Uploaded November 2025 | Updated September 2026, 3 weeks ago
Jared Palmer, SVP at GitHub and VP of CoreAI at Microsoft, joins Latent Space for an in-depth look at the evolution of coding agents and modern developer tools. Recently joining after leading AI initiatives at Vercel, Palmer shares firsthand insights from behind the scenes at GitHub Universe, including the launch of Agent HQ which is a new collaboration hub for coding agents and developers.
This episode traces Palmer’s journey from building Copilot inspired tools to pioneering the focused Next.js coding agent, v0, and explores how platform constraints fostered rapid experimentation and a breakout success in AI-powered frontend development. Palmer explains the unique advantages of GitHub’s massive developer network, the challenges of scaling agent-based workflows, and why integrating seamless AI into developer experiences is now a top priority for both Microsoft and GitHub.
00:00:00 Introduction and Jared's New Role at GitHub
00:01:00 From V0 to Agent HQ: The Evolution of Coding Agents
00:02:51 The V0 Origin Story: From ChatGPT to AI Playground
00:05:40 Building the AI SDK and ShadCN Collaboration
00:07:08 The Birth of V0: Prompt to UI Revolution
00:09:18 V0's Growth Journey and Model Evolution
00:11:05 Model Strategy: Composite Models vs User Choice
00:13:16 GitHub's Agent HQ and Model Marketplace
00:15:51 The Future of Agent Abstraction and Standards
00:16:33 Microsoft Core AI Integration and Workflow Vision
00:18:37 Dev Containers and Repo Setup Challenges
00:24:10 Agent Quality and Infrastructure Reliability
00:27:05 Using Coding Agents for Non-Coding Tasks
00:29:11 GitHub Homepage Redesign and Community Feedback
00:30:27 Stacked Diffs: GitHub's Most Requested Feature
Jared Palmer, SVP at GitHub and VP of CoreAI at Microsoft, joins Latent Space for an in-depth look at the evolution of coding agents and modern developer tools. Recently joining after leading AI initiatives at Vercel, Palmer shares firsthand insights from behind the scenes at GitHub Universe, including the launch of Agent HQ which is a new collaboration hub for coding agents and developers.
This episode traces Palmer’s journey from building Copilot inspired tools to pioneering the focused Next.js coding agent, v0, and explores how platform constraints fostered rapid experimentation and a breakout success in AI-powered frontend development. Palmer explains the unique advantages of GitHub’s massive developer network, the challenges of scaling agent-based workflows, and why integrating seamless AI into developer experiences is now a top priority for both Microsoft and GitHub.
00:00:00 Introduction and Jared's New Role at GitHub
00:01:00 From V0 to Agent HQ: The Evolution of Coding Agents
00:02:51 The V0 Origin Story: From ChatGPT to AI Playground
00:05:40 Building the AI SDK and ShadCN Collaboration
00:07:08 The Birth of V0: Prompt to UI Revolution
00:09:18 V0's Growth Journey and Model Evolution
00:11:05 Model Strategy: Composite Models vs User Choice
00:13:16 GitHub's Agent HQ and Model Marketplace
00:15:51 The Future of Agent Abstraction and Standards
00:16:33 Microsoft Core AI Integration and Workflow Vision
00:18:37 Dev Containers and Repo Setup Challenges
00:24:10 Agent Quality and Infrastructure Reliability
00:27:05 Using Coding Agents for Non-Coding Tasks
00:29:11 GitHub Homepage Redesign and Community Feedback
00:30:27 Stacked Diffs: GitHub's Most Requested Feature
![[State of Research Funding] Beyond NSF, Slingshots, Open Frontiers — Andy Konwinski, Laude Institute](https://i.ytimg.com/vi/ZagdY6UJYL4/mqdefault.jpg "[State of Research Funding] Beyond NSF, Slingshots, Open Frontiers — Andy Konwinski, Laude Institute
From co-founding *Databricks* and *Perplexity* to launching the *Laude Institute*—a dual venture fund and nonprofit designed to turbocharge the path from *research breakthrough to breakout company*—*Andy Konwinski* is building the infrastructure to recreate the Databricks motion at scale: fund researchers doing open work, help them ship products that matter, and turn paradigm-shifting ideas into trillion-dollar companies. We caught up with Andy live at *NeurIPS 2025* to dig into the origin story of Laud (right resource, right researcher, right time), why the *Databricks founding model* (eight co-founders, deep research scars, years of collaboration) is becoming the gold standard for AI startups (not an anomaly), how Laudes *venture arm* backs researchers-turned-founders with 50+ professors and PhDs as LPs (Jeff Dean, top Berkeley/Stanford faculty, Databricks and Perplexity co-founders), why the *nonprofit arm* does no-strings-attached grants to fund open research before incorporation (the upstream funnel that feeds the next generation of companies), the *slingshot program* funding breakthrough projects like *DSPy, Terminal Bench, LMArena, and continual learning research,* why *NSF isnt broken but insufficient* (its $1B/year for computer science when we need $10-100B, and Silicon Valleys picker model can deploy capital more effectively), how the *post-post-training layer* (prompt optimization, context management, RAG, memory curation, tool usage) is becoming the new frontier above pre-training and post-training, why *Chinese labs are outpublishing Western labs* in open research (Moonshot, DeepSeek shipping twice as many interesting papers as American startups because OpenAI and the frontier labs stopped publishing), the launch of *Open Frontiers*—a live-streamed conference in San Francisco bringing together the 100 most influential open researchers (Yann LeCun, François Chollet, Jan Leike, Percy Liang, Berkeley AI Research, Allen Institute, and more) to share roadmaps and unify the ecosystem, why the *Laude Lounge at NeurIPS* became the VIP gathering spot (Starlink WiFi, free food, couches, and the gods of AI hanging out because conferences need a place for the VVIPs to actually sit down), and his thesis that *open research is the path to world-changing impact*—and Laud is the bridge from grant to company, from paper to product, and from researcher to billionaire founder. We discuss:
