Thoughts on Jeanphilo Blog

Do Not Let AI Drive You: Keep the Ability to Build Independently

Mon, 08 Dec 2025 00:00:00 +0000

How to avoid copy-paste dependence when using AI for coding: Feynman technique, deliberate practice, retrieval practice, and a practical self-check workflow.

How to Build a Blog System

Fri, 14 Nov 2025 15:04:02 +0800

Build a Hugo Blog with GitHub Pages in 10 Minutes

Subtitle / Abstract

This guide takes you from zero to a deployed Hugo blog on GitHub Pages with GitHub Actions. It is beginner-friendly and explains the key moving parts.

Target readers

Hugo beginners
Developers who want a quick technical blog
Users of GitHub Pages and GitHub Actions
Anyone who wants free static hosting

Background / Motivation

Common pain points when publishing a blog:

How to Publish with Hugo

Fri, 14 Nov 2025 15:01:32 +0800

How to Publish with Hugo: From Markdown to Online Blog

Subtitle / Abstract

This guide explains how to create, manage, and publish Hugo posts: front matter, drafts, images, directory structure, local preview, and deployment.

Target readers

Hugo beginners
Developers building a technical blog with Hugo
Writers using Markdown + static sites
Users of PaperMod, DoIt, and similar themes

Background / Motivation

After setting up a Hugo site, common questions include:

API Standards

Thu, 06 Nov 2025 00:00:00 +0000

Title

How to Write a Qualified API Document: From Swagger to Modern OpenAPI

Subtitle / Abstract

Want developers to actually enjoy using your API? This article covers the structure, examples, and best practices of high-quality API documentation based on Swagger/OpenAPI (originally by Tony Tam).

Target readers

Beginners who want a standard API doc structure
Mid-level developers improving maintainability
Architects and leads defining API standards

Background / Motivation

Common problems in API docs:

Thoughts on AI Systems

Fri, 31 Oct 2025 00:00:00 +0000

For a system, a single thread should be a single assistant. We should provide each user with one assistant and optimize that assistant.

Providing many parallel threads per user is too expensive and unnecessary.

How to Write a Perfect Machine Learning Document

Fri, 24 Oct 2025 00:00:00 +0000

Bengio-style ML Task Specification: From Research to Engineering

Subtitle: How to write a reproducible, explainable, and comparable fine-tuning task document based on Yoshua Bengio’s methodology.

Reading time: 10 minutes Tags: ML documentation, fine-tuning, technical standards, deep learning practice Audience: mid to senior ML engineers, researchers, technical writers

1. Why do we need this document?

In ML projects, teams often run fine-tuning experiments. Months later, nobody can reproduce results or explain why a learning rate or LoRA layer was chosen.

A New Frontend Idea for AI Assistants

Wed, 27 Aug 2025 00:00:00 +0000

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

Mastering a Paper

Tue, 26 Aug 2025 00:00:00 +0000

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.

Reading an NVIDIA Paper on Small Language Models

Tue, 26 Aug 2025 00:00:00 +0000

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.