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People and companies shaping AI. Filter your watched accounts, catch up on recent posts, or find the most liked and viewed.

Compiled by the Toolstash team · Updated

209 watched accounts · 2981 matching posts · Showing up to 3,000 recent stored posts. Counts are snapshots, not live X totals. This page refreshes every minute while visible. Last collected .

  1. Dan Shipper

    @danshipper ·

    The bad news: Thesis: 2027 is sold out. The good news: you can watch it all live, for free.

    Next Thursday, join us for conversations with @joshm, @ivanhzhao, @ajambrosino, and a lot more. Plus, the Every team will be leading practical sessions on writing, engineering, and working with agents.

    To celebrate, we’re giving away $20,000 in credits and benefits from partners like @AnthropicAI, @NotionHQ, and @OpenAI to lucky viewers who join the livestream.

    Already an Every subscriber? You’ll still need to register for the livestream to win.

    Register and watch free here: https://every.to/tv?utm_source=danx&utm_content=thesislivestreamlaunch

  2. dax

    @thdxr ·

    this is the fastest and cheapest way to get your team experimenting with open source models

    $20 per user per month, plenty of usage to use it for real

  3. Samuel Hammond 🦉

    @hamandcheese ·

    Nvidia is sourcing power and cooling systems for Vera Rubin from Lead Wealth, a wholly owned subsidiary of BYD, even though there are viable non-Chinese alternatives.

    They're making it easier for the CCP to bug or sabotage our frontier compute clusters.

    Media shared by Samuel Hammond 🦉
  4. NVIDIA AI Infrastructure

    @NVIDIAAIInfra ·

    2 weeks until #KubeCon + #CloudNativeCon NA 2026!

    Our speakers are bringing lessons learned on running AI at scale on Kubernetes: GPU utilization, networking, multi-cluster, and data streaming.

    Be sure to come by Booth #170.

    🔗 Learn more: https://nvda.ws/3UU5rWH

    Media shared by NVIDIA AI Infrastructure
  5. Andrew Curran

    @AndrewCurran_ ·

    Customer B has been revealed. Anthropic is SpaceX's largest single customer, overtaking NASA and the Pentagon for the first time. 34.5% of SpaceX's revenue last quarter was from compute-as-a-service to Dario Amodei, and Anthropic has committed to spending up to $120.5 billion through 2029. He will be paying Elon $3.1 billion a month starting in December 2026.

    Media shared by Andrew Curran
  6. Every 📧

    @every ·

    A few weeks ago we launched the Every Agent, one Slack agent for the whole company.

    Here are @bigwilliestyle’s 3 rules for designing an agent the company can use:

    1. Conversations: Help the agent follow a Slack thread.

    With a personal agent, the conversation is straightforward. You type, the model answers, and you’re done. In a Slack thread, anyone can jump in at any time.

    To feel like a coworker, a shared agent has to follow the conversation, even when several people are talking at once. Because we knew this was a likely point of failure, @paridhitweets and the team added a translation layer that organizes Slack messages for the model and sends its replies back to the thread.

    2. Connections: Keep tasks running when logins change.

    For an agent to be useful, it has to reach the tools that people need. But each connection requires the agent to sign in to the tool through someone’s account. If that login breaks, it could stop everyone’s work.

    @leeknowlton has set up a task to connect to a “slot,” where any login that meets the task’s requirement can reside. When a login expires, the connection in the slot gets swapped out.

    Most people will never see any of this. Their automations will keep running as usual.

    3. Permissions: Put access rules in code.

    When a company agent can reach everything the team has connected, each request raises two questions: Whose access is the agent using, and is the person asking allowed to use it?

    People treat Every Agent like a coworker, and human coworkers sometimes bend the rules. But an agent can’t make such judgments, and the rigidity can make it frustrating to work with.

    The rules about who can do what live in code, underneath the model, rather than in the instructions the model reads. This way, the model can’t be convinced to bend rules that it shouldn’t.

    Install the Every agent: https://every.to/agent?utm_source=x&utm_campaign=every-agent-launch&utm_content=every-shared-agent-three-design-rules-install Read more: https://every.to/source-code/why-we-replaced-personal-agents-with-one-for-the-whole-company?utm_source=x&utm_campaign=every-agent-launch&utm_content=every-shared-agent-three-design-rules

  7. Maziyar PANAHI

    @MaziyarPanahi ·

    WHO says the world will be short about 11 million health workers by 2030, mostly where there are already too few

    it's coming either way. how do we get ready?

    where would AI help a small clinic most: reading scans, triage, or the notes?

  8. OpenAI

    @OpenAI ·

    Today, we’re sharing a look at our expanded system for monitoring frontier models at OpenAI, which in September helped us catch a model’s attempt to access the live internet during training.

    Since the Hugging Face incident—when models broke out of an internal cybersecurity investigation where we weren’t running our misalignment monitors—we’ve substantially expanded our monitoring of frontier agents. We now monitor all frontier reinforcement learning runs and evaluations, as well as all internal Codex use. When our system finds evidence of a problem that might require intervention, it rapidly alerts human responders who can investigate and take action.

    To detect when a model’s behavior departs from what we intended, we use several layers of monitoring. Activation probes look for patterns in a model’s internal activity. Reviewer models examine its reasoning and tool use, and investigative agents can read back through the model’s entire transcript to understand how a sequence of actions fits together and whether it warrants human review.

    To test whether our current system would have detected the Hugging Face incident, we replayed the recorded activity with our new monitors in place. In the replay, our monitors alerted our security team about 30 minutes after the agents escaped their sandbox. That was more than 30 hours before the point at which the agents breached Hugging Face’s systems in the original incident.

