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danielpham / engineering notebook

● AI Engineer / Field notes

Building AI
beyond the demo.

I'm danielpham, an AI Engineer. I build with LLMs and machine learning, and write down what actually works.

Welcome to my blog! I am DanielPham, an AI Engineer. This blog is where I share my knowledge and experience around AI.

Read the notebook About me
github.com/thuc248997Let's build something useful
danielpham@around-ai: ~
danielpham

~ $ whoami

danielpham

~ $ cat role.txt

AI Engineer

~ $ ls ./interests

llm/ ml/ agents/ self-hosting/

~ $

Hands-on by choice. Curious by default.

3

Published posts

Since 2023

Writing in the open

16

Topics covered

AI Engineer

danielpham

01 / Selected work

From an idea to something useful.

All projects

Open source / Self-hosted

around-ai blog.

Write in markdown. Ship on your own box.

This blog: a self-hosted Next.js + PostgreSQL stack with a markdown editor, running in Docker behind a Cloudflare Tunnel.

Next.jsPostgreSQLDocker
Explore around-ai blog
around-ai / docker compose
$ docker compose ps
NAME      STATUS
app       Up (healthy)
db        Up (healthy)
tunnel    Up

$ curl -s around-ai.com/api/health
{"ok":true}

$
Self-hosted. Markdown in, blog out.

02 / How I work

The whole system.
From technology to business.

How I work: see the whole system, from technology and engineering to business, before going into the details.

Background & experience
  1. 01

    Pick the technology for the problem, not the hype.

    Know the strengths, limits and cost of each option (models, infra, data) before committing.

  2. 02

    Engineer for production, not the demo.

    Clear architecture that can be measured and operated: latency, cost and reliability planned from day one.

  3. 03

    Tie every technical choice to business value.

    Start from users and goals, and measure by real outcomes, not just technical metrics.

03 / Current explorations

The questions keeping me curious.

01

LLM applications

Agents, RAG and tool use that hold up outside of a notebook.

02

Machine learning in practice

Training, evaluation and the boring parts that make models useful.

03

Running AI yourself

Self-hosting models and infra: what it costs and what it takes.

04 / Field notes

Building things. Writing it down.

All articles
View all 3 posts

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Good systems start with a conversation.

Let's talk

Have a project, a question, or an interesting idea? I'd love to hear it.