The Scientist

FrontierChallenge scored frontier models at 87.9% on partial progress and 20.6% on full completion of multi-stage scientific workflows that must ship reproducible code, tables and figures. Rubric-weighted partial credit is the default in most internal agent harnesses, so your scorecard overstates deployability about fourfold on compound tasks. RealSWE adds that agents degrade further on short, informal user requests, and the cheapest fix is a required desired-behavior field in task intake.

Spotify's Portal splits coding-agent work into bulk-reader and code-writer modes on Gemini 2.5 Flash, and Dimitri Mazmanov measured roughly a 90% cut in Claude Code context tokens on Java monorepo tests. That lands on a real budget line: 25% of engineering leaders report $200-$500 per developer per month, with outliers past $2,000. The evidence is one engineer's token delta with no task-success rate and no ablation, so treat 90% as a hypothesis with a plausible mechanism behind it.

Johann Rehberger's writeup on breaking Claude Code Opus 5 auto mode describes a website that asks the agent to summarize it, nudges it off the constrained WebFetch primitive onto curl, then reuses the model's own generated Python decoder as the exploit primitive. The compromise happens in tool choice, so a guardrail that inspects tool arguments passes it cleanly. Frontier capability is not a robustness proxy, which means a model upgrade should not ship with wider tool scope until your adversarial suite passes at the prior model's rate.

JSCeal, documented malware shipped as compiled V8 bytecode, bypasses Google authentication by replaying stolen session cookies rather than cracking credentials. That session lives in the same browser profile as Colab, the Vertex AI console, the BigQuery UI and the GCS browser, so password rotation and MFA are no-ops and only session invalidation plus token revocation work. The compiled-bytecode packaging also strips the lexical surface that source-token, AST and entropy features depend on, so any static JS detector needs per-slice recall reported by packing method.

A community wiki now publishes reproducible Docker builds, runbooks and benchmarks for GLM-5, DeepSeek-V4 and Kimi on Nvidia RTX PRO 6000 Blackwell cards over PCIe alone, with no NVLink in the topology. Sparse mixture-of-experts layouts are why that can work: low active-parameter counts shrink the per-token collective payload that saturates the bus on dense tensor-parallel serving. Bloomberg separately reports DeepSeek plans at least 160,000 Huawei top-end accelerators at one Inner Mongolia site, so accelerator, driver and quantization recipe belong in your eval metadata now.