Mosaic

The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO

Jul 13, 2026 · 49:44

Engram co-founder and CEO Dan Biderman tells hosts Allen Park and Sean that long context and RAG aren't enough for AI memory—continual learning and gradient-based weight updates are needed. He argues Engram's approach compresses company knowledge into 'cartridges' that let models reason with far fewer tokens, overcoming 'context rot' and the inefficiency of re-reading massive corpora. Biderman draws on his background in Israeli special forces and computational neuroscience to explain how training creates intuition beyond text retrieval, citing examples like Harvey's legal queries where holistic understanding beats search. He envisions personal AI weights that improve like Tamagotchis, driven by user-specific feedback loops, and stresses that token efficiency and intelligence are inseparable—doing more with less enables harder problems. Engram is hiring infrastructure engineers to deploy millions of continuously updated memories.

The Agent Cloud: Databricks’ Bet on the Future of AI — Matei Zaharia and Reynold Xin

Jun 24, 2026 · 1:10:06

Databricks cofounders Matei Zaharia and Reynold Xin argue the company is moving beyond the lakehouse into a full data-and-AI operating system, anchored by two new initiatives: Omnigent, an open-source meta-harness for combining coding and enterprise agents, and LTAP, a unified storage layer that gets most HTAP benefits without collapsing query engines. Omnigent provides a common API for agent sessions, files, streams, tool calls, and cancellation, solving portability, collaboration, and security issues across Claude Code, Codex, Cursor, and custom agents. LTAP writes transactional data directly in columnar Parquet format, eliminating brittle CDC pipelines—Reynold jokes CDC means 'continuous data corruption'—and enables instant analytics without overloading the source database. The episode details Databricks’ culture of rapid prototyping, where an engineer built the LTAP prototype without a formal design doc, and the thesis that traditional software will be rewritten once data is in the right place with agents on top. They also cover Mosaic’s shift from general frontier models to specialized fine-tuned models like document parsing, internal agent usage, and security features such…

How to train a Million Context LLM — with Mark Huang of Gradient.ai

May 31, 2024 · 1:12:14

Mark Huang of Gradient.ai explains how his team extended Llama 3 to a 1 million token context window using curriculum learning, RingAttention, and EasyContext, achieving near-perfect GPU utilization. They employed theta scaling from the RoPE paper to interpolate positional encodings, trained on carefully curated datasets including SlimPajamas and synthetic data from GPT-4, and validated with benchmarks like Ruler and needle-in-a-haystack. Huang details the trade-offs between full fine-tuning and LoRA adapters, the challenges of pushing to 4M tokens (degradation from floating-point precision limits), and why long context matters for state management across sessions and grounding multimodal inputs. He calls for community collaboration on long-context evaluations and pairwise multimodal datasets.

Building an open AI company - with Ce and Vipul of Together AI

Feb 8, 2024 · 1:15:18

Together AI co-founders Vipul Ved Prakash and Ce Zhang explain why openness is core to their mission, detailing their journey from Apple (Vipul) and Stanford research (Ce) to building an open AI platform. They discuss RedPajama’s evolution into a modular dataset with 40 quality signals, the need for 5,000 tokens/second inference speed, and their investment in state space models like Mamba and Hyena as alternatives to transformers. The company, which runs 7,000-8,000 GPUs (mostly H100s), sees training as a larger workload than inference, with fine-tuning driving top models. They advocate for independent inference benchmarks, publish open research like FlashAttention, and keep some software proprietary. With 38 employees and 45% researchers, they are hiring across the stack, from CUDA to DevOps.

The AI-First Graphics Editor - with Suhail Doshi of Playground AI

Jan 2, 2024 · 1:09:23

Suhail Doshi, co-founder of Mixpanel and founder of Playground AI, argues that image generation is still in a GPT-2 moment and that training open-source foundation models from scratch is necessary to unlock real utility, exemplified by Playground v2's 2.5x preference over Stable Diffusion XL on a 1K prompt benchmark. He explains the pivot from Mighty (cloud-streamed browser) to AI, driven by the belief that shifting compute elsewhere aligns with AI's parallel computation. The episode covers Playground's unique UI—not just a prompt box but a full canvas with preview rendering, seed control, and style filters—and the difficulty of balancing safety with artistic expression, especially around NSFW content. Suhail discusses the under-investment in graphics AI compared to language, the open-source community's role, and his choice to release pre-trained weights for academic research. He also shares lessons from running GPU infrastructure (harder for training than inference) and advises founders to follow curiosity and build projects rather than relying on books.