If you're brand new to RAG, start with RAG Explained and RAG Architecture. This post is for the point where you've built a RAG pipeline that mostly works…...

1 skill, 23 commands, and curated anti-patterns for impeccable frontend design. Works with Cursor, Claude Code, Gemini CLI, and Codex CLI.

How do we fix code fast when the bug reports arrive faster? Multi-agent orchestration tools like Squad may be the answer.

Most companies don't have an AI quality problem. They have a measurement problem.

As LLMs hit the limits of scale and cost, specialized SLMs are emerging as the faster, cheaper, and more private workhorse for the autonomous enterprise.

Running LLMs on your own hardware

When it comes to software developers, there are a few distinct types. For example, the extroverted, chatty type, who is always going out there to share the latest and newest libraries and projects …

Watch now | Six layers, three example builds, and the case for owning your AI infrastructure end to end.

Watch now | What Karpathy, Shopify, and a small YC startup just figured out — and what it's going to cost the teams who don't.

The agent-led growth playbook: how to make AI agents discover, use, and pay for your developer tool, and defend against the ones you didn't invite. LLM discoverability, agent-first onboarding, agent payments, AX security.

Watch now | Every agent product is solving the wrong problem — and the right one sits upstream of all of them.

How I use AI in my daily work as a maintainer and developer, from coding to triaging PRs and CI failures

What I expect to come next and why, focused on the open-closed gap.

Most agent frameworks optimize for recall. Open-strix optimizes for forgetting — and that turns out to be the whole trick.

Most AI SEO advice is unproven. We tested what ChatGPT, Claude, and Perplexity actually read on our own site. Six LLM visibility techniques that worked, eight that didn't, and the metrics to tell the difference.

Step-by-step setup for running a quantized Qwen3.5-27B model on a remote GPU via llama.cpp, exposing it over Tailscale and using it as a provider in OpenCode (optionally with Codex).

StrongDM's field notes on non-interactive agentic development: specs + scenarios, validation harnesses, feedback loops, and the supporting components.

Llama.cpp is a powerful and efficient inference framework for running LLaMA models locally on your machine. Unlike other tools such as…

A comprehensive guide covering the local LLM stack from hardware requirements to production deployment. Compare Ollama, LM Studio, llama.cpp and build your first local AI application.

Build with LM Studio's local APIs and SDKs — TypeScript, Python, REST, and OpenAI and Anthropic-compatible endpoints.

Watch now | A new infrastructure stack is forming underneath your AI agents.

Cursor, Claude Code, and OpenAI Codex are forming a composable AI coding stack with orchestration, execution, and review layers instead of consolidating into one tool.

Watch now | The variable that decides who wins the AI infrastructure war isn’t a faster chip or a better model. It’s a compression algorithm.

Why intent and control are becoming the new software architecture

Watch now | Everyone’s talking about the Tamagotchi -- here’s what actually matters.

Credit: Museums Victoria / Unsplash There’s a lot of conversation right now about “context engineering” for dev work; structuring what you feed an LLM so it can do useful things. …

Large Language Models (LLMs) like OpenAI’s GPT-4 have transformed natural language processing, enabling applications ranging from chatbots…

A deep dive into effective caching strategies for building scalable and cost-efficient LLM applications, covering exact key vs. semantic caching, architectural patterns, and practical implementation tips.

In this article, you will learn how to add both exact-match and semantic inference caching to large language model applications to reduce latency and API costs at scale.

A deep dive into exactly how text-only language models are finetuned to *see* images