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I made a simple, sequential task runner (in Python)

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I made a simple, sequential task runner (in Python)

While temporarily residing in the US (and the UK for a short while), far away from the comfort of home, and being surrounded by three kids (two of which are not my own), I decided to create a little and useful tool as a challenge to myself (I'm not much of a developer) and to help keep my sanity intact. This tool lets me define a list of commands as a YAML file to be executed sequentially, without touching code. We actually use something similar at work to onboard/update people's dev environments, but all tasks are pre-set Python classes. The thought was to try and create a complimentary tool (i.e. not necessarily as robust as the one we use), that will not require to rebuild and push an entirely new binary when making changes to a task or a sequence of tasks, nor require Python knowledge. Note: I personally use this little tool, and sometimes find and fix issues. I know this can be coded much better, and I'm hoping to learn and improve as I go. If you experience any abnormalities, do tell!

Visit productView on Hacker NewsJan 9, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, tasks, code · Missing: mac, agents, macos
69%69% 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
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
52%52% 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
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
37%37% 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
13%13% 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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