slaptijack

AI coding agents are useful in the same way junior engineers, build scripts, and sharp shell aliases are useful: they can remove friction, accelerate boring work, and occasionally surprise you with a clever path through a problem.

Bazel's old WORKSPACE model had a long run.

Bazel 9.1.0 is not the kind of release that should make an engineering team drop everything and schedule a build-system migration party.

The Staff Engineer's Path by Tanya Reilly is a career book for engineers who have discovered that senior technical work does not automatically become clearer when the title gets bigger.

A Philosophy of Software Design, 2nd Edition by John Ousterhout is a small book with a large target: the everyday design judgment that determines whether code stays understandable after the original author moves on to the next problem.

Designing Data-Intensive Applications, 2nd Edition is the kind of software book that should make an experienced engineer slightly uncomfortable in a productive way.

Bazel 8.0.0 was not just another build-tool release.

Natural language developer queries sound like a toy until you watch someone spend ten minutes answering a question the platform already knows: "Who owns the checkout service?"

Backstage is already where many platform teams want developers to go for service ownership, docs, APIs, runbooks, and operational metadata.

Git is usually the first place developers look when they need to understand a system.

Bazel is fast, reproducible, and battle-tested at scale — but when something breaks, good luck deciphering its logs.

CI/CD build failures can be brutal — especially when the logs are long, noisy, and cryptic.

Secure prompts are not magic words.

Pre-push hooks are your last line of defense before questionable code hits the remote repo.

Merges often bring in massive changes — sometimes dozens of commits and hundreds of lines of code — and the first thing developers ask is: “What just happened?”

Keeping changelogs up to date is one of those development chores that everyone agrees is important… and everyone forgets to do.

Now that you’ve got a fully functional, Python-powered Git hook CLI backed by OpenAI, the next step is sharing it — the right way.

Building a local Git hook with Python is great, but if you want others on your team (or across multiple repos) to use it, you’ll want to package it as a reusable command-line tool.

Git hooks are one of the most powerful — and most underutilized — features in the Git ecosystem.

GitHub Copilot is impressive, but what if you could build your own AI-powered Git assistant tailored to your workflow?

GitOps has already transformed how we think about infrastructure: declarative, auditable, and version-controlled.

Rebase and merge are not rival moral systems.

Source Code Management (SCM) has evolved dramatically over the past few decades.

When working with Google Cloud Storage (GCS) via gsutil, most file operations run smoothly.

In a previous article, we explored how to count lines of code by language using tools like cloc.

If you want to count lines of code by language in a repository, start with this: cloc .

When working with large Bazel code repositories, it’s not always clear how targets are distributed across directories.

Over the course of this series, we’ve explored how to build client/server applications with Thrift and Protobuf/gRPC in three different programming languages: Java, Rust, and Python.

We’ve now built similar RPC setups using Thrift and Protobuf/gRPC in Java and Python.

In our previous articles, we explored Thrift and demonstrated how to build cross-language systems with Java, Python, and even integrated Rust.

In the previous article, we built a simple Thrift-based client/server application where the server ran on Java and the client ran on Python.

In our first article, we introduced Thrift and Protobuf and laid out our plan to build cross-language client/server applications.

Modern distributed systems often span multiple programming languages, architectures, and runtimes.

As modern distributed systems become increasingly complex, developers rely on efficient, language-agnostic communication mechanisms to ensure seamless interactions between services.

Open source projects thrive on collaboration, transparency, and reliability.

Achieving hermeticity in software development brings numerous benefits, such as reproducibility, reliability, and security.

In the world of software development, consistency and reliability are paramount.

GitHub Copilot and Amazon Q Developer overlap where most developers first meet an AI assistant: editor chat, inline suggestions, code generation, and help understanding a codebase.

In the ever-evolving landscape of Python web frameworks, two names have been gaining significant attention: Quart and FastAPI.

Building a chatbot from scratch using Python and the OpenAI API has been an exciting journey.

You've built a sophisticated chatbot using Python and the OpenAI API—congratulations!

Creating a chatbot that can interact naturally with users is an exciting challenge.

In our previous articles, we've built a basic chatbot using Python and the OpenAI API that can engage in simple conversations.

With our development environment set up and our first API call to OpenAI under our belt, it's time to bring our chatbot to life.

Now that we have our development environment set up, it's time to dive into the exciting part—making our first API call to OpenAI.

Before diving into building our chatbot using Python and the OpenAI API, it's crucial to set up a development environment that is both efficient and comfortable to work with.

The rise of artificial intelligence has revolutionized the way we interact with technology.

Creating a chatbot has become an essential skill for modern software engineers, especially with the rise of AI-driven applications.

If you're a software engineer like me, you've probably spent countless hours tweaking your development environment to squeeze out every bit of productivity.

In the rapidly evolving world of technology, continuous learning and professional growth are not just optional—they are essential.