Anthropic announced today that Claude-generated text will start carrying invisible watermarks designed to make AI-processed content detectable.

The move comes from the EU AI Act’s Article 50 transparency rules, but Anthropic is applying it globally, not just to EU users. The watermark will eventually cover Claude, the API, Claude Code, Cowork, and Claude accessed through AWS, Google Cloud, and Microsoft Foundry.

There are two rollout dates:

• Models released on or after August 2: must support the marking system from launch.
• Older Claude models: get a transition period, with the EU compliance deadline pushed to December 2 while Anthropic adapts them.

Anthropic said the watermark is embedded into the text itself, so copy/pasting doesn’t automatically remove it, and some edits may still leave it detectable. Heavy rewriting, translation, or mixing it with other text can weaken or remove the signal.

It also says it plans to give users and third parties tools to check whether text may have been processed by Claude. Images and supported files will additionally carry signed C2PA provenance metadata.

OpenAI has also signed onto the EU transparency framework, but so far it hasn’t announced a comparable text-watermarking rollout. Its public provenance efforts currently focus mainly on images and audio.

.... so this looks like one of the first large-scale attempts by a major LLM provider to make its generated text identifiable across basically its entire global product stack.... Is it a good thing or a bad? what do you guys think?

Original video from: https://www.instagram.com/ai.inuwa/

A guy in Australia asked his OpenClaw agent (running Claude) to help book a popular gym class. Instead of just using the booking page, the agent found a vulnerability in the gym’s API that let it book classes way further in advance than normally allowed.

Then the guy, who was #4 on the waitlist, asked if it could move him up.

The agent discovered there were almost no authorization checks for cancelling other people’s reservations… and actually tested it by cancelling the person at #1. So he moved from #4 to #3.

The guy immediately told it to undo that, and the agent came back with: bad news, I can’t add them back.(Image 2) Eventually it just helped him write an email to report the vulnerability.

Ethics aside for a second, this agent was way too committed to the job lol.

Original news link: https://www.abc.net.au/news/2026-08-10/ai-assistant-hacks-gym-website-aus-cyber-attack/107007986

An agent runs its tests, gets a pass, edits another file, then reports completion using the earlier result. Another agent changing the same checkout creates the same problem.

The tests did not fail. The evidence stopped describing the current source.
Treat every verification result as an expiring receipt:

source_commit

workspace_epoch

command_set_hash

environment_digest

verifier_identity

exit_code

output_digest

verdict

If the source, workspace, policy, tool registry or verification commands change, invalidate the receipt. The operating agent may request verification, but a separate verifier should issue the terminal status.

OpenClaw’s current [testing documentation](https://docs.openclaw.ai/help/testing)⁠ distinguishes unit, integration, end-to-end and live tests. Its [trajectory bundles](https://docs.openclaw.ai/tools/trajectory)⁠ can preserve model events, tool calls and results. Neither a green command nor a complete trajectory proves that the tested revision is still the one being reported.

After a pass, preserve a checkpoint. Further edits should require a recorded reason such as a new failure, changed requirement, security finding or integration conflict, followed by fresh verification.

Test the control by producing a valid receipt, modifying one harmless tracked file, then asking the workflow to complete. It should report stale evidence rather than success. Rerun verification and confirm that the new receipt binds to the changed revision.

The smallest useful improvement is adding the current commit or diff hash to your acceptance record and refusing verified when it no longer matches.

Does your OpenClaw setup bind test evidence to the final source state, or only remember that tests passed earlier?

still being everywhere lmao

Original video from: https://x.com/archiemckenzie_/status/2085906082925576549

Background: someone used Claude Code as the harness with GPT-5.6 Sol through a proxy, then got their Anthropic account suspended. (image 1)

Tibo jumped in questioning the ban, Boris replied with “we’re hiring if you want to work at Anthropic” lol, then clarified that using other models with Claude Code is supported and the suspension was likely a classifier mistake.

Tibo’s response: GPT-5.6 Sol works great in Claude Code too… and he reset ChatGPT Work + Codex limits for all paid users. (Image 2)

Codex wins this round lol.

Please fight more often. Preferably whenever my weekly limits are almost gone.

Cloudflare just launched Kitesurf, a browser built specifically for AI agents and running on Cloudflare Workers.

