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 with important trade-offs around model capability, battery, latency, and privacy. Below I walk through...
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 installed my first smart home hub because I wanted fewer apps, fewer latency issues, and — frankly — fewer cloud...
I spend a lot of time testing models and reading the fine print of AI evaluation papers. Over the past few years I’ve...
I’ve spent many hours with field technicians—on service vans, in telecom huts, and crouched under factory...
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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I’ve been testing compressed vector embeddings for search pipelines for a while now, because the promise is irresistible: save storage and speed up retrieval while keeping relevance high. In practice it’s a balancing act. Below I share hands‑on experiments, the failure modes I’ve seen, and...
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I spent the last few weeks pushing a Pixel Tablet through a set of pro-photography chores: rapid culling, raw adjustments, masked edits, and a handful of “magic” fixes that promise to save time. My aim was simple: figure out whether the Pixel Tablet’s on-device AI is fast and private enough...
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I bought a cheap smart plug for my kitchen lamp last month. It was under £50, tucked into a sale, with shiny app screenshots and hundreds of five-star reviews. On the face of it, it solved a small convenience problem: turn the lamp on and off from my phone, schedule it, and integrate with my voice...
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