Insights That Matter Today
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Your ML System Is Not “Done” at Launch: A Pragmatic Guide to Evaluation, Monitoring, and Drift
Why this matters this week A pattern is repeating across teams rolling out applied machine learning systems: Models ship that look great in offline benchmarks, then quietly decay in production. Infra cost for “AI features” creeps up 3–5x over a quarter with no corresponding business lift. Incidents are now “the model did something weird” instead…
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Stop Gluing LLMs to Forms: A Pragmatic Path from RPA to Real AI Automation
Why this matters this week The last 12–18 months were about “getting an LLM into production.” The next 12–18 will be about “removing humans from the middle of boring workflows without blowing up risk, compliance, or uptime.” The pattern that’s now repeating across real businesses: RPA bots, integration scripts, and shared inboxes are the current…
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Stop Treating AI Codegen as Magic: Design It Like a System, Not a Demo
Why this matters this week The “AI coding assistant” story has shifted from novelty to line-item in engineering budgets. In the last month alone, several vendors have: Announced “full repo” codegen and refactors. Pushed “AI test generation” into their core offering. Started talking about “AI agents” that file and merge pull requests. Most teams I…
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Solid-State Batteries: What’s Real, What’s Hype, and What to Plan For
Why this matters this week Over the last few weeks, several solid-state battery announcements have landed in the same window: One major automaker publicly reaffirmed 2028–2030 as its “mass deployment” target for solid-state EV packs. A leading solid-state startup disclosed pilot-line yield numbers (still low, but finally not hand-wavy). A large Asian cell manufacturer quietly…
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Policy-as-Code or Policy-as-PDF? Getting Real About AI Governance Before Audit Season
Why this matters this week If you’re running production AI systems in any regulated-ish environment (B2B SaaS, fintech, health, infra, security), you’re now being asked some version of: “Where does the model send data?” “How long do you keep prompts and outputs?” “Can we audit what the model saw and decided?” “Is this SOC2 /…
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Serverless Isn’t “Free”: Designing for Cost, Reliability, and Observability on AWS
Why this matters this week AWS costs are spiking for a lot of teams that went hard into “serverless” and platform abstraction over the last 2–3 years. The common pattern: Lambda, Step Functions, EventBridge, DynamoDB everywhere Microservices and event-driven designs that looked elegant in diagrams CloudWatch bills and cross-service data transfer costs that now rival…
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