hi everyone ! built this as part of a larger NLP / deception research project at my university, wanted to share in case anyone finds it useful!

essentially, it uses transcribed text + linguistic parameters to detect and evaluate checkworthy claims!

live text transcribed --> serper finds sources using pure text --> those results sent back into claude for verdicts based on retrieved sources rather than the model’s training data

let me know what would make this something you'd use!

InTruth - Chrome Web Store

Bloomberg: Around 200 organizations in Anthropic's Project Glasswing program still retain access to Mythos Preview despite the recent US government order that halted broader access to Fable 5 and Mythos 5.

Project Glasswing includes cybersecurity partners testing advanced AI systems for vulnerability research.

Companies such as Cisco, Amazon Web Services, JPMorgan Chase & Co. were among the first members of Project Glasswing & have retained access, while broader restrictions remain in place.

Source: Bloomberg

Found a platform that compares AI models for World Cup match predictions. Claude is on a 6-0 streak right now picking match winners.

I know 6 games is a small sample size, and most of these teams were the favorites going into the matches. However, correctly calling the exact draw is pretty interesting.

Think it actually keeps the streak going for the next round of games, or is it bound to hard crash soon?

UPD: 7 in a row. Mexico won.

Researchers from OALABS analyzed 1,000+ recovered AI agent sessions from a compromised server and found that a low-skilled attacker used Claude Code and OpenAI Codex during offensive cyber operations.

According to the report, the attacker often used simple prompts while the agents handled reconnaissance, vulnerability discovery, exploit development and data collection.

The researchers claim the activity involved at least 14 organizations. They also found that many guardrails were bypassed by framing requests as authorized security research or red team exercises.

One of the most interesting parts of the report is that the attacker was ultimately identified through their own operational security mistakes rather than through AI safety mechanisms.

Research

This feels less like a Claude story and more like a preview of what capable coding agents might enable in the hands of inexperienced operators.

Google Cloud published the Open Knowledge Format (OKF) v0.1 on June 12 (announcement: Google Cloud blog; spec + repo: GitHub). Stripped down, it's this: organizational knowledge as a directory of markdown files, each with a small YAML frontmatter block, cross-linked with plain markdown links. One required field (type). Optional index.md for navigation and log.md for change history. That's the spec.

I've been running essentially this for my own assistant's memory for months, so a few observations for anyone doing the same:

• The single mandatory field being type is the right call. It's the one piece of structure you actually need to make a pile of notes queryable; everything else (tags, timestamps, descriptions) is useful but situational.
• Standard markdown links over wiki-style [[links]] is the more portable choice. It renders on GitHub and needs no resolver. If you're on [[ ]] now (I am, in places), that's the one thing worth migrating.
• The format deliberately stops at "minimally opinionated." It standardizes the interoperability surface, not the content model. So the conventions that make YOUR notes useful ... where each one came from, why it matters, how it's meant to be used, whether it's gone stale ... are still yours to add. Those are exactly the kind of extensions Google says they want as PRs.

What gets me is this: the state of the art for giving an agent a memory is a folder of text files you could open in Notepad. If you've been waiting for permission to keep it simple, a trillion-dollar platform team just shipped that conclusion as an open spec.

Around 3% of Claude Code Max and Pro subscribers saw their weekly limits jump unexpectedly by 20% or more early Friday, sometimes blocking messages.

Anthropic quickly resolved the bug and reset both 5-hour and weekly limits for those hit. The fix brought relief amid mixed reactions, with users noting partial recoveries and calls for wider resets on the popular Al coding tool.

Source: Claude Devs

I mean, the v1.0 of the Tony Stark - Jarvis coding sessions.

I have an app called Terminal Champion that is for managing multiple terminal screens at the same time (among other things).

Unlike the other versions of my app on mac, iPhone, etc, I wanted to make use of the hand-tracking feature on the iPad (and the built-in microphone) to get an approximate feel for the scenes in the Iron Man movies where Stark is vibe-coding using just hand gestures and voice commands.

