Andrej Karpathy helped start OpenAI. He later ran Tesla's AI team. Now he runs a small company called Eureka Labs.

On November 1, Karpathy posted a weekend project on X. He built a site called HackerTube. The site shows old YouTube videos. Hacker News readers once recommended every one of them.

Karpathy had Claude Opus 5.5 do almost all the work. The AI agent read 43 million Hacker News comments. It found every comment that linked a video. It sorted 6,454 videos into 150 topics. Karpathy said the whole job took about two hours.

He called the result "significantly better" than YouTube's own picks. His post pulled 41,287 likes in a day. It drew 1,704 replies.

The story spread past X fast. It reached the top of Hacker News. That post pulled 2,603 points. It drew 573 comments, the top story on the site today.

A coder named roansh saw the post and copied the idea. Roansh had Claude build a second site. He called it HackerBooks. That site tracks 16,500 books. Hacker News readers have named each one twice or more.

Karpathy said he barely touches a terminal now. He mostly talks to Claude. The agent handles the rest, start to finish.

Readers on X called the two-hour build a sign of the times. A task that once took a small team weeks now takes one coder an afternoon.

That speed cuts two ways. It lets one person build a useful site fast. It also lets anyone copy the idea within a day, the way roansh did.

Karpathy is one of the most followed voices in AI research. When he shows what an agent can do, other builders notice fast. Many of them try the same trick the very next morning.

A coder named -p-e-w- just shipped Heretic 2.0. Heretic is a tool. It strips safety rules out of open AI models.

The news went up on Reddit's r/LocalLLaMA forum. The post called it "the biggest Heretic release ever." Five months of work went into this version.

Heretic already has real reach. It carries 37,744 stars on GitHub. It has 4,303 forks from other coders. People have used it to modify more than 8,500 models on Hugging Face. Those models carry 88 million downloads in total.

Version 1 of Heretic did one job. It removed a model's refusals. A refusal is the polite "no" an AI gives to a risky prompt. Version 2 does much more. It can cut filler text, which the post calls slop. It can lower a model's urge to flatter the user. It can even shift a model's tone.

The new version uses a method the coder calls Arbitrary-Rank Ablation. The old method edited a model's weights in a blunt way. The new method edits fewer numbers to get the same result. That makes it run faster. It also works on more kinds of models.

The post drew heavy traffic on Reddit. Readers left more than 125 comments. A copy of the same release notes reached Hacker News too.

Heretic sits in a messy corner of open AI. Labs add safety rules so a model will decline harmful requests. Heretic exists to remove those same rules, free and in public.

The project runs under the AGPL license, built to keep code open. Anyone can read Heretic's code. Anyone can run it on their own machine.

A hobbyist can use it to stop a chatbot from lecturing them. A bad actor could use the same tool for worse ends. The project's reach, now past 37,000 stars, shows how many people want that choice for themselves.

A file on Hugging Face claimed to be a secret Anthropic model. Within a day, a Reddit user proved it was fake.

The file sat under the account "burner22," with the name "numbat." It claimed to hold a 14-trillion-parameter checkpoint. The checkpoint supposedly came from Anthropic's Mythos line. Mythos is Anthropic's line of frontier models, shown only to a few partners.

The claim first spread on X on November 1. One early post about it pulled 876 likes. It drew 98 replies within hours.

A Reddit user named johnxreturn then ran the numbers. The post checked the file's own math. A model's output layer and its input layer usually differ, since Anthropic trains them apart. In this file, the two lined up almost exactly, row after row.

Real model weights also swing a lot from one input to the next. This file's weights barely moved at all. The post called the pattern noise. Trained models leave real structure behind; this file carried only randomness.

The account itself raised flags too. It was made one week before the file appeared. It had posted on Hugging Face just once, for this file alone.

Even so, the file held some real details. Its claimed size, 7.72 terabytes across 772 shards, checked out exactly. Its model name is real too. Anthropic does use "Mythos" for models it shows only to a few partners.

By the time of this post, the file had drawn 260 likes. It had 7,108 downloads on Hugging Face.

The debunking thread pulled 7 comments on Reddit. Word of the forensic work spread faster on X than on the forum where it first ran.

The case shows how a leak can look real at first glance. Real file sizes and a real model name can sit beside numbers a human invented. One coder with free tools found the gap within a day.

