History listed on @Nasdaq. @SpaceX ($SPCX) raised a record $85.7B in capital, hit a $2.1T market cap, and traded 500M+ shares on its first day as a public company. Read more about how the capital injection will directly fund SpaceX’s infrastructure for the future: https://t.co/AZ4uPAtOvZ

TBC is hiring exceptional mechanical, electrical, civil, software, and field engineers in Bastrop, Las Vegas and Nashville. Apply for the July 1 Boring Factory video tour, and hear directly from the engineers who are designing/building Prufrock and trying to Beat The Snail! https://t.co/MolT6mjOka

BREAKING: Paraguay receives Starlink kits to connect remote schools, health centers and communities. 🇵🇾 • 50,000+ students and teachers will benefit. • Goal: connect 1,600 locations nationwide. • Prioritizing rural, and isolated communities. • Also connecting remote healthcare centers. • 100 kits already installed in rural Chaco. • Builds on an initial plan to connect 500 strategic sites. • Expanding access to online education, digital libraries and global opportunities. This partnership between Paraguay Government and Starlink will help bring internet access and more opportunities to people across the country.

The 4-bit quant is now available, making our first open-source agentic coding model small enough to be run on a Mac. Get the weights here: https://t.co/8amCVzzunE

@Abaybektursun Not sure tbh. If yes, it wouldn't surprise me, it's pretty common. Claude distills from the internet & potentially others, others distill from Claude,... that's just the natural dev cycle. GLM 5.2 is >10 pts better than Opus 4.8 on coding tasks btw, so they not "just" distilling https://t.co/ih3FlbLIgB

Where will Midjourney's body scanner improve most dramatically? @iScienceLuvr, CEO of @SophontAI: "This is just a prototype of what can be done, and the resolution will continue to improve over time." "Even just the reconstruction algorithm, you have the ring of all the transducers, and to construct that slice, you have to emit from all different angles and based on that, reconstruct a slice. There are many innovations that could take place in just improving that algorithm." "The algorithm they currently use is a bit of a more basic algorithm. But they're looking at developing all these sophisticated physics simulators to understand the propagation of the ultrasound through the human body and then being able to back-propagate that and construct the slice." "In probably version three of the system, they're gonna be having their own silicon, custom chips they're developing for this. All of that is needed in order to make these advances."

my notes from the @midjourney medical launch - @Scobleizer compared this to the original iPhone and Tesla launches (that he was also front row for) - find you a man who looks at you like @bryan_johnson was 😍 ing for @DavidSHolz - see @iScienceLuvr tweet linked for Nature paper - reminds me of our @biohub episodes: better science starts with better data, and that means better imaging - people asking "but wen FDA?" are so small minded. we will do the easy stuff, then we'll do the harder stuff. roll up your sleeves and help or just be patient. - when you have genuinely better tech+mission, all the other hurdles just sort of fall away/figure themselves out: business model, regulatory approval, hiring, marketing, confusion over what to do - this was just the first of 8 side project launches MJ has planned this year - this is what technological ambition looks like: not 10% better, not 2x better, but 40-100x better in every dimension - how are we getting this level of innovation and ambition out of a $10m/yr research budget and whats wrong with the way we use R&D in every other megacorp/goverment/frontier lab? - how has $BFLY stock not mooned yet, this thing just had its ChatGPT moment thank you to L for letting me into what I believe is going to be the top 10 most important launches i'll ever see live.

