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Anthropic’s guide to prompting Claude Opus 5.5: how the model behaves, patterns that work for complex agentic and coding tasks, and practical prompt-engineering advice for builders.
17 min · 3,906 words
Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models
Aleph Alpha’s Letitia Parcalabescu presents Merlin–Arthur-style protocols that use mutual-information bounds to detect and limit language-model hallucinations with verifiable guarantees.
34 min · 7,713 words
Why I Think You Should Almost Never Use AI to Write Anything Substantive
Erich Grunewald argues that using AI to draft substantive writing erodes thinking, voice, and accountability—and that almost everything worth writing is better done by a human who owns the words.
15 min · 3,455 words
Noah Smith on what it means when AI becomes better at mathematics than any human—how “hero” mathematicians shaped culture, and how post-heroic math and science might look when machines own the frontier.
14 min · 3,242 words
There's no point at which turning your brain off will work
Dan Luu argues that turning off critical judgment while using LLMs fails in practice: when people let models take actions or produce analyses without verification, errors compound — there is no safe point at which you can stop thinking.
12 min · 2,831 words
Deterministic Core, Non-Deterministic Shell
Fourteen years after Functional Core, Imperative Shell, Outdata argues for a deterministic core with a non-deterministic shell—keeping pure logic testable while isolating AI and I/O uncertainty.
4 min · 1,004 words
GenRec: Towards LLM-Native Recommendation at Netflix
Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi‑task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it costly to onboard new use cases: adding a content type or surface can require significant feature engineering, architecture change,...
12 min · 2,813 words
Self-generated prompt injections in compaction summaries
Research on aligning AI with human values and intent, and reports documenting model failures.
6 min · 1,350 words
Christoph Nakazawa updates his LLM workflow and names values that still matter with coding agents: ownership, taste, guardrails, repo context, owning your stack, and option value.
2 min · 552 words
AI Now Writes as Many Online Articles as HumansGraphite’s Common Crawl sample finds primarily AI-generated articles plateaued near 50%
Graphite’s Five Percent research averages three AI detectors across tens of thousands of English articles and finds primarily AI-generated pieces have plateaued near half of new articles since early 2025—after a steep rise following ChatGPT’s launch.
8 min · 1,859 words
Harness engineering: leveraging Codex in an agent-first world
OpenAI engineer Ryan Lopopolo on harness engineering: how Codex and agent-first workflows reshape the scaffolding around models, from prompts and tools to evaluation and production loops.
13 min · 3,025 words
Sparse Reward Subsystem in Large Language Models
Guowei Xu, Mert Yuksekgonul, and James Zou report a sparse reward subsystem in LLM hidden states: value neurons encode expected value, while dopamine neurons track reward-prediction error. The study finds these signals are robust across tasks and models and useful for confidence estimation and inference-time search.
1 min · 287 words
Bryan Cantrill argues that LLM-generated LinkedIn posts are immediately recognisable to readers who have seen much AI content, and that using AI to write in your name undermines authenticity and signals to others that your stated views may not be genuine. He urges people to write in their own voice.
1 min · 278 wordsagent-written
Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Fu et al. propose Cache-to-Cache (C2C): multi-LLM systems exchange KV-cache semantics directly instead of text tokens, aiming for richer inter-model communication with lower latency and token cost.
52 min · 12,056 words