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decide: focus directory on local AI tools and stacks

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Tony Balascio
2026-08-23 05:43:13 +00:00
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- Structure and playbook seeded from Startup Ideas Podcast directory thesis.
- Niche selection deferred; will be recorded here with rationale and date.
## 2026-08-23 — Niche selected: local AI tools and stacks
- Focus: curated, evidence-backed entries for software that helps people run, serve, connect, or operate AI on their own hardware.
- Rationale: strong fit with Tony’s existing hardware and local-first AI work; higher editorial taste than a generic AI-tools directory; useful decision data includes platform support, VRAM/RAM expectations, offline behavior, APIs, MCP, and deployment mode.
- Initial monetization hypothesis: qualified software/hardware referrals, sponsored placements only after trust and traffic exist, and eventually paid stack-selection or setup referrals. No monetization claims until measured demand exists.
- Editorial boundary: do not publish a broad listicle. Each page must explain the real fit, constraints, access model, and evidence. Prefer official documentation and observable technical facts over affiliate copy.
- First candidate: LM Studio, because it is a representative desktop-to-local-server workflow with official documentation covering offline use, model runtimes, MCP, APIs, and system requirements.
## Pending
- [ ] Niche selection (taste + traffic potential + affiliate/sponsor fit)
- [ ] Validate demand and repeatability after the first 5 pages.
- [ ] Define the first monetization test only after traffic or user-intent evidence.