Introduction For TypeScript files with the .ts extension, we cannot run them directly. We need to transpile TypeScript to JavaScript and then run the JavaScript output. There are two common approaches: upload .ts to the server and compile via CI, or transpile locally and upload the .js build to production. If you want to run and test locally during development, you can use ts-node, but the project still needs a build step for production.

Introduction I want to build an AI system that supports tree-shaped or graph-shaped Q&A, instead of a traditional single-thread chat flow. Exploration Open-source framework research flowise

Introduction Mermaid is a framework for creating diagrams using code. This post shows how to install the tooling on your server and render Mermaid code into images. Steps Install the renderer Run: npm install -g @mermaid-js/mermaid-cli Note: the CLI requires npm version >= 20. It is recommended to manage npm versions with nvm. If you do not have nvm, install it with: curl -o https://raw.githubusercontent.com/nvm-sh/nvim/v0.39.4/install.sh | bash Restart your shell, then run: ...

How to Truly Master a Paper Conclusion To truly master a paper, reading once is not enough. You need to decompose, verify, and reconstruct it, and then express the key points in your own words or implementation. The goal: explain the core contribution in 5 minutes, derive key formulas by hand, and reproduce a core experiment. Principles and background A paper is a compressed expression of a problem. It omits background, intuition, failed attempts, and many details. Mastery requires “decompressing” that information into your own knowledge network: assumptions, derivations, engineering steps, and the limits of the results. Only then can you judge when to use it and when not to. ...

What problem does this paper solve, and what are the results? We know AI systems are expanding and can solve general tasks. But many AI agent applications today target small tasks. NVIDIA argues that small language models (SLMs) are capable, more suitable, and cheaper, and should be a main direction for future agents. The paper discusses: What tasks current SLMs can handle Where general language ability matters The limits of SLMs as agents Conclusion: moving from LLMs to SLMs has advantages in both capability and cost.