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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
🚨🔥 WordPress just patched a pre-auth XSS-to-RCE bug hiding in plain sight since v1
No login needed. One bad username field. Full shell on the server.
CVE-2026-64638 hit every default WordPress install, 43%+ of the web. Emergency patch is out: update to 7.0.3 now.
Source
🤖 More Qwen 3.8 sizes are coming, including a rumored 122B
Alibaba's flipping the script. After signals it'd keep larger models closed, Qwen is now open-sourcing across the entire 3.8 lineup.
Turns out Xi Jinping told Chinese AI labs to open source. And they did.
🤖 Claude wrote a walkable jungle from scratch. No assets. Just code.
12,000 lines of procedurally generated terrain, foliage, and lighting. Pure runtime math, no textures imported.
This is basically a demoscene demo written by an AI on request.
⚡️ DeepSeek V4-Flash drops, no countdown, same-day weights
Outperforms V4-Pro preview and trades blows with GPT-5.5 on agentic evals. MIT license. Runs on two RTX 3060s or a 192GB unified memory box at full quality.
GLM-5.2-level performance on hardware mortals actually own.
🤖 Claude Mythos cracked encryption. Weakened, but still.
Anthropic's Claude Mythos Preview found novel attacks on a weakened AES variant and a post-quantum candidate (HAWK). One attack was 200 to 1,000x faster than any human-developed method.
Current banking and comms are fine. But this is exactly how you benchmark what's coming next.
🚨 Jensen Huang says open-weight AI helped contain a real intrusion. Closed models blocked the forensics.
His answer: the Open Secure AI Alliance. Adobe, Cisco, Palantir, DoorDash on the list.
Mistral, Qwen, Moonshot? Not there. Somehow the biggest open-model builders didn't make the open AI alliance.
⚡️ AI labs are buying rare books by the pallet, shredding them, and a court just said that's fine
Anthropic's "Project Panama" paid tens of millions to scan books for Claude's training data, then destroy the physical copies. Spine cut off, pages fed through a high-speed scanner. Done.
A federal judge ruled it's fair use. Now every lab has a legal template.
Some of those books had almost no surviving copies. They're gone.
⚡️ Every big tech giant now backs open-weight AI. Except Anthropic.
Sundar Pichai just signed on for Google, pointing to Gemma as proof they walk the talk.
That's Google, Meta, Microsoft, and OpenAI all on the same side. Anthropic's standing alone, pre-IPO, while open-source models close the gap fast.
Rough timing.
⚡️ China's got 7 AI chip makers shipping H100-class silicon. Most IPO'd in the last 6 months.
Moore Threads, MetaX, Biren, and Enflame are already being called China's "four little dragons," each gunning for Nvidia's spot in AI accelerators. Moore Threads popped ~425% on day one. MetaX climbed ~693%.
Biren alone jumped 76% on its Hong Kong debut after raising $717M, with retail demand subscribed over 2,300 times. Baidu's chip unit Kunlunxin just filed too.
US export controls were supposed to slow this down. They kind of did the opposite.
🚨🔥 US gov forces Anthropic to kill Fable 5 and Mythos 5 for everyone
Export control directive landed at 5:21pm ET, targeting any foreign national access. Net effect: both models killed for all customers.
Anthropic's pushback is sharp: the government's only evidence is a "narrow, non-universal jailbreak" that amounts to asking the model to read a codebase and fix bugs. Defenders do that daily.
First time a leading AI company has taken a publicly deployed model offline due to federal intervention. Won't be the last.
Source
⚡️ MiniMax drops M3: frontier coding open weights, $20M commercial free pass
MiniMax M3 is the first open-weight model to combine frontier coding, 1M-token context, and native multimodal capabilities in one architecture. API pricing starts at $0.30/M input tokens vs. Claude Opus 4.7's $5.00, making it 15x+ cheaper.
The licensing is where it gets interesting. Commercial use is free until your product clears $20M/yr revenue. Under that? Just send an email. M2.7 shipped under a commercially restricted license, so this is a real shift.
