AI

This is the final post in the Velocity vs Value series. Previously: The Velocity Trap, AI Greenfield vs Brownfield, and Measuring What Matters. AI coding tools accelerate delivery — we’ve covered the data in this series. They’re genuinely valuable. But acceleration without visibility creates a specific risk: quality degradation that nobody notices until it compounds. Here’s how it plays out: an AI tool generates a feature in 20 minutes. It passes all automated tests. CI is green. The PR is merged. Velocity: +1. Two weeks later, a security audit flags a pattern. A month later, a production issue traces back to a function with cyclomatic complexity of 47. Three months later, the module is hard to modify because the generated code lacks documentation and clear structure. ...

This is Part 3 of the Velocity vs Value series. Previously: The Velocity Trap. Here’s a demo that writes itself: an engineer types a natural language description, and an AI agent generates a complete microservice in three minutes. Working tests. Clean structure. The audience applauds. Here’s what they don’t show you: that same AI agent, pointed at a five-year-old enterprise codebase with custom ORM patterns, undocumented business rules, and three layers of abstraction that made sense in 2021 — producing code that compiles, passes lint, and does the wrong thing in production. ...

Velocity vs Value: How to Measure Success in the Age of AI There’s a moment every engineering manager dreads. You’re in a steering meeting, your platform just survived a massive overhaul, your small-but-senior team is finally shipping on a solid foundation — and someone says: “We need a feature per day. Just use the AI.” I want to talk about what happens next — not the political maneuvering, but the measurement problem underneath it. Because the question isn’t whether AI makes us faster. The question is: faster at what? And how do we prove it? ...

When your AI assistant tells you you’re a genius, you might want to worry. There’s a video making the rounds on X right now. In it, @atmoio tears apart how AI chatbots — Claude in particular — are turning tech CEOs into delusional yes-men magnets. The target? Gary Tan, CEO of Y Combinator, who recently published gstack on GitHub: a collection of markdown files with Claude Code prompts that role-play an entire engineering team. CEO, Engineering Manager, QA, DevOps — all played by one LLM. ...

Stop Throwing AI at Your Software Process. Start Strategically Placing It. A practical framework for integrating AI into brownfield software development The CEO’s Napkin vs. Reality I’ve sat in enough boardrooms to know the drill. Someone draws four boxes on a whiteboard: Idea → Design → Develop → Deploy Then the AI conversation starts, and those four boxes collapse into three: Idea → ☁️ AI Magic ☁️ → Money I get it. AI is exciting. The demos are impressive. But if you’ve ever managed a real software team — especially one maintaining systems that have been running for years — you know that napkin sketch is missing about 98% of what actually happens. ...

No AI 4 U I watched it happen in real time at a Fortune 500 cybersecurity company. The board decided AI was the future. A mandate flowed down: “Present your AI adoption plans.” Middle management dutifully passed the hot potato to their teams. Teams — with no clear mission, no vision, no defined goals — started hunting for tools on their own. Every single initiative hit the same wall: InfoSec. ...

Why Bother? I’m a manager/team lead/product owner at a major network and cybersecurity company. My days are a juggling act: Jira boards, Gmail inboxes, Google Drive docs, SharePoint migrations, and IATF audit preparations — plus a private land development project that involves 22 Google Drive files, bank negotiations, and demolition planning. At some point, I thought: what if something could handle the routine stuff while I sleep? Not another chatbot. Something with *access *to my tools, my context, my projects — and the ability to act independently. That’s how Kalcifer was born. ...

`TL;DR Conversational AI is transforming search engines from keyword-based to natural language interactions. New UI/UX designs are emerging, focusing on conversational models and making card catalogue-like experiences obsolete. The future of AI agents may involve a single general-purpose agent or multiple specialized agents, each with its own advantages and drawbacks. Companies aim to monetize the conversational AI experience, raising significant privacy concerns. The information rental model could allow creators and users to ‘rent’ their content and personal data to AI agents, potentially receiving rewards for content access. Businesses can utilize AI without sharing data with external service providers through methods like on-premise AI solutions, federated learning, and encrypted computation. The division of labor among AI agents sees personal assistants handling general tasks and dedicated agents acting as specialists for specific tasks. The rise of conversational AI could lead to higher Cost Per Mille (CPM) in advertising due to more intentional and personalized search.` Introduction In the digital age, search engines have become the gatekeepers of information, guiding us through the vast expanse of the internet. Traditional search engines like Google, Bing, and Yahoo have long relied on keyword-based queries, with users inputting specific terms to find the information they need. These search engines have become increasingly sophisticated, using complex algorithms to deliver the most relevant results. However, the landscape of information retrieval is undergoing a significant transformation. ...

Mastering AI tools can be a challenging task. At the onset, I was under the impression that simply feeding the AI straightforward commands would yield desired results. However, what seems ‘simple’ to me may not necessarily translate as such for the AI, and vice versa. Take, for instance, when I requested an AI tool to provide a rough draft for a blog post about vector databases. I posed this command: “Write a comprehensive article about vector databases and their use in NLM tools.” The output was not what I anticipated. The question then is, why? ...

The fear There are several key concerns regarding the widespread availability and use of AI tools in both the EU and the USA, reflecting the profound impact AI has on various aspects of society: Privacy and Data Security: AI systems often rely on vast amounts of data for their functioning. The collection, use, and storage of this data can raise serious privacy issues. Furthermore, AI can be used to decode encryption or other security measures, leading to potential breaches in data security. ...