Inside the Model Factory — Eiso Kant, Poolside AI
- ID
- 7097
- Status
- summarized
- Published
- 23 Jul 2026, 1:09 PM
- Fetched
- 23 Jul 2026, 1:40 PM
- Provider
- Latent Space
- Category
- developer-ai
- Original URL
- https://www.latent.space/p/poolside
- Source URL
- https://www.latent.space/feed
Summary
- Score
- 7.5
- Created
- 23 Jul 2026, 1:40 PM
- Tags
- Audience
- developersai_ml_learnerssaas_founders
What happened
Poolside AI co-CEO Eiso Kant discusses how a small team of researchers built a model factory that trained Laguna S, a 118B mixture-of-experts model that reportedly outperforms Thinky's ~1T open-weights model. The conversation covers their training infrastructure, methodology, and roadmap for what comes next.
Why it matters
For AI/ML learners and SaaS founders, this is a signal that small, focused teams can compete with massive-scale AI labs by optimizing model architecture and training pipelines. Understanding how a 118B MOE can beat a ~1T model has practical implications for anyone deciding whether to build on open-weights models, fine-tune, or train from scratch — a decision increasingly relevant as compute costs and model selection shape Southeast Asian AI startups' go-to-market strategies.
Discussion angle
What does a small team beating a 10x larger model mean for Malaysian startups choosing between open-weights models, API providers, and custom training — and where does MOE architecture fit in that decision tree?