Search "best AI tool for X" and you'll mostly land on directories where the order comes from who paid for a featured slot, who signed up for an affiliate program, or who submitted most recently. The tool listed first is often the one with the biggest marketing budget, not the one people actually use.
We built AI Use Cases to try a different approach. You type a task, like "transcribe a meeting" or "run an LLM locally", and get the top 3 tools for it. The order comes only from public evidence that anyone can check. Right now the catalog covers 257 tools across 45 tasks, and the scores are recomputed every night.
This post covers how the score works, where it falls short, and what it currently says for a few common tasks.
The problem with pay-to-rank directories
Most AI tool directories make money in one of three ways: paid "featured" placement, affiliate links, or paid submissions. None of these is evil on its own. The trouble starts when they quietly shape the order of the list. When the reader can't tell an ad from a ranking, the ranking stops telling them anything.
We set two constraints:
- Nothing paid can move a rank. The site's only paid product is a clearly labelled sponsor box that runs for 7 days. It sits beside the list, never inside it.
- The formula is public. It's published on the ranking page, and the raw numbers behind each score are shown on every tool page (for example, Ollama).
The 4 signals and their weights
Each tool gets a score from 0 to 100:
| Signal | Weight | What it measures |
|---|---|---|
| Popularity | 45 | The higher of log-normalized GitHub stars or log-normalized Hacker News stories mentioning the tool in the last 30 days |
| Momentum | 25 | Log-normalized sum of HN stories (30d) + mentions in our release feed (30d) + GitHub stars gained in the last 7 days |
| Freshness | 15 | 1.0 if active in the last 30 days, 0.5 if within 90 days, otherwise 0 |
| Access | 15 | Free tier = 7, open source = 4, plus up to 4 for platform breadth |
The normalization is log(1 + x) / log(1 + max over the catalog). Archived repos get their score halved.
A few design choices are worth explaining:
- Logs, not raw counts. Star counts follow a power law. Without the log, a handful of 150k-star repos would flatten everything else to zero.
- Popularity takes the max of stars or HN, not the sum. Closed-source tools have no GitHub stars. Taking the higher of the two signals lets a hosted product like ChatGPT or Claude compete on HN discussion alone instead of starting at zero.
- Momentum is a separate signal. A huge repo that has gone quiet shouldn't beat a smaller one that's shipping every week.
- Access is weighted lightly on purpose. A free tier helps, but it can't carry a tool that nobody uses.
Where it falls short (and what we learned)
Public evidence has real limits, and we'd rather show them than hide them. These two came up while building it:
Short or common names inflate Hacker News counts. A search for a tool called "Create" or "Speak" matches every story containing that ordinary word. Our fix: for names under 5 letters, we search "<name> AI" instead, and every tool page shows the raw HN count so you can judge it yourself. It's still imperfect. "Create", an app builder, currently has 233 HN stories in 30 days, which is almost certainly inflated.
Closed-source tools are judged on HN alone. With no star signal, their popularity rests on one source. That's why Cursor (66) scores below open-source editors like Zed (88) and Continue (82), even though plenty of people would pick Cursor first. The score measures public evidence, not market share, and we say so on the site.
One operational lesson: our first stats code wrote to Cloudflare Workers KV on almost every crawler hit. Once Googlebot, GPTBot and Applebot started crawling, that blew through the free tier's 1,000 writes a day, and stats stopped saving until midnight UTC. We now buffer counts in memory and write one record per day, capped at 300 writes a day. In a simulated day of traffic, writes dropped from 2,616 to 302. If you're building on a free tier, assume crawlers will be most of your traffic.
What the ranking says today
These are the current top 3 for five common tasks, taken straight from the published dataset (snapshot of Oct 26, 2026). Scores change nightly.
- Ollama: 95.4 (open source)
- llama.cpp: 93.4 (open source)
- Open WebUI: 93.3 (open source)
- ComfyUI: 97.6 (open source)
- Fooocus: 81.9 (open source)
- Stable Diffusion WebUI (AUTOMATIC1111): 71.1 (open source)
- Zed: 88.0 (open source)
- Continue: 82.0 (open source)
- Cursor: 66.1 (free tier)
- Granola: 27.2 (free tier)
- Fathom: 20.5 (free tier)
- Fireflies.ai: 11.0 (free tier)
- Gamma: 39.0 (free tier)
- Microsoft Copilot: 21.5 (free tier)
- Canva Magic Studio: 11.0 (free tier)
The pattern is clear. Developer categories are dominated by open-source projects with large, active GitHub communities, so their scores sit in the 80s and 90s. Consumer categories like meeting notes and slides are all closed SaaS, so the scores are lower and closer together. A Granola at 27 isn't a "bad" tool. It just means there's less public evidence to go on, and you should treat close scores as close.
For each task there are also free-only and open-source-only lists (for example, free transcription tools), "alternatives to X" pages like ChatGPT alternatives, and head-to-head pages like Cursor vs GitHub Copilot.
Grab the data
The whole catalog is free to use under CC BY 4.0. Use it in your own projects, analyses or tools, just credit AI Use Cases.
- Full dataset (tools, tasks and scores): https://aiusecases.dev/data.json
- Dataset page and endpoints: https://aiusecases.dev/data/
- Plain-text summary for LLMs: https://aiusecases.dev/llms.txt
- New releases RSS feed: https://aiusecases.dev/feed.xml
curl -s https://aiusecases.dev/data.json | jq '.rank | to_entries | sort_by(-.value.score) | .[:10]'
If you think a tool is missing, in the wrong task, or scored unfairly, corrections are free, and you never need to be a sponsor to get one. We're especially interested in better signals for closed-source tools, since that's the weakest part of the model right now.
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