Loading video...

Video Failed to Load

Go Home

Introducing Lab: A full-stack platform for training your own agentic models Build, evaluate and train on your own environments at scale without managing the underlying infrastructure. Giving everyone their own frontier AI lab.

3,154,871 views • 6 months ago •via X (Twitter)

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be Harvey, Factory, RamPrasad "RamP!" Moudgalya, etc. The list goes on. We held a summit Sequoia Capital to convene our portfolio on this topic, together with Gabe Pereyra (Harvey) on building Harvey Labs, Lin Qiao (Fireworks) on post-training, Harrison Chase (LangChain) on harnesses + evals, Brendan (can/do) () on RL environments and synthetic data, Arjun Karanam (Trajectory) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context

Sonya Huang 🐥

127,724 views • 22 days ago