Article of the day

Docker layer caching

One of Docker's biggest performance optimizations is layer caching.

Every instruction in a Dockerfile (FROM, COPY, RUN, etc.) creates a new image layer. When you build the image again, Docker doesn't blindly execute every instruction, it checks whether it can reuse previously built layers.

For example, consider this Dockerfile:

FROM node:22

WORKDIR /app

COPY package*.json ./

RUN npm install

COPY . .

CMD ["npm", "start"]

On the first build, Docker executes every instruction. But if you only change server.js and rebuild, Docker reuses all the cached layers up to RUN npm install and only rebuilds the final COPY . . layer. This means your dependencies aren't installed again, making the build significantly faster.

However, if you modify package.json, Docker must rebuild the COPY package*.json layer, the RUN npm install layer, and every layer after them. A change to one layer invalidates that layer and all subsequent layers.

This is why Dockerfile order matters. Place instructions that change rarely (like installing dependencies) near the top, and instructions that change frequently (like copying your application source code) near the bottom. A well-structured Dockerfile can reduce build times from minutes to just a few seconds during development.

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Who is gonna be my last guest😁

Let's go to 2k🔥

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Start reading the terms and conditions when u signing up, guys😁

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When you're trying to impress with your tech, but it failed😁

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Article of the day

Advanced git

Before pushing to main, make it a habit to sync your branch with the latest changes by rebasing on top of main. This helps you resolve conflicts early and keeps your commit history clean. If your branch has a lot of small "fix" or "oops" commits, use an interactive rebase to squash them into a few meaningful commits. A clean history makes reviewing code and debugging much easier later.

Before every push, review your changes with git diff and scan for anything that shouldn't be in the repository, such as API keys, tokens, passwords, or temporary debugging code. Then run your tests locally to make sure nothing is broken. Catching an issue before it reaches the remote repository is always easier than fixing it after deployment.

If you've rebased your branch, avoid using git push --force. Instead, use git push --force-with-lease, which checks whether someone else has updated the remote branch before overwriting it. Finally, tag important releases with versions like v1.0.0 or v2.1.0. Release tags make it easy to identify deployments, track changes between versions, and roll back quickly if something goes wrong in production.

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Git in CI

CI runners clone your repo on every job. For a 200 MB monorepo with 50k commits and 10k refs, a naive git clone burns 30-60 seconds per job before any test runs. Across a 40-job pipeline that is 30 minutes of pure clone wait per pull request. The fix is not "throw a faster runner at it", it is matching the clone strategy to what each job actually needs.

Shallow clone is the lazy fix. It downloads only the most recent commits, skipping history.

git clone --depth=1 https://github.com/org/repo.git

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Git in CI

While shallow clones (depth=1) are fast, they break commands that rely on Git history like git log, git blame, git merge-base, changelog generators, and release tools.

A better option is a partial clone, which downloads the commit history but only fetches file contents when they're actually needed. This keeps history available while making clones much faster and smaller.

# Download history, fetch file contents on demand
git clone --filter=blob:none https://github.com/org/repo.git

# Skip blobs larger than 1 MB
git clone --filter=blob:limit=1m https://github.com/org/repo.git

For medium and large repos, especially monorepos, partial clones can significantly reduce clone time and disk usage without losing access to Git history.

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Prompt of the day

"Build Windows 12 and make no mistake"

LOL😁

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Article of the day

LLM Integrations

Everyone gets excited when their AI feature finally works, but making one successful API call is the easiest part. What actually matters is everything around it before it reaches production. If you don't plan for failures, retries, rate limits, token budgets, or cost tracking, your application will eventually fail when real users start using it.

A production-ready LLM integration should be built with resilience in mind from day one. Abstract your provider so switching models is painless, retry only transient failures with exponential backoff, stream responses instead of making users wait, validate every tool call, and count tokens before every request to avoid expensive errors. None of these features make your demo look cooler, but they'll save you countless hours in production.

The biggest lesson is that reliability is a feature. Add observability, track your LLM costs, enforce sensible timeouts, and always have a fallback model ready. Production isn't about proving your AI works—it's about making sure it keeps working when everything around it doesn't.

Read full article 👉 [LINK]

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Here's a simple fallback implementation in Go. Instead of failing the request when one provider is down or rate-limited, it just tries the next provider in the chain until one succeeds. It's a small pattern, but it makes a huge difference in production where outages and rate limits are normal. Graceful degradation is almost always better than showing your users an error.

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Btw guys, today I sat at my desk at 2 PM, and it's almost 2 AM now. I literally worked for almost 12 hours straight.

I think this is the longest work session I've had this break. The good part is I finally finished the project.
The bad part... I'm not feeling my back 😭.

never had this kind of back pain before tho😭

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