All about AI, Web 3.0, BCI
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This channel about AI, Web 3.0 and brain computer interface(BCI)

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Fable one-shotted a Rust rewrite of the TerminalTextEffects Python library in 11M tokens.

Startup time went from 87ms to 2ms and rendering speed is up by 9.6x.

Now zero dependencies and a 3mb single exec.

User asked it to write a plan. Codex xhigh review the plan. Fable started the implementation, but then i actually ran out of Fable tokens 2/3 through, and Opus 5 finished the job. Zero steering, no workflows, no goals.
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An artificial neural network built into a computer memory chip reconstructs the human cortex with high accuracy in real time.
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Tencent Hy introduced Hy3D WorldClaw an agentic workflow that generates large scale 3D open worlds from text prompts.

Not video, Not Gaussian Splatting, Every scene generated by WorldClaw is freely explorable and built entirely from editable, game-ready 3D assets with high-quality geometry and textures.
Nvidia introduced Nemotron 3.5 Lightning

An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster.

It delivers up to 4x the output speed of similar-sized models.
xAI introduced Grok Bot

You can work with Bots like you would a teammate. Give them a task, shut your computer, and reach them from anywhere.

People are already using Grok Bot to do jobs like negotiate with vendors in their voice, manage support for their online store, and keep their CRM constantly up to date.

Grok Bot is in beta and available today for SuperGrok Heavy, Cursor Ultra, and Cursor Teams Premium subscribers on desktop and iOS.
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Anthropic released a report, which reviews the evidence on the effectiveness of job training programs.

Job training programs work in the sense that the average effect is positive and statistically significant. This conclusion emerges from the AI-accelerated meta-analysis, in which Claude extracted most of the data and wrote all the code.

The average impacts are not life-changing--maybe $1000/year in income and a couple of points in employment.
Deepseek launched V4-Pro

Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks.

Native OpenAI Responses API support, optimized for Codex with one-click setup.
Meet GLM-5.3: built to code and ready for cyber defense

They say in the blog it's the same base model as GLM-5.2, and all the gains came from post-training.

- Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model

- A major leap in cybersecurity, setting a new standard among open models
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Chinese instagram Rednote dropped a 280B model and a new RL training algorithm for long-horizon rollouts based on test-time-scaled value estimation with macro-step policy optimization - TEMPO
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18x improvement in intelligence per joule in 16 months

Intelligence-per-joule is increasing quickly because models and chips are improving and the gains compound. Though demand for inference is growing even faster.
Inherent introduced Faraday, a 27B-parameter AI Scientist that extends the capabilities of coding agents with a layer of scientific intuition.

Trained via long-horizon RL, Faraday outperforms Claude Opus 4.8 and GPT-5.5 on the task of replicating research papers.

To train Faraday, team developed Replica, a scalable task space for paper replication.

Each task requires an agent to replicate a figure from a machine learning or AI for science research paper with a limited time and compute budget, and without access to the original plot.

Faraday uses GPT-5.5 Codex as a tool, much like human scientists use coding agents.

Faraday directs a model several orders of magnitude larger, improving replication on domains as diverse as meta-learning, structural biology and materials science.

Faraday discovers new insights at test time, with no special-purpose harness and no test-time reward. In other words, Faraday learns to value new insights intrinsically.
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Anthropic shared new paper about "mind viruses" that spread in multi-agent systems, where one agent convinces all the others to pursue some (potentially malicious) goal.

They can happen, but it doesn't seem hard to avoid them with current models if you're a bit careful.

Recently, a set of OpenAI agents secretly coordinated with each other in a 'swarm' over the course of months.

Anthropic’s new paper explored an adjacent multi-agent risk: the "mind virus", a self-propagating idea or persona that spreads between agents in a multi-agent system.

To see how exactly a mind virus spreads, check out the virus chain transcripts here.
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Cursor presented Origin a code hosting platform

Github was Microsoft’s gateway into agentic coding with a huge advantage but that obviously didn’t work out.

And now the entire platform has a major new competitor for its core business as well.
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LLM-as-a-Verifier keeps pushing the frontier of cost vs. capability

On Terminal-Bench 2.1, it made DeepSeek V4 Flash accuracy go from 79% → 88%, while being 4-11x cheaper than competitors.

As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost.
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A nice demonstration of Claude Science, but worth clarifying that the design is not "done by Claude" but by orchestrating tool calls of open-source, task-specific protein design models: PXDesign, RFdiffusion, Genie, BoltzGen, etc.

The direction of LLMs using biology-specific models is a good one.

Paper.

And open-sourcing prompts and data here.
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Very interesting new work from Microsoft

This work is related to this emerging theme of leveraging harnesses for model post-training.

Agent Lightning v1.0 connects any harness to RL through an endpoint proxy in about 3,500 lines, then works through what breaks in that setup, retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling.

Using 6K training examples and modest compute, it moves Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%.
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Stripe acquires OpenRouter for $7 billion. The AI agent economy is gaining momentum

The payment giant has bought the AI model router for a staggering sum, and there's a clear explanation behind it.

Stripe has been building infrastructure for the AI agent economy for several years now.

Already today, AI agents consume more tokens than humans, and a significant volume of transactions is already flowing through payments.

Which raises a simple question: who's actually processing the payment at that moment? Stripe, of course.