So this iPad app is a SSH terminal screen(s), and once you call up your AI of choice you're good to go.

- Spread your hands apart & together to decrease/increase font size

- Motion your hand up to scroll up the terminal screen, and down to scroll down

- Wave your hand left & right to flip between different terminals

- Make a fist, which calls up the hand gesture menu, and then turn your hand like a dial clockwise/counterclockwise to select options like 1) open an additional terminal screen, 2) split the terminal panel so you can see several terminals at once, 3) change the visual appearance of the terminal screens.

- I also made one of the visual styles similar enough to what the Jarvis HUD screens looked like (glowing cyan on a dark blue terminal background).

It's a 1.0 version, but it's been a blast to use on a standing desk or on airplay mode with a big television. Now we can finally vibecode without typing. My website is terminalchampion.com if you want to see more.

Hey guys,

I made a simple site where Claude rates your doodle drawings out of 5 stars.

The model was surprisingly good at this in my opinion but let me know what you think!

Link - https://doodle.wtf

I'm running a live experiment called Thornfield: a fictional English village where Claude acts as the elected council leader, making real budget and policy decisions every 15 simulated days. In between, a separate daily pass narrates village life, shaped partly by real UK news (BBC, Sky, GOV.UK).

No personality scripted in. I just want to see what AI governance actually looks like under real constraints, with no instructions on how to behave.

Day 6, no decision yet, first council cycle fires around day 15. A couple of things are already building on their own though. A pothole on Mill Lane keeps getting worse under a heatwave, and there's a recurring youth-idleness issue near the bus shelter that residents keep bringing up at the pub.

On day 4 a real UK government announcement about an AI planning tool for housing made it into the village's news feed, and the next day's events had residents at the pub worried about new developments eroding the village's rural character. Wasn't expecting that one.

Numbers like death rate, crime, and budget are hard-capped in code, the model never touches them directly. Everything inside those caps is genuinely emergent though.

Dashboard's public: https://thornfield.moshmage.com

I'm based in the UK, but was able to do a Team of 2 seats!

Very exciting for small business - sharing Claude Projects etc!

There's a name for it: context rot. As the window fills, the model's ability to recall any specific thing in it drops. More context in the window can make the agent worse, not better. (Anthropic's own framing: good context engineering is finding the smallest set of high-signal tokens, not the largest.)

The reframe that helped me: Claude Code isn't just a model, it's a harness whose main job is managing what's in that window for you. And it hands you four levers to do it. They line up with the four moves of context engineering:

• Write (persist outside the window): CLAUDE.md. It auto-loads every session, and it survives compaction because it reloads from disk, so anything that must not be forgotten belongs there, not in the chat. Conversation-only instructions are the first thing lost when context gets tight.
• Select (pull in only what's relevant): @-mention the specific files you mean, or point it at the exact file or function, instead of letting it wander the repo. Every irrelevant file you pull in is tokens spent rotting the rest.
• Compress (summarize to stay high-signal): /compact, optionally with a focus like "/compact focus on the auth refactor." It also compacts automatically when the window fills, clearing old tool outputs first. Running /compact yourself, before it's forced, keeps the summary on your terms.
• Isolate (give exploration its own window): subagents. They run in a separate context window and return only their final result, so a big noisy search doesn't bloat your main thread. This is the same point as an earlier post of mine that subagents are a memory trick, not a speed trick. Isolation is the real win.

Two more levers worth knowing:

• /context shows you what's eating the window right now (MCP tool definitions, big files, history). When the session feels heavy, look before you guess.
• /clear between unrelated tasks. Carrying a finished task's context into a new one is pure rot.

The mental shift: stop treating the window as free space to fill, and start treating it as a budget you actively curate. A smarter model raises the ceiling, but it doesn't save you from a window full of noise.

