Article

🐈💀 When My AIs Died: The Rise and Fall of Lynqbit & BarnOwl

Not all code runs forever. Some of it purrs once... then fades away.

A while ago, I decided to build my own AI systems from scratch.

Two of them became especially memorable: Lynqbit and BarnOwl.

🐈 Lynqbit AI — The Cat That Learned to Think

Lynqbit was my first serious AI experiment.

I wanted it to have personality, so it didn't just answer questions — it acted like a cat.

• Built with PyTorch
• Custom BPE tokenizer
• Around 94 million parameters
• Trained using my own dataset of 57k+ questions

After roughly two months, it started producing surprisingly good responses.

Then reality hit.

The model became too heavy for my available storage and computing power. Training slowed down, databases struggled, and eventually I had to let Lynqbit go.

RIP Lynqbit. 🐈💔

But it taught me that even a failed experiment can completely change how you think about building.

🦉 BarnOwl AI — The Night Scholar

I wasn't finished.

BarnOwl was supposed to be different — smaller, quieter, and more thoughtful.

I wanted it to observe and reflect rather than simply throw information at people.

For a while, it worked.

Then came the resource problems.

Training repeatedly crashed, datasets disappeared, and my hardware simply couldn't handle what I was trying to build.

After countless retries, I had to accept it:

BarnOwl couldn't fly. 🦉💀

💭 What They Taught Me

These projects taught me that AI isn't just algorithms.

It's compute, planning, patience, resources, and persistence.

Both AIs died.

But the lessons survived.

And honestly?

I'd build them again.

#AI #Python #Programming #MachineLearning #BuildInPublic #ArtificialIntelligence