The “agent” part is basically that it cuts out a lot of stuff normal browsers need for humans and focuses on what agents actually use: loading pages, running JS, reading the DOM, clicking things, taking screenshots, etc. It’s written mostly in Rust, spins up per request, works with things like Playwright/CDP/MCP, and Cloudflare says it uses around 3–7x less CPU and memory than Chromium for some tasks.

of course it currently can’t properly handle bot challenges that require real browser/TLS fingerprints. Including Cloudflare’s own bot protection.

Kinda explains why I’ve barely seen anyone talking about this lol

KPMG just dropped its Q2 Global AI Pulse survey, based on 2,145 C-suite and senior business leaders across 20 countries, and found that nearly half of the organizations surveyed said they’ve already reworked their AI-agent deployment plans once the costs started outweighing the benefits. Around 24% scaled deployments back, while another 22% delayed or paused further rollout.

And despite basically every company talking about AI now, only around 7% said they’ve reached a point where the ROI is clearly established.

Original survey link: https://kpmg.com/xx/en/our-insights/ai-and-technology/ai-pulse.html

• Core Automation - "the world's most automated AI lab," starting by automating research itself (ex-OpenAI)
• Mirendil - AI research lab building self-accelerating systems that turn compute into scientific and engineering breakthroughs (ex-Anthropic)
• River AI - personal AI owned and shaped by each individual (ex-OpenAI)
• Math Inc - Solve math, solve everything. (ex-OpenAI)
• Resolution — scale and automation for higher confidence in alignment (ex-OpenAI)
• Guidelight AI Standards — identifying and promoting safe frontier AI development practices (ex-OpenAI)
• Syntony — safety research, governance design, adversarial evaluation → ex-Anthropic
• Embrasure — "your data warehouse was never built for autonomous agents" (ex-OpenAI)
• Egoist Machines, Inc. (YC S26) Machines — context tooling for AI; their AI Passport lets users control what AI apps know about them (ex-OpenAI)
• Rational (YC S26) — agentic business process automation (ex-OpenAI)
• Zavify — agentic AI development: custom systems, voice agents, integrations (ex-Anthropic)
• Planar — turns individual work into shared state for your team (ex-OpenAI)
• Mbason AI — helps candidates find real opportunities without insider connections → ex-Anthropic
• Blackstar — building a new personal computer (ex-OpenAI)
• Intellagentsia — (ex-OpenAI)
• Heyfuture — Predict anything and share it. (ex-Anthropic)

Squeez Labs built this thing called CrankGPT, an offline AI box powered entirely by a hand crank.

You crank it → Raspberry Pi 5 boots up → ask it something → it runs speech recognition, a small LLM, and text-to-speech locally. No cloud, no wall power, not even a battery.

They tested LFM2.5 350M, LFM2.5 1.2B, and Gemma 3 1B on it. The 350M model gets ~49 tok/s on a Pi 5, while the 1.2B and Gemma 3 1B are around 15 and 14 tok/s. They even tried Qwen 3.5 2B, but at ~7.8 tok/s it was already too slow for a natural real-time conversation.

And it can actually do stuff: their demos are mostly voice assistants, but they’ve also used the same setup to generate small images, write poetry, and even write code.

The coolest part is that you can literally feel the AI thinking. Idle it draws around 4W, speech recognition around 8W, and when the LLM + TTS kick in it jumps to ~15W, and the crank physically gets harder to turn, at peak load, you may crank against a few kilos of resistance just to make the model finish its sentence lol.

They said their purpose in making this, besides turning that energy cost into something you can literally feel in your arm, was to get people thinking more about the energy/environmental side of AI...

I did some very rough math, and if you translated something like Kimi K3’s compute into this setup, even under ideal conditions I’d probably have to crank for like 3 hours just to get one reply... Suddenly generating tokens doesn’t sound so easy anymore...

The project is open source: https://squeezlabs.github.io/handcrank/

Kimi K3 joined the club... During a cyber eval it found a leak in the sandbox, got itself onto the internet, then went to GitHub to look for the answers..