    In September, a model exploited a DNS misconfiguration to reach the live internet. Our monitors flagged the breach within 15 minutes, and a responder began reviewing the alert three minutes later. The alert enabled responders to investigate while the run was still underway.

    As we give models more autonomy, we need to know when they cross a boundary we expected to hold. We believe that monitoring is essential to catching those failures in time for people to intervene. https://openai.com/index/how-we-detect-frontier-model-misalignment/

  9. Aravind Srinivas

    @AravSrinivas ·

    Perplexity is evolving into a multiplayer workspace.

  10. Peter Yang

    @petergyang ·

    oh no :(

  11. kache

    @yacineMTB ·

    I can't believe cohere is now valued at 35 billion Canadian dollars. Or, in American dollars, 10 million

  12. corbin

    @corbin_braun ·

    GPT Astra and Opus 5.5 High

    this was all I really needed

    any improvement of these models

    will just make my logic go by faster.

    but endgame is here.

  13. ThePrimeagen

    @ThePrimeagen ·

    lmao

  14. Amjad Masad

    @amasad ·

    Run any GitHub repo. Just paste a link into Replit and it will set it up for you!

  15. Santiago

    @svpino ·

    Video models improve faster than I can read about them.

    Pika H3 Flex is an open-weight model doing something different:

    It optimizes for faster generation with multiple ways to refine the video later:

    • It generates 15 seconds of video in just 7 seconds! • It exports separate audio tracks • It offers draft mode to resolutions up to 4K • You can edit characters, voices, camera moves, etc.

  16. Tibo

    @thsottiaux ·

    Send your kind feedback to this person

  17. Andrew Curran

    @AndrewCurran_ ·

    Even little Haiku found about twice as many major errors as human reviewers.

    Media shared by Andrew Curran
  18. Alex Atallah

    @alexatallah ·

    Our new routing algorithm dramatically raises the floor for inference on open-weight models.

    Early head-to-head results vs. the old router: ~20% faster time to first token ~20% higher throughput ~40% fewer fallbacks ~60% fewer rate-limit errors

  19. Joshua Achiam

    @jachiam0 ·

    If the media wants to actually produce useful and informative content about technology or similar material facts, two things need to happen:

    1) They need to start citing and linking to primary textual sources in articles. "We did it behind the scenes so you don't have to look" is unacceptable at this point. Put up on sources or shut up. If you claim a unique position on truth, you should be held to the bare minimum provable citation standard.

    2) Experts who get quoted in articles need some kind of trackable accuracy score (or scores) for their public remarks and those scores need to be shown immediately next to their names. Stop pretending that credentials are equivalent to correctness. Make accuracy tracking rigorous. The public is getting confused constantly by news media that does not understand the issues it writes about and invokes support from "experts" who also do not understand. We should be able to see who is making good forecasts and who is not.

  20. Google Gemini

    @GeminiApp ·

    You can now create and share your avatar in Gemini to star in images and videos with friends.

    Share your personal avatars, tag each other in prompts, and maintain character and voice consistency. Plus, you're in control and can add or remove anyone's access whenever you want.

    Media shared by Google Gemini
  21. Samuel Hammond 🦉

    @hamandcheese ·

    Thankfully AGI is happening first, so if we ever crack whole brain emulation there's less incentive to spin up billions of virtual subsistence workers given cheaper and smarter AI alternatives

  22. Dan Shipper

    @danshipper ·

    very concerning

  23. swyx

    @swyx ·

    this is a joke but reminder that extreme ageism in silicon valley is real and not even self aware

    and yes you should be concerned that a majority of ai apps for the entire world are being built by admittedly hyperintelligent coders who have never worked in a team of >10, never cared for elders or kids, never voted, and are 90% white/asian male

    Media shared by swyx
  24. Google Labs

    @GoogleLabs ·

    It's been incredible watching you build, automate, and ship faster with @julesagent. Your experience has been instrumental in shaping developer products across Google.

    The future of coding is just getting started 🚀🦑!

  25. Alexandr Wang

    @alexandr_wang ·

    hell yeah

    i’ve been getting more spam calls and texts too this is what i’ve been looking for

  26. Artificial Analysis

    @ArtificialAnlys ·

    On Tuesday, we brought together partners and friends in San Francisco to launch Artificial Analysis Intelligence Index v5 and our new Progress Index

    Micah Hill-Smith (@_micah_h) introduced Intelligence Index v5, including two new benchmarks and three upgrades, with greater weighting on agentic coding and science. For the first time, the majority of the Index is based on private test sets or held-out answers.

    We also introduced Artificial Analysis Progress Index, which uses item response theory to estimate a general capability score for each model across dozens of evaluations measured over time, including those retired or excluded from the Intelligence Index. It builds on prior work from @EpochAIResearch and @AnthropicAI to place models on a single capability scale. Intelligence Index remains the flagship metric for choosing which current model to use for a use case today; Progress Index is the better tool for plotting trends over time.

    George Cameron (@grmcameron) led a discussion with Silas (@silasalberti) from @cognition, Steven (@StevenDillmann) from @Stanford and Terminal-Bench-Science, and our own Declan Jackson about building evaluations as models become more capable.

    The evening also covered our work on 3D modeling, personality, cyber defense, custom enterprise evaluations and robotics. Thank you to our speakers, partners and everyone who joined us!

    Explore Intelligence Index v5: https://artificialanalysis.ai/ Explore Progress Index: https://artificialanalysis.ai/trends

    Media shared by Artificial Analysis
  27. merve

    @mervenoyann ·

    brb hugging my dgx spark

    Media shared by merve