Cole Medin walks through his personal AI content engine and a free skill for building a customized version in about 14 minutes.
Filter before asking a model
- Collect from RSS, Reddit, YouTube, Hacker News, GitHub, research papers, and product changelogs.
- Store seen items centrally so repeat announcements do not trigger repeat model calls.
- Remove stale items and other obvious rejects with ordinary code before spending tokens.
- Clean headers, footers, and irrelevant fields out of source text before model processing.
Use a cascade, not one expensive judgment per item
- Jev provides the first model-based relevance decisions; an LLM handles remaining ambiguity and digest preparation.
- Ask whether an item is relevant, genuinely new and concrete, or merely a toy demonstration.
- Medin says removing the LLM altogether made his filtering worse: both useful omissions and irrelevant inclusions increased.
- His example run processed 864 posts for about $0.03 in Jev calls and $0.42 in Claude Haiku calls—not a complete bill for collection, hosting, or maintenance.
- Engagement thresholds are his shortcut, not a quality guarantee; they can bury useful niche work.
Build around your interests
- The build-signal-engine skill interviews you about interests, sources, access methods, and information you previously missed.
- Start with one working source end to end, then add connectors, filters, decisions, digest formatting, and scheduling in tested stages.
- Apify is Medin’s collection option, not a requirement; the description includes a referral link.
- Keep source definitions in one JSON configuration and preferences in a readable profile.
- Editing that profile does not change the running pipeline automatically: ask the coding agent to implement the differences.
“We should truly understand how it’s working.” — Cole Medin, 2:24