Toybox – A Laravel TALL starter kit for solopreneurs

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Hey peeps! Here's a project I've been working on for the last two months. It initially started as trying to be a simplified boilerplate for Laravel, but I think it's gone a bit beyond that. With recent tooling releases in the Laravel ecosystem, there's a ton of exciting tools to work with, but it's either annoying to set it all up every time, or it's difficult to try and gather it all up for every piece you'll need for your SaaS. To that end, Toybox attempts to collate what is in my opinion the best of modern Laravel, and while being simple to start, run and launch with. This is how I also came up with the name - pick and choose all your favourite Laravel toys and see how quickly you can build products with it. I've never been a fan of Docker and similar tools. While I see their value, I've generally not had a good experience and it always felt like a waste of my time trying to debug infrastructure instead of writing application code. So on that front Toybox goes back to basics - it targets only one stack, and is made to be able to initialise its repository, provision the server it is currently on, and deploy itself from your local machine, all with Bash scripts. Lastly, I tried to include guidance and support for scaling beyond an MVP and Bash scripts: The README includes tons of recommendations for various services & tools for all manner of things you need for your SaaS.

Visit product View on Hacker NewsAug 29, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

82%82% 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, dock, code · Missing: agents, macos, agent

56%56% 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

45%45% 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, way · Missing: mobile apps, ios, personal

33%33% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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 · 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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