52 Dispatches from the engineering frontier — Deep dives, insights, and explorations
Handling payroll calculations and real-time attendance tracking for 50,000+ employees isn't just a database challenge; it's a bottleneck waiting to happen. When I started scalin...
Security is often treated as an afterthought in rapid prototyping, but when you’re building production-grade distributed systems, relying solely on server-side checks isn't enou...
I used to spend hours context-switching between my IDE, documentation, and various LLM chat windows. It was the standard developer tax—copy-pasting snippets, manually updating t...
We’ve reached a point where standard benchmarks like MMLU or GSM8K have become effectively useless. If you’re still basing your model selection on those static leaderboards, you...
Standard vector search often hits a wall when your data has deep, multi-hop relationships. If you ask a question that requires joining information across documents—like "How doe...
LLMs are great at writing code, but they are notoriously bad at writing SQL for complex, production-grade schemas. I’ve spent the last six months building internal data-querying...
Most LLM-based agents suffer from a classic case of amnesia. Once the context window hits its limit or the session resets, your agent loses everything it learned about the user’...
When I started building LLM pipelines for fintech clients last year, the conversation rarely started with "how smart is the model?" Instead, it was always, "where does the data ...
Getting LLMs to return reliable JSON is the single biggest headache in building production-grade AI agents. When you're building a system that needs to extract entities, categor...
Terminal windows are where software engineers spend the bulk of their day. Yet, when we integrate AI into our workflows, we often settle for clunky web interfaces or bloated VS ...
OCR in food tech is rarely about just reading text; it’s about converting noisy, unstructured pixels into structured nutritional data. When I built my first production-grade foo...
When I started scaling our LLM-powered features last year, the monthly AWS and OpenAI bills were the first thing that kept me up at night. It’s easy to prototype with a few API ...
When I started scaling my vector search infrastructure last year, I hit a wall. We were stuffing 1536-dimensional OpenAI embeddings into a managed vector database, and the cost ...
LLMs are great at chatting, but they’re essentially goldfish. They lose context the moment the request ends, and they can’t touch your database or call an API on their own. I sp...
When I started building LLM-integrated apps in Next.js, the choice between Vercel AI SDK and LangChain felt like picking between a surgical tool and a Swiss Army knife. If you’r...
By mid-2026, text-only RAG feels like trying to navigate a city with only a map but no street signs. We have moved past simple PDF parsing. Today, the real value in enterprise d...
Running LLM-powered applications at the edge—whether on Cloudflare Workers, Vercel Edge Functions, or distributed nodes—creates a unique security headache. You need low-latency ...
Most RAG pipelines hit a wall once the vector database starts returning "okay-ish" results. You can tune your chunking strategy or swap embedding models all day, but often, the ...
When I started building agents for internal workflows, I quickly realized that copy-pasting logic between repositories is a productivity killer. You end up with "version drift,"...
I spent the better part of 2022 wrestling with Redux boilerplate. If you’ve ever found yourself creating three separate files—an action, a constant, and a reducer—just to toggle...
We’ve all been there: you’re on a train, the tunnel hits, and your app turns into a white screen of death. As developers, we often build for the "happy path"—high-speed fiber an...