I remember the first time my team relied on an AI system in production: a recommendation model that quietly drifted for weeks until business metrics diverged and users complained. We had alerts—sort of—but they told us symptoms, not causes. That experience shaped how I evaluate AI observability tools: they need to reveal silent failures, expose real costs, and help teams act quickly. Below I...
Embedding drift is one of those slow, sneaky problems you don’t notice until your semantic search suddenly returns...
I spent the last two weekends tearing down the privacy posture of three affordable Wi‑Fi 6E mesh systems so you...
I’ve been experimenting with on-device GPT-style models on phones and tablets for over a year now, trying to answer...
I’ve been watching models quietly change behavior in production long enough to know the moment a product team tells...
I’ve spent the last few years helping teams build real-time features—live dashboards, multiplayer sessions,...
I recently spent a few weekends trying to answer a practical question I keep getting from field engineers and IT...
I’ve been experimenting with on-device GPT-style models on phones and tablets for over a year now, trying to answer the question that keeps coming up in meetings, DMs, and comment threads: can on-device GPT replace cloud inference for offline mobile apps? The short answer is: sometimes — but...
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I’ve been watching models quietly change behavior in production long enough to know the moment a product team tells me “we didn’t see it coming.” Silent model drift — where performance degrades without obvious feature changes or clear data pipeline failures — is the kind of slow leak...
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I’ve spent the last few years helping teams build real-time features—live dashboards, multiplayer sessions, collaborative editors—and one thing keeps coming up in post-launch retrospectives: egress bills. Real-time apps are chatty by design, and pushing bits out of a cloud region to end-users...
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I recently spent a few weekends trying to answer a practical question I keep getting from field engineers and IT managers: can you run a reliable on-device LLM for field techs on a Raspberry Pi 5? The short answer is: yes—with important caveats. In this piece I’ll walk you through the realistic...
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I installed my first smart home hub because I wanted fewer apps, fewer latency issues, and — frankly — fewer cloud services hoarding sensor data about what I do at home. Over the years I’ve tested hubs that promise “local-first” operation and those that lean on cloud conveniences, and...
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I spend a lot of time testing models and reading the fine print of AI evaluation papers. Over the past few years I’ve watched the same problem crop up in every product demo, policy brief, and internal risk review: generative models confidently produce false or misleading outputs —...
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I’ve spent many hours with field technicians—on service vans, in telecom huts, and crouched under factory conveyors—watching how they solve problems with limited tools and even less patience for latency. Bringing a local LLM into that environment can be a game changer: instant troubleshooting...
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I’ve been tracking npm supply-chain incidents long enough to know that most successful attacks share the same fingerprints — if you know what to look for, you can catch many of them before they ever touch your CI. I’m not talking about exhaustive auditing or complex formal verification here:...
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I recently spent weeks comparing three deployment paths for large language models: using hosted API services (OpenAI, Anthropic, Cohere), running Mistral-style open models locally or on dedicated servers, and doing local fine-tuning or instruction-tuning of a model you control. Each option has...
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Hello — I’m Anaïs Dupont. Over the years I’ve spent time inside engineering teams, poking at data pipelines, and helping organisations ask the awkward questions about what exactly is feeding their models. In this piece I’ll walk you through a practical audit mindset for enterprise AI...
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