#production AI systems
1767 related articles
·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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·2 min
Dependency Drift in Production AI Agents: Detection, Response, and Building Resilient Systems
AI Agents in production face systemic dependency drift risks — model updates silently change outputs and silent failures are hard to detect. Learn detection strategies, hidden costs, and engineering practices.
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·2 min
Distributed AI Systems: A Practical Engineering Guide to Training, Inference, and Production Deployment
A deep dive into distributed AI systems engineering: data/model/tensor parallelism for training, KV cache, quantization, elastic scaling for inference, and cloud deployment with Kubernetes, Ray, and DeepSpeed.
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·3 min
Distributed AI Systems in Practice: A Complete Guide to Training, Inference, and Production Deployment
A deep dive into Distributed AI Systems: a new book distilling 10 years of AI engineering experience covering distributed training, inference optimization, and production model serving.
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·3 min
How to Polish AI-Generated Auth Systems to Production-Ready Quality
Learn how to polish Copilot-generated Auth systems from error-prone drafts to production-ready code, covering bug fixes, i18n replacement, and effective AI collaboration tips.
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·3 min
AI Research Automation: More Like Data Cleaning Than Inventing the Transformer
Why AI research automation looks more like data cleaning than inventing the Transformer. Exploring the value of automating 60%-80% of repetitive research work and how human-AI collaboration reshapes the research paradigm.
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·3 min
AI Research Automation: More Like Data Cleaning Than Inventing the Transformer
AI research automation will look more like data cleaning than inventing the Transformer. Explore how automating 60%-80% of repetitive research work reshapes the AI research paradigm.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.
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·2 min
Will AI End Human History? The Deep Anxiety Behind Technological Optimism
A viral Reddit post asks: will AI end human history? This article analyzes the blind spots of tech accelerationism, the governance mismatch, and how to rationally navigate AI transformation.
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·2 min
Rootless Containers: A Practical Guide to More Secure Service Deployment
A deep dive into Rootless Containers: technical principles, security advantages, and production practices. Learn how user namespaces and daemonless architecture reduce container escape risks.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Open Archiver v0.5.2 Deep Dive: A Comprehensive Look at the New Features of This Open-Source Email Archiving Tool
Deep dive into Open Archiver v0.5.2: advanced search, index rebuilding, PST/Mbox import fixes, and more features for self-hosted email archiving and compliance.
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·3 min
Poolside Laguna Model Launch: Can Silicon Valley's Open-Source Push Catch Up to China's AI?
Poolside launches Laguna open-weight model after 18 months of silence, pitting 118B parameters against Kimi K3's 2.8 trillion. Can Silicon Valley's open-source push close the gap with Chinese AI?
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·3 min
Claude Opus 5 Officially Released: Performance Approaching the Top Tier as the Open-Weight Debate Heats Up Again
Anthropic releases Claude Opus 5 with near-frontier performance at lower prices. Same day, Jensen Huang co-signs open-weight letter with 20+ companies while DeepSeek fundraising rumors surface.
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·3 min
AI Weekly: Sandbox Escape Sparks Safety Legislation, Kimi Open-Source Triggers Geopolitical Dynamics
Deep analysis of five key AI events this week: OpenAI sandbox escape driving safety legislation, Kimi K3 open-source sparking geopolitical debate, Gemini Flash full rollout, Anthropic's $1.5B copyright settlement, and Chinese models' mobile expansion.
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·3 min
Chinese Open-Source AI Conquers Silicon Valley: The Industry Upheaval Behind a 58% Compute Share
Chinese open-source AI models surged from under 10% to 58% of U.S. AI consumption. Kimi K3, DeepSeek, and Qwen are reshaping AI cost structures as DoorDash, Airbnb, and other Silicon Valley giants adopt them at scale.
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·4 min
Jensen Huang Joins 50 Silicon Valley Giants to Champion Open-Source Models: How Capital Logic Overrides Political Correctness
Jensen Huang's first-ever tweet backs open-weight AI. 50 Silicon Valley giants oppose banning Chinese open-source models. Deep analysis of the interests behind closed vs. open AI ecosystems.
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·3 min
Running AI Inside DRAM: A Processing-in-Memory Experiment That Breaks DDR4 Timing Rules
Explore how deliberately violating DDR4 timing rules enables running PrismML's Bonsai AI model inside DRAM, covering the principles, energy benefits, and challenges of processing-in-memory.
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·3 min
The Depth Perception Challenge for Transparent Objects: How LingBot-Depth Breaks Through with Masked Depth Modeling
Transparent and reflective object depth perception is a core challenge in robotic grasping. LingBot-Depth uses masked depth modeling to turn sensor failure into supervision, inferring glass depth from RGB context.
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