GenAI has one job: produce a plausible response. Not accurate. Not correct. Plausible. That's the bar the technology is optimised for. Test your knowledge of AI Failure Modes in Development Teams. agilepainrelief.com/quiz/ai-fail...

Ask an LLM why it made a mistake. It doesn't replay an internal trace. There isn't one. It invents a plausible explanation in real time, using the same process that produced the mistake. How many AI Failure Modes can you spot in Development Teams? agilepainrelief.com/quiz/ai-fail...

AI code generators promise speed. The promise leaves out the part where the bill arrives later. Code is cheap to write, expensive to maintain. agilepainrelief.com/blog/the-rea...

Security defects in AI-authored code: ~1.57x the rate of human-authored. Models trained on whatever was on the internet. Vulnerabilities and all. More AI code = more AppSec load. Test your knowledge of AI Failure Modes in Development Teams. agilepainrelief.com/quiz/ai-fail...

Feature Factory: a team that ships features with no discovery, no outcome measurement. Layer GenAI on top and you get a turbocharger with no steering wheel. Way out: outcomes, experiments, kill features that didn't help. agilepainrelief.com/glossary/fea...

Heavy AI-adopting teams: change failure rates +30%. Incidents per PR +23.5%. Speeding up code generation didn't speed up delivery. It just moved the queue downstream into review and incidents. Think you can spot the AI Failure Modes in Development Teams? agilepainrelief.com/quiz/ai-fail...

Cognitive Complexity in agent-assisted repos: +39%. Static analysis warnings rose with it. Code that's harder to read is harder to change. The bill arrives later, not in the sprint AI sped you up. How well do you know the AI Failure Modes in Development Teams? agilepainrelief.com/quiz/ai-fail...

AI agents promised faster delivery. They also delivered +39% Cognitive Complexity and +30% static analysis warnings. Reinforcing loop: more AI code -> complexity -> debt -> velocity drops. Speed gains gone in months. agilepainrelief.com/blog/ai-gene...

Cognitive Complexity in agent-assisted repos: +39%. Static analysis warnings rose with it. Code that's harder to read is harder to change. The bill arrives later, not in the sprint AI sped you up. Test your knowledge of AI Failure Modes for Agile Teams buff.ly/YPOBf7z

AI agents promised faster delivery. They also delivered +39% Cognitive Complexity and +30% static analysis warnings. Reinforcing loop: more AI code -> complexity -> debt -> velocity drops. Speed gains gone in months. agilepainrelief.com/blog/ai-gene...

AI-generated code: ~1.7x more issues per PR than human code. At the 90th percentile, that doubles. Code is cheap to generate. Reviewing and refactoring it isn't. The bottleneck didn't move, it just got buried. Test your knowledge of GenAI Failure Modes in development buff.ly/YPOBf7z

Feature Factory: a team that ships features with no discovery, no outcome measurement. Layer GenAI on top and you get a turbocharger with no steering wheel. Way out: outcomes, experiments, kill features that didn't help.

Heavy AI-adopting teams: change failure rates +30%. Incidents per PR +23.5%. Speeding up code generation didn't speed up delivery. It just moved the queue downstream into review and incidents.

Cognitive Complexity in agent-assisted repos: +39%. Static analysis warnings rose with it. Code that's harder to read is harder to change. The bill arrives later, not in the sprint AI sped you up.

AI agents promised faster delivery. They also delivered +39% Cognitive Complexity and +30% static analysis warnings. Reinforcing loop: more AI code -> complexity -> debt -> velocity drops. Speed gains gone in months.

AI code generators promise speed. The promise leaves out the part where the bill arrives later. Code is cheap to write, expensive to maintain.

Coaches keep promising AI chatbots 'trained' on their writing. Most are just RAG. Works when the question maps cleanly to your docs. Falls apart when it doesn't, and the user can't tell which they got.

#Intuit #QuickBooks continues to disappoint. - I have a passkey and it still expects an additional code via SMS - It insisted I create a new service for a one off invoice and then overwrote my existing entry

AI-generated code: ~1.7x more issues per PR than human code. At the 90th percentile, that doubles. Code is cheap to generate. Reviewing and refactoring it isn't. The bottleneck didn't move, it just got buried.

AI agents promised faster delivery. They also delivered +39% Cognitive Complexity and +30% static analysis warnings. Reinforcing loop: more AI code -> complexity -> debt -> velocity drops. Speed gains gone in months.

Senior devs felt 20% faster using AI on familiar codebases. METR measured them 19% slower. The 39 point gap between feeling and reality is the part nobody puts on the roadmap. 12 questions, 3 minutes.

Smaller teams, end-to-end ownership, fewer management layers, fewer countries. What Agile coaches have urged for years.

Tests built for humans assume the test-taker passes or fails like a human. LLMs break that. A model can define a haiku, then write one that doesn't fit, then misclassify its own output.

GenAI prototypes: built in minutes, not weeks. Game changer? At #ProductCamp Ottawa 2026, an informal poll showed few people test hypotheses with real users before shipping AI code.

