AI engineering, November 7, 2026

Top News · 284 HN points

Inkling 2 Small is Thinking Machines’ second-generation model for coding and autonomous work. Thinking Machines reports a 30.6 AA Index v5.0 score, ahead of GLM-5.3-Flash at 27.8 and MiniMax-M3.1-Flash at 29.1.

Here's what it offers:

• Inkling 2 Small scored 70.5% on JobBench, the highest result in the company's comparison.
• Coding scored 72.1% on DeepSWE v1.1 and 86.3% on SWEBench Pro v2 (Hard).
• The MoE has 594 billion total parameters, with 17 billion active. It accepts image inputs and supports variable effort.
• You can use it in Tinker Playground, or fine-tune and sample it on Tinker.

One catch: Initial access uses closed weights. Thinking Machines plans an Apache 2.0 weight release after external safety testing and red-teaming.

Sources: thinkingmachines.ai

Top News · Reported · vllm.ai

REINFORCE Zero is an asynchronous RL recipe. In the reported comparison, REINFORCE Zero kept reasoning quality close to synchronous training. The authors report 2.40× training-step throughput over 12,000 updates on 16 GPUs.

Here's what changed:

• Training took 68.55 hours, versus 164.77 hours for the synchronous run.
• The 1.5B reasoning comparison reached 48.80% macro pass@1 at lag 2, versus 48.91% for synchronous training at 6,000 updates. At lag 8, asynchronous training reached 48.53%.
• The recipe uses plain REINFORCE with group-relative rewards, without a value model or sample-rejection gates.

One catch: You need aligned rollout and training kernels, plus unclipped token-level importance sampling to correct policy-weight lag.

Try it: hijkzzz/REINFORCE-Zero for the paper and code, NVIDIA-NeMo/labs-molt for training code, and Inferact/zero-mismatch-rl for alignment recipes and model-specific fixes. Also available via Amazon Web Services, releasebot.io

Sources: vllm.ai

Top News

A TODO pointing at a bug can pull a coding agent away from your request. Google's engineering study associates these unprompted detours with 105% more wait time, after controlling for task and developer factors.

Here's what Google found:

• Detours were associated with 70% more steps and 77% more tokens per session, alongside the longer wait.
• Closed or otherwise dead-end tickets accounted for 41% of unprompted detours. Another 57% involved open bugs outside the developer's request.
• Cleanup agents inspected bug, file, and code-change history before proposing removals. The work removed thousands of obsolete references over six months, with more than 95% human-review acceptance.
• Google recommends presubmit analyzers, corpus scanners, and automated agents to catch and remove obsolete TODOs.

One catch: The measured effects are associations tied specifically to inline TODO(bug/...) and FIXME(bug/...) references.

Sources: developers.googleblog.com

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