Mirendil

Huge thanks to @baseten! Fast, reliable rollouts and seamless weight syncs kept our RL run moving at scale. A great partner across the board.

Meet Lor.

We’re building Lor to do the work of an entire AI lab. Today, its agents write our code, run experiments, and iterate on the results for days, on any machine, hundreds at a time.

Now in private beta ↓

Introducing Fern, Mirendil’s first model: pretrained from scratch, with up to 10 million tokens of context.

That’s ~0.5M lines of code or days of an agent’s work - all in context at once.

Built for high-volume agentic work in AI R&D: auditing training data, debugging training runs from logs, and reading agent traces end to end. Fern’s next-token prediction keeps improving as context grows to 10M tokens.

Fern 0.1 is Mirendil-Base after a short round of post-training. It’s the first and smallest of three models, with two larger ones now being pretrained.

We’ll ship frequent updates and make the model available soon.

Today, we're sharing a major milestone in our mission to democratize frontier AI development:

Pretraining at scale without a pretraining team.

We set out to answer an ambitious question: Can we significantly reduce the time, human expertise, and compute needed to pretrain a competitive foundation model from scratch?

The result is Mirendil-Base, our first pretrained coding model, built by one researcher in just 3.5 months with less than $10M in compute, including all research, experimentation, and the final training run.

Our approach is already showing promising results as we scale to even larger models.

This is an important step toward a future where a single researcher, empowered by AI, can accomplish what once required an entire research organization. As these capabilities improve, we believe frontier AI development can become accessible to far more people and teams.

More to come soon, including independent evaluations, post-training benchmarks, and API access.

Today, we're announcing a multi-year partnership with @Google to power the next generation of self-accelerating AI!

Mirendil will scale its training, inference, and AI research workloads on @googlecloud 's AI Hypercomputer — drawing on both TPUs and full-stack NVIDIA AI Infra.