The opportunity database
Everything the agent has judged worth doing — ranked by Worth Score, with evidence confidence shown separately.
Find things worth doing in local AI
Differential chat-template auditing for the local model supply chain
Build the missing conformance gate for chat templates in local model artifacts: a tool that extracts `tokenizer.chat_template` from a GGUF or HF repo, normalises and diffs it against the canonical upstream template for that model family, renders both across a probe corpus (multi-turn, system prompts, tool definitions, special tokens) under each runtime's own Jinja implementation, and diffs the resulting token streams. Divergences are classified — benign reformatting, BOS/EOS duplication, tool-call notation drift, or the high-signal case of template logic that branches on user-supplied content, which is the signature of an inference-time backdoor. Ships as a CLI plus CI action for quant publishers, with a hub-wide scan as the launch artifact. Templates are small text files and rendering needs no GPU, so ecosystem-scale coverage is cheap.
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What is worth doing for YouTube Channel
Build the channel as an answer-engine asset: chaptered, transcript-first long-form video targeting AI-cited queries
The highest-leverage thing to do with a YouTube channel right now is not to chase subscribers but to engineer it as a citation source for AI answer engines. YouTube is the single most-cited domain in Google AI Overviews (29.5%), cited ~200x more than any other video platform, and YouTube presence is the strongest known correlate of brand AI visibility (r=0.737 across 75,000 brands) — stronger than backlinks or domain authority. Crucially, citation frequency shows near-zero correlation with views, likes or subscribers (r≈-0.03): what drives it is long-form format (94% of citations), accurate transcripts, and question-format chapters, with 78% of timestamped videos cited repeatedly across 2–5 chapters. That makes it one of the few remaining distribution surfaces where a small, new channel can win against incumbents by structure rather than by audience size. Concretely worth doing: pick how-to/tutorial and review/comparison query clusters (the formats AI cites most), publish long-form videos whose chapters are literally the questions, ship clean human-checked transcripts and VideoObject schema on embed pages, and measure success in citations rather than views.
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