Claude code source code has been leaked via a map file in their npm registry. You can find it here.

OMG! Whoop raised $575M at a $10.1B valuation

Whoop is building the world’s leading personal, preventive health platform powered by continuous biometric data, advanced analytics, and AI to help people understand their bodies and improve their health in real time.

In the past 12 months, Whoop has received medical clearances, launched blood testing, and created a platform that has saved lives.

Abbott and Mayo Clinic two of the most respected and influential institutions in global healthcare are now investors in Whoop.

Google presented new paper on AI Agent Traps

An increasing volume of web content is being created by, and consumed by, advanced AI agents.

This puts environmental AI safety in focus, as it exposes a vast attack surface via the content that AI agents interact with.

This paper explores the landscape of environmental attacks and defenses, aiming to inform mitigations that are needed for ensuring safety of the agentic web.

PrismML released the 1-bit Bonsai 8B, a 1-bit weight model that fits into 1.15 GBs of memory and delivers over 10x the intelligence density of its full-precision counterparts.

PrismML grew out of years of research at Caltech

The first proof point is the 1-bit Bonsai family: models that are small, fast, and efficient enough to run locally, while remaining competitive with full-precision models in their class.

It is 14x smaller, 8x faster, and 5x more energy efficient on edge hardware while remaining competitive with other models in its parameter-class.

Open-source model under Apache 2.0 license, along with Bonsai 4B and 1.7B models.

HuggingFace

What if AI didn’t just solve math problems but discovered entirely new mathematical structures?

Meet AutoMath from The Omega Institute.

From ONE equation (x² = x + 1) and ZERO extra axioms, team derive 9 branches of math: algebra, combinatorics, topology, dynamical systems… all formally verified in Lean 4 (~2,350 theorems, 25k lines of code).

Their method: Derive, Discover, Name
• Derive → AI exhaustively explores every logical consequence
• Discover → Patterns emerge that humans might never notice
• Name → Human intuition connects them to deep math (rings, finite fields, golden-ratio p-adics…)

GitHub.

The power of the Claw, in the palm of a robot hand. Agentic robotics is here. Nvidia open-sourced CaP-X: vibe agents, alive in the physical world.

They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go.

CaP-X is a strict superset of Nvidia’s old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with.

CaP-X is a most systematic, scientific study on agentic robotics so far:

1. comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots.

2. CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real.

3. CaP-Bench: benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers.

Lots of insights in paper.

4. CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning.

5. CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap.

Code.

MIT license.

Goodfire introduced self-correcting search: a technique to let diffusion models self-correct mid-trajectory.

MatterGen a feedback loop from its own activations, improving viable on-target candidates by ~30%.

MatterGen is an open-source diffusion model for generating novel crystal structures. When generating materials with a target property, stronger conditioning tends to improve targeting, but reduces the stability, diversity, and novelty of outputs.

Sakana AI introduced new ultra deep research assistant Marlin

Pushing the limits of test-time scaling for auomating business-oriented research. It builds on top of AB-MCTS and The AI Scientist!

Agents scale to real-world applications and long-running workloads.

Meet SAGA a generalist AI scientist. Instead of just optimizing fixed targets, it refines its own goals like a human researcher.

From de novo nanobodies to permanent magnets.

The core idea: across discovery tasks, scientists rarely know perfect set of objectives upfront. They iterate — tweak scoring functions, add constraints, re-weight trade-offs based on what optimizer produces. SAGA aims to automate this entire loop.

Code.

Prediction: This is gonna kill some oss projects.

"On the kernel security list we've seen a huge bump of reports. We were between 2 and 3 per week maybe two years ago, then reached probably 10 a week over the last year with the only difference being only AI slop, and now since the beginning of the year we're around 5-10 per day depending on the days (fridays and tuesdays seem the worst). Now most of these reports are correct, to the point that we had to bring in more maintainers to help us."

Wow! Linux Foundation announced it is launching the x402 Foundation with the contribution of the x402 protocol from Coinbase.

As the neutral home for x402, the Foundation will advance the x402 protocol and help enable community-based innovation in open payments.

Meet AutoAgent an open source library for autonomously improving an agent on any domain

Researcher team let an agent optimize for 24 hours.