Better answer: a startup’s risk profile isn’t a mature company’s. Quality may hold for the first 100-200k lines, not long-term. The flaws are fundamental: they’re in how models are trained. Claude Code leak: print.ts has a 3000-line function, 12 levels of nesting.

With 150GW solar, India achieved its target of having 50% of its total installed electricity capacity from non-fossil fuel sources in June 2025, reaching this milestone 5 years ahead of its original 2030 deadline set under the Paris Agreement www.pv-magazine.com/2026/04/10/i...

And how can we make it easier for people to speak up? Read Speaking Up for some tips. Read more 👉 https://lttr.ai/Ap9Su #DontLetProblemsHIde #voice #DontLetProblemsHide

This heartbreaking footage of Ukrainian tennis player Marta Kostyuk preparing for tournaments amid air raid sirens and missile attacks is a stark reminder that russians shouldn't be allowed to participate in international sporting events until they stop their genocidal war and are brought to justice

"Boroda" ended up trapped under the rubble in a basement - pinned beneath concrete slabs, with a broken arm and leg. The rescue operation lasted 10 days. Throughout that time, his brothers-in-arms cleared the debris by hand under Russian shelling. The injured Defender was saved.

Tiger Woods’ Liver Photographed Crawling Out Of Golfer After Crash https://theonion.com/tiger-woods-liver-photographed-crawling-out-of-golfer-after-crash/

Three takeaways: • GenAI doesn’t understand your prompt, it’s pattern-matching • No memory between requests • More parameters = better mimicry, not intelligence Run it before your next retro. 10 min, index cards, a die.

It’s a reinforcing loop: more AI reliance → less skill practice → less ability to judge output → even more reliance. Junior developers are especially at risk, building confidence in AI before building confidence in themselves.

Brian Graham calls it the Knowledge Cliff: automating away understanding you need. GPS replaced taxi drivers’ mental maps. NASA lost rocket expertise to retirement. In software, every LLM decision erodes skill while giving false confidence.

The antidote? Use AI to validate faster, not ship faster. ScrumMasters: frame Sprint Goals as “learn whether X solves problem Y.” Product Owners: run three small experiments in the time it used to take to build one feature.

AI agents promised faster delivery. They also delivered 39% more Cognitive Complexity and 30% more static analysis warnings. Research found a reinforcing cycle: AI generates more code → complexity rises → debt accumulates → velocity drops → teams write even more AI code to compensate.

GenAI is making it easier to switch tool providers. I’m frustrated with our email platform: most features are GUI-only, no API access. I’ve written code to extract our entire newsletter archive + stats. When the time comes, exporting will be painless.

AI models are trained to bluff. Pass/fail training offers no reward for admitting uncertainty. Instead of "I don't know," you get a confident wrong answer. Researchers call it "test-taking mode." #AIinSoftwareDevelopment #BuildInPublic

Multiply that across a team. Six people, ten AI-assisted tasks each per week. Even a small error rate means several unnoticed mistakes weekly. The real cost? Catching errors requires exactly what we're short on: domain expertise, critical thinking, and time.

Most of the analysis was right. Mistakes were easy to spot. But compressing a week into a day doesn't reduce cognitive load; it might double it. I'm far more exhausted than after a normal workday.

Also discovered the SEO structure I paid $$$ for is now obsolete. Used Claude Code to restructure internal links across our Astro site. First batch done in an hour. Manually? Days. It even caught broken redirects.

Apple is using the Wizard of Oz MVP to test how we expect to interact with AI Agents that work on our behalf. I never thought I'd see this from Apple. machinelearning.apple.com/research/map...

Last Minute seat just became available in next week’s Certified ScrumMaster (CSM) Workshop: buff.ly/pcAmkUf #ExperientialLearning

Speed is not an advantage. With GenAI, Quality and Fit for Purpose will differentiate.

The emotional attachment some people have to GenAI amazes me. Rather than reading the research on its flaws, they say it's dated or "works on my machine." I'm not saying stop using the tools—just understand their limitations.

Jamie Twiss found newer models silently remove safety checks rather than fail visibly. The result: code that looks right but breaks in production at 2am. The tool matters less than how you use it.

"Reasoning" models don't reason. They rely on statistical pattern matching—replicating patterns from training data. That's not logical deduction. More effective than models without it? Sure. But it's still a stochastic parrot.

"Two hours of Sprint Planning. Zero confidence we can deliver." Sound familiar? The problem isn’t Planning—it’s what happened before you walked in.

AI-generated code has 1.7x more issues, 30-41% more tech debt, 39% more cognitive complexity. Some teams think TDD is the fix. They're solving the wrong problem.

Renaming your teams "Squads" didn't help, did it? The Spotify Model was a snapshot of one company's culture — autonomy, psychological safety, continuous improvement. Most adoptions skip to the labels and ignore the hard work. Structure without culture is just a reorg with fancier names.

Anthropic claims orgs are building everything with Claude Code at quality. If it works so well, why not share the recipe? The data elsewhere: - 1.7x more correctness issues - 1.64x worse maintainability - 1.57x more security issues - Only win? Spelling.