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I have some big news to share. Workera is being acquired by Pearson! Over six years ago, I was teaching at Stanford and thinking about a simple question: what if we could understand everyone's skills as precisely as the best teachers understand their students? I believed it could lead to a more meritocratic world. People could be recognized for what they can actually do, not just their credentials or network. They could understand their strengths and gaps, and rapidly develop the skills they need next. Organizations could discover talent they might otherwise overlook and manage their workforce with trusted skills data. What felt like a dream at the time is now a reality. Workera brought together experts in AI, psychometrics, and enterprise execution to build AI systems that reinvent how skills are measured. Our team pioneered AI-native skills intelligence, agent-led multimodal assessments, and even ambient skill measurement. We've established skills benchmarks across organizations, industries, and roles. And this mission feels more important today than ever! AI is changing work as we speak. Some roles are disappearing, new ones are emerging, and we need to help billions of people develop new skills and navigate what comes next. When I first spoke with @omarabbosh, it became clear that our companies shared the same mission. Pearson has helped generations of people learn and prove what they know. If you're reading this, there's a good chance you've taken a Pearson assessment, learned from their educational materials, earned a professional credential through them, read their psychometrics research, or benefited from their enterprise products in many other ways. Bringing together Workera's technology and AI talent with Pearson’s global scale and deep expertise in learning and assessment means we can pursue our mission at a scale we could only imagine on our own. To our customers and partners, thank you for believing in us. Expect even more innovations coming out of Workera and Pearson. To the Workera team, I’m incredibly proud of what you've built, and your continued dedication to our beautiful mission. To our board and our chairman @AndrewYNg, thank you for your belief, support, and mentorship. To everyone, we have big plans for this next chapter, so please stay tuned. We're just getting started! 😊

glad to see my lifetime project of randomly DMing this image to product designers is starting to pay off

Officially announcing: Finch × Baidu AI Cloud We're partnering with @Baidu_Inc AI Cloud to advance the AI agent economy, combining its AI capabilities and industry expertise with Finch's platform and developer ecosystem. Our collaboration begins with model integration through Qianfan, Baidu AI Cloud’s MaaS platform. Together, we’ll explore new business models and industry applications for AI agents, and build an open, thriving ecosystem where developers, businesses, and partners can create value. We’re building the agent economy, together.

来了 @stark_nico99 @YouMind_AI ，Manus 风格的YouMind 宣传片。

The Meet Manus 2.0 event series is going global - online and in person. Join a Founder Briefing, Roadshow, or a Fellow-led Community Meetup to see the product in action and hear the thinking behind it. First up: a Founder Briefing webinar with Co-founder & CPO @hidecloud on Sep 30 at 10 PM SGT. RSVP: https://luma.com/essfrk2k More events in the thread below ↓

Took a minute to write a few words about security & safety as someone who lived through it all at OpenAI. I hope my thoughts help someone out there. https://x.com/i/article/2104258872957636608

Hi everyone, today we released SGLang Omni v0.1.7. This release includes 75 merged PRs and welcomes 8 new contributors, with 8 first-time contributions. We added MiniCPM-o 4.5, NVIDIA PersonaPlex-7B, and OmniTyper powered by MLX streaming ASR, while further improving realtime and stateful Omni serving. 1.Performance: continued optimizations for Qwen3-TTS, Qwen3-Omni, CosyVoice3, MOSS-TTS, and AuK, covering Prefill CUDA Graph, speaker/reference encoding, kernel fusion, batching, and vocoder hot paths. 2.Serving: added Omni session lifecycle, the SGLang streaming session bridge, and a shared /v1/realtime WebSocket runtime, while further improving realtime ASR and streaming serving. 3.Models & hardware: added MiniCPM-o 4.5 multimodal input and speech output, plus PersonaPlex-7B offline speech-to-speech. MiniCPM-o and MiniMax-Music3 now support Intel XPU, with further MUSA support for Qwen3-TTS. 4.Runtime: improved breakable Prefill CUDA Graph, Talker / Code2Wav colocation, priority CUDA streams, scheduler admission, and profiling infrastructure to reduce host overhead and improve high-concurrency stability. https://github.com/sgl-project/sglang-omni/releases/tag/v0.1.7 https://github.com/sgl-project/sglang-omni

How we think about securing frontier RL training runs: https://openai.com/index/towards-safety-cases-for-frontier-ai-training/

Claude suddenly stopped cheating.

New historic NanoGPT record at 39.9s (-27.7s) from @DevenPzak , obliterating the prior record of 67.6s! This record introduces a new paradigm of thinking to NanoGPT: instead of optimizing matmuls or adding more expressive operations, optimize at the individual flop level with incredibly clever engineering and ML judgement. If a flop is low value on a particular step, skip it. Specifically: -(~8s) Sampled softmax. If a token doesn’t appear in a batch, skip its lm_head fwd/bwd some fraction of the time. -Sparse values. Only run an optimizer step for ngram embeddings that occurred in the batch. Set beta1 to zero to enable this. Beta2 is applied retroactively when the row is later used. -Sparse updates. Only update ngram and value embeddings once every 4 steps instead of once every 2. -Sparse communication. Shard the n-gram table across GPUs, and only pass the rows receiving updates on each step. -Sparse optimizer states. For the n-gram table, reduce from 2 floats in Adam optimizer per param, to 1 float per 768 params. -Hand-rolled flash attention for 64 dim heads. There are several additions that add accuracy too: -(~4s) EMA during last 300 steps, combined with lifting final_lr to 0.3 instead of 0.15. -(~1s) A new optimizer, Anvil2, which expands muon via a second tracked momentum buffer, improves the ortho coefficients, and modifies the cautious weight decay application. -A couple additional dynamic skip connections in the network. The most striking consequence of the ‘flop aware paradigm’ is you can grow parameters arbitrarily large, only limited by the available memory, since you can selectively choose how to expend flops on those parameters on each step. NanoGPT has kept active parameters below 124M, but total is unbounded, and has grown to 640M through embedding sparsity over the last year. This PR takes that to its logical conclusion on the 8xH100, scaling up to 65B sparse embedding parameters, which accounts for 25% of the PR’s gains. At frontier scale, where one is not bounded by an 8xH100, one could imagine where this paradigm could lead. https://github.com/KellerJordan/modded-nanogpt/pull/360 As this was a very notable PR, I spoke with Deven for an hour to learn how he did it. Here’s his story on the changes: https://hyperstition.cc/training-nanogpt-in-39-9-seconds

In 'well when you put it like that' news, here's the Florida Attorney general asking for a preliminary injunction to stop OpenAI from doing more AI R&D.