RightAIChoice VS ShellNewHandler

RightAIChoice's answer

Most AI tool sites are written once and left to rot. We re-verify ours continuously: every tool in the catalogue is re-probed on a rolling weekly cycle, and 100% are re-checked at least thrice within 30 days. Every answer we give carries the date it was verified, and if we can't date it, we don't say it.

That matters because AI tools die constantly. We probed 2,291 of the top Product Hunt launches from 2023 to 2025 and found that 24.4% of the 2,066 with a definitive verdict are dead, with their landing pages still online and still being recommended.

The second thing that makes us unusual: nothing here is purchasable. Not rankings, not scores, not verdicts, not inclusion in any list.

RightAIChoice's answer

Three reasons.

Freshness you can check. Every verdict carries the date behind it, so you can judge for yourself whether it's current instead of trusting a page with no timestamp.

Costs that aren't on the pricing page. We document tier gates, credit top-ups and annual-only rates. Notion's AI features aren't on the Plus plan, they start on Business. Grammarly's $12/month Pro rate requires annual billing; monthly is $30.

Sentiment from real discussion, not stars. We synthesise what people actually say on Hacker News, YouTube, Product Hunt, app stores, Stack Overflow and GitHub, and publish the mention count with every verdict so you can weigh the confidence yourself.

And placement is never for sale. Paid and free listings are treated identically in all rankings and recommendations.

RightAIChoice's answer

Two groups.

People choosing tools: founders, operators, developers and teams deciding what to adopt, who need to know whether something is still maintained, what it really costs, and what to use instead if it isn't.

AI assistants: our data is available free through an MCP server, so ChatGPT, Claude, Gemini and Cursor can verify a tool is alive before recommending it. Increasingly the answer reaches people through an assistant rather than a browser, and we'd rather be the source it checks than the page it skips.

RightAIChoice's answer

It started with a recommendation for a tool that no longer existed.

That turned into a question worth measuring: how much of what gets recommended is already gone? We built a verification pipeline to find out, probed 2,291 top Product Hunt launches from 2023 to 2025, and found that 24.4% of the 2,066 with a definitive verdict were dead.

An honest finding shaped everything after it. Our own catalogue skews toward living tools, so we couldn't derive a death rate from it without cheating, which is why the study uses an external cohort probed by our pipeline instead. The same principle became the rule for the whole product: date every claim, publish the method, and never assert what we can't check.

Built in public by a solo founder in India.

RightAIChoice's answer

Next.js and TypeScript on the front end, PostgreSQL via Supabase for the catalogue and its full change history, and Cloudflare in front. The verification pipelines run as scheduled jobs that re-probe vendor links, re-read pricing pages, and synthesise sentiment from public discussion using LLMs. Our data is also exposed through a Model Context Protocol server so AI assistants can query it directly.