#heuristic optimization
70 related articles
decisionrl: An Open-Source Reinforceme…
·2 min
decisionrl: An Open-Source Reinforcement Learning Library Built for Operational Decision-Making
decisionrl is an open-source RL library for operational decisions, with built-in environments for inventory, pricing, supply chain, and OR baselines for comparison. Built on PyTorch, MIT licensed.
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Deep Dives
Deep Dive into AI Agent Skill Design: …
·3 min
Deep Dive into AI Agent Skill Design: Engineering Practices from Anthropic and Perplexity
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
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CUDA Kernel Fusion: A Practical Guide …
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CUDA Kernel Fusion: A Practical Guide to Reducing Memory Bandwidth and Launch Overhead
Learn how CUDA Kernel Fusion merges multiple GPU kernels to reduce global memory traffic and launch overhead, with real-world examples from AI inference and deep learning.
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·3 min
Gemini's Code Highlighting Theme Update Sparks Developer Discussion
Gemini quietly swapped its code syntax highlighting theme, sparking developer discussion. We break down One Dark, Dracula, and how Google's Material Design shapes its coding UI.
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·4 min
Do You Still Need to Hand-Code Machine Learning Algorithms in the AI Era? The Answer and the Method
With AI tools everywhere, is it still worth hand-coding SVM, decision trees, and other ML algorithms? This article explores the real value of hand-coding, the limits of AI tools, and smarter learning strategies for beginners in the AI era.
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·3 min
Arcaide: Understanding Codebase Structure Through Multi-Level Call Graph Visualization
Arcaide is a code comprehension tool based on multi-level call graphs, helping developers explore function calling relationships from macro to micro, speed up onboarding, assess change impact, and identify technical debt.
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·3 min
Why Does Reviewing AI Code Take Longer Than Reviewing a Junior Engineer's Code?
An engineer found that reviewing AI-generated code took four times as long as reviewing a junior engineer's code. This article dissects the trust mechanisms, process traceability issues, and the right approach to code review in the AI-assisted development era.
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·4 min
Learn ML Free at Public Libraries: A Complete Guide to O'Reilly Tech Book Resources
Getting O'Reilly machine learning books free at public libraries? It's no myth. This article reveals hidden tech learning resources at libraries, including online platform subscriptions and digital database access, helping self-learners build AI knowledge at zero cost.
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·5 min
GPT-5.6 Launches: Comprehensive Upgrades to ChatGPT Work, Sites, and Desktop Client
OpenAI releases the GPT-5.6 model family, launching enterprise-focused ChatGPT Work, one-click ChatGPT Sites, and a major desktop client upgrade, with coding now ahead of rivals. Meta, Google, and Kimi follow intensively.
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AI Crushes Humans! OpenAI Model Solves All 5 Problems in AtCoder World Tour Finals Algorithm Contest
OpenAI's model completely solved all 5 problems in the AtCoder World Tour Finals Algorithm Contest, while no human competitor solved more than 3. A deep dive into this milestone: AI surpasses top programmers in both symbolic reasoning and heuristic optimization.
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The Boundaries of AI Safety Guardrails: Deep Reflections Sparked by a Single Prompt
A Reddit post sparks debate: what happens when a user asks AI to "push guardrails to the limit"? An in-depth look at AI safety guardrails, jailbreaks, and content balance.
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AI Agents for SGLang Kernel Optimization: Real-World Performance Gains and Engineering Methodology
SGLang's team converted expert knowledge into agent skills, achieving 71.4% throughput gains, TTFT reduced from 456ms to 168ms. A deep dive into agent-assisted kernel optimization methodology.
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·5 min
AlphaEvolve Goes Commercial: Google Cloud Opens Algorithm Discovery to Solve Enterprise Optimization Challenges
Google is bringing AlphaEvolve to Cloud enterprise customers. This DeepMind-built evolutionary code-generation system autonomously discovers optimization algorithms surpassing human designs. A deep dive into its principles, use cases, barriers, and industry impact.
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·7 min
Turning Any AI Agent into an Orchestration Hub: A Deep Dive into Multi-Agent Collaboration Architecture
An in-depth analysis of the "any Agent as an orchestrator" design philosophy, exploring the technical implementation of multi-Agent collaboration, context management, and workflow automation.
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·6 min
Introduction to Prompt Engineering: Principles, Methods, and Practical Case Studies
Prompt Engineering is the core skill for harnessing LLMs. This article covers principles and design methods through real cases like translation role-setting and DeepSeek image generation.
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AI Agent: A Complete 6-Week Learning Roadmap from Beginner to Production
A complete 6-week AI Agent learning roadmap covering core architecture (planning/memory/tool use), the ReAct paradigm, multi-agent collaboration, RAG integration, and production deployment.
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·7 min
Context Warp Drive: A Deep Dive into Deterministic Context Folding for AI Agents
An in-depth look at 'Deterministic Context Folding' from Context Warp Drive: solving AI agent context window management with reproducible, cacheable, debuggable context compression for production-grade agents.
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·6 min
Made by Google Event Confirmed: New Pixel Phones with AI Capabilities Coming Soon
Google confirms the Made by Google hardware event in NYC, unveiling next-gen Pixel phones with new Tensor chips and deep Gemini AI integration. On-device AI gets a major upgrade.
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·6 min
Hands-On with Mixar: An AI-Native Blender Fork That Handles Baking and LOD with a Single Prompt
Mixar is an AI-native fork of Blender 5.0 that embeds AI into the kernel layer. This hands-on review tests texture baking, LOD generation, mood boards, image-to-3D, and more, comparing it to MCP. Fully open source and free.
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·7 min
Apollo Chief Economist Warns: AI Assets May Face Painful Repricing
Apollo's chief economist warns that current AI-related asset valuations may have severely detached from fundamentals, risking a painful systemic repricing.
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