10 articles
Most arXiv-to-Claude guides need a Skill, MCP, or Python. This one doesn't. Clean LaTeX-preserving paper summaries in Claude.ai with one click.
Prompt caching is the biggest cost lever for repeated-context AI in 2026. Most devs skip it. Those who use it save 70-85% per session past the first turn.
When a Reddit thread is the source you want Claude to read like a paper — full reply tree, scores, stance mapping. A workflow for researchers, not scrapers.
Wikipedia is the canonical first-source for AI research, but its HTML is heavy with cite-numbers, navboxes, and edit links. Extract clean Markdown for Claude.
YouTube transcripts are the richest audio knowledge on the open web — and the worst-formatted for LLMs. The pipeline that turns a 90-min talk into clean Markdown.
DeepSeek R2 is the cheapest Chinese reasoning model. The bottleneck is feeding it clean text from Xiaohongshu, WeChat, Zhihu, Bilibili. The pipeline.
GPT-5.5's browse tool is genuinely good for many research tasks. It is also bounded in specific ways that matter. The honest comparison after months of using both.
Claude Opus 4.7's 1M context holds ~500 Reddit threads. The bottleneck isn't 'will Claude read this' — it's how to get 500 threads into one paste.
Reddit is the largest source of real human opinion on niche topics. Feeding it to ChatGPT, Claude, or NotebookLM needs clean text. The 2026 workflow.
Claude Opus 4.7 has a 1M token context — roughly 200 long articles. The bottleneck isn't the model; it's how to get 200 articles into one prompt.