Within the Creator 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 powerful full-stack background, he designs end-to-end options that stability technical depth with enterprise worth. Alongside consumer 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 info scientist position?
I do, however not within the sense of personally constructing each layer. For me, full-stack means understanding how architectural choices at one layer form system conduct, danger and value over time. That perspective is crucial when designing programs that must survive change.
This angle additionally influences how I view the info scientist position. 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. Information 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 concentrate on subsequent, and the way are you aware when a brand new subject is value exploring?
I are likely to observe recurring friction. After I see a number of groups wrestle with the identical issues, whether or not technical or organizational, I take that as a sign that the problem is structural slightly 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 at the moment going through or reveals dangers that aren’t but broadly understood. Lastly, I write about matters I personally discover attention-grabbing 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 largest false impression you suppose individuals have about AI brokers in the present day?
Brokers are genuinely highly effective and open up new potentialities. The misunderstanding is that they’re easy. It’s straightforward in the present day to assemble cloud infrastructure, join an agent framework, and produce one thing that seems to work. That accessibility is efficacious, nevertheless it masks plenty of complexity.
As soon as brokers transfer past demos, the true challenges floor. State administration, permissions, value management, observability, and failure dealing with are sometimes underestimated. With out clear boundaries and possession, brokers develop into unpredictable, costly, and dangerous to function. They aren’t simply prompts with instruments; they’re long-lived software program programs and have to be engineered and operated accordingly.
In your article on Layered Structure, you point out that including options can usually really feel like “open-heart surgical procedure.” For a newbie or a small information group trying to keep away from this, what’s your key recommendation on establishing an structure?
“The one fixed is change” is a cliché for an excellent motive so optimize for change slightly than for preliminary supply pace. Even a minimal type of layered pondering helps: separating area logic, software circulation, and infrastructure issues.
The aim isn’t architectural perfection on day one or good categorization. It’s about creating clear boundaries that permit 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 isn’t at all times higher.” In a manufacturing ML pipeline, what’s a situation the place you’ll intentionally select a slower, safer insertion technique?
When correctness, traceability, and recoverability matter greater than uncooked throughput. In lots of pipelines, lowering runtime by just a few seconds presents little profit in comparison with the chance launched by weaker ensures.
For instance, pipelines that feed regulatory reporting, monetary decision-making, or long-lived coaching datasets profit from transactional security and express validation. Silent information corruption is way extra pricey 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% personal, 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 every 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 alternative to not route my private or delicate information by way of exterior cloud suppliers since there are restricted ensures on how information is dealt with over time. By protecting the system self-hosted, I might 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 most important or costliest 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 vital a part of the work occurs earlier than writing code: aligning with stakeholders, understanding the issue area, and designing options that match current programs and groups.
This upfront work turns into much more essential as agent-assisted improvement accelerates implementation. With out clear targets, context, and constraints, brokers amplify confusion slightly than productiveness.
In 2026, information professionals will spend extra time shaping programs, defining boundaries, validating assumptions, and making certain accountable conduct in manufacturing environments.
Wanting forward at the remainder of 2026, what massive matters will outline the yr for information professionals, in your opinion? Why?
Generative AI and agent-based programs will proceed to develop, however the larger shift is their maturation into first-class manufacturing programs slightly than experiments.
That transition is determined by reliable, high-quality, accessible information and sturdy engineering practices. Because of this, full-stack pondering and system-level design will develop into more and more essential for organizations that need to apply AI responsibly and at scale.
To study extra about Mike’s work and keep up-to-date along with his newest articles, you may observe him on TDS or LinkedIn.
