Become Undeniable AI Systems Builder

Become Undeniable AI Systems Builder | Generated by AI

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Question: What should I do in the next few months?

Given your current trajectory (AI engineer transition, already training LLMs, building agents, running consulting, blog/community, and having strong backend experience), I would optimize for becoming visibly undeniable as an AI systems builder rather than collecting more knowledge.

The next 3–6 months should be:

1. Build a public AI engineering portfolio (highest ROI)

You already have rare experience:

trained GPT-2 124M from scratch on RTX 4070
trained nanochat-scale models
debugged CUDA / FlashAttention / vLLM
built agents
ran production systems

Most “AI engineers” cannot show this.

Turn it into artifacts.

Project A: “Train LLM from scratch” series

Repo:

github.com/lzwjava/llm-from-scratch

Structure:

01-tokenizer
02-nanogpt-baseline
03-pretraining-gpt2-124m
04-distributed-training
05-eval
06-inference-engine

Write blog posts:

“I trained GPT-2 124M on RTX 4070”
“What happens inside the transformer forward pass”
“Building vLLM-like inference from scratch”

This attracts:

AI startups
recruiters
researchers

2. Build one serious AI agent product

Don’t build another chatbot.

Build something with business value.

Examples:

Option 1: AI coding agent

Like a mini Claude Code:

Architecture:

User
 |
CLI
 |
Agent loop
 |
+-- planner
+-- tool executor
+-- code search
+-- git operations
+-- test runner
 |
LLM

Your advantage:

Java/backend experience
Linux
infra
LLM knowledge

Possible repo:

ww-agent

Features:

ww fix bug
ww explain repo
ww refactor module
ww generate tests

Option 2: AI consulting demo

Create:

AI Enterprise Agent Platform

Demo:

PDF
 |
RAG
 |
Agent
 |
ERP/CRM/API tools
 |
approval workflow

Target:

Chinese enterprises
Singapore companies
overseas SMBs

You can sell this.

3. Learn inference deeply (next frontier)

You already touched training.

The industry value is moving toward:

Training
   |
   v
Inference systems
   |
   v
Agents
   |
   v
AI products

Spend 1–2 months understanding:

vLLM internals

Implement:

PagedAttention
KV cache
continuous batching
scheduler
tensor parallel

Minimal version:

tokens
 |
scheduler
 |
KV cache manager
 |
attention kernel
 |
GPU

Your background fits this very well.

4. Improve your English technical presence

Your advantage is unusual:

Chinese + English + AI engineering.

Write English posts:

Examples:

I trained GPT-2 124M on a $600 GPU.
Here is what I learned.

Understanding vLLM by implementing it from scratch.

Building an AI coding agent in Python.

Post:

GitHub
Hacker News
Reddit r/MachineLearning
X/Twitter

One viral technical post can create opportunities.

5. Don’t spend too much time on fundamentals now

You already passed the beginner stage.

Avoid:

watching endless courses
reading random papers
trying every new model

Your bottleneck is not knowledge.

Your bottleneck:

knowledge
   |
   v
artifact
   |
   v
users
   |
   v
money/opportunities

Suggested timeline

August 2026

Goal: portfolio

finish:
- GPT training repo
- 3 technical blogs
- GitHub cleanup

September

Goal: inference

implement:
- mini vLLM
- KV cache
- batching

Publish.

October

Goal: product

Ship:

AI coding agent
or
enterprise AI agent demo

Find first users.

November-December

Convert:

portfolio
    |
    v
job offers / consulting clients / startup revenue

One strategic observation

Your current profile is actually closer to:

Karpathy-style builder
+
Chinese startup engineer
+
production backend engineer

The missing piece is public proof.

A person who says:

“I know LLMs”

is common.

A person who says:

“I trained GPT-2 on my RTX 4070, implemented attention, built an inference engine, and shipped agents used by customers”

is rare.

Your next months should maximize that gap.

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