Practical write-ups from the Geeks Invention team — production lessons from AI agent fleets, machine learning, IoT and software delivery.
We handed a product team's entire bug lifecycle to a fleet of AI agents: 150 issues imported straight out of a messy spreadsheet, and 50 of them fixed, reviewed and sitting in merge-ready pull requests ten hours later.
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A rewrite concentrates all the learning at the end; incremental spreads it. Which one is right depends on your codebase, not on which sounds cleaner.
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Running two frameworks in the same page is how you migrate a front end without a rewrite. Here is the code that mounts the boundary, and the four things that break once you do.
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88% of enterprise AI pilots never reach production. The reason is almost never the model — it is the data, integration, ownership and measurement nobody scoped.
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The Digital Omnibus deferred the high-risk obligations to 2027. It did not touch Article 50 transparency, which applied from 2 August 2026 to almost everyone.
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86% of teams run coding agents against production code; only 8.6% have other agents live. The gap is verification, permissions, memory and observability.
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Routing routine steps to a small model instead of a frontier one can cut inference spend by 20× or more. How to find out what it would save you.
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28% of the Fortune 500 have deployed Model Context Protocol. What it takes to expose an internal system to an AI assistant without regretting it later.
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Roughly 80% of AI output quality comes from what is in the context window, not how the prompt is worded. What that changes about the way you build.
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Generative tools made iteration cheap and moved the scarce skill to judgement. What that changed about creative work, and the rights and disclosure questions still open.
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Search and fraud detection reliably pay in e-commerce. Personalisation often doesn't, and the difference is usually trustworthy event data rather than model sophistication.
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Some development work collapsed, some barely moved, and a new engineering discipline appeared. Two years of evidence on what AI actually absorbed.
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Ambient documentation and imaging triage reached the bedside; sepsis prediction largely did not. What separated them was integration and safety design, not model quality.
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Connected-device projects rarely fail on hardware. They fail on device management, OTA updates and data pipelines — the four systems every fleet needs before it scales.
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A prediction is only worth the decision it changes. The three questions that kill most predictive projects before modelling starts, and where the discipline reliably pays.
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