#LLM tool augmentation
99 related articles
·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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·2 min
Modern Web Guidance: Bringing Modern Web Standards to Your AI Coding Assistant
Modern Web Guidance is an agent skill & CLI tool that injects modern Web standards into AI coding assistants like Claude Code and Copilot CLI, replacing outdated polyfills with Baseline-validated solutions.
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·3 min
AI Video Editing in Practice: How to Compress 5 Hours of Rough Cuts Down to 30 Minutes
A deep dive comparing ChatCat and VSCALE AI editing tools, showing how rough cuts shrink from 5 hours to 30 minutes — covering multi-cam sync, lossless DaVinci import, and AI's real limits.
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GPT-5.6 Practical Guide: 7 Tips to Save Tokens and Boost Efficiency
7 practical GPT-5.6 optimization tips for Codex: control context windows, choose thinking modes, limit sub-agents, and trim agents.md to save tokens and cut costs.
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Getting Legal AI Right: Why the Coding…
·2 min
Getting Legal AI Right: Why the Coding Agent Paradigm Doesn't Translate
Most legal AI products are just general-purpose models wrapped in RAG and prompt engineering scaffolding. Learn why the coding agent paradigm fails in law and what real legal AI requires.
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·3 min
Metaview in Practice: Building a Prompt System That Evolves With User Preferences
Metaview engineer Nick Mayhew explains how to build self-evolving prompt systems: Markdown over rules, layered workflows to cut token costs, and agents that learn user preferences for human-centered AI recruiting.
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·2 min
500+ AI Research Tools: Automating the Entire Research Workflow
A curated open-source repo of 500+ active AI research tools covers the full workflow—literature review, code reproduction, paper writing, and LaTeX formatting—potentially saving 80% of research time.
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·3 min
Getting Started with LangChain 1.3: Building Intelligent Agent Applications with Deep Agent
Learn LangChain 1.3 core concepts including LLM model abstraction, RAG retrieval-augmented generation, and Agent orchestration. Build a Deep Agent with planners, tools, and reflection modules.
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A Human-Centered AI Future: The Humani…
·2 min
A Human-Centered AI Future: The Humanist Tech Philosophy of Thinking Machines Lab
Thinking Machines Lab's 'The Future Worth Building Is Human' argues AI should augment, not replace, people. Explore the humanist philosophy, community debate, and real-world challenges of human-centered AI.
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·3 min
Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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·2 min
Deep Dive into MCP: How Large Language Models Connect to the Outside World
MCP (Model Context Protocol) is the standardized protocol connecting AI models to external tools and data — the 'USB-C port' of the AI era. Learn its origins and value.
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Building an Offline RAG App from Scrat…
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Building an Offline RAG App from Scratch: A Complete Guide to Local PDF Q&A
Learn how to build a fully offline RAG app using Ollama, ChromaDB, and Flask for local PDF Q&A. Covers chunking, vectorization, retrieval, and hallucination prevention.
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·2 min
Complete Spring AI Tutorial: ChatModel, RAG, and MCP Practical Guide
A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.
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·1 min
Verbalized Sampling Lands at ICML: Does Prompt Engineering Belong at Top Conferences?
A prompt engineering paper on "verbalized sampling" accepted at ICML sparked fierce Reddit debate: does a prompting trick that mitigates mode collapse belong at a top ML conference?
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Loving LLMs, Hating the Hype: How Engi…
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Loving LLMs, Hating the Hype: How Engineers Rationally Evaluate Large Language Models
Engineers love LLMs for real productivity gains but hate the hype around AGI narratives, glossed-over hallucinations, and valuation bubbles. Here's how to find the rational balance.
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·6 min
GPT-5.6 vs Grok 4.5 on the Same Day: A Packed Week of Flagship AI Model Launches
OpenAI's GPT-5.6 launches with Sawa, Terra, and Luna sub-models the same day as Musk's Grok 4.5, while Anthropic, Meta, and NVIDIA make their moves. A packed week of flagship AI launches.
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AI Costs Out of Control: Real-World St…
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AI Costs Out of Control: Real-World Strategies for Enterprises to Rein In Soaring Bills
More enterprises are finding AI operational costs spiraling out of control. This article dissects token billing traps and blind flagship-model use, and maps out cost-reduction strategies like model routing, open-source self-hosting, and semantic caching.
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·6 min
The Eight-Step Method for Agent Development: A Practical Path from Pitfalls to Production
Many enterprises fail at AI Agents due to choosing the wrong tools and lacking methodology. This article outlines an eight-step Agent development method—from cognitive foundations, scenario selection, hand-writing ReAct, and structured output to Tool Use, RAG, evaluation sets, and production fallback.
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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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·3 min
GitHub Daily · July 12: The Explosion of the Agent Skills Ecosystem and the Rust Rewrite Wave
July 12 GitHub trending: Agent Skills/MCP ecosystem explodes with superpowers hitting ~900 stars, pgrust rewrites Postgres in Rust passing 100% tests, plus solid engineering foundations.
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