Interconnects, the Substack of former AI2 post-training lead Nathan Lambert, is the inside-the-labs newsletter for the models and reasoning layer under agentic development, minus the hype.
He explains the research and the open-model ecosystem from someone who built it, which makes him the direct bridge between the frontier labs and the engineer choosing a model or an agent today.
What it is #
A Substack newsletter, website, and interview podcast by Nathan Lambert, a machine learning researcher who was a post-training lead at the Allen Institute for AI (Ai2) and co-led the OLMo open-model effort.
The newsletter describes itself as the cutting edge of AI from inside the frontier labs, minus the hype.
His Get Good at Agents (2026-01-21) piece and his
Claude Code reviews document how he actually operates coding agents in his own RL research.
Status #
Active and prolific as of 2026-10-04.
The newsletter reports over 85,000 subscribers as of 2026-10-04, and his most recent posts are “The current balance of power in open models” (2026-09-21), the expanded form of testimony he prepared for Congress, and a podcast with JS Denain of Epoch AI (2026-09-22), following the RSI-skeptic essay “Why I still haven’t bought into true RSI” (2026-09-19).
He released the RLHF Book (‘26) with a companion post-training course, and his site links to open-model tools he maintains: the Artifacts Hub, an Adoption Dashboard, and the ATOM Project.
The “currently doing something new” tease after leaving Ai2 has landed: on 2026-10-01 he co-founded
Trillium Labs with Tom Zick, a nonprofit for the open science of frontier AI that will build fully open post-training recipes and open infrastructure to study RSI, reward hacking, and multi-agent systems, with early support from Halcyon Futures and Schmidt Sciences.
Strengths #
- He writes about post-training, reasoning, and open models from direct authorship rather than press coverage, so the technical claims are reliable.
- He uses the coding agents under discussion in his own research work, documenting real multi-agent and RL workflows rather than demos.
- The focus on open models and candid comparisons gives readers a usable lens for model selection without relying on benchmark marketing.
- The RLHF Book and course are a rare, grounded curriculum for the post-training layer that most practitioners never get.
Cautions #
- The newsletter is opinion-driven and fast-moving, so takes can shift and need re-checking against primary sources.
- Some posts are analysis and commentary rather than new findings, and readers must separate signal from churn.
- The writing is research-oriented and assumes familiarity with fine-tuning and RL concepts.
- He is an open-model advocate, so the open-versus-proprietary framing is a side he argues from, even when balanced.
Pricing #
Free to read, reader-supported via paid subscriptions. The RLHF Book is paid; parts of the course and companion materials are free.
Compared to #
- Lilian Weng: both explain the model and reasoning layer, but Weng surveys the broader research canon while Lambert tracks the current open-model and post-training state.
- Chip Huyen: the model-training microview versus the application-systems macroview; Lambert is upstream of where Huyen’s survey starts.
- Andrej Karpathy: both are researcher-writers who set vocabulary, but Lambert is high-cadence and tool-engaged while Karpathy is low-frequency and conceptual.
Bottom line #
Recommended for the engineer who wants to understand the reasoning-and-post-training layer behind the agents they use, and to pick open models with good information. Not for anyone who wants a mostly-vendor-neutral or application-level overview; this is the model-side view, argued from an open-model vantage.
Top 5 recommended reading #
Get Good at Agents - His documented personal workflow for scoping and running coding agents, the piece most directly useful to engineers who use them daily.
Reinforcement Learning from Human Feedback - His RLHF and post-training textbook with its companion course, the durable curriculum behind everything he writes about the model layer.
Changes #
- 2026-08-29 - Created as the model-and-post-training band of the people and publications category expansion.
- 2026-09-16 - Subscriber count moved to over 83,000 (from over 82,000), with no new posts since the September 8 to 11 cluster.
- 2026-09-20 - New essay “Why I still haven’t bought into true RSI” (2026-09-19) ended the quiet stretch after the September 8 to 11 posts.
- 2026-09-22 - Two posts after the RSI essay: “The current balance of power in open models” (2026-09-21, expanded Congressional testimony) and the Epoch AI podcast with JS Denain (2026-09-22).
- 2026-09-24 - Added the Top 5 recommended reading section.
- 2026-09-27 - Subscriber count moved to over 84,000; no new posts since the September 21 to 22 pair.
- 2026-10-04 - Window audit: added the Trillium Labs launch (2026-10-01), the nonprofit he co-founded with Tom Zick for open post-training science, resolving the “doing something new” teaser; subscriber count refreshed to over 85,000 as of 2026-10-04.
See also #
- Model Selection for Coding Tasks - the decision his model-and-reasoning coverage feeds
- Agentic Coding Tools Landscape - the harnesses he uses and evaluates in his own research
- Lilian Weng - the complementary research-survey voice
- Scaling the LLM Agent Company - the supervision and model questions his multi-agent RL work raises
References #
https://natolambert.com/ - his site with background, the RLHF Book, and the open-model tools
https://aiweekly.co/alerts/nathan-lamberts-rlhf-course-reaches-tool-use-and-agents - a third-party note that grounds his course contents and its stated limits
https://blog.trilliumlabs.org/cp/218529442 - the Trillium Labs launch announcement (2026-10-01), grounding the nonprofit, its co-founder Tom Zick, and its open post-training mission