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Speedrun – Markdown to build tools (OSS)

Hacker News

Speedrun – Markdown to build tools (OSS)

You go to the docs to do something but there's no button to click on to do it. Further, there's all that nonsense of getting credentials, gathering and formatting inputs and finding and replacing text. Enter Speedrun, simple markdown that lets you build tools right into your documentation where they belong. It powers up your README's, runbooks and documentation with the ability to prompt for inputs, run JavaScript, get temporary AWS credentials and reference configuration. Now instead of merely telling your users what to do, they can solve their exact problem with a click. Think of it like a Jupyter notebook but on top of GitHub markdown. In its current incarnation, it runs as a TamperMonkey script that finds Speedrun blocks in your GitHub repo and lights them up. It can be used to wrap a command line with a ui, federate to an exact location in the AWS console, as a ui for authorizing and invoking an AWS Lambda or Step Function and many other use cases like building a SQL statement. The neat thing is that you aren't really building new tools, you're speedrunning the tools you already use by wrapping them to make them faster, safer and easier. And since it's markdown, you can build a tool your team can instantly use in less time than it takes to complain about the problem. Here's an example that will allow only authorized users to get temporary credentials and invoke an AWS Lambda function with some json it builds based on the name it prompts you for. ``` #!lambda {functionName:"HelloWorldService"} { "name": "~~~Name {default:'Samuel L. Jackson'}~~~" } ``` You can see the above example in action, see the sizzle reel with more use cases and play around in my AWS accounts with the live demo at the website: https://speedrun.cc The script is licensed under the MIT license and it's the perfect time to try it. From now until January 15, 2024, I'm running a fun contest called "Done before the bass drops." You'll use Speedrun to speedrun AWS in less than 90 seconds while EDM plays in the background and you need to be "Done before the bass drops."

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
71%71% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, new, plain · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
28%28% 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
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.

Incorrect prediction on native model

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