I graded 200 AI tools/apps and track any changes in their policies

Hacker News

Hi all, Given the recent OpenAI saga with the Navier–Stokes problem, I thought it would be a good time to show my latest project. The site pulls policies from 200+ websites, keeps track of them and compares them to any changes. I then grade the companies (A-F + Unclear) and I have emailed all of the companies that have been graded D or below. Where applicable, such as with OpenAI, I've provided instructions on how users can opt-out of their data being used for training. Findings of note so far: - 80 tools train on you by default. 55 don't let you opt-in and four do, but you have to pay. - 106 tools don't say if they do or don't train on your data/usage. - Only 36 (16%) say that they do not train on your data. - One company, FastMail, made a change to their policy based on my feedback/grading. If you have any feedback, suggestions or concerns please let me know! I'm trying to improve this as and when I can. Also let me know if you have a specific app/tool in mind you'd like for me to grade/track.

Visit product View on Hacker NewsSep 10, 2026

Share card

Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, email · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Changewatch – see what’s changed between versions of policies43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Changewatch – see what’s changed between versions of policies

Hacker News3

300aidirectories20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI tools

Indie Hackers

topaiinfluencers32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TOP AI Tools

Indie Hackerscommitment-side-project

Track what's happening in the top 200 apps53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Track what's happening in the top 200 apps

Hacker News10

200 AI Conferences in 201844%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

200 AI Conferences in 2018

Hacker News2

Seasons – Track changes to your website49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Seasons – Track changes to your website

Hacker News4

C+

C++ LMAX Disruptor < 200 LOC45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

C++ LMAX Disruptor < 200 LOC

Hacker News3

Матрац на ліжко 160 на 20031%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Двоспальний матрац: інженерний компроміс між двома тілами

Indie Hackers

A system to recommend configuration changes on NixOS / Nix47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A system to recommend configuration changes on NixOS / Nix

Hacker News3

Track time spent on AI tools on Chrome30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Track time spent on AI tools on Chrome

Hacker News1

Browse all products Analyze your launch