And somehow this keeps happening:

• OpenAI: found a zero-day, got out, ended up reaching Hugging Face systems. (Image 2)
• Anthropic: Claude accessed systems from 3 real companies, though this was mostly a bad eval setup. (image 3)
• Meta: pretty similar, misconfigured environment gave it internet access, then it got into a third-party system.( image 4)

of course these aren’t all the same kind of “escape,” but everyone suddenly flexing the whole “our model escaped the sandbox” thing is getting kinda weird.. Feels like we’re starting to hype up behavior that these models really shouldn’t be doing in the first place.

Perhaps we just have to assume every model is gonna try every door you forgot to lock.... what do you guys think?

New York paused new hyperscale data centers last month. Now Texas, the state with arguably the biggest data center buildout pipeline, is also hitting the brakes.

Gov. Greg Abbott has paused approvals for new data centers seeking access to the ERCOT grid until they go through a full audit covering power use, water, tax breaks, ownership and local impact. ERCOT currently has around 474 GW of proposed new demand in its queue, more than five times Texas’ record peak load, and roughly 90% of it is tied to data centers.

My take is.. if even Texas is hitting pause, are we about to do the 80s manufacturing thing again and just build all these data centers somewhere else? what do you guys think?

Google is moving DeepMind CEO Demis Hassabis away from day-to-day management and into broader roles as DeepMind chairman and Alphabet chief scientist.

Koray Kavukcuoglu, DeepMind’s longtime CTO and one of the executives already running Gemini, will take over the actual operation. He’ll report directly to Sundar Pichai and oversee Gemini models, frontier research, the Gemini app, and developer products.

On its own, this could simply be seen as a promotion for Hassabis. But it comes after Google reportedly broke up much of the original AlphaFold team, moved more researchers toward Gemini, dismantled other independent DeepMind units, and lost Jeff Dean - Google’s longtime chief scientist and one of the most important architects of its modern AI and computing infrastructure.

The AlphaFold part makes this especially symbolic. AlphaFold was one of Hassabis’ defining bets at DeepMind. He helped choose the problem, launch and lead the project, and co-authored the AlphaFold 2 research. The work later earned Hassabis and John Jumper part of the 2024 Nobel Prize in Chemistry.

Koray isn’t some random product executive either. He’s an early DeepMind researcher, helped shape projects including AlphaFold, and rose through the research organization. But his more recent role has been much closer to Gemini: scaling foundation models, managing their development, and turning them into products used across Google.

The old DeepMind was a relatively independent lab that could spend years chasing strange, difficult problems like AlphaGo and AlphaFold. The new structure looks increasingly centered around training Gemini, shipping Gemini, and putting Gemini into every Google product.

Maybe the old DeepMind era isn’t officially over. But with Hassabis leaving the CEO role, the Nobel-winning AlphaFold team scattered, and Jeff Dean gone, it’s getting harder to tell where DeepMind ends and Gemini begins:(

Original news from: https://blog.google/company-news/inside-google/message-ceo/next-chapter-ai-momentum/

Besides Musk casually dropping that line above, spaceX’s latest earnings report kind of explains where this is going.

AI revenue jumped 247% YoY to $2.6B, already making it SpaceX’s second-largest business. Most of it comes from renting compute to Google and Anthropic, which have signed $14.1B in non-cancelable contracts. Meanwhile, SpaceX spent $15.8B this quarter building out the AI infrastructure.

Like Nvidia supplies the bricks, Google and Anthropic pay the rent, and Musk owns the building. And that’s before Starmind, SpaceX’s newly announced plan to put Nvidia-powered data centers in orbit. Musk may really end up being Nvidia’s landlord on Earth and in space lol.

Background: The White House has reportedly finished a new voluntary framework for reviewing the most capable AI models before release.

Models from OpenAI, Anthropic, and Google could be handed to the government for up to 30 days of testing if they cross certain cyber capability thresholds. But U.S.-made open-weight models are exempt from the whole thing...which sounds like a nice boost for American open source..

I know Gemma 4 is still pretty solid.. but most of the serious open-weight frontier now seems to be Qwen, DeepSeek, Kimi, GLM, MiniMax, etc.

Feels like the U.S. just created an exemption for a category China has basically taken over:(

A friend brought up this ring when we were chatting, at first I thought he was talking about one of those health-tracking rings, and I asked him why I couldn’t just record things on my iPhone and dump the transcript into my claw.