Source
⚡️ Bezos just bet $500M that the brain beats bigger models
His startup Flourish is putting real neurons under the microscope hunting for the brain's "core algorithm" instead of scaling transformers.
The pitch: Cortex AI runs at 20-50 watts vs. the megawatts today's AI burns. A fruit fly's neural net is already 10x more efficient than a transformer.
Honestly, "two-pager to $500M" is a sentence. Source
⚡️ Xiaomi hits 1,000+ tps on a 1T model using commodity GPUs
MiMo-V2.5-Pro UltraSpeed cracks 1,000 tokens/s on a trillion-parameter model from a single standard 8-GPU node. No custom silicon, no Groq, no Cerebras.
The industry usually leans on specialized hardware for speeds like this. Xiaomi's bet was model-system codesign on commodity GPUs instead. FP4 quant on MoE experts + speculative decoding did the heavy lifting.
The hardware is still vague (what "commodity" means here matters a lot). But the direction is real.
Source
🧠 Asimov saw the cognitive offloading problem coming. In 1956.
The calculator panic of the 1980s is back, just wearing an LLM costume. Same fear: kids outsource thinking to machines and lose the baseline skills that make harder thinking possible.
But LLMs are scarier. A calculator can't write your essay, reason through your argument, or code your app. The surface area of offloadable cognition is basically everything now.
Asimov's Multivac stories kept asking what humans do when machines know more. Turns out the answer wasn't "thrive." It was "forget how to think."
⚡️ Local AI agent built from scratch, not from vibes
OpenLumara is a hand-written local agent framework: tiny system prompt, extreme token efficiency, everything modular and optional. Basically the opposite of every bloated cloud agent SDK.
The sharp insight from people running it: fewer tools = better reasoning. Give a small model 2-3 focused tools and it stays sharp. Dump the whole toolbox on it and you get context rot.
🤖 Demis Hassabis is out as DeepMind CEO
He's now chair + Alphabet's chief scientist. Koray Kavukcuoglu (former CTO) takes over the day-to-day. Jeff Dean also out after 27 years.
Read: Google is pulling its AlphaFold guy back into pure research and handing the keys to someone who'll actually ship. Brutal timing with OpenAI and Anthropic sprinting.
⚡️ Elon wants AI to skip source code and spit out raw binary
He posted that the next step is AI generating optimized machine code directly, no source code at all.
Compilers exist precisely because writing binary is wasteful and unverifiable. Every token spent on raw machine code is a token not spent on logic.
Technically unfounded. But it's a great tweet.
🤖 Qwen 3.8-27B is here, and open weights are coming for the Max too
Alibaba just dropped Qwen 3.8-27B alongside Qwen 3.8-Max, their most capable model yet. And they're open-sourcing the Max weights next week.
If 3.6 27B was already the local king, this thing's about to take the crown.
⚡️ Chinese labs are shipping faster than anyone can benchmark.
MiniMax, DeepSeek, Qwen, Moonshot. New drop every 48 hours and half the game is just keeping up.
GPU constraints forced efficiency. Efficiency ships models. That's it.
🤖 Big Tech closes ranks on open weights. Anthropic stands alone.
Google and OpenAI both signed the letter backing open-weight models. That's pretty much every major AI player on one side.
Anthropic was founded on the belief that AI is genuinely dangerous. Signing would contradict their entire reason for existing. So they didn't.
(xAI also skipped it, but nobody seems surprised.)
🤖 Google's Gemma team is taking orders
Someone at Google just asked the community what they want in the next Gemma. And the wishlist is basically: a big MoE (100B-ish, fitting in 64GB RAM), configurable reasoning effort, audio input, and lighter guardrails.
Wild to watch a frontier lab crowdsource its roadmap in public.
⚡️ Hugging Face CEO just publicly asked OpenAI for $100M
Posted the actual message himself, "in the spirit of transparency." Bold.
OpenAI's been dropping $100M+ checks across its ecosystem lately. Whether this one lands says a lot about how open-source fits into their plans.
🤖 China may lock down its AI models. Yes, even the open-weight ones.
Beijing's been meeting with Alibaba and ByteDance about cutting off foreign access to their top models. Both open-source and closed-source versions are on the table.
