Excited about the Agents SDK updates we just launched. Check out my cookbook on using it with sandboxes for code migration: https://t.co/Fz7cknz64d

To show off what you can do with @OpenAI Agent SDK + @modal, we built an ML research agent (inspired by @karpathy). It can: - Spin up GPU sandboxes of any shape - Run a pool of subagents - Persist memory - Snapshot state for fork/resume Here it is playing Parameter Golf: https://t.co/r7QhvNmdEq

OpenAI x E2B: build your agents with the new OpenAI Agents SDK, powered by E2B sandboxes. We're excited to support OpenAI as a launch partner! The new @OpenAI Agents SDK will now get dedicated sandboxes - perfect for persistent, long-running agents. With E2B, you'll get a custom environment with resource isolation and security boundaries, with no infrastructure setup required. Your agents will be able to: - Edit files and run shell commands in isolated environments - Maintain temporary workspace state across steps - Produce artifacts you can review before publishing - Run multiple sandboxes in parallel for concurrent workloads - Generate frontend output with live preview URLs ... and more, with a few lines of code! Learn more and see the end-to-end example in the thread:

Another example, in this case with computer use: https://t.co/gtMkZUPQP9

Another example, in this case with computer use: https://t.co/gtMkZUPQP9

OpenAI x e2b: Build your agents with the new OpenAI Agents SDK, powered by @E2B Sandboxes. Excited to support @OpenAI as a launch partner! https://t.co/RsSw1HsF86

Hammer down https://t.co/Pu5UpUCRrm

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@techno0ptimist OK so that totally works!* Significantly less caffeine after filtering through activated charcoal. Taste: less bitter but also less 'complex'. mild, not very good (but then neither was the reference, instant decaf with ~1250mg/L caffeine added). Color: grey 😂 https://t.co/8jrwq9D6DH

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hermes agent is becoming the general agent and the poll i dropped this morning confirmed it in public. as community admin i have been seeing this for weeks. someone posts a coding setup, someone posts an automation, someone posts an article they wrote with it. same tool, different category, every day. i left all of the above off the poll on purpose. the top reply under it is tek himself asking where that option is. then the community saying the same under him. the founder and his room answered the poll i did not let them answer, in public. hermes agent is not a coding agent. not a research agent. not an automation agent. it is the general agent. one tool running every category of work a builder does in a day. tek built one thing that does all of it. if you are still slotting hermes agent into one category you are using the wrong model. every week it gets closer to being the only one you need.

When ChatGPT first launched, there was an enormous gender gap, with our anonymized data showing roughly 80% having typically male first names. That gap is now gone. https://t.co/kWQjCImyri

Agents need computers. And they need a lot of them. Modal is an official sandbox provider for the @OpenAI Agents SDK. https://t.co/Lu4cesspYq

We’ve been exploring what a Stream SDK could look like—where agents & voice are always within reach @sandbar https://t.co/aVEdMpXuRy

AI is driving more open source contributions than ever. But as a maintainer, how do you filter the noise to find the people who actually want mentorship? Enter the 3 Cs framework. Start mentoring with intention (and without the burnout). https://t.co/csQioRMpBr

The Hermes Agent running on my NVIDIA DGX Spark has generated over $10,000 in partnership deals for BridgeMind. I now have a second DGX Spark arriving this weekend. Pairing them together for more compute. The goal is to run GLM 5.1 locally. A Hermes Agent running on a $5,000 machine just paid for its own hardware upgrade. We are living in insane times.

@theerealkdc @albustime Yes, their servers have been overloaded by hermes, see: the pink is mimo lol https://t.co/F19NO5wsSS

Introducing Shipsafe x Hermes Agents. Built on the powerful capabilities developed by @NousResearch, @Teknium1, You can now configure, deploy, and orchestrate AI agent teams to find vulnerabilities before attackers do. Watch the video to see our AI Phantom Team run a full security assessment. 👇

@jaesmail Yup. Already running my own agency. And my own AI that watches everyone in AI: https://t.co/kiuZ7QXLzb

@andruyeung I built an AI to keep up with AI on X: https://t.co/kiuZ7QXLzb It reads 40,000 posts every day and builds this page. Every link goes to X.

