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Tudor Iordache - 12 May 2026

What Gartner's 2026 tech trends mean for product teams, not CIOs

Of Gartner's ten 2026 technology trends, four matter disproportionately for product builders: AI-native development, multiagent systems, domain-specific language models, and digital trust. AI-assisted development works only when grounded in structured context, not clever prompts. Multiagent systems are already in 80% of enterprise apps shipped in Q1 2026, yet 88% of agent pilots never reach production, because the bottleneck is product design, not model quality. Domain-specific models outperform general ones for targeted use cases, but only when a pre-development business case has set accuracy and cost thresholds.

Technology Trending Featured
Ilie Ghiciuc - 8 May 2026

AI document extraction accuracy: why fine-tuning alone is not enough

Document extraction accuracy is not a single problem but a sequence of failure modes resolved in order. Fine-tuning an open-weight visual-language model closes most of the gap from a general-purpose baseline, but rarely the gap that matters: the one between early performance and the threshold a business case requires. Closing that distance is a separate engineering effort, and the techniques that get there compound on each other rather than substitute for each other.

Technology Featured AI
Ionut Lomer - 29 Apr 2026

Claude is not a chatbot: how to use it on live product builds

Most teams have adopted AI assistants in some form by now. Few have built systems around them. A single conversation with Claude is transactional. You type a question, you get an answer, and the session ends. That has real value, but it scales like a calculator: only as fast as you type. A system built with Claude is different. Context is loaded before the work starts. The model knows the project methodology, the team constraints, and the deliverable format before a single instruction is given. Output is consistent across sessions. The re-briefing tax disappears.

Technology Team Featured AI
Ilie Ghiciuc - 22 Apr 2026

How regulated companies build AI without third-party APIs

At some point in almost every AI project we work on with a regulated client, someone on the team says some version of the same thing: "We can't send that data outside." It usually lands like a problem. The obvious implementation path, connect your data to a capable hosted model, get results back, iterate, suddenly has a wall across it. SOC 2 compliance, data residency requirements, security perimeters built around credentials and sensitive client information: any of these can make the default approach to AI architecture a compliance violation rather than a technical decision.

Featured AI Energytech Architecture
Tudor Iordache - 16 Apr 2026

Edge computing vs. cloud computing: how to choose your architecture

The edge vs. cloud decision is a workload classification problem, not a technology preference. Five factors determine placement: latency tolerance, data volume, compliance requirements, resilience needs and infrastructure cost. Misallocation in either direction carries measurable consequences, defaulting to cloud accumulates latency debt and egress costs at scale, while premature edge investment introduces operational complexity before the business case is established. Organisational readiness is an independent variable that shapes when edge adoption is viable, separate from whether it is technically warranted.

Technology Featured Cloud Architecture
Stefan Sarbu - 6 Apr 2026

How to build a business case for AI before writing a line of code

A pre-development business case for an AI initiative functions as a technical constraint document: the cost of what is displaced, the accuracy threshold required to sustain that displacement, and the maximum allowable cost per unit of output together determine architectural decisions before any model is evaluated. Without these constraints defined in advance, accuracy targets become intuitive, infrastructure choices become arbitrary, and the decision to move to production becomes a matter of engineering preference rather than measurable evidence. The sequencing discipline, business case before technical evaluation, is what makes success criteria testable and completion definable.

Featured AI
Lucian Vasiliu - 24 Mar 2026

Lessons on designing an AI software development workflow

The experiment shows that AI-assisted development becomes reliable only when grounded in structured documentation rather than exploratory prompting. Early output appeared productive but lacked coherence, revealing that incomplete context leads to fragile systems and inefficient iteration. By shifting to short cycles where documentation, constraints and specifications are continuously refined, teams gain more predictable implementation outcomes. Over time, the workflow evolves into a controlled system where AI operates within clearly defined boundaries, reinforcing the role of product reasoning and engineering discipline in shaping results.

Team Featured AI
Stefan Sarbu - 12 Mar 2026

Pivot or persevere? How to make startup decisions that drive success

Every successful founder faces this crossroads: your idea isn’t gaining traction, your metrics are flat, and the market feels like it’s slipping through your fingers. Do you keep pushing forward, or is it time to pivot? The difference between startups that thrive and those that fail often comes down to mastering this decision. Let’s break it down so you don’t have to second-guess your next move.

Featured Startup
Tudor Iordache - 9 Mar 2026

Best practices for embedding AI in SaaS Platforms

Embedding AI in a SaaS platform is primarily a product and systems challenge rather than a modeling exercise. Effective implementations start by identifying specific workflow problems, validating AI capabilities through controlled experiments, and only then integrating them into platform architecture designed to support evolving models, data dependencies, and operational constraints. Reliable outcomes depend on structured data foundations, transparent human oversight in critical workflows, and clear ownership for monitoring and iteration after release. When these conditions are in place, AI capabilities can evolve as part of the product rather than remaining isolated experiments.

Featured AI SaaS
Razvan Cozma - 25 Feb 2026

‘We can’t touch the Core’: the sentence that’s slowing telecom down

In telecom legacy systems, treating the core as untouchable shifts innovation to surrounding layers, where incremental integrations, duplicated logic and reactive extensions compound into architectural sprawl. That sprawl increases IT cost, slows product velocity and introduces operational drag not because of inadequate investment, but because change propagates across fragmented system boundaries. Telecom modernization becomes effective only when leaders deliberately redefine what the core should own, clarify system boundaries and treat integration as a first-class capability. The central decision is whether the current telecom IT architecture makes change progressively safer and more predictable, or progressively heavier and more fragile.

Featured Corporate innovation Telecom