How Reddit Migrated Comments Functionality from Python to Go
Tap compare, what a clever way to migrate from one tech stack to another in production. Sounds like a beginner interviewer shadowing an experienced one in real interviews.
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Stop using MySQL in 2026, it is not true open source
Development has ground to a halt. The thing is plagued by security issues. As fondly as I remember MySQL from my formative years, the sad reality is it stopped being a viable option soon after its Oracle acquisition. Why Sun, why did you sell yourself?
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How DoorDash Moved to a Service Mesh to Handle 80M Requests/Second
A good case study in picking just part of an off-the-shelf platform solution and building out the rest based on your org’s unique architecture and constraints. ‘Most important is to stay grounded in real problems rather than chasing architectural trends.’
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Your Job Is Not Your Career
Add intentional ‘thinking time’ to your workdays. Use it for reflecting on hurdles encountered, measuring the impact of a completed task, and planning for future value creation. Otherwise, all you are doing is keeping the lights on rather than investing in your growth: good for your company, bad for your career.
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A pragmatic guide to LLM evals for devs
TIL about Evals, the automated testing analogue to traditional unit/integration tests. Since running LLMs (for evaluation) in CI pipelines isn’t cheap, it’s good to prioritize test scenarios based on top buckets of real-world user issues.
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The Trillion-Dollar “Why”
Dr. Gandhi’s piece confirms my prophecy about the AI dust settling by late 2025 or start of 2026. 100% automation shouldn’t be the goal. Human knowledge is priceless (goal mine as per Dr. Gandhi). Rather than replace humans with AI, augment agents with humans for the best outcomes. Good for business, good for humanity.
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How LLMs Learn from the Internet: The Training Process
In my 2017 book Artificial Intelligence for .NET (co-authored with Nishith), I described how language models of that era worked. A combination of gradient descent and supervised fine-tuning gave them predictive and classification superpowers for the given domain/context. Those models were next token predictors in the truest sense.
The attention/transformers architecture together with data-science-optimized GPUs revolutionized language models and made today’s LLMs possible. The fundamentals of pre-training and teaching models have stayed the same.
It’s incredible how a single breakthrough in software architecture can lead to generational advancements.
Beautifully written article by Alex Xu, so easy to digest. Highly recommended even if you aren’t familiar with the technical details.
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Saga Pattern Demystified: Orchestration vs Choreography
TIL that there’s a name for the inter-service communication pattern that I implemented in our microservices solution five years ago.
It’s called Choreography-based Saga. This is where each service listens for interesting events from other services to trigger actions.
The alternative is Orchestration-based Saga where a central orchestrator listens to events from and issues commands to services.
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High-Agency Engineering: Why You Want To Be Blocked
It works! This is how I approached my first Jira ticket at my new job. Rather than waiting to digest the entire codebase or for code walkthroughs to happen, I simply dove into unfamiliar waters and created a pull request on day one. I suddenly had specific, pointed questions to the team to inform my approach rather than generic questions that might not have been helpful in solving the problem.
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The Prison Of Financial Mediocrity
The double-whammy of insecure job market and social media’s constant feed of ‘a better life’ is creating a massive FOMO and driving people to invest in modern day gambling. But who’s profiting? The Dream11s and Coinbases of the world. The house wins. Most players lose.