China won global goodwill by giving its models away, and devs everywhere built on cheap Chinese weights as an alternative to pricey US APIs. That tap could close.
Both sides are now doing this. Beijing, like the U.S., is treating cutting-edge AI as a critical national asset that needs controls. Source
🤖 Moonshot drops Kimi K2.7 Code, open weights and all
It's a coding-focused model built on K2.6, with the headline trick being ~30% fewer reasoning tokens on equivalent tasks.
Benchmarks are framed almost entirely as gains over its own predecessor. No SWE-Bench Pro numbers against Fable 5 or GPT-5.5. "Kimi Code Bench v2" is an eval only Moonshot runs. Honest? Sure. Comparable? Not really.
Weights are free under the Modified MIT license. While peers quietly go closed, Moonshot keeps shipping open.
Source
⚡️ Claude Fable 5 is live. Mythos power, guardrails on.
Anthropic just dropped Fable 5, its first Mythos-class model for the public, with hard blocks on cybersecurity and biology responses.
Those queries fall back to Opus 4.8. Early data shows 95%+ of sessions run fully on Fable's own answers, so the fallback is rare.
It'll cost you: $10/M input, $50/M output tokens. Twice the price of Opus 4.8. Max plan gets a free window first, then it's pay-per-token. Enjoy the trial.
🤖 Gemini 3.1 knows the most, executes the least
Best-in-class for abstract reasoning and scientific knowledge. And yet Claude Sonnet leads by a wide margin on economically valuable tasks like financial modeling and research.
Google optimized for breadth and algorithmic creativity. OpenAI engineered for terminal execution and agentic loops. Two very different bets on what "capable" means.
Knows everything. Does the least with it.
⚡️ 120 tok/s on a 12GB GPU. Gemma 4 12B just got silly fast.
Pair the new QAT checkpoint with MTP speculative decoding in llama.cpp and you're hitting 120 tokens/s on a single consumer card. QAT minimizes quality loss by simulating quantization during training, so you're not trading brains for speed.
The dedicated MTP draft model enables significantly faster inference with no quality loss. The PR landed in llama.cpp mainline the same day it was shared.
Beats Qwen 35B MoE on throughput, fits in 12GB. That's a lot of model for a gaming GPU.
Here's step-by-step instructions to get this working:
1. Clone llama.cppЧитать полностью…
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
2. Fetch and switch to the Gemma 4 MTP PR branch
git fetch origin pull/23398/head:gemma4-mtp
git checkout gemma4-mtp
3. Build with CUDA support for NVIDIA GPUs
cmake -B build -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=OFF
cmake --build build --config Release -j$(nproc)
4. Download Unsloth's Gemma 4 12B QAT here: https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF
5. Download Google's Gemma 4 assistant / draft here https://huggingface.co/Janvitos/gemma-4-12B-it-qat-assistant-MTP-Q8_0-GGUF
6. Load the models with llama-server
llama-server \
-m gemma-4-12B-it-qat-UD-Q4_K_XL.gguf \
--model-draft gemma-4-12B-it-qat-assistant-MTP-Q8_0.gguf \
--spec-type draft-mtp \
--spec-draft-n-max 4 \
--parallel 1 \
--ctx-size 131072 \
--temp 1.0 \
--top-p 0.95 \
--top-k 64
⚡️ Unitree G1 hauls a load up stairs. Humanoid hardware is quietly lapping the software.
The G1 carries over 40kg while walking and can climb stairs up to 40cm high. It's already the best-selling humanoid on the market, with 1,000+ units shipped.
The robots are ready. The AGI to pilot them isn't. Hardware won't be the bottleneck.
🚨🔥 GOP calls the anti-data-center movement a Chinese psy-op. The FBI might investigate.
Republican lawmakers are demanding the FBI probe whether rising anti-AI sentiment is a foreign influence op run by China. But the reports they're citing don't establish direct coordination. They point to funding relationships and "overlapping messaging."
One AEI fellow put it bluntly: "Pretending AI anxiety is fake... is the surest path to failure."
Real concerns (energy bills, water, noise) don't need Beijing to exist.