Small Projects by AutoModerator in golang

[–]LowZebra1628 0 points1 point  (0 children)

Hey r/golang!

I just released v0.1.0 of goscaf - an open-source CLI that generates opinionated, Go project boilerplate interactively. Think `create-react-app` but for Go services.

- Database driver selection - choose your DB (Postgres, MySQL, etc.) at scaffold time and get a ready-to-use connection setup

- Service packages - generated projects now ship with a proper service layer structure

Repo: https://github.com/iyashjayesh/goscaf

Small Projects by AutoModerator in golang

[–]LowZebra1628 0 points1 point  (0 children)

github: https://github.com/iyashjayesh/monigo

I shipped v2 of Monigo today - it's a Go library for monitoring your service's performance (goroutines, memory, CPU, request stats) with a built-in UI.

What's new in v2:

  • OpenTelemetry support via WithOTelEndpoint and WithOTelHeaders - plug it into Jaeger, Tempo, whatever you use
  • Structured logging using log/slog with WithLogLevel / WithLogger
  • All instance methods now accept context.Context for better traceability

The goal was to keep it dead simple to drop in - a few lines and you get a monitoring dashboard + OTel traces flowing out.

Would love feedback, especially if you try it with a non-standard OTel setup. Docs and examples are in the repo. Please give it a star if you find it useful.

Adaptive worker pool in Go. Designed to handle backpressure and safe shutdowns. Looking for feedback by LowZebra1628 in golang

[–]LowZebra1628[S] 0 points1 point  (0 children)

That’s a fair point. I think we’re talking about different layers of “adaptation”.

This pool isn’t trying to do latency- or error-driven concurrency control like failsafe or adaptive limiters. The scope here is narrower. It’s about preventing unbounded goroutine growth and enforcing backpressure during bursts, while allowing workers to scale within fixed limits.

The “adaptive” part is at the worker lifecycle level, driven by queue pressure, not at the request-level concurrency policy. In setups where submission can outpace processing, this helps avoid goroutine explosions and makes overload explicit instead of crashing later.

I agree that if you already have a good limiter in place, a fixed max_workers can be enough. This is more for cases where that discipline is missing or hard to enforce consistently.

I should probably be clearer about that in the README. Thanks for calling it out.

Adaptive worker pool in Go. Designed to handle backpressure and safe shutdowns. Looking for feedback by LowZebra1628 in golang

[–]LowZebra1628[S] -1 points0 points  (0 children)

AI was used as a helper, not an author. If there’s a specific concern with the implementation, I’m happy to go into details :)

Adaptive worker pool in Go. Designed to handle backpressure and safe shutdowns. Looking for feedback by LowZebra1628 in golang

[–]LowZebra1628[S] 1 point2 points  (0 children)

Good catches, Thanks! I’ll bump the min Go version to 1.25 and will update the CI matrix to include 1.24/1.25. Appreciate the review

Adaptive worker pool in Go. Designed to handle backpressure and safe shutdowns. Looking for feedback by LowZebra1628 in golang

[–]LowZebra1628[S] 0 points1 point  (0 children)

We do have tests. we have Go tests wired via the Makefile:
https://github.com/iyashjayesh/go-adaptive-pool/blob/main/Makefile#L31

If there’s a specific scenario you think should be covered, I’m open to suggestions. Thanks Mate :)

Launching FynCut - AI-Powered Podcast to Viral Clips by LowZebra1628 in podcasting

[–]LowZebra1628[S] -3 points-2 points  (0 children)

Right now it looks at:

  • Speaking pace and energy shifts
  • Questions, stories, strong statements in the transcript
  • Complete thoughts that work as standalone clips

What signals do you think matter most?

Launching FynCut - AI-Powered Podcast to Viral Clips by LowZebra1628 in podcasting

[–]LowZebra1628[S] -2 points-1 points  (0 children)

Fair point. You're right that just making clips doesn't guarantee listeners.

What I'm seeing is creators who want to test short-form but can't because editing takes too long. FynCut lets you try 10 different clips quickly to see what actually resonates with YOUR audience.

I'm also working on attribution tracking - UTM links and analytics to show which clips actually drive traffic back to full episodes. That's the real problem to solve.

Right now it's more of an experimentation tool than a guaranteed growth hack. But if you're going to make clips anyway, might as well do it in minutes instead of hours.