52 Dispatches from the engineering frontier — Deep dives, insights, and explorations
Most RAG pipelines fail not because the LLM isn't smart enough, but because the retrieval process is essentially "garbage in, garbage out." If you’re still splitting your docume...
Building persistent, multi-turn AI agents usually hits a wall when you move beyond simple chat loops. If your agent crashes midway through a complex tool-use sequence or a multi...
Retrieval-Augmented Generation (RAG) pipelines often fail when relying solely on vector search. If a user searches for a specific product SKU or a niche technical term, dense em...
Running a 70B parameter model on a single consumer GPU used to be a pipe dream. A couple of years ago, you needed a cluster of A100s just to move the weights around. Today, betw...
Most engineers treat AI agents as linear pipelines—a single prompt leading to a single completion. But when I started building production-grade automation for enterprise clients...
Retrieval-Augmented Generation (RAG) is prone to hallucinations when the retrieved context is irrelevant or noisy. I’ve spent the last few months moving away from standard "naiv...
Building RAG pipelines in 2026 feels a lot different than it did two years ago. Back then, we were just throwing embeddings into whatever index was easiest to spin up. Now, with...
LLMs are notoriously bad at following strict formatting instructions. If you ask a model to "return a JSON object," you’ll often get a chatty preamble like "Sure, here is the JS...
Running heavy language models on your own hardware used to be a nightmare of dependency hell and mismatched CUDA versions. With Ollama and Docker, we’ve finally hit a point wher...
Waiting for a large language model to generate a full response before showing anything to the user feels like 2023. In modern applications, perceived latency is everything. If y...
When you’re building pipelines for Large Language Models, the backend isn't just a CRUD wrapper; it’s the traffic controller for compute-heavy tasks. Over the last year, I’ve mi...
When Meta dropped the weights for Segment Anything 2 (SAM2), the computer vision community finally got a tool that treats video segmentation like a first-class citizen. Unlike t...
Static analysis tools often fall into two camps: pattern-based linters that miss context, or heavy-weight scanners that generate endless noise. When I started building a custom ...
The era of "chatting" with an LLM to generate a single function is over. We have moved into the era of Swarm Orchestration, where autonomous agents collaborate to build, test, a...
Building dashboards for defense environments is a different beast compared to standard SaaS development. You aren't just dealing with intermittent Wi-Fi; you're dealing with "de...
Running LLMs in production is a constant battle between keeping GPU costs down and ensuring latency doesn't spike when traffic hits. If you leave your pods running 24/7, your cl...
GPU compute is expensive, and keeping a cluster idling is a quick way to burn through a startup’s runway. As I moved from managing persistent Kubernetes clusters to more fluid, ...
LLM-based applications are notoriously brittle. Unlike traditional software where a unit test checks if 2 + 2 = 4, prompt engineering is probabilistic. A minor tweak to a system...
When I first started integrating LLMs into production pipelines, I treated them like standard REST APIs. I figured a simple try-catch block would handle the occasional 429 error...
I recently spent three weeks debugging a production LLM pipeline that was leaking internal system instructions because of a simple prompt injection. It’s a wake-up call: if you’...
Latency is the silent killer of LLM-powered products. When I’m building production apps, I find that waiting for a 70B parameter model to stream tokens at 15 tokens per second f...