button 🎉

All Codex Go/Plus/Pro subscribers around the world are receiving a reset in the bank! Happy Codexing!

[Romain Huet @romainhuet]

Make your way to the OpenAI booth now if you’re at @aiDotEngineer! 🚨

At 1pm, you’ll get to see the Codex reset button in action.

Rumor has it it’s headed back into a top-secret underground vault after today. 🤫

[OpenAI Developers @OpenAIDevs]

⚙️ We debugged a year’s worth of crashes in our data infrastructure and found one issue in the hardware and another that has been unnoticed in open-source code for 18 years.

Here’s how we tracked them down:
https://t.co/5c13Knw69o

[OpenAI Developers @OpenAIDevs]

As agents take on longer-running work, engineering shifts to setting direction, reviewing work, and designing better systems around the models.

@steipete at @aiDotEngineer

[Romain Huet @romainhuet]

Backstage at @aiDotEngineer! The energy here is incredible.

Excited to kick off the opening keynote with @embirico in a few minutes.

We might even be joined by a special guest… 👀

[Alex Xu @alexxubyte]

How OpenAI Built Its Data Agent

Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't.

Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design.

We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it.

The article covers:
- The architecture behind the data agent
- The six layers of context that make a single LLM reliable across 90,000 tables
- How OpenAI Uses Codex Internally: 3 Use Cases
- Five practical lessons for any team building a domain agent
- Where OpenAI's data platform is headed next

[OpenAI Developers @OpenAIDevs]

To bring Codex to Windows, we had to answer a hard question: how do you let coding agents stay useful without forcing developers to choose between constant approval prompts and full machine access?

Here’s how we built the Windows sandbox for Codex:
https://t.co/U8JfOe3WIG

[Greg Brockman @gdb]

Multipath Reliable Connection (MRC): a new open networking protocol for large AI training clusters, deployed in production on our largest training clusters.

[Uday Ruddarraju @udayruddarraju]

There is a lot of news about compute being the bottleneck for AI. There is less visibility into the engineering it takes to make large-scale compute actually work reliably.

In my view, this is one of the most interesting computer science problems in the industry right now. It is not just about getting more GPUs. It is about making every layer of the system work: networking, scheduling, hardware health, storage, orchestration, reliability, observability, security, and the developer experience for researchers.

This blog gives a rare preview into the depth of engineering happening across the stack at OpenAI, starting with MRC and supercomputer networking. We're excited to start sharing more about designing, building, and operating compute at planet scale.

https://t.co/eQVylBAGa3

Join us: https://t.co/BwHYeXnfMo