AI models flub these intelligence tests. Can you fare any better?

Essay argues neolabs are asked to deliver both a research breakthrough and a venture-scale business, each a 1-in-100 outcome, making combined odds 1 in 10,000. It proposes borrowing pharma's approach to risky R&D so frontier labs, neolabs, and investors all come out ahead.

An r/ExperiencedDevs thread argues AI does not raise velocity much outside startups because it shifts the bottleneck to code review. The poster says political, procedural and practical friction in large enterprises absorbs most of the time anyway, citing academic studies.

Peter Werry, founding engineer at Unblocked, argues vector search can't answer queries like "which PRs did I merge last week?" while a relational context engine can. The engine pulls in PRs, Slack threads and incident data beyond code search, reducing token burn.

A r/LocalLLaMA thread argues local models plus a good harness are about a year from writing 90% of code, with commenters pointing to Qwen3.8 Next Flash as already close.

A Reddit r/Singularity post tracks how compute allocation moved from pretraining to post-training over the past two years. The post carries no article text, figures, or linked source.

Nvidia is in early-stage negotiations to acquire or further invest in Reflection AI, a US developer of open-weight models, per the Financial Times. No financial terms were reported.

A 20-year software engineer ran local models on tasks analogous to his actual work, testing Gemma4-31B, Qwen3.8-27B and 6.1-Sol. He posted the observations to r/LocalLLaMA as informal field notes rather than a benchmark.