AI testing
# Bridging AI Evaluation with Real-World Business Impact *Magdalena Picariello reframes AI conversations around measurable business value, iterative development, and feedback-driven optimization.* --- ## Introduction Magdalena shifts focus from **algorithms and metrics** to **tangible business outcomes**, advocating for evaluation systems that go beyond accuracy. Her approach: - **Continuous feedback loops** - **Iterative
Ray
Scaling AI Workloads on TPUs with Ray Engineering teams are increasingly using Ray to scale AI workloads across diverse hardware, including GPUs and Cloud TPUs. While Ray provides a robust scaling framework, developers have traditionally needed to address the unique architectural and programming models of TPUs — including their distinct networking
Apple AI
Pico-Banana-400K: High-Quality Dataset for Text-Guided Image Editing Pico-Banana-400K is a curated dataset of 400,000 images developed by Apple researchers to advance text-guided image editing models. The dataset was generated through the following pipeline: * Source Images: Real photographs from the Open Images collection. * Image Editing: Google's Nano-Banana applied
decentralized architecture
Key Takeaways * Teams can make any decision after seeking advice from affected parties and documenting it in an Architectural Decision Record (ADR). * Context maps ensure clear team ownership of system areas. * Use architectural principles, immutable ADRs, and weekly advisory forums to maintain alignment. * Teams evolve through four phases: disbelief → excitement
sequence parallelism
00 — Preface Long-Sequence Training Challenges Training ultra-long sequences is a critical aspect of large model development. In real-world inference — especially within Agent pipelines — a model’s generalization ability to handle long contexts directly impacts its reliability. However, long-sequence scenarios place heavy demands on training resources due to the O(N²
incident postmortems
# Don’t Let Incident Postmortems Become Mere Formalities ## Use AI to Extract Value from Every “Fall”   This article examines why **incident postmortems** often drift into superficial
SRE
# Successful Public Speaking and Site Reliability Insights *By David Blank-Edelman — SRE Academy Program Lead, Microsoft* --- ## Introduction I’m David Blank-Edelman, and a big part of my work involves **helping people with public speaking**. To kick things off, I’d like to share a selection from my *Top 10 Tips
Perplexity AI
## Disclaimer The details in this post are based on information publicly shared by the **Perplexity Engineering Team**, **Vespa Engineering Team**, **AWS**, and **NVIDIA**. All technical credit belongs to the respective teams. Links to original articles and sources are provided in the **References** section at the end. We have added our
AI coding
# Six Months, 300,000 Lines Refactored — Lessons from Claude Code > Six months of real-world project work. > 300,000 lines of code rewritten. > It sounds like fiction — but for **JokeGold5455**, it was reality. --- ## Overview Developer **JokeGold5455** single‑handedly undertook the refactoring of a complex TypeScript microservices project.
Docusaurus
# Docusaurus 3.9 Release — AI Search, Runtime Upgrades, and i18n Enhancements **Date:** 2025-11-03 13:31 Beijing Docusaurus is a **React-based static site generator** maintained by Meta’s open-source team. The **3.9 release** brings major runtimes, search, and localization improvements — all while maintaining backward compatibility. 
Robotaxi
Robotaxi Track Welcomes New L2 Upgrade Route Player Yuanrong Qixing aims to launch its Robotaxi business based on consumer-grade mass-produced cars by the end of this year. --- What Are Consumer-Grade Mass-Produced Robotaxis? A consumer-grade mass-produced car in this context means: * No additional customized sensors or dedicated hardware * Vehicles are
AI strategy
Z Potentials — 2025-11-03 11:58 Beijing > “High-quality data often resides for long periods in fragmented, highly sensitive, or hard-to-access domains. In these areas, data sovereignty and trust often outweigh sheer model compute power or general capabilities.” Image source: unsplash --- 📌 Z Highlights * When infrastructure providers also become competitors, startups