Batting Averages and Slugging %s
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This document discusses alternatives to using team velocity as a metric for software development teams. It proposes measuring the frequency of delivering value to users through high value releases, prioritizing work that provides the best value for effort, and recognizing individuals who consistently deliver high value features. Specific metrics proposed include an L value batting average measuring weeks with high value releases, slugging percentage measuring value delivered, and a silver slugger award for top performers.
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Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
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With the cost of using Generative AI services dropping exponentially and the array of available models continually expanding, integrating AI into FME workflows has become inexpensive, accessible and effective. This presentation explores how GenAI within FME can cost-effectively transform data workflows by automating data extraction, validation, classification and augmentation tasks. Well discuss how FMEs no-code flexibility enables users to combine Generative AI and Computer Vision tools that create efficient workflows tailored to specific challenges. Using recent practical examples, well demonstrate how these integrations can simplify complex tasks, save time and enhance data quality.Quantum AI: Where Impossible Becomes Probable
Quantum AI: Where Impossible Becomes ProbableSaikat Basu
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Imagine combining the "brains" of Artificial Intelligence (AI) with the "super muscles" of Quantum Computing. That's Quantum AI!
It's a new field that uses the mind-bending rules of quantum physics to make AI even more powerful.Coordinated Disclosure for ML - What's Different and What's the Same.pdf
Coordinated Disclosure for ML - What's Different and What's the Same.pdfPriyanka Aash
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Coordinated Disclosure for ML - What's Different and What's the SameMPU+: A Transformative Solution for Next-Gen AI at the Edge, a Presentation...
MPU+: A Transformative Solution for Next-Gen AI at the Edge, a Presentation...Edge AI and Vision Alliance
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/mpu-a-transformative-solution-for-next-gen-ai-at-the-edge-a-presentation-from-fotonation/
Petronel Bigioi, CEO of FotoNation, presents the MPU+: A Transformative Solution for Next-Gen AI at the Edge tutorial at the May 2025 Embedded Vision Summit.
In this talk, Bigioi introduces MPU+, a novel programmable, customizable low-power platform for real-time, localized intelligence at the edge. The platform includes an AI-augmented image signal processor that enables leading image and video quality.
In addition, it integrates ultra-low-power object and motion detection capabilities to enable always-on computer vision. A programmable neural processor provides flexibility to efficiently implement new neural networks. And additional specialized engines facilitate image stabilization and audio enhancements.AI Agents and FME: A How-to Guide on Generating Synthetic Metadata
AI Agents and FME: A How-to Guide on Generating Synthetic MetadataSafe Software
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In the world of AI agents, semantics is king. Good metadata is thus essential in an organization's AI readiness checklist. But how do we keep up with the massive influx of new data? In this talk we go over the tips and tricks in generating synthetic metadata for the consumption of human users and AI agents alike.Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdf
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdfPriyanka Aash
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Cracking the Code - Unveiling Synergies Between Open Source Security and AIYou are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
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MPU+: A Transformative Solution for Next-Gen AI at the Edge, a Presentation...
MPU+: A Transformative Solution for Next-Gen AI at the Edge, a Presentation...Edge AI and Vision Alliance
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Batting Averages and Slugging %s
- 1. Batting Averages and Slugging %s Matt Story // VP Engineering Handshake
- 2. Why I Dont Care About Team Velocity And neither should you.
- 3. Why Process Infrastructure? Were usually optimizing for up to 3 things
- 5. Predictability Consistency of time to value
- 6. Sustainability Value over the long haul
- 8. 0 25 50 75 100 Velocity E鍖ort Actual Team E鍖ort Sprint-over-Sprint
- 9. It Rewards Padding Which isnt really predictability
- 10. And its a bad predictor When you have historical data 50% 75% 98%
- 11. E鍖ort <> Value Unfortunately e鍖ort is not that important
- 12. L Value Batting Average Weeks with a L Value Delivery / Weeks
- 13. It Rewards Iteration More L Value Releases the Better
- 14. It Rewards Value/E鍖ort Arbitrage L Value / S E鍖ort is best
- 15. Informing Prioritization Decisions Stack ranking for Value / E鍖ort
- 16. And Producing Results In 鍖rst quarter using stack, against .357 the prior quarter .769 10 weeks with launches / 14 weeks
- 17. But it Over-values Home Runs ... wrong town for this slide*
- 18. But Over-values Home Runs Much better.
- 19. XL Value Eq Batting Average Weeks with the equivalent of an XL Value Delivery / Weeks
- 20. Slugging % L Value Equivalents / Weeks
- 21. Slugging % In 鍖rst quarter using stack, compared to .446 quarter prior. 1.654
- 22. Silver Slugger Award Given every month to engineer with highest Slugging %
- 23. Not Counting Weeks on Support Which are like sacri鍖ce plays