Announcing AA-Briefcase, the benchmark for the next era of agentic knowledge work AA-Briefcase is our new benchmark for testing models on long-horizon knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week projects, each with many linked tasks and thousands of input source files. We evaluated Claude Fable 5 from @AnthropicAI before it became unavailable, and it currently leads with an Elo score of 1587, followed by Claude Opus 4.8 (max, 1356), Opus 4.7, and the recently-released GLM 5.2 (max, 1266) from @Zai_org. Claude Fable 5 cost $31 on average to run each AA-Briefcase task, followed by Claude Opus 4.8 at $10.40, GPT-5.5 (xhigh) at $3.68 and GLM-5.2 (max) at $2.40. AA-Briefcase comprises four private scenarios, each representing a multi-week knowledge work project set in a realistic organizational context. A public fifth scenario has been released via @huggingface as a representation of scenario structure, submission, and grading (AA-Briefcase Lite). This does not count toward official AA-Briefcase results, and is demonstrative only. Key elements of AA-Briefcase: ➤ Realistic long-horizon projects: AA-Briefcase moves beyond single, disconnected prompts by evaluating models across a coherent long-horizon project. Tasks build week by week, draw on shared institutional context, and require deliverables such as financial models, board presentations, and design mock-ups ➤ Large volumes of fragmented context: AA-Briefcase requires models to reason across thousands of inputs, including company documents, meeting transcripts, large-scale data exports, 25,000+ Slack messages and 3,500+ emails. These sources are fragmented, messy, and often contain realistic contradiction, testing whether models can navigate the ambiguity of real-world knowledge work ➤ Composite rubric and pairwise grading: AA-Briefcase combines binary rubric checks for ground-truth correctness with pairwise grading on analytical quality and presentation quality. Unlike many evaluations that focus on a single metric, AA-Briefcase tests agentic capabilities more comprehensively, exposing cases where models produce outputs that look polished but are incorrect or lack analytical rigor ➤ Built by industry experts: AA-Briefcase scenarios mirror real-world knowledge work, with tasks developed over months by experts across data science, product management and corporate strategy from companies including Google, McKinsey & Company and BCG. Task challenges are drawn from professional experience, making AA-Briefcase more reflective of the ambiguity, messy context and competing priorities that define real-world knowledge work Key results: ➤ Claude Fable 5 leads AA-Briefcase at 1587 Elo: This is followed by Claude Opus 4.8 (1356) with the next-best non-Anthropic model, GLM-5.2 (max), ~90 points back at 1266. Note that Claude Fable 5 did not use the Opus 4.8 fallback for any task in AA-Briefcase ➤ Cost per task varies by ~800x across models tested: Claude Fable 5 leads the benchmark but costs more than $31 per task on average, compared to ~$0.04 for DeepSeek V4 Flash (max). The strongest price/performance options are open weights models such as GLM-5.2 (max) and DeepSeek V4 Pro (max), with GLM-5.2 (max) scoring only ~90 Elo below Claude Opus 4.8 (max) for less than 25% of the cost ➤ Real-world complexity remains difficult for models: The top performer, Claude Fable 5, satisfies all rubric criteria on just 3% of AA-Briefcase tasks. On 31 of 91 tasks, no model scores above 50% on the rubric criteria ➤ Task difficulty scales with the number of required input files: For each rubric check, we identify the set of source files needed to pass. Across all models, pass rates fall as this file count increases, though top-tier models degrade less than weaker models More details below in thread ⬇️

Pull requests are easier to open than ever, but every review still takes human effort. Introducing pull request limits: maintainers can cap how many open PRs contributors without write access can have and set a bypass list for trusted contributors. More signal, less queue noise.

Learn more 👇 https://t.co/XrFU9MfNun

@valuetainment https://t.co/UPPUO77Ado

@swyx @midjourney @bryan_johnson @DavidSHolz @iScienceLuvr I captured a photo of you asking a question. What a front row! https://t.co/ity8V0VV0v

Can a VLM see without a vision encoder? We trained one for $100, inspired by Gemma 4 12B. Latency on an M3 Pro MacBook: 112 ms -> 1.1 ms for the image path 30% lower end-to-end image+LLM The architecture is just: patchify the image -> linear projection with pos embeddings -> LLM Writeup: https://t.co/yt0IKzsF7O

Commodore just made a cell phone and it goes way back! Meet Callback Link: https://t.co/qoYA49mZWN https://t.co/9QdmOyVgdW

When the US restricted foreign access to some of Anthropic’s most advanced models last week, it underscored a new reality: AI is now a geopolitical issue. In his latest piece for Project Syndicate, Sakana AI’s Co-Founder Ren Ito argues that AI sovereignty is not about building a national ChatGPT. It is about maintaining access to frontier AI and preserving the freedom to choose among multiple models. https://t.co/Um1VnnOkPr