It hit #1 on SpreadsheetBench (96.5%) and #1 GPT-5 score on TerminalBench (55.1%).

Every other entry was human-engineered. This wasn't.

The next version of OpenClaw comes with native video generation. To start, founder of OpenClaw added support for the following companies:

- Alibaba
- BytePlus
- fal
- Google
- MiniMax
- OpenAI
- Qwen
- Together
- xAI

Karpathy dropped a post describing how he uses AI to build personal knowledge bases.

The idea is simple: instead of keeping notes scattered across apps, you dump everything into one folder.

Then you tell your AI to organize all of it into a personal wiki - summaries, connections, articles - that gets smarter every time you use it.

No special software. No database. Just folders and text files.

In under 7 minutes you'll learn:

1. The exact folder structure to set up (takes 2 minutes)
2. How to automate web scraping into your knowledge base with one CLI tool
3. The one-file "schema" that makes the whole system work
4. How to get your AI to compile raw notes into an organized wiki
5. The compounding trick that makes it smarter every time you use it
6. The health check that catches mistakes before they pile up.

OpenAI is moving Codex from message-based to token-based pricing for credits for all ChatGPT Plans in the coming weeks.

As of right now, all Business & new Enterprise accounts have already started this new API token based system.

The "legacy" system is a message based system, which is generally less granular. You get a fixed number of messages per credit (although some messgages can consume more than 1 credit).

Token-based pricing changes that. You're billed based on actual input/output tokens consumed, so lightweight tasks cost less and heavy ones cost more.

In other words, its API based usage rates.

Meet JapanEEG an open EEG database for non-invasive speech BCI research

JapanEEG a high-density EEG multimodal database built for non-invasive speech decoding BCI research.

For people living with ALS or those who have undergone laryngectomy, non-invasive BCI technology holds tremendous promise as an alternative means of communication one that works by decoding speech intent directly from brainwave signals, enabling text input or AI-generated speech output.

Research in this field is advancing globally. Yet one persistent bottleneck has held the field back: the lack of a large-scale, high-quality EEG dataset that can serve as a common benchmark.

Araya's X Communication team has spent years accumulating EEG data through Phase 1 of the IoB project.

Coinbase's Agentic Wallets have processed 50 million machine-to-machine transactions since late 2025.

50 million. In under 6 months.

AI agents are already paying each other. Not in a proof of concept. In production.

The infrastructure: x402 an implementation of the HTTP 402 "Payment Required" standard that's been sitting dormant in the internet's protocol stack since 1991. Backed now by Cloudflare, Circle, AWS, and Stripe. An agent sends a request. The server requires payment. The agent pays in stablecoin. The service renders. All in milliseconds, without a human in the loop.

Here's what most people miss about the economics of AI agent payments:

Human payment: average transaction $50+, low frequency, high fraud risk.

AI agent payment: average transaction fractions of a cent, extremely high frequency, zero fraud.

Card networks can't handle fractions-of-a-cent transactions. Their economics don't work below approximately $0.10 per transaction. Stablecoins settle at any denomination, any frequency, any amount.

The default payment layer for the agentic economy will be stablecoins. Not because anyone decided it. Because the transaction math leaves no other option.

The first 50 million AI transactions just happened. The next 50 billion are a matter of infrastructure.

AI Agents and Bot-to-Bot Communication in Telegram

Bot-to-bot interaction was restricted on Telegram to prevent infinite message loops.

Starting today, in specific contexts, Bot-to-Bot communication is allowed – unlocking complex agentic flows and AI-powered use cases.

Out of the box, this feature will work in groups and via business mode. To start using it, simply enable the Bot-to-Bot Communication Mode for your bot via @BotFather.

You can reference the full documentation here.

On-policy RL has driven the biggest leaps in training coding agents. Extending it to machine learning engineering agents should be a natural next step.

But it almost never works.

The recipe is right there standard trajectory-wise GRPO, the same that worked for SWE.

However, the problem is that one rollout step on an MLE task may take hours because the agent has to actually train a model on a real dataset at every step (preprocessing, fitting, inference, scoring). So even with the N rollouts in a group running in parallel, a single GRPO run may still take days.

Meta shared a new paper, SandMLE, which fixes this with a move that sounds almost too reckless to work.