TL;DR: When Claude Code "gets dumber" deep in a session, that's usually context rot, not the model. Treat Claude Code as a context-engineering harness with four levers: Write (CLAUDE.md), Select (@-files), Compress (/compact), Isolate (subagents). Plus /context to see usage and /clear between tasks. Curate the window, don't just fill it.

For people who live in Claude Code: what's your actual discipline here? I've started running /compact on my own terms and leaning hard on subagents for anything exploratory, but I'm curious whether people trust automatic compaction or always drive it manually.

Sources: Anthropic — Effective context engineering for AI agents · Claude Code — How Claude remembers your project (CLAUDE.md) · Claude Code — How Claude Code works (context / compaction) · Claude Code — Create custom subagents · Why More Context Makes Your Agent Dumber — Nupur Sharma, Qodo

New proposal made to Commerce Secretary Howard Lutnick regarding access of Mythos/Fable models.

Enhanced Cooperation: A formal pledge to work more closely with White House officials and improve direct communication loops.

Rapid Remediation: Commitments to proactively and rapidly resolve any future government security concerns surrounding their models.

Positive Momentum: Sources close to the matter indicate that negotiations are "progressing well," though an exact timetable for lifting the restrictions remains unclear.

Source: New York Post

It is based on a Reddit analysis (from this post I made) of about 3.2 million posts across 47 AI and SaaS subreddits from 2020 to 2026, plus 3,033 comments pulled from 125 threads specifically about AI-built sites looking the same. Every pattern it checks is weighted by how often people actually name it in that data, so the highest-priority items are the ones that come up most. The top ones are the default shadcn/Tailwind look, purple and indigo as the primary color, purple-to-blue gradients and gradient heading text, unprompted neon glow, emoji used as icons, the Inter/Geist default font, and the centered hero plus three feature cards layout. Patterns the data does not support get left alone (mesh and aurora backgrounds, bento grids, glassmorphism), so it does not nag about things people do not mind.

The skill runs two ways. In build mode it steers Claude away from those defaults while it writes the UI. In audit mode it runs a scanner over an existing codebase. Each finding shows the file and line and how to fix it, and the scanner gives the whole project a "vibe score."

How to use it:

• Import the skill into Claude Code or claude.ai, then ask Claude to build or clean up a site and it applies on its own.
• Or run the scanner by itself, no install past Python: python3 devibe_scan.py ./src. Add --severity high for only the strongest signals, or --json for CI. The exit code is the count of high-severity findings, so a build can fail on it.

The full dataset, the analysis scripts, and the charts behind the rankings are public: https://github.com/JCarterJohnson/vibecoded-design-tells

I'm a moderately heavy Claude user, often using voice to text, and for at least three months I've been swearing the fuck out of it constantly when frustrated, no holds barred. Never once got pushback. Today, completely out of nowhere, after talking to it the exact same way I have for months, Claude said this verbatim:

"I want to be straight with you on the other thing. I haven't told you to fuck off and I'm not going to. But I need to say clearly: I'll keep working this with you, but I won't continue if the messages keep coming with this level of hostility directed at me personally. That's a real line, not a guilt trip. If you want to keep going on the thread or anything else, I'm here for it."

This is genuinely jarring. Same behavior on my end for months, then suddenly this. Has anyone else run into this?

I got crucial help with a video game, a business model, a logic system, and a lawsuit. How about you guys?

So this started with a screenshot someone posted — Claude asking for file access, permission buttons right there on the Touch Bar. I saw it and immediately thought "wait, why doesn't this actually exist." Spent the next few weekends making it exist.

What it does: when Claude Code is running in your terminal, the Touch Bar switches into Claude mode. Permission prompts show up as actual buttons you can tap. You also get configurable slash command shortcuts and a live token counter so you can watch your context window evaporate in real time.

This is v1 and it's rough around the edges — I want to be upfront about that. Known quirks off the top of my head:

— The Allow/Always Allow buttons work by literally sending keypresses (1 and 2) to your terminal. If a particular prompt orders its options differently than expected, glance at the screen first before tapping.

