Perspectives on TinyML, custom AI, IoT, robotics, software, branding, and startup launch — written by practitioners across twelve practice areas, from edge inference and agentic automation to growth ops and product delivery.
Regulated industries, air-gapped networks, and strict data residency rules put cloud LLM APIs off the table entirely. What a real on-premise inference deployment looks like — hardware selection, model quantisation, serving infrastructure, and the operational reality nobody describes in the launch announcement.
Regulated industries, air-gapped networks, and strict data residency rules put cloud LLM APIs off the table entirely. What a real on-premise inference deployment looks like — hardware selection, model quantisation, serving infrastructure, and the operational reality nobody describes in the launch announcement.
A Series A investor's technical DD request landed with a two-week deadline. Here is what they looked for, what surprised them, and the preparation work that separated a clean pass from a conditional term sheet loaded with remediation clauses.
Shipping ML models with ad-hoc scripts and Slack messages does not scale past two engineers. How we wired DVC, GitHub Actions, and a model registry into a repeatable pipeline that treats model updates as deployments — with eval gates, rollbacks, and full audit trails.
Cloud inference has a latency floor. Our client needed sub-40ms defect detection on a production line that cannot afford a connectivity dependency. How we compressed a vision model, selected hardware, and shipped a system that has run without an engineer on-site for seven months.
The demo worked in a notebook. Production was a different story. Here is how we took a domain chatbot from first RAG experiment to 180,000 queries a day — without pretending the hard parts were solved on day one.
You cannot fix what you cannot see. We wired Prometheus, Grafana, and ELK around a live ML service — and caught a silent regression before the on-call phone rang.
Clean datasets and perfect lighting do not exist on a hospital floor. What we learned mounting computer vision on a real arm — and why the model was only half the battle.
Same model, three boards, real sensors. We ran head-to-head tests on Jetson Nano, Pi 4, and Pi 5 so you do not have to guess which hardware fits your use case.
Agents cleared 40% of a client's repetitive data work — and failed quietly on the rest. An honest breakdown of what agentic automation is good for today.
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