Cazac.tools – a set of tools (inc. yet another favicon generator)

Hacker News

cazac.tools is a collection of mini tools that I found myself needing while building stuff online. I have found myself time and time again googling and using very clunky and outdated sites when I needed tools like favicon generators, data converters, CSS gradient generators, hashers, etc. It initially started with some basic tools like: 1. Favicon generator - I wanted to make sure there is a possibility to use an icon from any icon set as well as text and images for generating the favicon. The output is the files and the html code. 2. PHP array to JSON (and back) converter - I needed this when working with my PHP app where often I'd need to convert a JSON payload to a PHP array, to use in tests or other similar situations. 3. CSS Gradient generator - I needed a gradient generator that looks good, is easy to use, and also supports interpolation for beautiful gradients. I've later added several other minitools and will keep on adding them while trying to keep the site as minimal and simple as possible.

Visit product View on Hacker NewsJul 26, 2024

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42%42% predicted probability of success on TrustMRR, 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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, code · Missing: mac, agents, macos

29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

15%15% 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.

Correct prediction on native model

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