Tx

Txtl – Fast static website of text utilities

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Txtl – Fast static website of text utilities

I often need simple text tools (e.g. diffing, epoch time conversion, number base conversion) and was frustrated that I couldn't find a simple option that was as fast as I wanted. Many (SEO-optimized) websites exist, but they unnecessarily send your input to their servers. I used tools like DevToys but their diff has been broken on Mac for a while [1] and it requires installing an app, and CyberChef is good but requires too many interactions (yes, I'm picky). So, I decided to build my own. txtl tries to autodetect what mode you want based on the input, and in general is fast and gets out of your way as much as possible. For example, if you paste in a number then it will show base conversions, but if it looks like an epoch timestamp then it will display the corresponding date. And of course it's open source at https://github.com/viveksjain/txtl/ This was partly also a way for me to try using LLMs to build a frontend/new project. This could probably be a whole blog post, but I see why some people can say that LLMs can 10x their coding productivity (I certainly didn't find this true in my day job) - I was really impressed with what o1 can do, it probably got me 70% of the way there in one shot. And as someone not familiar with frontend development, iterating was way faster. But, it still requires plenty of feedback and requires handholding - I had to completely rewrite the diff rendering logic, no matter what I tried I couldn't get an LLM to get it right. Would love feedback on the autodetection and overall usability! [1] https://github.com/DevToys-app/DevToys/issues/1296

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent
81%81% 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, ide · Missing: https docs, excited, just released
69%69% 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
43%43% 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
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.

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