Research

My summer at Overmind

by Shashank

A summer internship reflection on autonomy, hard problems, and building across the Overmind product.

Small is the new frontier

by Tyler Edwards

Why specialized models trained on your own production data are becoming the practical path to owned intelligence.

When bigger isn’t better

On specialized tasks, Overmind-trained small language models outperform frontier models on accuracy, hallucination, and cost.

How to train your agent

by Pritam Soni

Learnings from building, deploying and improving agents in production.

Unwrapping the wrappers

by Sam Brunt

Application layer companies are training their own models. Why?

How do you turn traces into a training dataset?

by Rohit Gupta

Every agentic system can create its own training data.

Prompt engineering vs fine-tuning

by Rohit Gupta

Prompt engineering changes what you send the model. Fine-tuning changes the model's weights. Start with prompts because they are fast, cheap and reversible. Fine-tune when the eval stops improving, the task is narrow, and you have a few thousand real examples of the output you want.

What are the different types of fine-tuning?

by Rohit Gupta

What's the difference between SFT, LoRA and Distillation?

Open-weight vs frontier models: when to rent, when to own

by Pritam Soni

Frontier models are the most capable models available, and most are closed and rented through an API. Open-weight models publish their weights, so you can fine-tune them and run them where you choose. Rent while volume is low and the task is broad. Own once volume is high, the task is narrow, or data cannot leave your perimeter.

The anatomy of an AI agent

by Pritam Soni

An agent has a model, loop, tools and memory.

So, you have observability. Now what?

by Tyler Edwards

Observing your agent is table stakes.

Secret agent Overmind

by Tyler Edwards

Your agent is the most documented system in your engineering org and yet you can't answer simple questions.

Think smaller

by Tyler Edwards

Frontier models are overkill for most agent tasks. Specialized small language models beat them on cost, latency, and accuracy.