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Firefox from Ramdisk (macOS Only)

Hacker News

Firefox from Ramdisk (macOS Only)

I hacked this app for my needs in one single before noon in Swift. ChatGPT and Grok turned out to be super helpful. It solves the problem, where Firefox writes excessive amounts of data. Even if you disable disk cache. It still stores lot of data. cookies, session storage data... You might wonder why I made this as an app instead of using Automator or a bash script. The main advantage of an app is that it displays splash screen with progress, while the RAM disk is being created. With a script based approach, you typically just have to wait a few seconds while the profiles sync in the background before Firefox finally launches. Automator scripts also tend to display an unsightly spinning cogwheel in menu bar, which isn't ideal. It's MIT licensed and source code is provided. https://github.com/mauron85/Firefox-Ramdisk

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Actual performance

5points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
86%86% 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: mac, macos, chatgpt · Missing: agents, agent, cursor
83%83% predicted probability of success on Product Hunt, 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
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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
18%18% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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