Data and AI leader, ~15 years. Obsessed with one open problem in AI: your tools remember you, but not each other.

DFW
Let’s hear from our drivers, which mirror is adjusted correctly?
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agreed. but just a small sliver
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When I see this it confuses me. ssh into a computer for god sakes, or use codex remote feature w/ mac mini. Once you have been stuck in "i need to keep my laptop open" more than 3 times, its crazy to keep this insanity going.
tell me you work in tech without telling me you work in tech
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your battery dies way faster
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Faster than being open and awkward to hold and then accidentally dropping it? No I don't think so.
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had to share lol
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Would be less focused on other people's sentences if they got a job... lol
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Alright who was in the wrong here? The car coming from the right was in the far right lane and while I was mid-turn they decided to change lanes almost hitting me. FSD hit the brakes and they slammed on the brakes pretty hard themselves which if you look closely you can see the passenger get thrown forward. I felt comfortable letting the car take the turn because I saw the car was in the far lane but these turns always make me nervous for this exact reason. In most of these types of scenarios FSD is extra cautious but i still don’t think it made the wrong decision here. FSD 14.3.10 Speed Profile: Standard
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Your already making the turn with a clear path. They clearly could see you and decided to change lanes anyway - they are at fault. I probably wouldn't have turned but that's because I know there are an abundance of stupid people driving cars.
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How would you like your FSD?
People want FSD, so they go to tesla.com and pick what shape they want it in
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CyberSUV please. We can justify replacing the Tahoe with one of these tiny cars.
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The whole point of building Tavus has been simple: talking to a machine should feel as natural as talking to a friend or coworker. It’s hard to describe all the tiny nuances that make a conversation feel human. The little expressions. Moving around in your chair. Knowing when to speak and when to listen. The dance of it all. Griffin is by far the closest anyone has come to a model that can capture those nuances. The first time I saw it being used, I had no idea I was watching our model rather than just a normal video call. I’m so incredibly proud of this team and what they’ve built.
Introducing Griffin, the first model to pass the video Turing test. 48% of people who talked to it live thought it was a real human. Previous systems have had a pass rate <3%. It is #1 on NVIDIA's benchmark for full-duplex AI video. It’s the first Human Interaction Model (HIM).
Community note
The 48% figure and "video Turing test" claim are from Tavus's own study of 54 one-minute calls, not independently verified or using a standard protocol. Griffin-Lite leads NVIDIA's VideoFDB benchmark on their public leaderboard. cellcog.ai/blog/tavus-gri… research.nvidia.com/labs/amri/proj… tech-ish.com/2026/10/02/tav
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It's super cool... but her mouth still makes it very obvious that it's AI generated. Still great work but not quite as real as X is making it seem.
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Dot is going to be great! Please use it with voice! It will work now 😉
Introducing dots, powered by GPT-6 Astra. Remarkably capable, always-on agents built to handle everything.
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US based 20x sub and still no access so... meh.
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hey @thsottiaux why are dots not available in other supported countries apart from the us?
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Not available in the US either.
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Are Dots available in your region yet and where are you from? chatgpt.com/dots Let's track propagation
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No, US (Texas).
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I cannot believe how smart astra is
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O really
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I see what you did there.
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SuperGrok Heavy costs $300, and with Grok 4.7 you only get around 3.5B tokens in Grok Build Meanwhile, the $200 Codex plan gets me close to 20B tokens a month roughly 20K worth of usage These numbers are based entirely on my own personal usage Honestly, the pricing just doesn’t make much sense to me. Claude gives you around 15K worth of usage, and considering Grok 4.7 still isn’t really at the level of the top frontier models, it’s hard to justify paying that much for such limited usage This isn’t hate , it’s genuine feedback, I’d love to see them improve the limits and offer at least 10B+ tokens. Hopefully SpaceX takes a look at this and does something about it
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Free Cursor Ultra changed the equation. Without it, no chance
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Yeah they took that away very quickly tho.
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It’s over for SpaceXAI. In 2-3 months Anthropic and OpenAI will have something even better.
Replying to @notjazii
I am cautiously optimistic that SpaceX will have a Fable/GPT-6 level model in 2 to 3 months
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Newton level toast AI.
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E5, the only answer.
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Let's do things properly. I extracted my actual Codex usage for the past 7 days directly from the local session logs, including input, output, cache, and models used. So let's calculate what this week would have actually cost in Codex credits. My usage: > 1.76B input tokens > 97.65% cached > 41.2M uncached input > 5.54M output Applying Codex's credit rates to each model: > GPT-5.6 Sol: ~22185 credits > GPT-5.6 Terra: ~952 credits Total: ~23137 credits. At the 20% volume discount tier, that's roughly €607. That's what my actual week of Codex usage would have cost at pay-as-you-go rates.
Replying to @stemonteduro
You main Sol? And out of curiosity, you never buy credits?
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interesting, cause it should have around 6000-8000 USD worth of value in credits?
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Clearly it doesn't anymore though. Lots of people finding it's 2-3x less than it use to be.
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Jev is great at zero-shot classification, but specialist classifiers will dominate commercial use cases. @trycua tuned a tiny model that scored 99.7% on their form-filling eval. Hosted Jev scored 83.6%. I tuned GLiNER 2.5 on a task in 51 minutes yesterday and it crushes Jev. And it's local. And 8.8x faster: You too can do this. Linked post in comments.
1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua
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I feel like it's always been true that specialist finetunes beat generalists. The trouble is most people using these models don't know how to finetune and/or don't have the data required to train. Jev is a good step toward broad use of a pretty good but not exceptional across all use cases model.
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i'm a little bit disappointed with OpenAI. I'm running out of my weekly usage limit: • I can't upgrade to the $200 plan • I can't use Astra, and I haven't used it all week • Despite all of this, there has been no reset this week In less than 1h, I'll be forced to switch to Claude again. All of this in my first week as a $100 user. Not good at all
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I've been out of usage on the $200 plan for 4 days. It's pretty rough tbh. I have resubbed to Claude and Cursor just to not get blocked. I keep wishing I could go full ChatGPT but it's not enough usage.
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whoa this actually worked! Jev lets me control my browser in real time with my voice now > i talk > transcript sent to Jev > jev returns probabilities in ~300ms > browser clicks costs: $0.0002 per decision i'm stunned how fast this is. when i asked it to "go back", it even finished the request before i finished my sentence 😂
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So cool.
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I think I just cooked something 🔥 jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function. WHERE jev(people, 'could work from home') or WHERE jev(people, 'name sounds european') 129 rows judged in ~1s for $0.0009. Second run: 6ms from cache.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution
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That is very cool.
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so sorry it's going down! demand is beyond our wildest dreams 😭 we really want this up and available for everyone
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Just happens man; don't stress we aren't going anywhere!
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i made an llm from first principles with Jev 29 yes/no questions per character: should the next key be a–z, space, comma, or period? highest probability gets append to it, then fed the updated text back in & repeat an autoregressive loop made out of a classifier
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution
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