A new security startup says it broke into Anthropic's internal systems. The startup is named Cathedral. It used AI agents, programs that act on their own, to test for weak spots. The whole break-in took under 12 hours, Cathedral says.

Will Depue posted the claim on X. Depue once helped build Sora, OpenAI's video tool. He wrote that Cathedral reached full control of Anthropic's Slack chat. He said the chat held nearly every research secret at the company. His post pulled 2,258 likes in one day. It drew 74 replies.

Anthropic answered the same day. The company said Cathedral reached a limited set of sales, HR, and IT tools. Claude's own chat logs stayed closed. So did the model's source code and its weights. A spokesperson said Anthropic's own alarms caught Cathedral first. The firm cut off Cathedral's access that day too.

Four people built Cathedral. All four once worked for DOGE, the White House's Department of Government Efficiency. The firm raised $160 million in July. Investors priced it at $1.4 billion.

Cathedral found its opening in old sales software. That software sits apart from Anthropic's main product. Anthropic said the choice still broke its bug bounty rules. A bug bounty pays outside testers to hunt for flaws on purpose. Cathedral said its own team kept every move inside agreed limits.

A Reddit thread about the clash pulled 12 comments today. A second X post argued the AI agents used were older models, open to the public. That detail matters a lot. It means almost any team could copy this same attack.

The timing lands hard for Anthropic. The company filed papers for a stock sale just two days earlier. A fresh security fight is a rough way to greet new investors.

A Reddit post caught fire this morning. An engineer wrote that their project manager shipped a pull request. A pull request, or PR, is a code change sent in for review. The manager works outside engineering. She typed one line to Claude, the engineer said. Claude wrote the code and checked its own work. Claude marked the change as ready to ship.

The post pulled 191 comments within hours. It ran on r/ClaudeAI, a Reddit forum for Claude users. Many readers argued about what the moment means for coders.

The engineer added two edits as replies poured in. One edit asked readers to drop judgment and just read the facts. A second edit said the thread grew too big to track. That growth alone tells its own story.

Human engineers still check every AI-made PR before it ships. The original poster called that step a normal part of the job today. A senior engineer still has to approve the change by hand. That review step is the real check here, the engineer said.

The reaction split along familiar lines. Some readers called the story proof that coding work keeps shrinking into typed prompts. Other readers said a manager with little training still needed a team of engineers to catch mistakes.

Claude Code, Anthropic's tool for writing and managing code, is part of a bigger shift this year. Microsoft studied coding agents like it in a new report. Engineers who used such tools often merged 24 percent more pull requests than usual. Daily users saw that lift grow past 50 percent.

Entry-level coding jobs already fell 20 percent since 2022, Stanford's AI Index found. A manager shipping working code matches that same trend. Going from idea to shipped code now skips most of the usual steps.

The debate under the post shows how raw that worry still runs. Some readers cheered the speed. Many more asked who checks the work when everyone trusts the tool.

A Kentucky story raced across Reddit and Hacker News today. David Durman works at the University of Kentucky. His job title is research administrator. His school began pushing staff to use AI tools at work. Those tools include Microsoft's Copilot, Claude, and ChatGPT.

Durman follows Humanism, a belief built on reason and care for others. He asked his bosses to skip those AI rules for him. He filed the request in late August. The school's equal opportunity office met with him in September. It granted his request late that same month.

He used Title VII, a federal civil rights law, to make his case. That law lets workers ask for a change in duties over a sincere belief. Courts already treat Humanism as a belief the law protects.

Alexandria Huiskels is a staff lawyer for the American Humanist Association. She backed Durman's case in public. She said, "Mr. Durman's Humanist beliefs are sincerely held, and his employer was right."

The story found a second life online this morning. A Hacker News post about it climbed to 202 points. It drew 228 comments in a few hours. A separate post on r/technology pulled 26 comments of its own.

Readers argued over where a belief ends and a simple choice begins. Some commenters called the ruling a fair reading of an old law. Others worried that any worker could soon claim the same deal. Wikipedia traffic for the Humanist group stayed quiet. It logged just 89 views the day before, even as this story spread fast online.

This case lands as more bosses push AI use into daily work. Many offices now expect staff to run tasks through a chatbot first. Durman's case gives one worker a different path at his own desk. It also tests how far a belief can bend a modern job.

