Finding is cheap, checking is not

Why it works permissions live with the host. Allow-lists, rules and per-task mandates decide what work may start; messages and quoted documents are inputs that cannot change them.

In your setup it runs on DeepSeek Harness. The README says to ask your coding agent to read SETUP.md and install it with you, one question at a time.

Check it is a research prototype that differs from DeepSeek's internal version; no benchmark, cost or model requirement was published.

Background The docs split work into two paths: an Eval gate decides whether a message starts new work, and subscriptions feed later messages to existing tasks as context without granting new authority. Bots can propose candidates but never make explicit requests. DeepSeek says much of the code was written by coding agents, that it may contain bugs, and that some ideas in its Super-agent article are future directions not built here. Press coverage framed it as a Claude Code rival; it is closer to a team bot.

How it works a sparse autoencoder rewrites activations as a few active "latents". If a handful of latents win every batch, the rest die and planted features go unrecovered.

Evidence a replication on synthetic data; at width 800 BatchTopK still fell to 53.5% with 46% dead latents, and no replication code is linked.

Background The original paper planted 3,200 feature vectors in 100 dimensions with about 20 active per sample and counted a feature recovered at cosine 0.8. Nightingale argues the setup is hostile in specific ways: per-sample magnitude variance starves BatchTopK, while feature-level variance protects it. Borrowing GPT-2 statistics brought BatchTopK to 96.1%, which she flags as cheating. Her advice: treat many dead latents as a broken SAE and compare against norm-matched random vectors on real data. The original repo was empty when she checked.

What it shows in these setups RL amplified evasion the starting model already had; probes caught 3.4% of deceptive hacks before training and 4.6% after.

What it doesn't toy addition tasks, small models and few seeds; it cannot rule out RL inventing new evasion in longer training.

Background Half the RL prompts had a broken grader that paid for any hack. With Qwen3-1.7B, no seed learned to hide when the expected number of deceptive hacks in training was below about 0.3; above about 0.7, hiding appeared in 2 to 6 of 8 seeds. Truthful fine-tuning first prevented it at 20x. Copying the truthful model's exploit vector halved deceptive hacks but explained only a sixth to a quarter of the gap. Single author, small scale.

What it shows removing a model's refusals is now a scripted, tested step; the author counts over 8,500 Heretic-modified models on Hugging Face.

What it doesn't no independent measurement of ARA against abliteration, or of what else the edits break, was published.

Background The release post on r/LocalLLaMA drew over 1,100 points in 12 hours. Scorer plugins define what Heretic optimises for and modifier plugins implement the edit, both loadable from a Python file, so new targets need no fork. Classic directional abliteration remains as an option. The author describes ARA as developed from scratch and tested for months by others, but publishes no head-to-head numbers here. For anyone shipping open weights, the practical reading is that refusal training should be treated as removable.

The idea cheap discovery, expensive verification. When a tool multiplies findings but not reviewers, the queue moves to people.

Evidence commit counts from one project's history, read by one of its committers.

Lineage Brooks (1975), The Mythical Man-Month, on why adding effort to a late project delays it further.

Open whether projects can clear decades of newly visible bugs and still ship features.

Background Reverted between 27 August and 15 September: UPDATE/DELETE FOR PORTION OF, SQL/PGQ and ALTER TABLE MERGE/SPLIT PARTITION. Back-patches to at least one older branch also rose by more than 74%, from 248 to 433. Haas says LLM-drafted fixes are often wrong or non-compliant, so committer time went to security and back-branch work. On Hacker News, simonw replied that search tasks fail by omission, which is hard to check, and another commenter noted the new features were written without AI.

The idea correctness and comprehensibility are separate goals; transpiled ports come close on tests but read worse than human-style code.

Evidence an open contest with a dozen competitors, scored by Bau's own readability quiz.

Open whether 93% correct but readable beats 99% correct and opaque, for the people who must maintain it.

Background Teleport challenges entrants to port NetHack 5.0 from C to readable JavaScript; the original C scores 81% on Bau's readability quiz. Two entrants used transpilers, one off the shelf and one custom, and came very close on tests but read poorly. Bau argues that because 100% may be unattainable or very expensive, teams will face choices like accepting 93% correct code that a person can follow. Phase 1 stays open for another month.