Martin Bennett

The number of cool AI tools on GitHub is essentially infinite. The time to try them isn't.
Every interesting repo charges what I'd call a curiosity tax: 30-90 minutes of cloning, installing, fixing dependencies, and hitting a CUDA error before you can even find out whether the tool is worth your attention. The cost isn't in skill. It's in hours spent on setup. And hours are exactly what a lot of us don't have
So most of the interesting tools in AI quietly go untried.
Today we closed that gap. GitHub repo installation is live inside Extella. Browse 630M+ repos, install in one click. What you install becomes a reusable Expert in your workspace.
Try it now: https://www.producthunt.com/prod...
Drop a GitHub link to a tool you think everyone should know about but few people use.

This year again, Supabase surveyed 2,000+ startup founders to uncover what's powering modern startups: their stacks, their GTM motion, and how they approach AI.

Many things have changed between 2025 and 2026.

The research on how AI systems decide what to trust is clearer than you might think. It comes down to a few core signals that are measurable and actionable.

Citations are the strongest signal. A study from the University of Notre Dame and Deloitte found that simply having citations in an AI response increases user trust significantly even when the citations themselves are random . The presence of sources signals credibility. The act of checking them signals distrust.

E-E-A-T is no longer just Google's framework. It has become the core principle AI systems use to decide what (and who) to trust . AI systems prioritize Experience, Expertise, Authoritativeness, and Trustworthiness when evaluating sources. They look for signs that a real practitioner stands behind the advice detailed bylines, credentials, first-hand narratives, and verifiable experience

Perceived gatekeeping and information completeness matter. Users trust Google because it performs credible gatekeeping . Wikipedia earns trust through collective curation. AI systems look for similar signals: is there evidence of editorial oversight? Is the information comprehensive enough to answer the query completely?