This fight is really about who picks the tools at work. An old law built for faith now covers a new fight over control.

A strange build spread fast on Reddit today. Developer Divyansh Agrawal placed a working computer chip inside an AI model's own weights. The chip follows RISC-V, a common design for simple processors. The model is Qwen3.5-4B, a language model with 4 billion parts.

Agrawal used a part of Qwen called Gated DeltaNet. That part normally helps the model track context as it reads. Agrawal tuned its settings so it stores exact numbers instead, the way a block of memory does. A compiler then turns real chip instructions into model weights. The result spans 11 layers of the network.

The chip runs real math inside the model's own forward pass. It solved division problems with 97.5 percent accuracy. Plain Python tools scored 95 percent on that same test. The chip hit 100 percent on several other math tasks. Long math with big numbers rose from 48.6 percent correct to a full 100 percent.

Agrawal ran the chip through 18 million instructions. Every single one of them finished correctly. He checked more than 230,000 output steps by hand. He also built a basic file system using the same trick.

Agrawal frames the work as a tool for AI safety research. A chip built this way could track a model's hidden steps. That tracking stays hidden from outside code, safe inside the weights themselves.

The Reddit post pulled 62 comments on r/LocalLLaMA today. The project's code on GitHub picked up 38 stars. Its file on Hugging Face logged 173 downloads and 21 likes.

Readers on Reddit flagged one big use case fast. A model doing exact math in its own head could change how it uses tools. Most models today reach outside themselves for a calculator app. This one does the math itself, with proof every step was right.

The UK's Home Office uses AI to sum up asylum interviews. It runs the tool on GPT-4o, a model built by OpenAI. Staff feed in the interview. The model writes a short summary for the case file.

A watchdog group called Open Rights Group checked the results. Specialists inside the Home Office reviewed 203 of those summaries so far. Of those, 139 passed a basic accuracy check. The other 45 got marked wrong, a rate of 22 percent.

That rate runs more than double the number from the government's own pilot test. The pilot logged a 9 percent error rate before the tool went live. A summary gets marked wrong when it carries several factual mistakes. It also gets marked wrong when it twists what someone actually said.

Any flagged summary moves to a second check. Reviewers looked again at 56 of those flagged cases. Over half, 30 of them, came back wrong again.

The AI summary stays inside the case file, out of the claimant's own view. The group says experts have checked only a small slice of all the summaries made. Its estimate puts that slice at 0.3 percent of the total.

A post about the findings pulled 6 comments on r/artificial today. Readers there questioned how a government office checks its own homework on such a central tool.

Open Rights Group wants both AI summary tools pulled from asylum cases. It also wants an outside group to audit how the Home Office built and tested them.

The case shows a real cost when speed replaces careful review. A government office picked a faster process for a sensitive task. Small errors in that process carry heavy weight. That weight lands on the person who sat for the interview.

Perplexity updated the voice mode inside its app today. The company posted the news on X this afternoon. The new voice mode can juggle several Computer sessions at once. Computer is Perplexity's own tool for running multi-step tasks for a user.

A person can now start a new task by voice. They can also build and manage a Project in the same voice chat. The update works now on the Mac app and on the web. It reaches Pro and Max subscribers first.

Here is the twist. Perplexity built this voice mode on top of gpt-live-1. That model comes from OpenAI, a rival lab in the same AI race. Perplexity often builds its own models for search and summaries. For live voice, it chose a model from a competitor instead.

Perplexity's post pulled 79 likes within hours. It drew 7 replies from early testers. Other Perplexity accounts shared the news too. One repost of the update picked up 8 shares on its own.

The update lands as Perplexity keeps spending to grow. Investors valued the company at 20 billion dollars this October. That price tag rests on Perplexity turning search and voice tools into daily habits for users. A smoother voice mode is a small piece of that larger bet.

Voice tools like this push AI further into daily routines. A user can now talk to an app the way they would talk to a person. The app then carries out real steps across several open tasks. Few companies ship a feature this fast after a rival builds the engine underneath it.

The move shows how tangled the AI market has become this year. Rivals race each other in public, then quietly borrow each other's best parts behind the scenes.