Insights

Dive into the dynamic world of Thinslices updates, where tech trends, ideas, expert opinions, and our latest news are shared.

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.

Ilie Ghiciuc - 8 May 2026

Technology Featured AI

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

Ionut Lomer - 29 Apr 2026

Technology Team Featured AI
How regulated companies build AI without third-party APIs

Ilie Ghiciuc - 22 Apr 2026

Featured AI Energytech Architecture
Edge computing vs. cloud computing: how to choose your architecture

Tudor Iordache - 16 Apr 2026

Technology Featured Cloud Architecture
How to build a business case for AI before writing a line of code

Stefan Sarbu - 6 Apr 2026

Featured AI

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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
Ilie Ghiciuc - 19 Feb 2026

Technology due diligence that predicts execution risk

Technology due diligence is often treated as a risk-screening exercise, yet many post-acquisition execution failures stem from issues it fails to surface. Traditional diligence evaluates technology in its current state, but rarely assesses how systems, teams, and decisions behave under pressure. In regulated and fintech environments, this gap shows up quickly through slowed delivery, integration friction, and rising compliance overhead. Architecture, decision-making structure, regulatory design, and knowledge concentration ultimately determine how fast and safely a business can evolve post-close. When technology due diligence is used to inform value creation planning rather than just deal approval, execution risk becomes clearer and early momentum is easier to protect.

Featured Private equity
Iain Cox - 10 Feb 2026

Cyber risk without decision logic: the executive accountability gap

Cyber risk accountability has moved to senior leadership without a corresponding decision logic for prioritisation, risk acceptance, or explanation under uncertainty. In distributed systems, fragmented signals and weak synthesis make it difficult to link technical risk to business impact in a defensible way. Without a safe harbor for informed judgment, outcome bias pushes decisions toward visible mitigation rather than deliberate trade-offs. The result is an accountability gap in which cyber risk is experienced as personal exposure rather than as an organisationally supported decision-making discipline.

Featured Cybersecurity