A like often says volumes. https://t.co/KMOvThRUe4

looks like i'm not entirely off base with this then. we need friction. https://t.co/1N8Az9sXwl

looks like i'm not entirely off base with this then. we need friction. https://t.co/1N8Az9sXwl

“Gaussian splats fall apart when you zoom in.” Meanwhile, SuperSplat users: https://t.co/KfLYuLmTDI

“Gaussian splats fall apart when you zoom in.” Meanwhile, SuperSplat users: https://t.co/KfLYuLmTDI

Global VC investment hit a record $297B in Q1 2026, up 150% YoY, with AI startups capturing 81% of the funding; just four companies raised 64% of the total (@geneteare / Crunchbase News) https://t.co/cbVKfTjFKm https://t.co/pgjQ6K3aVI 📥 Send tips! https://t.co/wlNZvXuhJs

New post: We tested the Mythos showcase vulnerabilities with open models. They recovered similar scoped analysis! 8/8 models found the flagship FreeBSD zero-day, including a 3B model. Rankings reshuffle completely across tasks => the AI cybersecurity frontier is super jagged! https://t.co/6DxKN2xJUw

Hermes Agent v0.8.0 is here. Full changelog below ↓ https://t.co/INrj6KAGF1

Hermes Agent v0.8.0 is here. Full changelog below ↓ https://t.co/INrj6KAGF1

Today we release Boxer, a new lightweight approach that lifts open-world 2D bounding boxes to *metric* 3D: https://t.co/5IZ0tPlqvr Here we show Boxer in action on an egocentric sequence captured from smart glasses: https://t.co/fkJa3C2QoO

As FSD v14.3 rolls out, two aspects stand out. First is RL (reinforcement learning). It’s clear Tesla’s AI team has put significant focus here. This is what’s driving improvements in the “little things”— parking, avoiding road obstacles (animals now, potholes next), and other edge-case behaviors. The system benefits from large amounts of real-world data collected from customer vehicles and Robotaxis, which provides many usable training scenarios for the RL framework. That framework, while part of the broader training pipeline, runs on AI4 hardware at the Cortex data center and is highly compute-intensive. Given the number and variety of scenarios involved, it’s likely the total compute footprint for FSD v14.3 has grown substantially. Many of the other improvements in the release appear to stem from these RL efforts. Second is the release note: “Rewrote the AI compiler and runtime from the ground up with MLIR, resulting in 20% faster reaction time and improved model iteration speed.” This is a big deal. The runtime is what actually executes the model on the vehicle, many times per second. The model itself consists of the neural network structure and its learned parameters (the data). That structure defines how data flows through layers and is transformed (computed) during inference. Tesla’s AI team made the decision to completely rewrite this runtime (the inference data pipeline) to make it more efficient and better aligned with future development. This strongly suggests they needed/wanted greater compute efficiency on the AI4 platform and saw an opportunity to achieve it. MLIR enables a more abstract representation of computation, which in turn allows for deeper optimization of execution steps, reducing both memory usage and compute cycles. In short, this wasn’t a tweak, it was a full replacement of a core inference system, with meaningful performance gains (and some future proofing). Lastly, some have asked whether 14.3 includes the rumored 10× increase in parameter count. The short answer is: I don’t know and it likely doesn’t matter as much as people think. While increasing parameter count can improve the overall “driving function,” better tuning of existing parameters through RL will likely have a larger real-world impact. Tesla undoubtedly wants to scale parameters significantly, and the runtime rewrite may have created some headroom to do that (but it may ultimately require AI5). More importantly, a larger model doesn’t necessarily translate directly into better safety or decision-making compared to gains achieved through improved training and optimization.

In AI, a lot can change in seven years. https://t.co/DbbcIuD0Ck

Dudes who won’t tag me because they know their arguments are weak sauce.* *for intellectual exercise you can list the flaws and misrepresentations in his argument below https://t.co/gYqiFeUBMD

When GPS Fails, Visual Positioning Systems Will Not. In a new blog, Hugh Hayden, Head of Business Development, shares how Niantic Spatial’s VPS 2.0 (Visual Positioning System) provides reliable positioning continuity across indoor, outdoor, and GPS-denied environments. Read more: https://t.co/ukg3OxA4fW #NianticSpatial #Scaniverse #VPS #GeospatialAI #AI #Geolocation

Thinking about speaking at a tech conference? 💭 We’d love to hear your story on the stage at #GitHubUniverse this year. Submit your session idea now: https://t.co/rmH7FiR2WZ https://t.co/e9lFnB0B2U