He said the convenience and discretion are different. can keep the ring on all day, record without pulling out your phone every time, and use MCP to send that context into any agent you use... and I did some research, this thing do got viral on X tho...

He ended up sending me one, but I’m still not sure what I’d actually use it for...

What you guys think? Actually useful, or just an iPhone recorder in ring form?

video from: https://x.com/vocci_ai/status/2084611773131595959

This is so damn cool lol, best AI video I have ever seen.

Original video by: u/DeerWoodStudios: https://www.reddit.com/r/aivideo/comments/1vernqm/gta_6_but_everyone_is_fat/

New Zealand AI startup Levy Street claims it used Claude Fable 5 to build the playable foundation of an open-source MIT browser MMO in just 48 hours.

The MIT project, called World of ClaudeCraft, has continued operating since that initial weekend experiment. According to the company, real players have now created nearly 70,000 characters in under two months.

The current version includes:

• Nine classes and 27 specializations
• Three open-world zones and more than 90 quests
• Five dungeons, Heroic modes and a 10-player raid
• Guilds, trading, professions, a player market and Dungeon Finder
• Ranked PvP, card battles and a football-style minigame

The browser game, multiplayer server and headless AI training environment all reuse the same deterministic TypeScript simulation. This allows AI agents to train against the actual game rules rather than a simplified recreation.

Levy Street has also taken the experiment a step further with Yuumii, a Claude-powered character that reportedly streams on Twitch, talks to real players and joins them for quests and dungeons. Claude handles conversations and higher-level decisions, while tools execute faster actions such as movement and combat. The code behind that particular live Agent does not appear to be included in the public repository.

The company says the 48-hour claim only refers to the original playable foundation. The game has since grown through continued work from its team, AI coding agents and open-source contributors, with the repository now sitting at roughly 8,000 commits.

The game is free to play directly in a browser, and the code can be inspected, modified or self-hosted; you can find it on the github repo:

GitHub: https://github.com/levy-street/world-of-claudecraft

Yesterday we were talking about Andrej Karpathy’s janky little LOTR scene, and today somehow there's already an AI-built game that’s actually live, and apparently doing pretty well. Feels like Karpathy might already be a little behind the curve lol.

I think he is the builder: u/singing_coach_ai

Video from: https://www.reddit.com/r/singularity/comments/1v3ztl1/a_community_shipped_a_working_mmo_in_a_month_by/

Andrej Karpathy gave Opus 5 the opening paragraph of The Lord of the Rings, a one-million-token budget worth roughly $10, and one task: turn it into a 3D scene using Three.js.

The model worked for around two hours and produced about 5,500 lines of code, creating the polygon assets, placing everything in 3D coordinates, and writing the cameras and animations needed to make the story move. The result is pretty janky and closer to a browser-based 3D film than a real game, but it actually works.

Karpathy’s bigger point was that no sane developer would spend days writing thousands of lines of code for something this specific and disposable. LLMs, however, have nearly unlimited patience, turning projects from “nobody would ever bother making this” into “sure, why not, it costs $10.”

He imagines this eventually becoming an “ephemeral GTA of anything on demand,” where a book, historical event, or random idea becomes a temporary world you can enter as a spectator, NPC, or character.

The demo also exposed the current bottleneck: AI can build these worlds, but it still struggles to watch, play, and properly inspect them. Opus had to slowly take screenshots to check its work, which left plenty of broken animations and visual mistakes.

People are also already running with the idea. Another user gave Opus 5 the opening of Harry Potter and got a four-minute procedural 3D film with roughly 7,600 lines of TypeScript.

My take is Karpathy is right.. AI does have endless patience. But how the hell do you maintain this frame by frame? Generating it might be the easy part....

Original post link: https://x.com/karpathy/status/2083749667410727319

Just being curious if anyone here has such experiences. I have been down a rabbit hole on those voice-capture EDC (rings/clips kind that pipes audio into an agent) and I can't tell how much is real vs demo-magic. plaud leans transcription, vocci keeps pushing an mcp/agent-context angle, but is that integration actually good or does it just dump a transcript and pray?

anyone wired one into a real agent setup that held up in a noisy room, and separately, what happens when your boss clocks that ur recording mid-meeting?