A new AI test says even the best models are far from ready for real jobs.

The analysis firm SemiAnalysis posted the benchmark on X on November 2. It is called GDPx. The name plays on GDP. It measures tasks tied to the richest parts of the economy, like chip design, finance, and biology.

SemiAnalysis wrote that the best model scores just 12 percent on the full test. That is about one task in seven, done well enough to pass.

A thread on Reddit's r/singularity forum picked up the news fast. One reply listed scores with safety filters set aside. GPT-6 Astra led at 11.8 percent. Claude Opus 5.5 followed close behind. Google's Gemini 4 Carbon scored lower still.

SemiAnalysis's post on X pulled 2,157 likes. It drew 222 replies. The Reddit thread pulled 19 comments of its own.

Commenters argued about what a low score like 12 percent really means. One reader asked how an average person would do on the same tasks. Another said people rarely do this work alone either. Every field here already runs on teams, the reader wrote, rarely on single people.

One reply raised a sharper point. SemiAnalysis had said months earlier that Google's odds of regaining the AI lead had fallen to almost nil. A commenter noted the firm's own new benchmark now puts a Google model close to the top score.

GDPx covers eleven fields in total. Those fields include hardware design, legal work, and AI safety research itself. The test skips broader fields like government work and real estate on purpose.

The test lands at a moment when AI firms sell agents as ready to run whole jobs alone. A score of 12 percent argues otherwise, for now. SemiAnalysis built GDPx to track how fast that number climbs as each new model ships.

A leaked usage chart suggests OpenAI is already testing its next flagship model.

On November 1, a user named Mashiro_0619 posted screenshots on X. The screenshots came from Codex, OpenAI's coding tool. They showed a new label next to the current model inside a usage chart. The post said three different users had seen the same thing.

A second user, Pankaj Kumar, shared the find in English soon after. He wrote that OpenAI was already routing some users to the new model. The post called it gray testing. Gray testing means a company quietly shows a new version to a small group first.

Pankaj Kumar's post pulled 94 likes. It drew 16 replies. The original screenshot post from Mashiro_0619 pulled 346 likes on its own.

Readers on Reddit's r/OpenAI and r/ClaudeAI forums picked up the story the same day. One Reddit post asked a blunt question about Astra's fate. Another guessed that OpenAI and Anthropic may launch new models the same week. Anthropic's own site code had hinted at a coming Claude Mythos update.

OpenAI has kept this release off every official channel so far. The company skipped a planned GPT-6.1 update earlier this year. It added a fresh round of safety testing instead.

Public interest in OpenAI's main product stayed high the same day. The Wikipedia page for ChatGPT drew 51,799 views on November 1 alone.

A leak like this rests on thin evidence. It is one label in one billing chart, seen by a handful of users. It would hardly be the first time a usage chart hinted at a model early. Readers now wait to see what happens next. The label may become a public release. It may also quietly fade, the way some test labels do.

A hobbyist coder just ran eight AI coding agents at once on two home gaming cards.

The coder, who posts as o2beast, shared the project on Reddit's r/LocalLLaMA forum. Each of the eight agents held its own 250,000-token memory, called context. That is enough room to hold a large codebase in one agent's head at once.

The setup ran on two Nvidia 3090 cards, the kind a gamer might already own. Together the eight agents produced around 545 to 790 tokens a second. The exact speed depended on the task.

The trick is a new way to manage memory. Normal setups keep a model's full memory on the graphics card itself. That card runs out of room fast. This setup keeps the whole memory in regular system RAM instead. At each step, the model picks out only the one percent it actually needs right then.

O2beast built the patches on top of HyperQwen. HyperQwen is an open coding tool. It carries 2,481 stars on GitHub and 317 forks. The new patches sit on top of it as a separate layer. The original code stays untouched.

The post drew 21 comments on Reddit. Other coders asked sharp questions. One asked how well the agents could still find a single fact buried deep in a huge codebase. O2beast answered that recall tests passed 31 of 32 checks at the full context size.

Other commenters pushed back harder. One said compressing memory down to four bits, as this setup partly does, usually hurts a model badly. O2beast agreed more testing was still needed before trusting the setup with serious work.

The project shows how far a single coder can push cheap hardware today. Two home graphics cards, bought for gaming, now run work that once needed a server rack. The memory trick still needs harder tests before anyone fully trusts it.