Haney GPT & Haney CLI Coding Agent

I Built a 537M Parameter GPT Model and an AI Coding Agent as an Independent Learner

Over the past few years, I have spent much of my time learning and building in the AI space. While my path into AI engineering has been unconventional, one thing has remained constant: curiosity.

That curiosity led me to build two projects that taught me more than any course ever could:

🐱 Haney GPT

and

🐱 Haney CLI

Haney GPT

Haney GPT started as an attempt to understand how large language models work under the hood.

Rather than treating LLMs as black boxes, I wanted to learn about tokenization, transformers, training pipelines, datasets, optimization, and inference by building things myself.

Over time, the project evolved into a GPT-style model that I scaled to 537 million parameters.

The process taught me lessons about:

• Data preparation


• Training stability


• GPU limitations


• Evaluation


• Model deployment


• LLM architecture

Most importantly, it gave me a much deeper appreciation for the engineering challenges involved in modern AI systems.

Haney CLI

After working with language models, I became interested in AI agents and developer tooling.

That led me to build Haney CLI, an AI-powered command-line assistant designed for developers.

Install:

pip install haney

Haney CLI follows a BYOK (Bring Your Own Key) approach and currently supports multiple model providers, allowing developers to use the models that best fit their workflow.

Current capabilities include:

• 6 AI providers


• 10 MCP integrations


• Plan Mode


• Edit Mode


• Developer-friendly CLI workflows


• Extensible architecture

The goal is not to replace developers but to create a practical AI companion that can help with coding, research, planning, and automation tasks.

Lessons Learned

Building AI systems independently taught me that the hardest problems are rarely technical.

The biggest challenges are:

• Understanding user needs


• Creating good developer experiences


• Handling edge cases


• Maintaining simplicity while adding power

Every feature seems easy until real users start using it.

Building in Public

I continue to document my learning journey through projects, experiments, and open-source work.

Projects:

🐱 Haney CLI

Sakthi Wiki is an LLM-powered wiki where I document concepts, experiments, and learnings from AI engineering, machine learning, and software development.

What's Next?

I am continuing to explore:

• AI agents


• Coding assistants


• MCP integrations


• LLM training


• Developer tooling


• AI-powered career and productivity agents

If you're building in this space, I'd love to connect and learn from your experiences as well.

Small steps, consistent learning, and lots of curiosity have brought me this far.

Let's keep building.