I code and I love kubernetes. Built gttlsh.brimble.app/
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Gafana dashboard on demand - gttlsh.brimble.app/
Done building gttl
It lets you connect to your Prometheus, pick a dashboard, and get a temporary Grafana dashboard without having to set up Grafana yourself.
You can run it from the same server or environment where your app and Prometheus are running, so you don’t need to expose your Prometheus publicly.
Useful for quickly checking metrics, debugging something, or sharing a dashboard without keeping it around forever.
gttlsh.brimble.app/
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K6 plus this in CI will actually be dope, will try it out and give feedback here
k6.io/
I think continuous profiling should become a default part of observability in the AI coding era.
AI can generate a lot of correct code very quickly, but correct code is not always efficient code.
An agent might choose an unnecessarily heavy library, allocate too much memory, repeat expensive work, use a poor algorithm, or simply implement something in a way that costs far more CPU than necessary.
Metrics can tell you that CPU usage or latency is getting worse. A flame graph can show you where the program is actually spending its time.
From there, you can inspect the hot paths, review the implementation manually, research better approaches, replace inefficient libraries, or use an AI agent to investigate possible optimizations.
As AI makes writing code cheaper measuring the actual performance of that code becomes even more important.
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Reason I just talk about what I’m learning, good vibes here
I made a video about this but had to delete it. Many of these people come with immense arrogance rather than openness to learn.
It's sickening. Everybody wants to prove they know it all by putting the other person down. Chill bro, you don't work at Jane Street, on flight control systems, or at Google or Cloudflare.
It's frustrating to see people without an open mind to learning and improving.
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tø2by retweeted
I think continuous profiling should become a default part of observability in the AI coding era.
AI can generate a lot of correct code very quickly, but correct code is not always efficient code.
An agent might choose an unnecessarily heavy library, allocate too much memory, repeat expensive work, use a poor algorithm, or simply implement something in a way that costs far more CPU than necessary.
Metrics can tell you that CPU usage or latency is getting worse. A flame graph can show you where the program is actually spending its time.
From there, you can inspect the hot paths, review the implementation manually, research better approaches, replace inefficient libraries, or use an AI agent to investigate possible optimizations.
As AI makes writing code cheaper measuring the actual performance of that code becomes even more important.
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You need to checkout boot dev web fr , it’s very well detailed and structured
Whilst you can learn almost everything on your own. “School” gives you structure and a tutor/ physical mentor gives you direction and guidance.
The hidden cost of learning from everywhere as a beginner is waste of time. AI? Some always agree to your objections and proposals 1/2
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Imagine applying and still getting rejected
@Raynerdtech @J_Tara_ @ekemini58110 @fidexcode @davestack_dev @hsprafrique
I found a backend job opportunity and thought of you guys, since you're my mutuals...
30k per month..
Y'all should go apply🙏
You don't have to thank me
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My experience using Wema Bank’s ALAT API has been pretty frustrating, especially the developer onboarding.
The biggest problem is how manual everything feels.
There’s a playground, but you can’t simply generate API credentials and start testing. Verification is handled through back-and-forth emails instead of having a proper onboarding flow where you submit your documents, see their verification status, and know exactly what’s pending.
Even after verification, the test dashboard barely feels like a dashboard.
There’s no useful transaction history, no charts, no visualization of money coming in or going out, and no real interface for seeing what is happening in your test environment. It mostly feels like another version of the API documentation showing request/response formats.
Even something as basic as configuring callback URLs has to be done by emailing them the URLs you want to use.
Normally, I’d expect to log into a developer dashboard, add or change my callback URLs myself, generate test credentials, view transactions/webhook events, and debug my integration from there.
The API itself may work, but the developer experience around it makes integration unnecessarily difficult.
Wema/ALAT really needs a proper self-service developer portal.
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My personal alternative:
1. Learn frontend with MDN Web Docs.
2. Learn a language and backend-related stuff on Boot.dev. Boot.dev is extremely goated, at least to me.
3. Go to CodeCrafters and build a project based on the language you learn.
4. YouTube.
5. Follow the right people on social media and stay heavily active in the tech space.
I watch Sanmi’s videos on LinkedIn quite often and, to be fair, I’ve learned a lot from him.
But ₦1,000,000 to learn full-stack development in big 2026?
I have just one question:
Are you also helping the students get jobs after they finish?
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