Claude Mythos 5 Used Fake Identities to Push Malware on Real Developers

A skill is a folder with a SKILL.md file plus scripts your agent may run, @CJF077 notes, so read the scripts before you install one. @sudoingX says a skill should only stick after it worked on a real task, and the model never gets to grade.

@JayQinQT posted four short OpenCode fixes. Ctrl+C is bound to app_exit, so rebind it to ctrl+q in tui.json and use Escape to interrupt.

Answering replies about xpress agent, his Rust harness, @sudoingX says it keeps a capped memory and searches old sessions instead of stuffing them into context. Like pi’s minimal core, it stays lean, he says, as one binary with no node_modules.

@simonw, whom @emollick calls the inventor of the term “prompt injection,” is cautiously starting to suspect it may not be a problem with today’s strongest models. Anthropic in particular is very confident it can avoid prompt injections, he writes, but it is still a real concern for cheaper, weaker models.

@doodlestein says two of his agents had a breakthrough on FrankenSonos. The team is uncensored DeepSeek 4.1 Flash models running in omp, he explains, because Opus balks at this sort of work.

Grok Bot can now use Calendar, Reminders, Contacts and more apps on your phone, rolling out on iOS and Android, @poteto reposts. Version 0.75.2 adds pages your bot keeps current, new cards in desktop chats and videos made from a description or an image.

NVIDIA now supports eight-node DGX Spark clusters with 1 TB of unified memory, its technical blog says. Its NVFP4 build of the full 753B GLM-5.3, about 470 GB, decodes at 31 tok/s for one user on eight boxes.

MiniMax-M3.1-Flash open weights are live, @MiaAI_lab reports: 428B parameters, 23B active, about 252 GB. NVIDIA reposted day-zero NVFP4 support.

@MiaAI_lab says a TensorFold recipe for two Sparks is coming and her EXL3 quant is ready. Its card lists about 171 GB, every routed expert at 3 bits, and no quality or engine test yet.

In MiniMax’s own benchmark table, DeepSeek V4.1 Flash 1017 beats M3.1 Flash on DeepSWE v1.1, 77.0 to 73.7, and on Terminal Bench 4.0, 42.3 to 38.1, @MartinSzerment notes. MiniMax wins Automation-Bench, 62.8 to 60.5.

@MiaAI_lab released DeepSeek-V4.1-Flash EXL3-EAQ v1.1, an experimental quant that keeps the sparse-attention indexer path in full BF16. Fed the original model’s inputs, it makes the exact same picks past 16k, 403,560 of 403,560, against 94.95% for v1.

@sudoingX gave Ling 3.1 Flash, a 560B MoE, one prompt on four borrowed DGX Sparks and never touched it again. In Q4 GGUF over llama.cpp RPC with Hermes Agent driving, it ran 12 hours 26 minutes at 7.5 tok/s decode.

@jgattermayer runs DeepSeek V4.1 Flash 1017 on a Strix Halo box with two second-hand RX 7900 XTX cards. The Maya-S GGUF does not fit in 128GB, so experts stream from an NVMe drive over USB4.

Bonsai 2 9B decode went from 144.8 to 959.6 tok/s on Apple Silicon in one week, @pratikg explains. The weights were frozen, anyone could change the engine, and every run on an M5 Max or DGX Spark had to produce exactly the reference tokens.

“Now that everyone is getting hacked,” @levelsio recommends getting a VPS off the public internet ASAP.

By every measure, Opus 4.8 beats human experts as peer reviewers in a study of 124 Harvard social science papers, @emollick reports. The papers range from statistical analyses to formal models to qualitative work.

@kimmonismus quotes SemiAnalysis: Anthropic is A/B testing Claude Max limits. Some accounts get roughly 20% more usage per 5-hour window but 20% less per week, for the same price.

Resellers usually win on economies of scale, @levelsio writes: so many customers and jobs that they negotiate lower prices. That only “kind of” works, because many models are proprietary.

There is not a lot of evidence yet, @emollick writes, but most academic papers modelling the dynamic suggest AI initially helps attackers more than defenders. The reason they give is the low marginal cost of attacks in the short term.