The latest articles on DEV Community by David Eller (@david_eller_b4d0b9113980a). https://dev.to/david_eller_b4d0b9113980a https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4060995%2Fda474fb8-4ad5-4bc9-b24a-c901f2980c2c.png https://dev.to/david_eller_b4d0b9113980a en David Eller Tue, 06 Oct 2026 21:07:03 +0000 https://dev.to/indiciumai/welcome-to-the-indicium-ai-tech-blog-53g5 https://dev.to/indiciumai/welcome-to-the-indicium-ai-tech-blog-53g5 <p>There has never been more content about AI.</p> <p>Tutorials, threads, frameworks, and hot takes are being published at a pace no one can realistically keep up with. For anyone trying to learn, build, or make grounded technical decisions, that creates a clear problem: separating signal from noise and finding what actually holds up in production.</p> <p><strong>This blog is about the signal.</strong></p> <p>What you'll find here comes directly from real enterprise work: building and running data and AI systems inside complex organizations. Places where production means dealing with legacy architecture, imperfect data, strict security requirements, governance, scale, hard deadlines, and real users.</p> <p>The authors are the people doing that work every day: forward deployed engineers, AI engineers, data scientists, architects, and other technical specialists from Indicium AI's 600+ experts across five global locations, delivering for more than 50 enterprise clients.</p> <p>They'll share the details a case study doesn't have room for: the technical decisions behind complex architectures, which LLM evaluation approaches held up in production and which didn't, the trade-offs made when the ideal solution met real-world constraints, and honest breakdowns of what broke and what they learned from it.</p> <h2> What to expect </h2> <ul> <li>💻 Code</li> <li>📊 Benchmark results</li> <li>🏗️ Architecture decisions</li> <li>⚖️ Trade-off analysis</li> <li>💬 Opinions, backed by real work</li> <li>🔥 Lessons from what worked and what didn't</li> </ul> <p>Use what's useful. Question what doesn't fit your context. Build better systems with the rest.</p> <p>Welcome. 👋</p> ai machinelearning engineering architecture