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AI Learning Over Time • Cohort-Based

Cohorts and Workshops

These offerings are designed for groups who want to build practical AI capability together over time—using a repeatable, outcomes-focused approach. Explore the options below, then visit each class page for the full details.

  • Team Activation — align on goals, tools, and guardrails.
  • AI Audit — assess readiness, risks, and highest-value use cases.
  • AI + Scrum Cohorts — build habits across roles with hands-on practice.
  • AI for Scrum Teams — practical, role-based workflows your team can adopt.
Tip: If you’re not sure where to start, choose AI Audit first—then map a cohort plan from the findings.

Ready to start?

Pick your next step—start with free learning, watch the videos, or browse the full course catalog.

Prefer Virtual or On-Site delivery for your team? See Corporate Training Offerings.

Search Results

16 May 2025

Author: Rod Claar
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4 Sep 2025

Critical AI Development - Meta Achieves Recursive Self-Improvement

Critical AI Development - Meta Achieves Recursive Self-Improvement

The Breakthrough Meta announced on July 30th, 2025, that their AI systems have achieved true recursive self-improvement - what researchers call a "guttle machine." This isn't incremental optimization but rather AI that can access and rewrite its own code, making mathematically proven improvements to its own performance.

Author: Rod Claar
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13 Oct 2025

Keeping Up With AI is a Struggle — But You Don’t Have To Do It Alone

Keeping Up With AI is a Struggle — Summary

Feeling overwhelmed by AI? You’re not alone. New models and “breakthroughs” land daily, but the goal isn’t to chase every tool—it’s to integrate the right ones, intentionally and sustainably, into your existing Scrum workflow.

The Real Struggle

  • Teams: Pressure to “use AI” without a starting point.
  • Leaders: Noise, uncertainty, and fear of bad investments.
  • Scrum Masters: How to add AI without breaking Agile principles.

From Struggle to Strategy

AI-Enhanced Scrum connects AI directly to Scrum practices so you can:

  • Improve backlog refinement and sprint planning, reducing ceremony fatigue.
  • Turn vague requests into crisp user stories with acceptance criteria in seconds.
  • Automate repeatables (test cases, retro summaries) and free time for creativity.
  • Measure real ROI in hours saved and increased velocity.

Results reported: ~2× faster planning and 30–40% improvement in sprint throughput using these techniques.

AI Won’t Replace Scrum—It Amplifies It

Use AI to spot blockers early, surface patterns across months of work, and accelerate requirements discovery—while keeping human judgement at the center.

Join the Live Program

AI-Enhanced Scrum: Transforming Agile Development with AI
November 5–7, 2025 — Virtual, Live

Register now

Final Thought

Keeping up with AI isn’t about running faster—it’s about running smarter. Scrum teaches us to inspect and adapt; AI gives us sharper lenses to do it. Stop chasing the future and start building it—one sprint at a time.

Author: Rod Claar
0 Comments

28 Apr 2026

Rob Pike's 5 Rules — What They Mean for AI and Agents

Rob Pike's 5 Rules — What They Mean for AI and Agents

Rob Pike's 5 Rules — What They Mean for AI and Agents

Rob Pike wrote five rules for writing clean C code in 1989. They hold up surprisingly well today — especially now that AI tools and autonomous agents are showing up in our Sprints, our pipelines, and our backlogs.

Rule 1: Bottlenecks are never where you think. Before your team celebrates an AI cutting story-writing time in half, check your cycle time data. The real delay is usually in review, refinement, or deployment — not the thing you just automated.

Rule 2: Measure before you tune. Don't add AI everywhere at once. Run a few controlled Sprints, look at velocity and defect rates, then decide. Your Retrospective already gives you the structure to do this.

Rule 3: Fancy is slow when n is small. Large language models are expensive and complex. A simple query or regex handles a lot of small tasks faster and cheaper. AI earns its keep on genuinely large, messy problems — not ten-line standup summaries.

Rule 4: Fancy algorithms are buggier. AI-generated code looks polished and can still be wrong. TDD and ATDD are your safety net. Write the test first, let the AI write the code, and let the test decide if it worked.

Rule 5: Data dominates. Clean up your backlog before you trust AI to read it. Well-written user stories and consistent acceptance criteria produce better AI output. No model compensates for messy data.

The bottom line: Pike's rules and the Scrum framework are pointing at the same thing — measure, keep it simple, test rigorously, and treat your data as the foundation everything else rests on.

Author: Rod Claar
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