MT

MTTCleaner – Same as CCleaner but free, no ads

Hacker News

MTTCleaner – Same as CCleaner but free, no ads

I'm an IT consultant. CCleaner was a good choice to speed up clients' web browsing experience. Unless you paid, it keeps vomiting ads for the product. Which made it a bad choice. mTTCleaner does most of the same thing to clean most browsers by cleaning caches, compacting databases, and removing unnecessary metrics files. It can create it's own cron job/scheduled task to run itself every month, if you choose. It covers 28 browsers on 3 platforms, assuming you have PowerShell 7+. Please file any bugs if you find them. This isn't really going anywhere except to help internet users and my clients. Hope it's helpful PS - All of it generated with VS Code, Augmentcode AI ( https://www.augmentcode.com/ ), OpenSpec ( https://github.com/Fission-AI/OpenSpec ), and beads ( https://github.com/gastownhall/beads ).

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1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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 HuntUnlikely to reach the leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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: month, users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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