#Output Parser
129 related articles
AI Agents Accelerate Lightweight USD R…
·3 min
AI Agents Accelerate Lightweight USD Runtime Development: A New Paradigm for Physical AI Infrastructure
How AI agents accelerate lightweight OpenUSD runtime development for physical AI — covering spec understanding, code generation, and iterative optimization for robotics and digital twins.
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Smart Proxy: The Key Architecture for …
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Smart Proxy: The Key Architecture for Running AI Coding Agents Safely and Autonomously
Too much AI permission is risky; too little kills productivity. A smart proxy acts as a controllable middleware layer — intercepting, auditing, and policy-gating AI agent actions in tools like Claude Code and Cursor for safe autonomous operation.
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How DSLs Make LLM Outputs More Reliabl…
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How DSLs Make LLM Outputs More Reliable: Principles and Practice
LLM output instability is a core production challenge. This article analyzes how DSLs improve LLM reliability through verifiability, semantic convergence, and structural constraints.
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·3 min
LongCat 2.0: Meituan Trains a 1.6 Trillion Parameter LLM on Domestic Chips
Meituan open-sources LongCat 2.0, a 1.6T-parameter MoE model trained on 50,000+ custom chips without NVIDIA GPUs or Google TPUs, rivaling OpenAI and Google.
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Kova: The Open-Source Tool for Building Presentations with Markdown
Kova is an open-source Markdown presentation tool for developers. With Git support and content-style separation, it hit 250 GitHub Stars in just three weeks.
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·6 min
A Deep Dive into OpenAI Codex: How AI Engineers Are Reimagining Software Development with Agents
OpenAI Codex is redefining how AI engineers work: from code completion to autonomous Agents, from single-threaded to parallel Value Maxing. A deep dive into the Codex App architecture, open ecosystem, and Manager of Agents practice.
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Introducing LLM Gateways in Production: Five Key Concerns and the Prerequisites for Trust
Introducing an LLM Gateway in LangChain production brings unified APIs and auto-fallback, but also quality drift, cost spikes, and debug black boxes. This article breaks down the five key engineering concerns and what it takes to earn trust.
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·2 min
LangChain Chain & Memory: Building AI Applications with Persistent Memory
Learn how LangChain's Chain and Memory components overcome LLM limitations. Build intelligent AI apps with multi-step workflows and persistent memory.
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Structured Information Extraction with…
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Structured Information Extraction with Local 7B Quantized Models: Root Cause Analysis and Engineering Optimization
Using Qwen 2.5 7B quantized locally to extract 60+ fields from insurance/financial contracts? Learn why it struggles and how task splitting, RAG, GBNF, and smarter chunking can fix it.
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AI Agent in 100 Lines of Lisp: Minimal…
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AI Agent in 100 Lines of Lisp: Minimal Code Reveals the True Nature of Intelligent Agents
A fully functional AI Agent in just 100 lines of Lisp reveals the essence of the ReAct loop. Deep dive into Agent architecture, Lisp's unique strengths, and minimalist engineering philosophy.
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·4 min
The Ultimate RAG Interview Guide: 204 Questions, 12 Architectures, and 6 Failure Modes Explained
The open-source project "Interview System" offers 204 RAG interview questions, 12 architecture approaches, and deep analysis of 6 failure modes. Prepare systematically for RAG engineer roles.
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·4 min
A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions
Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.
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·4 min
Why Do AI Agents Crash the Moment They Go Live? A Practical Guide to Harness Engineering for Runtime
A deep dive into the Claude Code source code, systematically analyzing the five-layer Harness Engineering architecture: environment, tool, control, memory, and evaluation. Build a stable runtime system for production AI Agents.
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·3 min
Sidenote: Annotate Rendered Pages Directly, Let LLM Auto-Generate Git Commits
Sidenote is an open-source tool that lets you annotate rendered static blog pages in natural language, with an LLM auto-locating source files and generating Git diffs.
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·6 min
Getting Started with AI Agents: Principles, Frameworks, and a Guide to Building Enterprise-Grade Agents
In-depth analysis of AI Agent core principles: why LLMs need Agent technology, the evolution from Prompt to RAG to Agent, Agent Tuning methods, and enterprise cost evaluation to help you build enterprise-grade agent applications.
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·5 min
Getting Started with LangChain: LLM Invocation, Agent Practice, and Environment Setup Guide
Starting from the three limitations of LLMs, this guide systematically explains LangChain's core positioning, environment setup, API key prep, model init, and the message system. Learn init_chat_model and AIMessage/HumanMessage/SystemMessage.
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·3 min
Spending $200K to Test GPT-5.6: Have AI Coding Assistants Reached a Tipping Point?
Developer Theo spent ~$200K over 6 weeks testing GPT-5.6 across 67 projects — from 20-hour autonomous coding runs to fixing boot partitions and Rust rewrites.
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·5 min
ECC: An Agent Enhancement Framework for AI Coding Assistants — Five Core Capabilities Explained
ECC is an agent optimization framework for AI coding assistants like Claude Code, Cursor, and Codex, enhancing them with skills, memory, security, and research-first development capabilities.
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·2 min
yaml-cpp: The Go-To Open Source Library for Parsing YAML in C++
yaml-cpp is the most popular YAML parsing and emitting library for C++, supporting YAML 1.2 with a clean Node API and custom type conversion. Used by ROS and more.
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·8 min
GPT-5.6 Hands-On: Farm Automation, Mathematical Breakthroughs, and End-to-End AI Execution
GPT-5.6 (Sol, Terra, Luna) hands-on testing: a Hokkaido farmer controls a greenhouse with AI, a NYC small business builds custom software, and a Polish mathematician breaks a 3-year problem. A deep dive into end-to-end autonomous execution.
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