Within the Writer Highlight collection, TDS Editors chat with members of our neighborhood about their profession path in information science and AI, their writing, and their sources of inspiration. At present, we’re thrilled to share our dialog with Mike Huls.
Mike is a tech lead who works on the intersection of information engineering, AI, and structure, serving to organizations flip advanced information landscapes into dependable, usable programs. With a robust full-stack background, he designs end-to-end options that steadiness technical depth with enterprise worth. Alongside shopper work, he builds and shares sensible instruments and insights on information platforms, AI programs, and scalable architectures.
Do you see your self as a full-stack developer? How does your expertise throughout the entire stack (from frontend to database) change the way you view the information scientist function?
I do, however not within the sense of personally constructing each layer. For me, full-stack means understanding how architectural selections at one layer form system conduct, danger and price over time. That perspective is crucial when designing programs that have to survive change.
This angle additionally influences how I view the information scientist function. Fashions created in notebooks are solely the start. Actual worth emerges when these fashions are embedded in manufacturing programs with correct information pipelines, APIs, governance, and user-facing interfaces. Knowledge science turns into impactful when it’s handled as a core half of a bigger system, not as an remoted exercise.
You cowl a variety of matters. How do you resolve what to give attention to subsequent, and the way are you aware when a brand new matter is value exploring?
I are inclined to comply with recurring friction. After I see a number of groups battle with the identical issues, whether or not technical or organizational, I take that as a sign that the difficulty is structural moderately than particular person, and price addressing on the architectural or course of degree.
I additionally intentionally experiment with new applied sciences, not for novelty, however to grasp their trade-offs. A subject turns into value writing about when it both solves an actual drawback I’m presently dealing with or reveals dangers that aren’t but broadly understood. Lastly, I write about matters I personally discover fascinating and price exploring, as a result of sustained curiosity is what permits me to go deep.
You’ve written about LangGraph, MCP, and self-hosted brokers. What’s the greatest false impression you suppose folks have about AI brokers at the moment?
Brokers are genuinely highly effective and open up new prospects. The misperception is that they’re easy. It’s simple at the moment to assemble cloud infrastructure, join an agent framework, and produce one thing that seems to work. That accessibility is efficacious, however it masks numerous complexity.
As soon as brokers transfer past demos, the actual challenges floor. State administration, permissions, value management, observability, and failure dealing with are sometimes underestimated. With out clear boundaries and possession, brokers turn out to be unpredictable, costly, and dangerous to function. They aren’t simply prompts with instruments; they’re long-lived software program programs and must be engineered and operated accordingly.
In your article on Layered Structure, you point out that including options can typically really feel like “open-heart surgical procedure.” For a newbie or a small information workforce seeking to keep away from this, what’s your key recommendation on organising an structure?
“The one fixed is change” is a cliché for a very good motive so optimize for change moderately than for preliminary supply velocity. Even a minimal type of layered considering helps: separating area logic, software circulate, and infrastructure issues.
The aim is just not architectural perfection on day one or good categorization. It’s about creating clear boundaries that enable the system to evolve with out fixed rewrites. Small upfront self-discipline pays off considerably as programs develop.
You’ve benchmarked PostgreSQL insert methods and famous that “quicker is just not at all times higher.” In a manufacturing ML pipeline, what’s a situation the place you’ll intentionally select a slower, safer insertion methodology?
When correctness, traceability, and recoverability matter greater than uncooked throughput. In lots of pipelines, lowering runtime by a number of seconds affords little profit in comparison with the danger launched by weaker ensures.
For instance, pipelines that feed regulatory reporting, monetary decision-making, or long-lived coaching datasets profit from transactional security and specific validation. Silent information corruption is much extra expensive than accepting modest efficiency trade-offs, particularly when information turns into a long-term asset others will construct on..
In your Private, Agentic Assistants article, you constructed a 100% non-public, self-hosted platform. Why was avoiding “token prices” and “privateness leaks” extra essential to you than utilizing a extra highly effective, cloud-based LLM?
In my each day work I’ve skilled that trusting a system is key to system adoption. Token prices, opaque information flows, and exterior dependencies subtly affect how programs are used and perceived.
I additionally made a aware selection to not route my private or delicate information by means of exterior cloud suppliers since there are restricted ensures on how information is dealt with over time. By conserving the system self-hosted, I may design an assistant that’s predictable, auditable, and aligned with European privateness expectations. Customers have full management over what the assistant has entry to and this lowers the barrier for utilizing the assistant.
Lastly, not each use case requires the biggest or most costly mannequin. By decoupling the system from a single supplier, customers can select the mannequin that most closely fits their necessities, balancing functionality, value, and danger.
How do you see the day-to-day work of an information skilled altering in 2026?
Regardless of widespread stereotypes, information and software program engineering are extremely social professions. I strongly consider that probably the most important a part of the work occurs earlier than writing code: aligning with stakeholders, understanding the issue house, and designing options that match present programs and groups.
This upfront work turns into much more essential as agent-assisted growth accelerates implementation. With out clear targets, context, and constraints, brokers amplify confusion moderately than productiveness.
In 2026, information professionals will spend extra time shaping programs, defining boundaries, validating assumptions, and making certain accountable conduct in manufacturing environments.
Trying forward at the remainder of 2026, what large matters will outline the yr for information professionals, in your opinion? Why?
Generative AI and agent-based programs will proceed to develop, however the greater shift is their maturation into first-class manufacturing programs moderately than experiments.
That transition is determined by reliable, high-quality, accessible information and sturdy engineering practices. In consequence, full-stack considering and system-level design will turn out to be more and more essential for organizations that need to apply AI responsibly and at scale.
To be taught extra about Mike’s work and keep up-to-date together with his newest articles, you possibly can comply with him on TDS or LinkedIn.
