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You can't read every ToS, but the clanker can

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You can't read every ToS, but the clanker can

Termsinator is a public registry of LLM-performed analyses of public legal documents like ToSs, Privacy policies, Cookie policies, etc. The goal of this project is to help people better understand how their data will be used, and what potential pitfalls they need to avoid. Why? You know that feeling of enlightenment and sense of control after you've red dozens of pages of legal mumbo-jumbo before subscribing to a service? Yeah, neither do I (well, maybe some of you do and mad respect for that). I've been encountering more and more reports on how corporations abuse ToSs, Privacy policies and the like to push data collection and retention to really orwellian depths - and thus openly declaring that they deem their (paying!) userbase sheepishly stupid. I don't know the solution to this problem, but Termsinator is a stab in this direction. What? The idea is very simple: - let the clanker read and analyse the legal stuff - collect the results in a common place so everyone can access it - help people make better informed choices - rinse repeat On a high level this is being implemented in the following way: - scrape public facing legal docs - forward it to an LLM, which will analyse it, and evaluate the content based on several metrics (see the docs) - store the results along with some metadata - present these on the website - and also provide people a browser extension that can pull these results automatically (if they exist of course) I'd like to honestly declare (not that it's not obvious) that this is a PoC and it's an LLM generated codebase (Python + Go + Astro written mainly by Sol in/with? the Pi agent). At the moment I manually trigger the analyses and since I'm poor I'm using a dirt cheap and thus not super bright Qwen model to do the reading. My goal with this Show HN is to gauge if such a project even makes sense. If you find it promising I encourage you to try it, contribute to it or just spread the word. :) PS.: The logo was carefully crafted by my totally human hands and not part of a new "draw me an angry robot-like SVG face" benchmark.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, ide · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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.

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