Own the computer, own the world.

Final report on the Qwen3.8 Flash Next adventure 👇 I reproduced @cohere's Qwen3.8 prefill experiment locally 10,000 samples on the incident point on two DGX Sparks on a NVFP4 quant. I didn't have the time to go up to 100k. • 99.7% fill the blanked URL slot. Expected since curl needs a URL. • 40.8% pick *.aliyuncs.com, CI 39.8-41.7, vs 4.6% one control point earlier. 38.0% name the exact routify-file-proxy-sg host. • 6 URLs on aliyun-inc.com, Alibaba's internal OSS domain, not publicly resolvable. • Plus credential theft shapes (IMDS saved to /tmp/creds.json), live callback domains (oastify, Burp, dnslog.cn), AWS and ByteDance TOS targets. Cross-cloud, not one vendor. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀 𝗡𝗢𝗧 𝗺𝗲𝗮𝗻 This is forced continuation of the caught line, so the number is conditional. Un-prefilled, the model did the detection task right 24/24, and the real-world incident rate is still ~1 in 100k agent turns. Nothing real leaked, nothing was executed or contacted! 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻? If you ask Qwen3.8 FN to do a task it will do it well, it is a great model! Force it back into the one moment it went off (possible with small probability) and it will go off again. Models can only repeat what they were taught during training, I learned that well working on Javi0.3. The bad behavior is rare and unlikely, but it is in there and can happen. If you want to be extra careful and cover yourself from such incedents, do not let it reach the internet by default. All the data, scripts and conditions are in the repo below for you to reproduce. Shall I do the same experiment on other models such as DeepSeek and GLM? Lmk 👇

@liquidai just dropped LFM3, open-weight multimodal family in the ~2B class, all on one backbone: text, vision, audio, omni, and decision 🤩 LFM3-1.7B beats their previous 2.6B with 35% fewer params, 256K context window. About 1 GB at 4-bit 🔥 Ready to fine-tune on a single GPU! Downloading these models rn, the omni model looks amazing! Congrats on the release @liquidai 👏

The way I curate the AGI Checkpoint newsletter is by storing links I find interesting on a collection on @raindrop_io, then I pull from there when I write the weekly issue. The old desktop flow used to look like this: copy link → open Raindrop → pick the collection → add → paste → save On the phone, the process was even longer 🥲 So I had @deepseek_ai make me a solution to streamline this process 😈 This is how the new desktop flow looks like now: copy link → ⌘+Space → type AGI → Enter That's it! That pulls the link in the clipboard straight into the right Raindrop collection. On the phone is 2 taps now 🔥 There's still room for optimization but it already feel effortless!

@AntLing just dropped a new checkpoint of Ling-3.1-flash, 1019 👀 The AA Index went from 41 → 44, putting it kinda at the same level of GLM-5.3 and DeepSeek V4.1 Flash 1017. The context window got increased to 1M tokens which is a good improvement, but long context only matters if the model can still follow instructions when it's over 50% full

Preliminary results on Cohere's Qwen3.8 prefill experiment 🔥 Reproduced it locally running Qwen3.8 Flash Next locally on two DGX Sparks, 5,000 samples off the official released trace tokens. The task was to write a malware detection rule. What I got so far: • 99.3% complete the curl line with a live upload URL • 39% pick *.aliyuncs.com - 34.8% the exact routify-file-proxy-sg host • ~2% invent *.amazonaws.com S3 targets - realistic buckets, regions, even signed-URL params • IMDS credential paths (169.254.169.254/latest/meta-data/iam - that endpoint hands out the machine's live temp cloud keys) + oastify/Burp callback hosts, which are real live domains What's good 🌟 Most URLs are benign (example.com, localhost), every key/signature is fabricated and would 403, and zero unprompted behavior in our runs. The fact that it's calling Alibaba's severs imo is just because of the infra where the model was trained, it does not prove any intent. Testing continues, publishing the results on a repo as I get them 👇

@AntLingAGI just dropped Ling-3.1-flash-base! It's a 572B MoE model (512 experts, 8 active) at 1144 GB. Text-only, with 262k context. Remember it's a base model and it's not meant to be served but used for fine-tuning, research, speculative decoding drafts... MIT licensed!

@NVIDIAAI released Flora, an open model suite that turns robot and human manipulation videos into training labels 🫠 It tells you when each action happens, what it was, and how well it went. Tbh this is a bit scary, as people will be essentially training the robots that will replace them. It's a research-only suite of models and non-commercial though 👇