Production AI
QCon 2025 — Agentic AI Deployment in Enterprises Date: 2025‑11‑24 · Location: Beijing --- As Large Language Models (LLMs) evolve toward Agentic AI, enterprises must navigate significant challenges — security, efficiency, and trust — on the journey from proof‑of‑concept to large‑scale deployment. Without a solid data foundation and systematic
Production AI
# Unifying Local and Cloud Language Models on Apple Platforms with AnyLanguageModel Developers on Apple platforms often face challenges integrating **local** and **cloud-based** language models due to a fragmented ecosystem. Local models (via **Core ML** or **MLX**) provide **privacy** and **offline functionality**, while cloud solutions like **OpenAI**, **Anthropic**, or **Google Gemini*
AI news
PhysX-Anything: Single-Image to Simulation-Ready 3D Assets A single photo can now be transformed into simulation-ready 3D assets with realistic physical and joint properties. (Yes — in the image below, almost all objects are generated by AI.) --- Why Simulation-Oriented 3D Generation Matters As 3D modeling expands beyond static visual effects toward
Production AI
Token-Oriented Object Notation (TOON) — A Schema-Aware Alternative to JSON The recently released Token-Oriented Object Notation (TOON) introduces a schema-aware, human-readable alternative to JSON. Its core goal: reduce token usage while preserving accuracy in LLM prompts. Benchmarks show TOON can use up to 40% fewer tokens than JSON in certain scenarios,
Production AI
Reducing False Positives in RAG with Semantic Caching: A Production Case Study Author: Elakkiya Daivam This article examines why Retrieval-Augmented Generation (RAG) and semantic caching are powerful for minimizing false positives in AI applications, based on a production-level evaluation of seven bi-encoder models against 1,000 query variations. --- The
Production AI
# Introducing Zoomer: Meta’s Automated AI Debugging & Optimization Platform ## Overview **Zoomer** is Meta’s **comprehensive, automated platform** for profiling, debugging, and optimizing AI workloads. It supports **all training and inference operations** across Meta, delivering **deep performance insights** that: - Reduce **energy consumption** - Accelerate **workflows** - Improve **AI infrastructure
Production AI
# Accelerating Drug Discovery with Multi-Agent AI Systems AI agents, powered by **generative AI**, are transforming industries by acting as **intelligent, collaborative partners** that can interpret goals, plan multi-step actions, and operate independently across systems. This marks a **significant shift** in how businesses — and scientists — **discover, understand, and act on their
Production AI
AI 写代码的速度已经快到离谱了，一句话就能生成一堆“看起来能跑”的函数。但问题是：这些代码真正上生产环境后，往往不是崩就是漏，维护成本甚至比人类敲的还高。我们现在遇到的，不是“代码不够多”，而是“代码不靠谱”。那么，既然生产力爆炸了，为什么可靠性没跟上？又该怎么补上这块短板？这成了所有工程团队必须面对的新问题。 原文：https://thenewstack.io/ai-code-doesnt-survive-in-production-heres-why/ 作者 | Animesh Koratana责编 | 苏宓 出品 | CSDN（ID：CSDNnews） 我几乎每天都能看到类似的 AI 演示：只用一个提示词，就能生成一款完整的应用。对着 AI 助手，输入几句自然语言之后，啪的一下，一个“打磨精良”的产品就出现了。 但在这些火爆的趋势背后，有个耐人寻味的事实：我们并没有看到成品软件数量的提升，也没有看到预期中的创新速度。 谷歌一位工程副总裁近期说过一句话：
Production AI
Lessons from a Catastrophic Identity Migration At QCon San Francisco, HeartFlow’s VP of Engineering Sonya Natanzon shared five key lessons from navigating architectural recovery after inheriting a failed identity migration — one that locked users out of a healthcare portal on day one. Her insights show that successful recovery depends
AI Content Creation
The Rise of AIGC in E-Commerce Artificial Intelligence–Generated Content (AIGC) is transforming industries, with visual generation emerging as a key driver in reshaping the e-commerce ecosystem. As businesses shift from simple product display to content-driven strategies, the demand for massive, diverse, and precise visual assets is unprecedented. Traditional manual
Production AI
AIOps & RCA in Practice: Insights from Chen Dihao at the 2025 XCOPS Summit > Based on Chen Dihao’s online sharing at the 2025 XCOPS Intelligent Operations Management Annual Conference – Guangzhou Station. > (Playback link available at the end — don’t miss it!) --- Speaker Profile Chen Dihao — Head
Production AI
Netflix’s Centralized Data Deletion Platform: Architecture and Insights Netflix engineers presented their centralized data deletion platform architecture at QCon San Francisco — tackling a critical, often overlooked system design challenge. The platform coordinates deletions across heterogeneous data stores while balancing durability, availability, and correctness. Impact so far: 76.8 billion