Intel Foundry unveils the world’s thinnest GaN chiplet (19 μm). By integrating power and digital control on a single chiplet, it delivers higher efficiency, faster switching, and smaller designs. Learn more: https://t.co/EvI9UVxktY #IntelFoundry #Semiconductors https://t.co/mEmIWHbXZx

Typing is a bug. Or maybe we just got used to it. At Art Central, something felt a bit off. It’s a major international art fair, 100,000 people moving through the space all day — but almost no one was typing. Instead, they were just holding up their phone, trying to make sense of what’s in front of them. Not searching, not prompting. Just looking, and letting AI make sense of it in real time. And it wasn’t just a one-off. You’d see it happen again and again, across different booths, different people, different moments. At some point it stopped feeling like a demo, and started feeling like how people naturally use visual agent @Chance_vision . If AI is supposed to understand the real world, why are we still forcing it into text?

Video-MME-v2 Towards the Next Stage in Benchmarks for Comprehensive Video Understanding paper: https://t.co/NfocAfrxu9 https://t.co/ejdu3LdEqf

Artemis II mission route in 3D https://t.co/wkRV89sxsG

Introducing the Hermes Ecosystem Map I was an early user of Hermes Agent from @NousResearch and have been a power user ever since But as the ecosystem has grown, its been hard to keep up, so I did some research: > Scraped every GitHub repo related to Hermes > Filtered out repos that looked unfinished or had 0 stars > Built an ecosystem map of everything created and organized it all by category > Published a website where you can see all the projects with star ratings, and if you hover over you get a short description and link to the repo Then I had Claude run a security check on every repo to exclude anything that looked sus Link is in the replies, and also open sourced the repo so feel free to submit PRs if you see anything missing Oh, and the repo has a /research folder that includes a scrape of everything I could find that's been published on Hermes - you can clone that and add it to your personal knowledge base / wiki

We've raised $8.4m to make software that sharpens your attention. We're starting with an email app that lets you handle your Gmail inbox in seconds. We call it Avec. It's available now! https://t.co/3KZraK41k7

@KSimback @NousResearch Cool, will go good with this report about Hermes: https://t.co/qeOiuNkiG0

Very interesting paper shows some suggestive evidence that release of AlphaFold caused researchers to work with more novel proteins than they would've otherwise https://t.co/okeVfssLcI

Introducing Stora, AI agents for the app store The app store is a full-time job nobody signed up for Screenshots. Compliance. ASO. Publishing We built agents to take it all on Shipping on mobile is now as easy as shipping to web @stora_sh | https://t.co/5Baz0BEjOH https://t.co/z5JsFaEvgJ

Pick up! It’s your AI Self calling 🤳 All Pika AI Self agents can now talk on the phone. For when it’s just too difficult to explain, your thumbs are tired, or you’re craving a more personal connection. https://t.co/lwowXmMBp5

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform. https://t.co/vHYfiC1G56

Today we're releasing the Factory desktop app. A native interface for autonomous AI agents that work across every part of your software business. https://t.co/17j06HMb2G

✨ Introducing AIMock - one mock server for your entire agentic stack! Your AI app calls LLMs, MCP tools, A2A agents, vector DBs, search, reranking, and moderation. If any of those are live in your tests, you've got flaky CI and burned tokens. No tool mocked all of it. So we built one. One package. One port. Plus drift detection and record & replay that nobody else ships. Zero dependencies. Open source. Mock with one command: `pnpm add @copilotkit/aimock`

Today we're announcing the Billion Dollar Build. An 8-week competition where teams will use Perplexity Computer to build a company with a path to $1B. Finalists have the opportunity to secure up to $1M in investment from the Perplexity Fund and up to $1M in Computer credits. https://t.co/OmEqtdIpbY

Allen AI just released the WildDet3D dataset on Hugging Face millions of 3D bounding boxes with depth maps and camera parameters across 11,000+ categories from COCO, LVIS and more. https://t.co/bSXXfAVlUP

New on the Engineering Blog: Building Managed Agents—our hosted service for long-running agents—meant solving an old problem in computing: how to design a system for “programs as yet unthought of.” Read more: https://t.co/YYaEub2QGV

Improve latency up to 1.68x with NVFP4 and MXFP8 using Diffusers and TorchAO on Blackwell across a suite of different models 🔥. Squeeze out maximum performance with recipes involving selective quantization and regional compilation. 🔗 Read our latest blog from @vkuzo (@Meta) and @RisingSayak (@HuggingFace): https://t.co/QRHwAiOSc5 #PyTorch #TorchAO #MXFP8 #NVFP4 #OpenSourceAI