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Speed of all of your processes across entire stack in one dashboard

Hacker News

Speed of all of your processes across entire stack in one dashboard

I built Checkpoints App out of my experience of not being able to quickly and easily measure the speed of processes across my tech stack in my startup. All startups optimize for speed in all of their operations: deploying code, responding to API requests, loading the UI, and in background processes such as sending emails to users or processing data in an ETL pipeline. But the tools available to measure the performance of all these operations are separate and time-costly to integrate, in the first place. Checkpoints App allows you to measure the speed of processes across your entire stack with minimal overhead and collects that data into a single dashboard. Integrating it into your tech stack is as easy as dropping a `print()` statement in your code, while you're writing it. It comes with client-side Python, JS and Bash scripts. You drop checkpoint statements anywhere in the code, defining a process name and checkpoint name, for example: `./checkpoints.sh process1 checkpoint1` where process1 is your process name and checkpoint1 is your checkpoint name. Once you've dropped checkpoints in your code. It will automatically create the process pipelines in your dashboard along with the speed metrics i.e, how long does it take, on average, to go from checkpoint1 to checkpoint2 . Then you can start optimizing for speed. Another major problem I saw was that most lightweight tools out there let you measure performance in a single part of your stack, for example, you need to use Lighthouse in the front-end UI and CloudWatch for your backend API endpoints. With Checkpoints App, however, you can create tailored processes across your tech stack. For example, you can add the first checkpoint in your backend and the second checkpoint in your front end. I'd love to hear your feedback. If you'd like to try this out, signup at the landing page and I'll send you the access.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, single · Missing: mac, agents, macos
89%89% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
79%79% 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, pipe, io · 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 · Strong signals: users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal
32%32% 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
12%12% 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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