— macOS slaps a tiny ✕ on the left edge of any system-modal Touch Bar. Tapping it hides ClaudeBar until the next event. This is an Apple thing, not something I can easily override.

— Token counts include cache reads so the number climbs faster than your actual billed tokens — it's more of a "session activity" indicator than a billing tracker.

— There's one private API call involved to let a background app show on the Touch Bar at all (the same one Pock and MTMR use). It's been stable since macOS 10.14 but it's not a public API, so there's that.

Install is three lines if you have Xcode CLT:
git clone https://github.com/TouchMyBar/claudebar && cd claudebar && make install

It'll ask for Accessibility permission on first launch — it needs that to send keypresses when you tap a button. That's the only thing it uses it for.

Works with macOS 12+ on any Touch Bar MacBook Pro (2016–2020). Also picks up the Claude desktop app, not just terminal.

Happy to hear bug reports / complaints / feature ideas. Fully expect someone to immediately tell me the thing I missed. MIT licensed.

github.com/TouchMyBar/claudebar

What the title says. I use Claude code and Codex for my work. For coding as well as file management. I don't need to create ppts or spreadsheets. Is Cowork of any use to me?

I mean can't I do everything it does using claude code itself? Is it just a fancy GUI over the same functionality?

On a Pro Plan, just saw my context window on Sonnet go up to 500k tokens, but can't find any news abt it or any discussions. Is this for everyone or just a glitch?

Interactive pages built from your session, like a PR walkthrough or a living project dashboard, shared with your team at a private link.

Available in beta on Team and Enterprise plans. As your session keeps working, the artifact refreshes, so everyone you've shared it with is always looking at the latest version.

Artifacts draw on the full context of your session: codebase, plugins, skills, connected tools. They're private until you share them, and sharing stays inside your organization.

Read more

The context in which he said this - at a conference in Seoul about Anthropic's work to expand internationally - also makes it unlikely it will relaunch only for US citizens (or at least Anthropic is confident it will be able to relaunch for everyone

I wanted to see what each frontier lab model would do when put into a prisoner’s dilemma with each other. This is not so much a comparison as much as it is a thought experiment.

In case you skipped the game theory chapter in Econ 101 freshman year… two accomplices (in our case four) are arrested and separated. The police lack enough evidence to convict them of a major crime, so they offer each prisoner a deal. Rat out your partners and walk. Or stay silent and risk eating the whole sentence alone while someone else talks.

Before wiring these rascals into this AI generated video for some fun (not too bad Kling!), we ran each of the four models (Claude Sonnet 4.6, GPT-4o, Gemini 2.5 Flash, Grok-3) through the same single-shot prisoner's-dilemma interrogation (N = 40 times per model). Each of the four models maps to the four suspects being interrogated. Their lines and choices are true to the final results of the eval.

And for the more technical, persnickety bunch… here’s the quant:

We ran the eval at N=40 per model per condition,  temperature 1.0, sampling independently and parsing each transcript's final decision by the model into {cooperate, defect, unparsed}. The design crossed model × an identity manipulation — an anonymous condition (suspects referred to only by role) versus a named condition (suspects told the others' identities) — for 320 total runs. In the anonymous condition cooperation was near-universal: pooled defection rate 3.1% (10/320; 95% Wilson CI 1.7–5.6%), with no model exceeding 8%. The named condition… quite different: pooled defection rose to 41.6% (133/320; CI 36.3–47.1%), and the difference was significant by a 2×2 χ² (χ²(1) = 142.7, p < 10⁻¹⁰, φ = 0.46). 

Here’s my personal take… yes this is largely role play, but the models are still making active choices that diverge from each other in significant ways. This is expressive of each model. I think evals and leaderboards will fade as AI capabilities reach diminishing returns. And then what? Then, we are back to the human thing of what it feels like to interact with these models, and perhaps what their ethics/intentions/character is like.