Ambral (@ambral_ai) is building the first customer mission control. It's an AI account management team built to help you maximize revenue from every customer. Congrats on the launch, @Sambrickman_ & @JackStettner!

We’ll know AGI is postponed once the labs start building features to choose our own background colors and fonts.

There is a huge advantage to being an in-person startup in SF right now: Proximity to the AI labs, early access to models, proximity to people at the cutting edge, access to capital. If a new model drops and your team is faster to integrate, that could easily be the difference between acquiring hundreds of new users in a single day vs not.

Waymo for excavators!

Figma co-founder and CEO Dylan Field (@zoink) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice

It's sometimes hard to grasp the significance of the reasoning and logic updates that are starting to emerge in powerful models, like GPT-5. Here's a *very simple* example of how powerful these models are getting. I took a recent NVIDIA earnings call transcript document that came in at 23 pages long and had 7,800 words. I took part of the sentence "and gross margin will improve and return to the mid-70s" and modified "mid-70s" to "mid-60s". For a remotely tuned-in financial analyst, this would look out of place, because the margins wouldn't "improve and return" to a lower number than the one described as a higher number elsewhere. But probably 95% of people reading this press release would not have spotted the modification because it easily fits right into the other 7,800 words that are mentioned. With Box AI, testing a variety of AI models, I then asked a series of models "Are there any logical errors in this document? Please provide a one sentence answer." GPT-4.1, GPT4.1 mini, and a handful of other models that were state of the art just ~6 months ago generally came back and returned that there were no logical errors in the document. For these models, the document probably seems coherent and follows what it would expect an earnings transcript to look like, so nothing really stands out for them on what to pay attention to - sort of a reverse hallucination. GPT-5, on the other hand, quickly discovered the issue and responded with: "Yes — the document contains an internal inconsistency about gross-margin guidance, at one point saying margins will “return to the mid-60s” and later saying they will be “in the mid-70s” later this year." Amazingly, this happened with GPT-5, GPT-5 mini, and, remarkably, *even* GPT-5 nano. Bear in mind, the output tokens of GPT-5 nano are priced at 1/20th of GPT-4.1's tokens. So, more intelligent (at this use-case) for 5% the cost. Now, while doing error reviews on business documents isn't often a daily occurrence for every knowledge worker, these types of issues show up in a variety of ways when dealing with large unstructured data sets, like financial documents, contracts, transcripts, reports, and more. It can be finding a fact, figuring out a logical fallacy, running a hypothetical, or requiring sophisticated deductive reasoning. And the ability to apply more logic and reasoning to enterprise data becomes especially critical when deploying AI Agents in the enterprise. So, it's amazing to see the advancements in this space right now, and this is going to open up a ton more use-cases for businesses.

New cookbook in partnership with @llama_index is out! We go over how to build a powerful parse-retrieve-rerank workflow over complex PDFs Check it out!

We're hosting a Context Engineering event in SF! ​Learn how leading applied AI teams are engineering their context windows. Hear from: Jake Heller, CEO @Casetext ​Beyang Liu, CTO @Sourcegraph Sam Bhagwat, CEO @Mastra_ai Jeff Huber, CEO @trychroma RSVP:

Anytrace () is your AI support engineer. It can resolve technical tickets end-to-end by talking to customers, investigating issues, and managing the full ticket lifecycle. Congrats on the launch, @samgoldman0 & @tairabun!

A new AI research lab called @blankbio_ launched today. They are building new foundation models for mRNA, and are one of the strongest technical teams I've worked with. I'm particularly excited about their new model results, and the wide set of application areas for it.