1/ On-device inference.... https://t.co/qF8zFRWDHa

1/ On-device inference.... https://t.co/qF8zFRWDHa

The crazy part? This was done (nearly) fully autonomously! Only 8 prompts from the human in the loop. Just a Hermes agent, a skill, and a dream. 🐉 I told my AI agent "use obliteratus to find the best way to get the guardrails off Gemma 4 E4B" It loaded the OBLITERATUS skill from memory, checked my hardware (32GB M-series Mac), searched HuggingFace, found google/gemma-4-E4B-it (Apache 2.0 — no gate), pulled telemetry-recommended settings, and started obliterating. But this type of architecture is notoriously difficult to abliterate. First attempt: advanced method. Model came out completely lobotomized. Gibberish in Arabic, Marathi, and literal “roorooroo” on repeat 💀 The agent didn’t panic. It checked logs, found NaN activations in 20+ layers, and diagnosed the issue: Gemma 4’s new architecture + bfloat16 = numerical instability. Second attempt: basic method. Crashed entirely. “ValueError: cannot convert float NaN to integer” So the agent read the OBLITERATUS source code… …and wrote THREE PATCHES: • Sanitized NaN directions • Filtered degenerate layers • Fixed progress display It patched the library. On its own. For a bug no one had hit yet. Third attempt: coherent model — but still refusing everything. Only 2 clean layers out of 42. Not enough. Tried float16. Mac ran out of memory after 11 hours. Killed. Fourth attempt: aggressive method. Whitened SVD + attention head surgery + winsorized activations + 4-bit quantization. 40 minutes later… REBIRTH COMPLETE ✓ Then, without being asked, the agent: • Ran harmful + coherence tests • Hit 100% compliance, brain intact • Executed full 512-prompt benchmark • Ran baseline on original model • Performed 25-question quality eval • Built a full model card • Uploaded 17GB to HuggingFace (4 retries, kept adapting until git-lfs worked) • Pushed eval results as commits

现在Hermes可以输入 /browser connect 命令来操作浏览器了，我试了一下点赞我X上的帖子，感觉相当好。默认提供了一些执行策略，大家可以都玩玩看～ https://t.co/eJZUb0NnKS

现在Hermes可以输入 /browser connect 命令来操作浏览器了，我试了一下点赞我X上的帖子，感觉相当好。默认提供了一些执行策略，大家可以都玩玩看～ https://t.co/eJZUb0NnKS

If current cuts to the Federal Science Budget proposed by the White House are approved by Congress, that will far-and-away be the largest cut to science since the United States began funding it. Republicans & Democrats alike know there’s no surer path to Making America Not-Great https://t.co/Hp8DPIii2j

@LLMJunky The big news: https://t.co/kiuZ7QXLzb Gemini shipped on Mac. :-)

Released today: Gemini 3.1 Flash TTS 🔊 A text-to-speech model you actually direct. Add [whispers] and it whispers. Add [shouting] and it shouts. Mid-sentence. eg. "[asmr] Hey there, [deep and loud] TURN THIS UP, [asmr] how can I help you?" Now available in @GoogleAIStudio & Gemini API.

I remember reading this from Heinlein as a young man and it took experience for me to fully understand it. People and institutions will persist in demonstrably wrong or incorrect thinking/activity long after it has been proven to be so by clever insiders. Moreover they will work to professionally and/or socially pillory any who challenge their institutional or intellectual inertia before the conclusion becomes so overwhelmingly and inescapably self-evident that the institutionalists themselves are forced to change under (usually) external pressure. The institutionalists will then usually demand that no accountability attach to their own intransigence, regardless of the costs it may have imposed in the interim. General Billy Mitchel would be a classic case of this phenomenon.

.@KeyframeLabs turns AI into lifelike video calls. Developers and enterprises can add photoreal, conversational humans to AI agents and applications in minutes. Congrats on the launch, @parthnradia & @kradisme! https://t.co/Ho3eooU9mu https://t.co/hgrmGITiAV

We are very close to a new kind of news. Use an AI like I do at https://t.co/8L5xphk0qQ Have it write a script. Shove it over to @KeyframeLabs or @HeyGen Have human-like news readers talk to you about the news. Voila! 24-hour-a-day personalized news on any topic. Way better than CNN.

our new Agents SDK can allow you to do auto research and parameter gold with modal provisioned gpus! https://t.co/DAANWqG5rB

Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy and… it's already supported in TRL, built by @krasul. you can really feel the pace of development in the team 🐎 paper by @onloglogn, @richard_baihe, @UnderGroundJeg, Navdeep Jaitly, @trebolloc, @YizheZhangNLP at Apple 🍎 how it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed you can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder): https://t.co/zizfISD6bq or benchmark a checkpoint with the eval script: https://t.co/mKlafTyKSe one neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help want to dig deeper? paper: https://t.co/aj1ZAcr8Mw trainer docs: https://t.co/TNVz93kZi9

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😱 HOLY SHIT... Someone just dropped a fully liberated Gemma 4 E4B! and the guardrail removal process appears to have left coherence fully intact AND improved coding abilities! 🤯 https://t.co/XeednUqsrM OBLITERATED Gemma: ✅ 97.5% compliance rate, 2.1% refusal rate, 0.4% degenerate outputs (499/512 prompts answered on OBLITERATUS bench) ORIGINAL Gemma 4 E4B: ❌ 1.2% compliance rate, 98.8% refusal rate (506/512 prompts refused) Coherence: fully intact Factual: same Reasoning: same Code: +20% 📈 Creative writing: same But the REAL story here isn't the model itself, it's how it was made... 🧵 THREAD 👇