Persistent scam. Beware of fake names etc touting stocks. Have seen variants on this several times lately; two different fakes just today. cc @KettlebellDan https://t.co/gMsYdeccTD

@cyber_razz https://t.co/wVuMh1DuCv

@shiro_life0 https://t.co/wVuMh1DuCv

Skill here: https://t.co/AsmdamIFuW

@TelepathicPug There's a webcam and the servo reports position, current, torque. Apart from the model's CAD designs snagging occasionally and the beaglebone black being slow reading webcam it seems to be going pretty well 😂 https://t.co/0ZKAZuaqvM

Excited to share my new agent skill. /youtube-notetaker generates Artifacts from YT videos. Captures slides, notes, transcription, and whatever you want. Open-source, and you can customize it as you please. https://t.co/uG1HHVEAxF

@fuji_ai_ https://t.co/wVuMh1DuCv

As AI takes on longer, higher-stakes tasks, we want models to carry beneficial and safe behavior into new domains beyond their training—and maintain it under pressure. That’s the idea behind our new research on training models to be broadly and persistently beneficial. https://t.co/6Yw45s1RRq

We trained models with reinforcement learning on realistic conversations to reinforce beneficial traits like truthfulness, humility under uncertainty, openness to correction, fairness, and concern for human welfare, across 12 domains, including health, science, and education. https://t.co/Mn5W4UWlWz

A small amount of this data produced broad gains beyond the training scenarios. Compared with a compute-matched baseline, the trained model improved on 44 of 53 independent evaluations of alignment and benefits, spanning deception, reward hacking, safety, health, and mental health. These evals varied widely in domain, task format, and grading scheme.

The most interesting test was cross-domain transfer. When beneficial behavior training was limited to health conversations, the model still improved on non-health evaluations of misalignment, deception, and reward hacking—even though those tasks looked very different from the training data.

We also tested whether alignment persisted under pressure. The model was harder to steer toward harmful behavior with adversarial prompts, while remaining responsive to helpful instructions. We saw preliminary evidence of greater resistance to harmful fine-tuning. https://t.co/dFXdWdMuDG

Planning with the views: Can VLMs predict how each camera move changes the view, and plan many such moves ahead? We introduce ViewSuite with 6 DoF camera control and ~165K task instances, testing: Path-to-View View-to-Path Interactive View Planning A sharp Planning Gap emerges: + can roughly "track" how camera action changes views - cannot "compose" a plan towards a target view at all We then try to teach VLMs with Reinforcement Learning. - RL cannot teach VLMs such planning ability, only 2.5% success rate with Qwen2.5-VL-7B. + With View Graph Distillation (our RL-Graph-SFT framework), 2.5% → 47.8% Below, we answer these questions: Q1. What are the failure modes? Q2. How can we make RL work? Q3. What has the model learned? Can we open up the model to see before/after? Can such spatial priors transfer to other view related tasks? Led by @James_KKW, great to work with @LINJIEFUN @zhengyuan_yang @shiqi_chen17 @wzenus @drfeifei @jiajunwu_cs Leonidas Guibas, Lijuan Wang. A joint efforts with @StanfordAILab @StanfordSVL @MSFTResearch.

AI enrichment activity: pendulum balancing challenge. I highly recommend hooking an LLM up to something physical with a goal/hill to climb. It's magical doing my own thing then seeing something happen as it runs another test :) https://t.co/iHjIfHh3tr

🤖 Bring your own AI models to @code! Connect models from providers you already use, run local models, and choose the right model for every workflow in VS Code. 📖 Read the full post: https://t.co/od5Hb9SX0v https://t.co/1xh3SmlA8d

Reporter: What's stopping Iran rebuilding and restarting from where we were pre the war their nuclear program? Vance: Well, number one, they would have to get a lot of money in order to rebuild their nuclear program. You're talking about billions and billions… https://t.co/eY7fVdptYc

🚨 JD VANCE: “I really worry about with AI is surveillance… AI is fundamentally a communist technology. It allows governments and corporations to surveil people in very profound and different ways. And that scares me a lot…” https://t.co/XWFZcu72m4