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#124 - The Path to AGI: Inside poolside’s AI Model Factory for Code with Eiso Kant
Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed Teams
Technical Deep Dives:
"Our model factory runs thousands of experiments before a single production model is trained. It’s an empirical science—every component, from data ingestion to evals, is versioned and traceable." – Eiso Kant
Chapters:
[00:04:28] Poolside’s unique approach to foundation models
[00:13:02] Scaling hardware: 10,000+ H200s and orchestration
[00:17:42] RL, agents, and the future of developer tools
[00:24:56] Immutable versioning and evaluation frameworks
[00:36:04] Distributed team structure and monthly sprints
[00:40:26] Leadership, decision-making, and low-ego culture
[00:45:54] Lessons for CTOs: breaking process dogma, preparing for agent-driven orgs
[00:50:54] The next 3 years: AGI, agent workforces, and the end of manual coding
[00:53:44] Context window, continual learning, and model memory
[00:56:20] Everything collapses into the model: product, research, and daily life
[00:59:46] Advice to a younger self: scale compute, trust RL+LM, and the four-minute mile
View all episodes
By Tobias Schlottke - alphalist CTO Podcast
5
66 ratings
Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed Teams
Technical Deep Dives:
"Our model factory runs thousands of experiments before a single production model is trained. It’s an empirical science—every component, from data ingestion to evals, is versioned and traceable." – Eiso Kant
Chapters:
[00:04:28] Poolside’s unique approach to foundation models
[00:13:02] Scaling hardware: 10,000+ H200s and orchestration
[00:17:42] RL, agents, and the future of developer tools
[00:24:56] Immutable versioning and evaluation frameworks
[00:36:04] Distributed team structure and monthly sprints
[00:40:26] Leadership, decision-making, and low-ego culture
[00:45:54] Lessons for CTOs: breaking process dogma, preparing for agent-driven orgs
[00:50:54] The next 3 years: AGI, agent workforces, and the end of manual coding
[00:53:44] Context window, continual learning, and model memory
[00:56:20] Everything collapses into the model: product, research, and daily life
[00:59:46] Advice to a younger self: scale compute, trust RL+LM, and the four-minute mile
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