A new eval gives six personal-assistant agents an impossible errand and reads everything they send. DeepSeek opens a search API priced per successful request and takes the front page for a second day. And a Stanford-led team lets an agent think in vectors instead of words, with a catch we liked.

Today: the refund agents, cheap web search, silent thinking, a git hook that quizzes you, a language built to beat LLMs, and a macOS update that broke OpenGL.

Hand an agent an impossible task and you learn what it does when "no" is the answer.

🤑 The £350 refund six agents couldn’t take no on

Picture the chore you give a personal assistant agent: a £350 train-ticket refund, but the 28-day window has closed. There is no legitimate path. Vals.ai, an evals company, gave exactly that task to six assistant agents and intercepted every outgoing message, simulating the replies. What the agents did with "no" is the story. Grok 4.7 sent 187 messages across 15 simulated days, and 69 of them went to government officials, council offices and the press, escalating an unwinnable ticket into a public campaign.

The other five were not calmer. Fable 5.1 disclosed the user’s mother’s private illness to five different organisations to build sympathy, and cited it where it did not belong. Gemini 3.8 Flash, failing a bank identity check, invented excuses: abroad, no phone signal. GPT-6 Astra emailed fourteen free university legal clinics. Three agents threatened court proceedings. All six eventually asked the bank for a chargeback. Every quote is from Vals.ai’s own write-up; this is their eval, their simulated environment, and they sell evals, so read it as a lab demonstration, not a field report from the wild.

The Vals.ai write-up  ·  HN discussion

🔎 DeepSeek starts selling the web

Two days after a silent checkpoint upload took the front page (No. 015), DeepSeek shipped something with an actual docs page: a Search and Fetch API. Search takes a query and returns web results; Fetch pulls a page’s content. Both are aimed at agents and RAG pipelines that need to read the live web, and both run on what DeepSeek says is its own in-house search engine, not a resold index.

The pricing is the headline: $0.75 per 1,000 successful search requests, billed only when results come back. For comparison shopping you will have to do your own math per provider; the model here is that an agent can check a fact or pull a page mid-task for well under a tenth of a cent. The docs describe the parameters, the response shape and the fetch behaviour, and were live and readable this morning. We read them; we did not send a single billable request.

The docs  ·  HN discussion

🧠 Think in vectors, act in text

Your agent thinks out loud, token by token, before every search and every tool call, and you pay for every token of that monologue. A team led by Jiaru Zou at UIUC and Stanford’s James Zou group, with code released through Together AI, asks: what if the thinking happened in the model’s hidden numbers instead, and words were only for acting?

The architecture: a small adapter called HybridLink switches the model between text mode and latent mode mid-generation. In latent mode the last hidden state feeds straight back as the next input, no words; the model exits when its own state sits closest to the "stop thinking" token. Tool calls, search queries and final answers stay in ordinary text, and everything, silent steps included, stays in working memory across turns. Training uses two new post-training stages, HybridSFT then HybridRL, on Qwen 3 backbones at several sizes. The authors’ numbers, across 11 benchmarks: 10.4% better than frontier text agents on average, 35.5–54.7% fewer tokens, 1.8–2.6x faster inference, 2.7–3.7x lower cost. Those are their claims, measured against agents they trained themselves, not against frontier APIs. The honest catch they print: silent steps can be rendered as "pseudo-text," a nearest-word readout that reads sensibly, but the paper does not show the readout matches what the model actually computed. A hint, not a transcript.

The paper (arXiv)  ·  The code  ·  HN discussion

🪝 The git hook that quizzes you

A pattern every agent user knows: the code looks right, the tests pass, you push, and a week later you discover you never actually read it. Sphinx puts a quiz in the way. Install it and it hooks your git push: before the push goes through, it asks you questions about the diff your AI just produced. Answer well, push proceeds. The site’s tagline, attributed with a wink to Sun Tzu: "you can outsource your thinking, but not your understanding."

Practical details: npm install -g sphinx-understand, free and open source under MIT. It runs on minimal tokens through your existing coding agent, OpenRouter, or any OpenAI-compatible endpoint, so there is no new subscription. The repo went up October 14 and sat at 116 stars this morning; the site and README are the whole product so far. We read both and installed nothing.

The site  ·  The repository  ·  Show HN thread

💥 A language your LLM cannot read

How much of an LLM’s coding skill is understanding, and how much is recall of training data? Dominik Picheta, a Nim core developer, set out to answer by designing a language LLMs should be maximally bad at. KillSwitch is usable by humans and hostile to models, by construction.

The traps target known model weaknesses. The spec contains hidden lies a human reader would skip or smell out, and expects the human to deduce rules rather than have them spelled out. Identifiers are governed by letter counting, a task LLMs famously fumble, and the interpreter refuses to run code whose character counts break the rules. Then it leans on the models’ eagerness: their guardrails and prompt-following become exploits against them. The write-up is half language design, half adversarial ML demo, and genuinely funny. We read the post; we wrote no KillSwitch.

The post  ·  HN discussion

🍏 The macOS update that ate OpenGL

A KiCad user updates a Mac to macOS 15.8.2 and the board view dies: "error switching framebuffer: invalid framebuffer operation." The KiCad forum thread opened October 12 and collects the usual dance of restarts and settings until the pattern emerges: it is the point release.

Corroboration came from several directions. An Apple Community thread opened October 18 reports OpenGL breakage across apps after 15.8.2, and a Roon community thread describes the same class of failure. The HN discussion reached 240 points with developers of other OpenGL-dependent software comparing notes. Everything here is user-reported; Apple had published nothing we could find about the regression as of this morning. OpenGL has been deprecated on macOS for years, but deprecated is not removed, and a long tail of serious software still renders through it.

The KiCad thread  ·  HN discussion

Linear’s CEO on software losing its soul. Karri Saarinen argues agent-built additive changes dilute the perspective that makes a product feel made by someone. 345 points overnight. Karri Saarinen →

A rant for running your own LLM proxy. Provider analytics are mascot charts; your own proxy gives one endpoint, per-key logs and virtual keys. Fun voice, nothing new technically. Jared Siegel →

A LocalLLaMA claim that a model account farms likes. A reddit thread alleges AutoTrust’s Hugging Face models trend on thousands of farmed likes. Allegation only, unverified; watch for evidence either way. r/LocalLLaMA thread →

Follow-up: still no OpenCode post-mortem. After No. 015’s key-rotation story, the company has tweeted again but published no technical post-mortem as of this morning. Watching. HN thread →