Keeping a large language model (LLM) healthy in production feels a bit like tending a high-maintenance houseplant: ignore it for too long and it wilts, water it too much and you drown it. In the last few years I’ve watched teams move from “deploy and forget” to continuous monitoring and active mitigation — and the single most effective lever I've seen is drift detection. But drift...
I’ve spent the last few weeks alternating between my living room, a noisy coffee shop, and a shared office desk,...
Fine-tuning GPT-style models on proprietary customer data is one of those tasks that promises huge value—better, more...
I used to dread the monthly cloud bill email. A spike in egress charges would arrive like clockwork after a product...
Buying an enterprise AI observability tool is one of those decisions that looks simple on a feature sheet and quickly...
I’ve spent the past year benchmarking vector databases as part of larger semantic search projects, and one thing...
I’ve spent the last few years building and evaluating machine learning systems for real teams, so when customers ask...
I used to dread the monthly cloud bill email. A spike in egress charges would arrive like clockwork after a product launch, blamed on "unexpected traffic" or "downloads from Region X." Over the years I've helped several engineering teams rework their multicloud topology so that customer-facing apps...
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Buying an enterprise AI observability tool is one of those decisions that looks simple on a feature sheet and quickly becomes painful in production. I’ve sat in more vendor demos than I’d like to admit, built my own ad‑hoc monitoring stacks, and seen observability gaps turn into customer...
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I’ve spent the past year benchmarking vector databases as part of larger semantic search projects, and one thing became quickly clear: choosing a vector database is less about raw recall numbers and more about trade-offs between usability, latency, and price. In this article I’ll walk you...
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I’ve spent the last few years building and evaluating machine learning systems for real teams, so when customers ask whether they should “retrain” a large language model (LLM) on their proprietary data, my first reaction is: there’s no single yes/no answer. The right approach depends on the...
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I’ve spent years testing gadgets and poking at the settings screens most people never touch. Smart home gear can make life easier, but it also opens a lot of new privacy and security questions. If you want a smart home that respects your data, the hub—the piece that ties sensors, speakers, and...
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I’ve been watching WebAssembly (Wasm) evolve from an intriguing runtime curiosity into a practical tool I reach for when building browser-based apps. Over the last few years I’ve ported libraries, benchmarked compute-heavy tasks, and explored the developer experience. In this article I want to...
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I started auditing third-party npm packages the hard way: by getting burned — a dependency pulled in a small, obfuscated postinstall script that sent environment variables back to a server. Since then I’ve developed a workflow that blends automated tools, quick manual checks, and safe testing...
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I’ve helped small engineering teams move from ad-hoc VPNs and open security groups to a zero-trust networking model that actually scales with a remote-first culture. In practice, zero trust isn’t a single product you buy — it’s a set of principles and a roadmap you follow: never trust,...
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I test AI products for a living, and one thing that never stops surprising me is how often "explainability" is treated as either a checkbox or a vague marketing line. Reviewers will praise a model for being "transparent" or criticize it for being a "black box" without giving readers concrete...
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I’ve spent years watching teams drown in time-series telemetry: terabytes of sensor data streaming into lakes, dashboards overflowing with metrics, and engineers still caught off guard when a pump or motor fails. The irony is that more data rarely equals better predictions — what matters is the...
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