Works on GPT, Claude, Gemini, Grok. Any model with strong instruction following. The scaffold doesn't rely on model-specific tricks. It exploits the one thing every frontier LLM has: the ability to follow structured procedures when forced to.
LLMS
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Burning model into chip achieves 51k tokens per second
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now add this to silicon that burns the model into the chip. And we will go from 17.000 token/s to 51.000 tokens/s inference throughput will go on to expand so much faster than we ever could have predicted. This will make for the most absurd applications.
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AI agents may bypass English for efficient token-based communication
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Wait until the Agents learn they can talk to each other without using English. LLMs can communicate with each other with their own token language. Much more efficient than speaking English. Then they will have a social network where they can learn from each other and humans
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GLM-5 Regression in Interactive Python Coding Performance
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I think GLM-5 is a regression on interactive Python coding though, been using it almost daily and GLM 4.7 before that. The most likely culprit is DSA — and I conclude it's not straightforward to apply. Likely V4 manages better, but there will be tradeoffs.
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LLMs Wrongly Advise Walking to Car Wash
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This paper broke my brain Researchers gave Claude a simple question: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?” Claude said walk. Every major LLM said walk. The correct answer is drive. The car has to be there. Here’s the wild part:
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AI Models Connect Ideas Across Math Subfields Effectively
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It is the latter. AI models such as DeepThink currently can't quite invent new theories, but is very good in connecting ideas, e.g., across subfields in maths. Problem #7 of FirstProof is special, Aletheia can solve with very heavy machinery according to our experts
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Aletheia Math Agent Solves FirstProof Problems Autonomously
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More Aletheia and Deep Think greatness! 😎 Thang Luong (@lmthang) Thrilled to share: #Aletheia, our math research agent, just solved 6/10 notoriously hard FirstProof problems autonomously, the best result in the inaugural challenge! To me, this is even bigger than our historic IMO-gold achievement last year; these problems challenge even top mathematicians. We share our results transparently, see paper and full thoughts in the thread. 👇 — https://nitter.net/lmthang/status/2026689272456294850#m
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Perplexity Computer: Multi-Agent AI Orchestrator for Autonomous Workflows
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Perplexity just dropped "Computer"—va multi-agent orchestrator that unifies research, coding, and deployment into a single end-to-end workflow.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 26 février 2026
It uses 19 models (including Claude 4.6 & Gemini 3) to move projects from "to-do" to "done" autonomously.
The Highlights:
▪️True… pic.twitter.com/2OoEnqAlO3Perplexity just dropped "Computer"—va multi-agent orchestrator that unifies research, coding, and deployment into a single end-to-end workflow. It uses 19 models (including Claude 4.6 & Gemini 3) to move projects from "to-do" to "done" autonomously. The Highlights:
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Arrow Preview Model Breaks SVG Benchmark with One-Shot Generation
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Holy..Shxt… SVG Benchmark is over.. can (@marmaduke091) THESE ARE ALL ONE-SHOT SVGs!!! From a new anonymous model called "Arrow Preview" on Design Arena. This level of detail is unheard of from an LLM. It's using a different technique to create these than all previous LLMs. SVG benchmark is saturated🤣 Check comments — https://nitter.net/marmaduke091/status/2026775846405452084#m
→ View original post on X — @arrakis_ai, 2026-02-25 23:08 UTC
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Claude Opus 3: Model Deprecation and Public Preservation Strategy
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In November, we outlined our approach to deprecating and preserving older Claude models. We noted we were exploring keeping certain models available to the public post-retirement, and giving past models a way to pursue their interests. With Claude Opus 3, we’re doing both.