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AI Features for Jira Data Center – No Atlassian Cloud Required

Imagine this: after every customer meeting, structured Jira issues are created automatically. You just paste your notes into an AI, and it does the rest. Atlassian already offers that kind of magic in Jira Cloud: natural-language search, automatic summaries, and issue creation from unstructured text. But not everyone wants to move to the cloud, and many teams plan to keep using Jira Data Center through 2029. In this article, we show how to get many of the same benefits on-premises with Jira Server and your own AI stack: GDPR-compliant, resilient to Cloud Act exposure, and without data leaving your environment.

Where AI Helps (and Hurts) Across Different Coding Scenarios

Where does LLM-assisted software development improve developer productivity—and where does it fall short? Instead of treating AI in software development as a one-dimensional productivity booster, we explore the question across several dimensions suggested by a Stanford-adjacent study: project maturity, task complexity, and the popularity of the programming language. The goal is to create a more realistic expectation baseline for both software developers and leaders—well away from today’s hype.

Spec-Driven Architecture: When Agents Build, Architecture Must Speak

SDD describes how a system is built. SDA describes how systems fit together

REST Beats MCP

Instead of using dedicated APIs, agents can operate existing web applications directly. Like humans, they rely on the most consistent implementation of hypermedia and benefit from existing context, validation, and access controls. Using an expense report as an example, I show how agents can automate complex, context-dependent tasks—without having to implement new APIs.

Spec-Driven Development is Domain-Driven Design’s Impatient Cousin

Spec-driven development tools like BMAD promise to fix the requirements bottleneck that agentic development teams face. But is it actually a good fit for your organisation?

The right size of a Data Product

Autonomous Data Product Heuristics

Agents good in the end?

Software development consists of a constant chain of trade-offs. As long as I’ve been building systems, there have always been things you don’t do despite wanting to do them. But now, with agents, we can fulfill all our dreams and finally build everything we always wanted and had the feeling we were missing. Spoiler alert: we shouldn’t do that.

Understanding AI Coding Patterns Through Cognitive Load Theory

AI coding assistants either severely harm learning or enhance it beyond manual coding—depending entirely on interaction patterns. A recent study reveals six distinct patterns with dramatically different outcomes. We’ll examine why through cognitive psychology and explore the study’s limitations.

Neuland reloaded

I sandboxed my coding agents. Now I control their network.

I want my AI coding agents to work independently, but I don’t want them to have unrestricted access to the internet. In this post, I describe how I routed all network traffic from my development sandbox through a strict proxy allowlist, allowing only a small set of explicitly approved domains. This setup finally gave me enough confidence to loosen the guardrails without constantly staying in the loop.

From Vibe Coder to Code Owner

AI agents generate thousands of lines of code in no time. If you want to fully leverage their potential, you can no longer review every single line – but you’re still responsible for the software. How do you take ownership of code you haven’t fully read? The answer lies in the agent harness: a system of deterministic checks, AI reviews, and targeted human review that enforces quality instead of hoping for it.

Your Database Table is an awful API

Is database integration still a thing?

Let’s Not Normalize Insecure AI Assistants

AI assistants like OpenClaw promise convenience, autonomy, and increasingly personalized help. But beneath that promise lies an architecture that quietly combines private data, internet access, and exposure to untrusted content—the lethal trifecta of security risk. Sandboxing and physical isolation help, but they don’t address the core problem. As we add more capabilities, the potential blast radius only grows. This post is a case for slowing down, questioning defaults, and refusing to normalize insecure architectures.

What to Do While the AI Is Thinking

AI can write code while we wait — but that doesn’t mean we suddenly have free time. Whether we should think, multitask, review, or rest depends on what kind of work we are doing.

AI and Elaboration: Which Coding Patterns Build Understanding?

Developing with AI through the cognitive Lens

I sandboxed my coding agents. You should too.

LLM coding agents are extremely powerful because they can run programs on our computers using our permissions. However, this same power also makes us very vulnerable. It only takes one mistake or one prompt injection to compromise the whole system.

Leave It Better Than You Found It

Leave your code better than you found it. This advice, also known as the Scout Rule, sounds so simple, but what does it look like in practice? This is the story of how I followed the advice and cleaned up a fat controller along the way, and how this unexpectedly enabled us to handle new requirements quickly and with minimal effort. It’s a lesson in habits and continuous architecture.

Why not scatter @Transactional everywhere?

From FOMO to Focus

AI is on every agenda – but where do you start? Many companies launch multiple AI initiatives in parallel, driven by the fear of missing out. The result: scattered resources, lack of prioritization, and unclear business impact. AI Opportunity Mapping provides a solution: it systematically guides you from vague AI visions to concrete, prioritized use cases with real business value. In five steps, teams develop AI opportunities in a structured way and make well-founded decisions. The result: focus instead of FOMO, clarity instead of chaos.

Speed vs. Skill

Every AI coding tool promises the same thing: unprecedented speed, effortless productivity, freedom from tedious work. The pitch is compelling. But what if feeling more productive and being more capable aren’t the same thing? Research shows that automation makes us feel more productive while eroding our skills. Let’s examine this tension through the lens of cognitive psychology.

Context Engineering: Managing AI-Generated Code Complexity

Keeping your scope small was always a good idea

Swiss Army Knife for Salesforce: LLM with In-Memory Database

Large Language Models (LLMs) struggle with transforming large datasets, particularly when performing aggregations that require calculations - essentially anything where you’d normally use GROUP BY or ORDER BY in SQL. But what if we need to retrieve extensive data from a system and process it with an LLM, but only have an API with limited functionality available? In this blog post, I present a solution I’ve implemented and tested.

Git Clarity: Building Meaningful Commits and Linear History

Feeling overwhelmed by Git’s complexity? Discover a streamlined workflow that centers on creating single, comprehensible commits. Learn how this approach leads to clearer code reviews, fewer merge conflicts, and a beautiful linear commit history that tells the story of your code. This isn’t just about Git commands—it’s about aligning your tools with how your brain works.

Managing Geopolitical Risks with Enterprise Architecture

Achieving Digital Sovereignty with Standard Software

In the context of IT system landscapes, digital sovereignty is often equated with autarky— complete independence from third parties. By that logic, , a company developing and operating all applications in-house would be the ultimate example of digital sovereignty. But does this idea really hold up?