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Explore Agile, ways of working and practical AI for teams. Find recent articles from six sources, then follow an idea back to its author.

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Simon Willison AI for Teams

Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war

Yesterday was Grok 4.7 (pelicans) and MiMo v2.6 Flash/Pro (more pelicans). Today Anthropic released Claude Opus 5.5, and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna. It's going to take a while to get a good read on all of these new models, but here are my impressions so far. GPT-6 Sol and Luna are half the price of their GPT-5.6 equivalents GPT-5.6 Luna was already my favorite model for building applications against, because it combined excellent performance with being really cheap. Somehow GPT-6 Luna is half the price of that again - and GPT-6 Sol had a similar reduction com

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Scrum.org Agile By Steven Deneir

Your Team Isn't Slow. Your Handoffs Are.

Image Watch where the work actually spends its time. Not the hours someone is heads-down building. The days it sits, waiting. Waiting for the one person who knows that system. Waiting for the other team to finish their sprint. Waiting for a review, a sign-off, an environment, an answer from someone who's out until Thursday. Add up the building time and it's small. Add up the waiting and it's most of the calendar. That's the thing about a slow team. Usually it isn't slow. The people are working hard and well. The work is just passing hand to hand, stalling a little in every gap between one spec

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Simon Willison AI for Teams

Jev introduces a new shape of LLM - System One, aka Decision Models

Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores. TypeSafe describe Jev like this: Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. It's also v

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Scrum.org Agile By Yuval Yeret

Finding AI Gold With Lean Startup Techniques

Originally published on yuvalyeret.com.How do we deliver valuable real-world AI impact through solutions that solve problems, using some of what we've learned by building products over the last couple of decades?We're not seeing the value yetUnless you've been in a cave, you're aware that there's a gold rush happening out there. Certain companies are definitely making money building AI hardware and solutions, and a lot of people are heading out to mine for AI gold.A lot of the conversations inside organizations feel like they're more about the technology and the solutions than about what we ac

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Scrum.org Agile By Chuck Suscheck

Cognitive Trap: Planning Certainty

Planning Certainty TrapMost mistakes in Scrum aren’t because people don’t understand the framework—they come from applying reasonable thinking in the wrong context. Cognitive traps happen when decisions favor efficiency, control, or comfort over transparency, inspection, and adaptation. Here is one of ten cognitive traps, Planning Certainty. Over-investing in upfront certainty delays learning and reduces the ability to respond to change.Simulated Assessment QuestionA Scrum Team spends significant time refining Product Backlog Items to ensure they are fully understood before starting work. As a

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Scrum.org Agile By Sanjay Saini

Does Your AI Know the Scrum Guide? Twelve Questions to Find Out

Image More and more participants in my Professional Scrum Master classes arrive with Scrum "rules" they picked up from an AI chatbot. Some of those rules are accurate. Some stopped being true in November 2020. And a few were never part of Scrum at all.The trouble is that from the outside, the correct answers and the incorrect ones look the same. They are equally fluent, equally confident and equally well formatted. An AI that knows Scrum is useful. An AI that only sounds as if it knows Scrum is risky, especially when a Scrum Master repeats its answer to a Scrum Team or a stakeholder.This artic

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Scrum.org Agile By Stefan Wolpers

AI on Top of a Dysfunctional System (1): The Product Backlog

TL;DR: Polished Artifacts, Unchanged Decisions, Or Ten Backlog Anti-Patterns AI Makes WorseAdd AI to a Product Backlog process that already struggles, and everything seems to improve within an afternoon. The problem is that polishing artifacts with AI doesn’t fix the root cause: the basis for the team’s decisions doesn’t change; AI only removes the visible discomfort that used to signal something was broken, along with some of the pressure to fix it. AI applied to a dysfunctional system – here, the Product Backlog process – makes the dysfunction look like progress.This is the first article of

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Scrum.org Agile By Ralph Jocham

Scrum's Meetings Are Capped at Five Hours a Week. Something Else Filled Your Calendar.

Image Scrum has too many meetings. I have heard that sentence in training rooms since 2010 and for years I argued with it. Now I do the arithmetic on the whiteboard instead, because the Scrum Guide caps its own events and the cap is far below what anyone in the room guesses.Add up the limits as written. Sprint Planning is "timeboxed to a maximum of eight hours for a one-month Sprint," with four hours for the Sprint Review and three for the Retrospective. A Daily Scrum is "a 15-minute event," and it needs no slot of its own on the day Sprint Planning already has the team in a room. Nineteen of

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Scrum.org Agile By Steven Deneir

Psychological Safety is not Impunity

You can find him in the retro, though he rarely says much. He doesn't need to. The team has learned to write around him. Watch what happens to a good idea in this room. Someone proposes a real improvement, and before it's even finished, you can see a couple of people glance, just slightly, toward the corner where he sits. The idea gets softened. Then shelved "for now," because everyone already knows he'll sink it, loudly and personally and for the rest of the afternoon, and nobody has the energy for that today. He has never been given a scrap of authority. He runs the team completely. Every ag

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Martin Fowler Agile By Martin Fowler

I don't like LLMs

I have a lot of mixed feelings about AI and LLM technology. I’m fascinated by its effect on our profession, excited by the potential gains in productivity - and thus the products we could rapidly build. On the other hand, I’m fearful of the damage AI might cause: agent swarms taking over our virtual and physical infrastructure, designing bio weapons. But, back on my first hand, LLMs might also design miracle cures, and come up with clever ways to raise our prosperity. Fundamentally I don’t think we have a choice about riding on the AI technology train. It’s a wild ride and I just hope we’ll ge

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