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Stefan Sarbu - 13 Oct 2026

MVP in-house vs. development partner: where knowledge should live

Whether an MVP is built in-house, with a development partner or as a hybrid is easiest to decide by asking where the product's knowledge needs to live. Product and domain knowledge belongs inside the company from the first day, technical architecture knowledge needs an in-house owner by launch and delivery capacity can be rented for longest. Planning the move from partner to in-house team before the build starts keeps either model reversible.

Idea to MVP Featured Startup
Mihai Bâlea - 7 Oct 2026

What a clickable prototype should prove before the build

A clickable prototype no longer proves much by looking finished, since AI tools can now produce a convincing one in an afternoon. Before it is used to approve a build budget, it should pass four tests: it holds up beyond the happy path, the people who will use it have reacted to it, every screen maps to the feature breakdown and the assumptions behind it are written down. What it cannot show is demand, behaviour under real conditions or build effort.

Featured Product design
Ionut Lomer - 23 Sep 2026

What a product design phase needs from you before it starts

A product design phase is a four to six week engagement in which a product manager, a UX designer and a software development lead turn a product idea into a scope that can be estimated and built. Those three roles run the work, but several decisions that shape the outcome belong to the client and are made before it starts: who holds decision authority, how much time domain and technical experts can give, how much of the technical role survives price negotiation and what "complete" is taken to mean. This article sets out what each role does, what each needs from the client and how to check readiness before signing.

Featured Product design
Tudor Iordache - 16 Sep 2026

AI coding agents: measuring ROI before you scale them

Gartner's 2026 market guide for enterprise AI coding agents reports that 90% of engineering leaders see improvements from the tools, against a net average productivity gain of 19.3%. A separate Gartner analysis of technology adoption puts the share of software engineering leaders reporting significant ROI from AI across the development lifecycle at 35%. The distance between those figures is not a tooling failure. It is what happens when a team adopts agents without a pre-adoption baseline, budgets for them as seats while vendors move to usage-based pricing and expects acceleration in the parts of delivery that agents do not touch.

Featured AI
Mihai Bâlea - 28 Aug 2026

AI-assisted product design: what your design system must enforce

AI has entered product design at both ends of the process and barely touched the middle. Discovery now runs through AI research synthesis and synthetic participants, generation runs through prompt-to-prototype tools, and the design phase between them is the only part of the sequence that produces output that somebody can be held to. That distinction matters because generated design output becomes usable only where a system enforces constraints rather than documenting them, and enforcement is the part that most teams have not built. This article uses a three-zone model, discovery, design, and generation, to locate where AI-assisted product design breaks down inside a funded team.

UX/UI Design Featured AI Product design
Alex Marciuc - 21 Aug 2026

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.

UX/UI Design Featured Product design
Paula Cristea - 7 Aug 2026

7 steps to validate a product idea before you build

Product validation is the work of gathering evidence that a specific problem is worth solving for a specific audience, before any production code is written. A practical sequence runs through seven steps: define the segment, write the problem hypothesis, run discovery interviews, map existing substitutes, spike the riskiest technical assumption, prototype, then score the evidence. The output is not a feeling about the idea. It is a written argument that can survive a co-founder or an investor asking why.

Idea to MVP Featured Startup
Stefan Sarbu - 3 Aug 2026

How to choose a product design agency in 2026

Choosing a product design agency comes down to three things that are hard to read from a proposal: how the team thinks about problems, who actually does the work and whether the scope matches what the budget can carry. Price ranges are useful for spotting an outlier, but they rarely separate a good fit from a bad one. The more reliable signal is whether an agency asks difficult questions before it shows you screens.

Featured Startup Product design
Alex Marciuc - 30 Jul 2026

Why a product design phase comes before the build budget

A product design phase is four to six weeks of structured work sitting between an approved idea and a committed build budget. It produces a validated prototype, a prioritised feature breakdown with ranged estimates, a technical architecture with integration risk resolved, and a release plan with cost scenarios attached. The output is not a set of design artifacts. It is a narrower cone of uncertainty, and a scope that can be cut without breaking. Teams that skip the phase rarely save the money. They move it into the build, where it gets spent on rework nobody planned for.

Featured Startup Product design
Tudor Iordache - 2 Jul 2026

Six principles for AI systems that work in production

The AI projects that work in production share a small number of structural decisions. None of them is about the model. All of them are about the system around the model: the threshold defined before the build begins, the constraint treated as architecture rather than an obstacle, the surface designed as the actual product, the MVP scoped as the first instance of the platform, the human kept in the loop as a permanent feature rather than a transitional one. The discipline of building AI well is the discipline of getting these decisions right early enough for the model to do its job, and of refusing to defer them until the cost of deferral becomes unavoidable.

Technology Featured AI