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Product design in software development is not UI/UX design

Product design and UI/UX design are not the same service. UI/UX design is the craft of the interface: information architecture, user flows, visual direction, design system foundations. Product design in software development is the phase that runs before a build and decides what gets built, whether it is technically feasible and what it will cost. It is delivered by a trio of product manager, UX designer and software development lead, and it ends in a validated, estimated, buildable scope rather than a set of screens.

Alex Marciuc - 21 Aug 2026

UX/UI Design Featured Product design

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Tudor Iordache - 2 Jul 2026

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Stefan Sarbu - 29 Jun 2026

Five MVP decisions that turn an AI MVP into an enterprise AI platform

The MVP-to-platform transition fails not because the technical architecture is wrong, but because most teams treat it as a scaling problem when it is actually a redesign problem. The MVP gets you to one customer. The platform requires you to be honest about what was specific to that customer and what is genuinely transferable. The teams that get this right do not generalize reactively. They build the MVP knowing which decisions will need to be revisited at the platform stage, and they revisit them deliberately.

Technology Featured AI Architecture
Tudor Iordache - 24 Jun 2026

Human-in-the-loop AI systems where judgement stays in the workflow

Human-in-the-loop is not the version of AI you build while waiting for the model to get good enough to replace the human. It is the version of AI that actually works in domains where being wrong is expensive. The teams treating it as a transitional design are building systems that will eventually be replaced. The teams treating it as the destination are building systems that compound trust and improve over time.

Featured Publishing AI
Stefan Sarbu - 18 Jun 2026

How do you build RAG systems that work in production?

Most RAG implementations are built to produce answers. The ones that work in high-stakes environments are built to produce answers with calibrated confidence, source attribution the user can verify, and behavior that changes when confidence is low. The hard engineering work sits in four places most teams underestimate: chunking strategy, embedding quality, confidence calibration, and how the system surfaces its own uncertainty. The teams that get RAG into production treat confidence as a product feature rather than a model property, and they treat "I do not know" as a legitimate answer the system needs to be trusted to give.

Trending Featured AI
Ilie Ghiciuc - 10 Jun 2026

AI browser agents: the gap between demo and production

Browser-use AI agents are most valuable when they are a component in a larger system, not when they are deployed as a complete solution. The teams getting durable value treat the agent as a navigation layer feeding into deterministic downstream processes, with serious operational infrastructure around it. The teams that struggle deploy the agent and expect the rest of the system to follow.

Technology Trending Featured AI
Ilie Ghiciuc - 2 Jun 2026

Edge AI inference: what it means for your product architecture

AI inference is moving toward the edge because centralized cloud processing introduces latency, egress costs and data residency constraints that compound as inference volume scales. The decision of where to run inference is determined by five workload characteristics: latency tolerance, data volume, compliance requirements, operational resilience needs and cost profile over time. Most production architectures resolve this by splitting responsibilities between cloud and edge, with the operational overhead of managing a distributed inference fleet remaining the primary factor that determines when the transition is viable.

Technology Trending Featured AI
Ilie Ghiciuc - 25 May 2026

Why AI automation ROI is highest on repetitive, high-volume processes

Document extraction accuracy at scale is a sequence of failure modes, not a single problem. Fine-tuning an open-weight visual-language model on domain-specific data closes most of the distance from a general-purpose baseline, but rarely reaches the threshold a business case actually requires. Pushing past that ceiling depends on three engineering techniques applied in sequence, each addressing a failure mode the others cannot. There is a question that comes up early in almost every AI conversation we have with founders and product leaders: "Is our process a good candidate for this?" It sounds like a simple question. It is not. A recent MIT study reports that 95% of enterprise generative AI pilots fail to deliver measurable business impact, and that the primary cause is not the technology itself but the absence of workflow integration and a defined outcome before the build begins. Most teams answer the question by focusing on the technology first, evaluating what a particular model or agent framework can do, and then searching for a process to apply it. That sequence produces many promising pilots but leaves production systems in short supply.

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