Terafab·자가개선 에이전트·경쟁력 있는 모델 성과

Terafab will be built in Grimes County, Texas In April, we broke ground on our research fab on the North Campus of Giga Texas – the precursor to Terafab. Both Tesla & SpaceX will need far more chips than current & future global production can supply. This is why we're building

a fully open-source self-improving harness. Prime Intellect just shipped Prime Agent, which turns a frontier model into a Recursive Language Model. context becomes a variable the model programs over, and sub-agents become ordinary function calls. let's understand what all of this means: almost every agent you use today works inside fixed harness. someone wrote the system prompt, picked the tool schemas, decided when history gets compacted, and shipped it. the model spends part of every session working around that scaffolding instead of working on the task. when the same failure repeats three times in a long run, you are the one who fixes it, after the run is over. Prime Agent removes that ceiling with two pieces. the first is the RLM design. a persistent IPython kernel is the model's only tool, so long inputs never have to enter the prompt at all. the model greps, partitions, and spawns child calls over the data instead of reading all of it back in. the second is the Continual Harness. four kinds of state sit outside the conversation and stay writable: → Prompt: supplemental instructions the agent appends when it learns something the base prompt never told it. → Memory: findings from earlier turns that would otherwise die with the context window. → Skills: recurring workflows packaged as importable Python, so the next run imports the procedure instead of rediscovering it. → Sub-agents: specs for the children it spawns, tuned once and reused across parallel, background, and long-lived runs. the /refine command reads the current trajectory, applies the smallest edit it can justify, and records what triggered it. the base system prompt stays immutable, and any update can be rolled back by ID. with Opus 5 driving it, Prime Agent reports 95.5% on ARC-AGI-3, just past the reported human expert baseline of 95.4%. when that benchmark launched, frontier models were scoring under one percent. nobody trained a new model to close that gap. worth noting that in Factorio runs the same loop found and then optimized scoring exploits, which is roughly what you would expect once an agent can edit its own instructions. with Opus 5 driving it, Prime Agent reports 95.5% on ARC-AGI-3, just past the reported human expert baseline of 95.4%. when that benchmark launched, frontier models were scoring under one percent. nobody trained a new model to close that gap. the scaffold stopped being something you configure once and became something the run improves as it goes. Prime agent is MIT licensed, single command install, works with open and closed models. check it out on GitHub: https:// github.com/PrimeIntellect -ai/prime-agent … since we are talking about RLMs, i wrote a detailed article on how Recursive Language Models work. the article is quoted below.

To understand whether we're making genuine progress on reasoning, we entered our AI models in five international STEM Olympiad competitions this year. The results: Asian Physics Olympiad (APhO): Perfect score on the theory exam — gold medal International Physics Olympiad (IPhO): Perfect score on the theory exam — gold medal International Mathematical Olympiad (IMO): Gold medal, top 4% of human participants International Chemistry Olympiad (IChO): Gold-medal level performance Romanian Masters of Mathematics (RMM): Gold-medal level performance Three of these (APhO, IPhO, IMO) were live competitions and our solutions were submitted under real competition conditions and graded by the official judges using the same marking criteria applied to student contestants. A few things about the approach: • Models were internally trained versions from the Muse Spark family • Zero tool use: no search, no code interpreter, no calculator • Multi-agent orchestration with parallel reasoning We are excited about where this reasoning capability goes next; frontier research level across scientific domains and personal superintelligence. Super grateful to the organizing committees of APhO, IPhO, and IMO for supporting our live participation. We have deep respect for the contestants and organizers behind these competitions. And proud of the MSL team that pulled this together!

Predicting cyclones accurately can help save lives - and every hour of lead time counts. Published in @Nature , our AI model WeatherNext achieves state-of-the-art accuracy in forecasting a storm’s track and intensity, giving us a critical extra 24 hours to prepare on average.

OpenAI Developers

@OpenAIDevs

Build a plugin once and use it across compatible agent clients. Introducing Agent Plugins, an open standard developed with @awsdevelopers , @cursor_ai , @github , @code , and @vercel that packages Agent Skills and supports MCP server configurations in a shared format.

OpenAI Developers

@OpenAIDevs

Compatible agentic clients at launch: • Codex • @ChatGPT • @cursor_ai • @github Copilot • @kirodotdev • @code

DeepSeek-V4-Flash can now run 2× faster locally with DSpark! DSpark enables V4-Flash-0731 GGUFs to generate ~1.4–2× faster with no accuracy change. DeepSeek-V4-Flash-0731 can reach at 120 tokens/s. GGUFs: https:// huggingface.co/unsloth/DeepSe ek-V4-Flash-0731-GGUF … Guide: https:// unsloth.ai/docs/models/de epseek-v4 …

Baseten is now an official inference provider on @huggingface Run Kimi K3, DeepSeek V4 Flash, and GLM-5.2 on Baseten straight from any model page, or with just your HF token in your favorite harness. Get started here: https:// huggingface.co/blog/baseten

Big news: a new Adobe plugin is now available in @OpenAI 's @ChatGPT ! 70+ tools, including @Photoshop , @AdobeFirefly , @Lightroom , and @Acrobat , all in one chat. Mock up your photos. Repurpose a video for any platform. Turn a template into a polished PDF. Learn more: https:// adobe.ly/3TDrxff