AI, Lately

Addy Osmani publishes a guide on how to get the most out of Opus 5.5 in Claude and Claude Code.

His take: AI helps employees produce more material, more quickly — without taking responsibility for whether it is useful or correct. “We call those ‘slop grenades’ that people toss at each other,” he said. “And that’s definitely a bad thing.”

Claude Opus 5.5 is the default model in Claude Code on the Pro plans now. According to Anthropic, it performs at the level of Fable 5.1 on most work, while being ~30% faster and ~40% cheaper than Opus 5 per task.

DeepSeek trained V3 for roughly $5.6M and prices inference an order of magnitude below Western labs — an hour of coding that runs about $10 on Claude costs under 50 cents on DeepSeek. Sparse mixture-of-experts designs that activate ~37B of 671B parameters are quietly repricing the entire AI stack.

A wave of open-weight releases from Chinese labs — DeepSeek V4, Moonshot’s Kimi K2, Alibaba’s Qwen3, Zhipu’s GLM — pushed real-world coding and reasoning to within a few points of GPT-5.5 and Claude Opus 4.8, at a fraction of the price. For tool use, Kimi K2 is best-in-class. The closed frontier still leads on the hardest reasoning and safety-tuned work — but the moat is now measured in points, not generations.

If you’re a developer, The Developer’s Guide to AI is easily one of the best books on the market.

How are organizations closing the AI skills gap, especially on the technical side? The answer depends on who you ask.

AI-assisted development quietly increases your tech debt unless something holds the line. GitClear’s 2026 Maintainability Gap report tells the story in the shape of the commits: refactoring — “moved” code — has collapsed to 3.8%, while copy-pasted and duplicated code climb to record highs.

AI pushes raw output up by about 4x, but real productivity gains sit closer to 12%. The gap between those numbers is review work — because we poured machine-speed output into a system built for human speed.

AI is in your stack. Is it in your threat model? Harriet Farlow’s Practical AI Security is a hands-on guide to attacking, defending, and securing modern AI systems — prompt injection, data poisoning, model theft, and more.

OpenAI’s GPT-5.4 posted record scores on computer-use benchmarks (OSWorld-Verified, WebArena) — a marker of how fast agentic/computer-use capability is moving.

METR’s longitudinal work shows a 37-point swing — from a measured 19% slowdown in early 2025 to an ~18% net speedup a year later — as developers learned the tools.

Gemini 3 Pro arrived as a leading multimodal model, with a focus on agentic capability and fewer hallucinations.

Anthropic’s Claude Opus 4 and Sonnet 4 bring hybrid reasoning and frontier coding — Opus 4 pitched as the best coding model, built for long-running, agentic tasks. The model race tilts decisively toward agents.

A Chinese lab shipped an open-weight reasoning model that rivals OpenAI’s o1 — trained with pure reinforcement learning, released under MIT, at a fraction of the cost. The opening move in a year that would reprice the whole AI stack.

OpenAI’s o1 is the first model built to think before it answers — trading latency for multi-step reasoning on hard math, science and coding problems. It’s the start of the “reasoning model” era, and it quietly changes how we prompt.