Self-Hosted AI App Builder: Run Your Own (2026)
A hands-on 2026 tutorial: stand up your own self-hosted, open-source AI app builder from the ai-app-builder-open repo, with one API key and code you fully own.
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9 posts in this category.
A hands-on 2026 tutorial: stand up your own self-hosted, open-source AI app builder from the ai-app-builder-open repo, with one API key and code you fully own.
A runnable 2026 tutorial: give a Claude agent long-term memory that survives across sessions using SQLite, embeddings, and cosine retrieval. No LangChain, FAISS, or vector database.
A runnable 2026 tutorial: build short-term and long-term memory for a Claude agent in TypeScript with message buffers, rolling summaries, and a durable fact store. No vector database required.
A runnable Python quickstart: install the Claude Agent SDK, stream a run with query(), then give the agent your own tools with the @tool decorator and ClaudeSDKClient. Builds a weather agent that chains two tools.
A runnable 2026 quickstart: install the Claude Agent SDK, wire a custom tool with tool() and createSdkMcpServer(), and let the agent loop call it for you in about 40 lines of TypeScript.
Should you use the Claude Agent SDK or hand-roll the agent loop with the Anthropic Client SDK? An honest 2026 comparison with runnable TypeScript for both and a decision table.
Speed up your Claude agent loop by running independent tool calls concurrently in TypeScript. Copy-paste code for parallel tool_use, is_error handling, and disable_parallel_tool_use.
A complete TypeScript tutorial for the streaming agent loop on Claude: input_json_delta accumulation, multi-turn dispatch, AbortController cancellation, and the eager_input_streaming workaround for the verified 5 second first-content delay on tool use. About $0.03 per call with claude-sonnet-4-6 at June 2026 pricing.
An AI agent is just a loop: you call a model, the model asks to run a tool, you run it, you feed the result back, and you repeat until the model is done. In this tutorial you build that loop yourself in plain TypeScript against the Anthropic Messages API, no framework. You will wire up two tools (read a file, run a calculation), let the model orchestrate them, add a turn cap and basic guardrails, then verify the whole thing end to end. The result is a small research agent you fully understand and can extend with your own tools.