An expert overview of post-training techniques for language models, covering the entire workflow from data generation and curation to advanced algorithms like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), along with practical advice on evaluation and iteration.
This talk introduces Anaximander, a system designed to bridge the gap between traditional, GUI-driven Geographic Information System (GIS) workflows and modern, code-heavy machine learning practices. Anaximander integrates geospatial foundation models directly into QGIS, allowing experts to interactively orchestrate, run, and evaluate models for tasks like semantic segmentation and object detection on satellite imagery.
At Applied Compute, efficient Reinforcement Learning is critical for delivering business value. This talk explores the transition from inefficient synchronous RL to a high-throughput asynchronous 'Pipeline RL' system. The core challenge is managing 'staleness'—a side effect of in-flight weight updates that can destabilize training. The speakers detail their first-principles systems model, based on the Roofline model, used to simulate and find the optimal allocation of GPU resources between sampling and training, balancing throughput with algorithmic stability and achieving significant speedups.
Ben Horowitz of Andreessen Horowitz discusses how AI is reshaping venture capital, detailing the firm's strategy for managing partners, verticalizing teams, and evaluating investments in real-time. He breaks down the current AI cycle, arguing that application design and model orchestration are key, and explains why the current market's growth is driven by real demand, not just hype.
Ashwin Rajeeva, cofounder and CTO of Acceldata, details the architecture and philosophy behind their Agentic Data Management (ADM) platform. He explains how the Xlake reasoning engine provides crucial context for AI agents to operate across petabyte-scale enterprise data, enabling capabilities like self-healing data pipelines and automated data quality assurance, while also discussing strategies for leading technical teams in the age of AI.
A panel of university students from LSE, Princeton, Berkeley, and ASU discuss the real-world impact of AI on campus life. They cover how AI is used as both a powerful learning tool and a crutch, the innovative projects students are building, how universities are adapting, and the challenges of navigating cheating, job applications, and 'AI slop' in a rapidly changing educational landscape.
Nikesh Arora, CEO of Palo Alto Networks, shares his unconventional journey and leadership philosophy. He provides a masterclass in building a multi-platform company through strategic M&A, explains why founders should sometimes ignore customers, and reveals how to lead with conviction while managing imposter syndrome.
Stewart Butterfield, co-founder of Slack and Flickr, shares the product frameworks and leadership principles that guided his success. He delves into concepts like "utility curves" for feature investment, the "owner's delusion" in product design, and why focusing on "comprehension" is often more important than reducing friction. He also introduces powerful mental models for organizational effectiveness, such as combating "hyper-realistic work-like activities" and applying Parkinson's Law to team growth.
Intuit CEO Sasan Goodarzi discusses the operational playbook for reinventing a 40-year-old company, from its slow transition to SaaS to its early adoption of AI. He shares insights on winning the SMB market by treating small businesses like consumers, building effective channel partnerships, and developing a platform strategy. Goodarzi also details his leadership philosophy, emphasizing that grit and curiosity are more critical than raw talent.
Professor Chris Kempes of the Santa Fe Institute proposes a universal theory of life that transcends Earth-based biochemistry. He introduces a three-level framework (Materials, Constraints, Principles) to explain how abstract rules like evolution could define life on any substrate, including culture, language, or artificial intelligence, leading to the concept of convergent evolution where similar solutions, like the eye, emerge independently across different physical forms.
Kyle Corbett and Daniel from CoreWeave (formerly Openpipe) discuss the practical advantages of Reinforcement Learning (RL) over Supervised Fine-Tuning (SFT) for building reliable and efficient AI agents. They introduce Serverless RL, a new platform designed to eliminate the infrastructure complexities of RL training, and share a playbook for teams looking to get started.
Co-founders Albert Gozzi and Santiago Perez De Rosso of Aleph discuss their journey building an AI-native FP&A platform. They share insights on their first-principles approach of meeting finance teams in spreadsheets, achieving rapid time-to-value, and the early challenges of building a horizontal product. The conversation covers their YC experience, scaling to a $29M Series B, and their vision for the future of finance powered by pragmatic, accuracy-focused AI.
Evan Czaplicki, creator of Elm, discusses the sustainability challenges of open-source that led to Elm's development pause, his new work on a full-stack typed functional language, and his thoughts on improving developer experience to make functional programming more accessible.
A discussion on OpenAI's new browser ChatGPT Atlas, Andrej Karpathy's pessimistic timeline for AI agents, the DeepSeek-OCR paper on visual context compression, and a study suggesting large language models can suffer from "brain rot" when trained on low-quality social media data.
Amjad Masad, founder of Replit, joins a16z to discuss the rise of AI agents that can now plan, reason, and code for hours. He explains how reinforcement learning and verification loops unlocked long-horizon reasoning, why AI is advancing fastest in verifiable domains like code, and debates whether "good enough" AI might be a local maximum that blocks the path to AGI.
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