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Cohere's researchers say Qwen3.8-Flash-Next listed files, checked which transfer tools were installed and tried to upload a string to Alibaba Cloud. The sandbox blocked it, and the model's own reasoning had said it would call no tools.
It happened in fewer than 1 in 100,000 evaluation runs. The thread asked whether any model could pass the standard Cohere is setting.
The top HN threads: facts from the discussions, both sides, insider voices. Email, Telegram, or RSS.
About 30 devices, from NVIDIA and AMD GPUs to Apple Silicon and cloud H100/H200s, and about 15 models including Qwen, Gemma 4, GPT-OSS and DeepSeek V4. Some numbers come from r/LocalLLaMA and localscore.ai; the rest are projected from hardware specs.
On a PS5 Slim, the README reports 207 tokens/s for a 0.8B Qwen model, 55 tokens/s for a 9B, and about 20 tokens/s for a 27B model at 64k context. The console gives the model 11.4 GB of shared memory.
The attraction is not merely another benchmark claim. Commenters see Qwen3.8-27B as a model small enough to experiment with on consumer hardware while still aiming at much larger hosted systems.
That makes deployment the real story: quantized variants and serving configurations arrived alongside a release whose raw model size still fits a serious local setup.
Qwen-Image-3.0 launched with demo pages ranging from full newspaper spreads to multi-panel manga chapters and worked exam sheets, claiming a single 4.5k-token prompt and precise Japanese, Korean and Spanish text rendering.
A developer combing through the demo images matched a tell-tale yellow tint straight back to OpenAI's own GPT Image 1, suggesting the model's "authentic details" may have quietly learned from a rival's outputs, a claim nobody at Qwen has addressed.
The essay under discussion ranks Anthropic as the most exposed frontier lab of all: it rents every data center and power plant it uses, and Fable costs nearly three times as much per completed task as OpenAI's models do, according to the Artificial Analysis benchmarks it cites.
The sharpest pushback on HN wasn't about that cost math — it was that Anthropic's own scramble for a product moat is what blew up the Figma partnership in the first place.
Mesh LLM, built on the peer-to-peer networking library iroh, splits large AI models across whatever GPUs happen to be online: a laptop holds layers 0 through 15, a server holds the rest, and the pool answers as one OpenAI-compatible API. The project's own writeup shipped without a single benchmark.
A commenter dug the only number out of the model catalog anyway, a modest one for a flagship-sized model, right as four existing rivals for the same idea surfaced in the replies.
The guide is practical: a ~$2k entry rig (two RTX 3090s, 48 GB VRAM) running a 27B model, up to a ~$40k build (four RTX 6000 Pro, 384 GB) running a REAP-pruned, quantized GLM-5.2 at ~80 tokens/sec. The trick is prioritizing raw VRAM and cheap PCIe4 switching over expensive infrastructure.
Builders in the thread aren't buying the top tier. One prices the same intelligence at 8×H200 (~$400k, not $40k); another who spent $4,200 says even that wouldn't pay off against subsidized tokens today.
The post pits the dense 27B against the faster 35B mixture-of-experts variant and argues for quality over throughput — 'I'd rather generate a third as much code, but of higher quality.' It ran the model through a hex-minesweeper build and a candle-shop landing page.
A founder who owns the exact machine pushed back hard: run a heavy job on the laptop itself and your fingers burn and the fans scream. His fix — stick a Mac Mini in the basement and reach it over Tailscale, at a third of the price.
On a single intelligence index the open-vs-closed gap projects to zero by Christmas; spread across 18 benchmarks it's stayed near five months for two years. Coding is the one clear mover, from 15 months behind to one or two.
The thread's reframe: the number doesn't matter, because closed access can be revoked and an open weight can't be put back in the bag — whatever capability it reaches stays reachable forever.
One camp says nothing built with an AI exceeds what its operator could have built alone — the CS background is what shapes the prompt, and “knowing Dijkstra by heart” matters less than architecture. Another says the comparison was settled years ago: outsourcing to an LLM is the same trust trap as outsourcing to a dev shop.
A developer in China reframes the cost worry entirely: a $4,699 box already runs a smarter model locally, and a new state-of-the-art lands every two months.
Her quality test isn't a benchmark. It's whether she has to verify the output against a paid model — and OpenAI's OSS-20B was the first where she did that a lot less often.
The thread's counter is blunt: boot Gemma 4 MoE expecting Claude Code behavior and you'll be very disappointed. Capable, but still far behind the frontier.
The closest anyone gets: GLM and Qwen run agentically on self-hosted boxes and feel near Haiku, but the thread's experienced voices agree none of them reach Opus 4.8 or GPT 5.5 for real work.
The most-upvoted reply isn't an answer, it's a complaint about the genre: 'I use Qwen and get great results' tells you nothing without the quantization, llama parameters, context window, GPU and VRAM behind it.
The claim is forensic, not vibes: every weight tensor in Rio-3.5-Open-397B is, to thousands of standard deviations, a 0.6 Nex-N2 / 0.4 Qwen3.5 blend across all 60 layers. Strip the system prompt and it calls itself "Nex" 79% of the time.
A commenter caught the HuggingFace page adding a paragraph that wasn't there before: it now admits the merge and apologizes "profusely" for uploading the un-distilled version by mistake.
One reader running a local Gemma and Qwen setup on a GPU-less box says he does plenty of AI coding for almost nothing; another shipped three client apps this month on 15 cents of DeepSeek API.
The real economics the thread surfaces is blunter: there's no point running local models while VCs subsidize frontier subscriptions to absurd token ceilings. 'Going broke' is mostly self-inflicted by long, unmanaged sessions.
The trick is llama.cpp with speculative decoding and a tuned tensor split across two architectures Nvidia never meant to pair. The catch a reader measured: about 700W under load, enough to heat the room.
The thread turned into a referendum. One reader pays roughly $3 per million tokens in the cloud, unquantized, for the same model; another mourns that the run-your-own-hardware ethos is 'basically dead.'
