LLM articles
511 articles about LLM, newest first.
- Replace Your Back Office With Claude: How AI Automation Can Streamline Your Workflow — Learn how Claude AI can automate back‑office tasks, boost productivity, and cut costs with simple integration tips.
- Top GitHub AI Repos This Week: Transforming Development with Claude, Strix, and More — Explore the week's most impactful GitHub repos shaping AI, security, and productivity for developers.
- 5 Hidden Claude Features You Need to Know: Unlock AI Power — Discover five underrated Claude capabilities that can turbo‑charge your workflow, from prompt chaining to custom tool use.
- Why Understanding Beats Automation: Boost AI Agent Performance with Human Insight — Learn how human insight stays essential for AI agents, with practical tricks to understand and guide them faster.
- NapMem: How a 9B Model Beats a 397B LLM by Navigating Memory Like a Pro — Discover NapMem, a new memory‑as‑action framework that lets a 9B AI outperform a 397B model through reinforcement learning.
- How LLM Wikis Give Your AI Agents a Memory — Discover how LLM wikis let AI agents store and recall knowledge, with insights from LangChain's OpenWiki and real‑world building tips.
- OpenWiki Personal Brain: Build Your Own AI Memory Wiki in Minutes — Learn how OpenWiki 0.1.0 creates a self‑maintaining memory wiki for AI agents, with setup steps, connectors, and technical insight.
- Should AI Engineers Still Read Code in 2026? The Z/L Continuum Explained — Explore the Z/L Continuum debate on AI code generation, human review bottlenecks, and what top engineers really do in 2026.
- Building Agent Sandbox Clouds: Secure Compute for AI Workers — Learn how sandbox clouds give AI agents private, secure computers, solving isolation, storage, and scaling challenges for real‑world automation.
- Why AI Agents Need a Human Owner in 2026: The Maintenance Skill You Can't Skip — Learn why maintaining AI agents is the critical 2026 skill and how to assign ownership to keep them honest.
- Grok 4.5 Explained: How SpaceXAI's New Model Beats GPT‑5.6 at Lower Cost — Grok 4.5 is SpaceXAI's latest frontier model that challenges GPT‑5.6, Fable and Opus with lower cost and higher token efficiency. It shows rapid progress in open and closed AI labs.
- Why Your Job Might Disappear: The AI Coordination Test — AI is reshaping work — if your role is mostly coordination, it’s the first to be automated. Shift toward direct‑value creation that touches customers and revenue.
- This Week’s AI Breakthroughs: From Glaze to Grok 4.5 — The Next New Thing recaps 10 hot AI launches, from Glaze’s one‑click Mac app builder to Grok 4.5’s low‑cost Opus‑level performance, showing how they reshape coding, voice, music, video, and dictation.
- From Data Scientist to AI Engineer: Roadmap, Skills & Real‑World Projects — Learn how to transition from data science to AI engineering with a step‑by‑step roadmap, LLM basics, production backends, and portfolio projects you can showcase.
- Trace Claude Code Sessions in LangSmith: Step‑by‑Step Setup Guide — Learn how to install LangSmith's tracing plugin for Claude Code, configure a tiny JSON settings file, and view every agent interaction in LangSmith's Threads tab.
- Preparing Your Company for AI at Scale: Lessons from Nick Puru’s 2026 Guide — Nick Puru explains why AI demos fail in production and shares a simple framework to scale AI successfully in any organization.
- Training Agents 2: Master Model Distillation for Custom AI Agents — Explore model distillation techniques — off‑policy, on‑policy, and self‑distillation — to shrink AI models while preserving performance.
- Scaling AI Agent Fleets: What Breaks When You Go Multi‑Machine — A real‑world look at running dozens of AI agents across multiple machines, revealing scaling pitfalls and how to keep them alive.
- Blindfolded AI Gets Eyes: How Vision Boosts Performance and Trust — Discover how giving AI visual perception multiplies speed and builds user trust, with simple analogies and technical details.
- Parker Prompts Tests Every Google AI Tool—What Actually Works? — Parker tests all Google AI products, shows what they do, and rates which are worth using.
- Fable 5 Planning Patterns: Cut LLM Costs with Advisor & Orchestrator Techniques — Learn how Fable 5 acts as a planner, not a coder, and how advisor and orchestrator patterns slash token use while keeping accuracy high.
- Paper’s Agent‑Native Design Demo: How AI Can Draw UI Live — Watch a live demo where AI agents like Claude Code edit a visual canvas in real time, generating thumbnails, decks, and sites on the fly.
- Unlocking Fable 5: AI Automation Hacks You Can’t Miss — Discover how to leverage Fable 5 for AI-driven business transformation, with practical automation tips and community insights.
- Why Open Agent Systems Are the Future of Enterprise AI — Jensen Huang explains how open agent stacks let companies build specialized, cost‑effective AI agents that integrate tightly with their data.
- How DoorDash Scales Claude Code: AI Agents in Production — DoorDash reveals how Claude Code and AI agents are deployed at scale, with governance, verification, and real ROI insights for engineers.
- Building Production-Ready AI Agents with dcode, Nemotron 3 Ultra, and NemoClaw — Learn how to integrate dcode with NVIDIA’s Nemotron 3 Ultra, enable LangSmith tracing, use the new /goal feature, and secure the pipeline with NemoClaw for enterprise use.
- How AI Is Writing Chess Puzzle Explanations on YouTube — A Deep Dive — Explore how an AI agent creates daily chess puzzle videos with arrows, from simple concepts to technical implementation.
- How Agent Harnesses Keep Your AI Coding Assistant in Check — Discover why AI coding agents sometimes ignore instructions and how an agent harness enforces compliance in real time.
- Teaching Agents Better Retrieval: Beyond Grep and Vector Search — Learn how Mixedbread AI equips agents with advanced retrieval tools to close the Oracle Gap and boost accuracy in legal, finance, and research tasks.
- Adaptive Engineering: The Next Frontier Beyond Fixed AI Harnesses — Explore how adaptive engineering shifts from rigid frameworks to real‑time, context‑aware solutions, tackling messy real‑world problems with AI.