* What *Laude Institute* does: dual structure with a *venture fund* (backing researchers-turned-founders post-incorporation) and a *nonprofit* (no-strings-attached grants for open research pre-incorporation)
* The *slingshot program:* funding *DSPy, Terminal Bench, LMArena, continual learning, and Jepa-style prompt optimization* projects across Berkeley, Stanford, MIT, CMU, Wisconsin, Caltech, UI Urbana-Champaign, Toronto, Waterloo, and beyond
* The *post-post-training layer:* compound systems, prompt optimization, context management, RAG, memory curation, tool usage—the layer above pre-training and post-training where innovation is exploding
* *GEPA and DSPy:* evolutionary prompt optimization (genetic algorithms reinvented by PhD student Laxia) and the DSPy framework (a reverse compiler that takes code and compiles natural language)
* Why *NSF isnt broken but insufficient:* $1B/year for computer science (and theyre trying to cut it in half) when we need $10-100B for frontier AI research—Laud complements NSF with Silicon Valleys picker model and high-velocity grant writing
* The *PhD entrepreneurship clubs:* Computer Science Grad Entrepreneurs (CSGE) at Berkeley (started 2012), Agent at University of Washington, Research to Impact at Wisconsin, Saplings at Stanford, and more clubs forming at CMU, MIT, UI Urbana-Champaign
* *Open Frontiers:* a live-streamed conference in San Francisco (next five months) bringing together the 100 most influential open researchers (Yann LeCun, François Chollet, Jan Leike, Percy Liang, Berkeley AI Research, Allen Institute, and more) to share roadmaps and unify the ecosystem
* The vision: *open research as the path to world-changing impact,* and Laud as the bridge from grant to company, from paper to product, and from researcher to trillion-dollar founder
— Andy Konwinski
* Laude Institute: https://www.laude.org/lounge
* X: https://x.com/andykonwinski
00:00:00 Introduction: Andy Konwinski and the Laud Institute Vision
00:01:17 The Databricks Motion: From PhD Research to Billion-Dollar Companies
00:02:15 Lauds Two-Sided Model: Venture Fund and Philanthropic Grants
00:06:37 Slingshot Program: Funding the Layer Above Foundation Models
00:07:56 JEPA and DSPy: Evolutionary Prompt Optimization
00:10:29 Beyond Berkeley and Stanford: Expanding the Research Network
00:13:22 NSF Complementarity: Not Broken, Just Insufficient
00:17:03 The Laud Lounge: Creating a VIP Experience at NeurIPS
00:18:41 Open Frontiers: Reclaiming Leadership in Open AI Research
00:19:06 The Open Research Crisis: Why")
![[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI](https://i.ytimg.com/vi/botHQ7u6-Jk/mqdefault.jpg "[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI
From pre-training data curation to shipping *GPT-4o,* *o1,* *o3,* and now *GPT-5 thinking* and the *shopping model,* *Josh McGrath* has lived through the full arc of OpenAIs post-training evolution—from the PPO vs DPO debates of 2023 to todays RLVR era, where the real innovation isnt optimization methods but *data quality, signal trust, and token efficiency.* We sat down with Josh at *NeurIPS 2025* to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how *GRPO* from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you cant), why *token efficiency* matters more than wall-clock time (GPT-5 to 5.1 bumped evals _and_ slashed tokens), how *Codex* has changed his workflow so much he feels trapped by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL (way more moving parts than pre-training), why *long context* will keep climbing but agents + graph walks might matter more than 10M-token windows, the *shopping model* as a test bed for interruptability and chain-of-thought transparency, why *personality toggles* (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isnt producing enough people who can do *both distributed systems and ML research*—the exact skill set required to push the frontier when the bottleneck moves every few weeks.
We discuss:
* Joshs path: *pre-training data curation → post-training researcher at OpenAI,* shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model
* Why he switched from pre-training to post-training: Do I want to make 3% compute efficiency wins, or change behavior by 40%?
* The *RL infrastructure challenge:* way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly
* How *Codex* has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange trapped feeling of waiting for the agent to finish
* The *RLHF vs RLVR debate:* both are policy gradient methods, the real difference is *data quality and signal trust* (human preference vs. verifiable correctness)
* Why *GRPO* (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes)
* The *token efficiency revolution:* GPT-5 to 5.1 bumped evals _and_ slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows
* *Personality toggles:* Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model just a tool
* The *router problem:* having a router at the top (GPT-5 thinking vs non-thinking) _and_ an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge
* *Long context:* climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length
* Why the education system isnt producing enough people who can do *both distributed systems and ML research,* and why thats the bottleneck for frontier labs
* The 2026 vision: *neither pre-training nor post-training is dead,* were in the fog of war, and the bottleneck will keep moving (so emotional stability helps)
—
Josh McGrath
* OpenAI: https://openai.com
* https://x.com/j_mcgraph
00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI
00:04:37 The Shopping Model: Black Friday Launch and Interruptability
00:07:11 Model Personality and the Anton vs Clippy Divide
00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL
00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training
00:13:12 Token Efficiency: The 2D Plot That Matters Most
00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting
00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context
00:21:23 The ML-Systems Hybrid: Whats Hard to Hire For
00:24:50 Pre-Training Isnt Dead: Living Through Technological Revolution")
