Replicate

The Future of AI Infra: from Kubernetes to Agent Sandboxes — Akshat Bubna, Modal CTO

Jul 8, 2026 · 59:10

Modal CTO Akshat Bubna explains how the cloud platform evolved from a serverless runtime to an AI cloud built for elastic inference, agent sandboxes, and post-training workloads. He argues that Kubernetes was never designed for bursty GPU-heavy AI workloads, so Modal built a decorator-based infrastructure that co-locates compute requirements with code. The company added GPUs a year before ChatGPT and now powers inference for custom models at Suno, Runway, and robotics firms, using GPU snapshotting to slash cold starts. Modal's new Auto Endpoints incorporate open-source DeFlash speculative decoding for frontier-level performance. For reinforcement learning rollouts, Modal provides up to 100,000 sandboxes simultaneously. The platform spans 17 cloud providers, features private IPv6 networking via eBPF, and supports serverless multi-node training with RDMA. Bubna also reveals Modal's shift from developer experience to agent experience, noting that agents benefit from the same minimalist SDK and observability tools that humans use.

Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Eskildsen of Turbopuffer

Mar 12, 2026 · 1:00:32

Simon Eskildsen, founder of Turbopuffer, explains how his obsession with the cost of vector search at Readwise led him to build a search engine on object storage and NVMe SSDs, cutting costs by 95% for customers like Cursor. He outlines the three conditions for a major database company—a new workload (AI-driven search), a new architecture (object storage first, no consensus layer), and evolving to support every query plan. Turbopuffer's architecture uses S3's strong consistency and compare-and-swap for metadata, avoiding stateful systems. Eskildsen shares how they won Notion by absorbing latency issues with dark fiber, and discusses the shift from single RAG queries to agents making many concurrent searches. He also describes the "P99 engineer"—obsessive, trade-off minded, able to bend software to first principles. Future plans include expanding full-text search, reaching Common Crawl scale, and eventually adding simpler OLAP queries.

Why Your AI Agents Don’t Work with Dex Horthy of HumanLayer | In-Context Cooking

Mar 6, 2026 · 24:59

Dex Horthy, founder of HumanLayer and creator of "context engineering," joins Alan to recreate Dan Dan noodles while explaining why most AI agent demos fail in production and how the top 1% of AI engineers build differently from the rest. Horthy describes how his NASA JPL internship at 17 taught him pathfinding with Dijkstra's algorithm, and later coining "context engineering" after observing that public hype around open-source frameworks doesn't match enterprise reliability needs. He defines AI "slop" as low-effort derivative content often produced without reading, and introduces the "dumb zone"—a rule of thumb to start thinking about context compaction at 40% usage unless you have strong intuition. Horthy argues that while smarter models narrow the gap, the frontier of reliably achievable tasks keeps shifting, so context engineering remains valuable. He predicts software engineering will evolve from writing code to producing working code, emphasizing shipping over coding, and notes HumanLayer helps enterprises standardize AI adoption with guardrails rather than vibe coding.

[State of AI Papers 2025] Fixing Research with Social Signals, OCR & Implementation — Team AlphaXiv

Dec 31, 2025 · 34:45

AlphaXiv founders Raj and Rayhahn describe building the go-to platform for navigating AI research by obsessing over UI and getting authors like the Laura team to comment directly on papers, then evolving from comments to feeds to benchmarks to AI assistants. They detail how they beat Hugging Face Papers by enabling direct paragraph-level commenting and early traction with high-signal authors, and how they use social signals (views, comments, Twitter) to filter the archive firehose. They discuss the broken academic review process—20% AI-generated reviews at ICLR, quality collapsing under exponential submission growth—and argue papers are becoming less important than implementations, Docker containers, and real-world usability. Their vision: making it trivial to spin up any trending paper's code directly in browser, focusing on the top 0.1% of implementable papers that applied researchers actually care about.

A Technical History of Generative Media

Sep 8, 2025 · 1:04:44

Fal.ai founders Gorkem and Batuhan detail their pivot from dbt pipelines to generative media inference, which now serves 2M developers and 350 models, crossed $100M ARR, and raised a $125M Series C. Key inflection points included Stable Diffusion 1.5 (company pivot), SDXL (first $1M revenue), Flux (jump from $2M to $10M monthly revenue), and Veo 3 (text-to-video with perfect lip-sync). They built a proprietary inference engine with 100+ custom kernels and a serverless GPU stack managing 10,000+ H100 equivalents across 6 cloud providers, typically delivering 1.5-10x speedups over stock PyTorch. Video models now drive 50% of revenue, up from 18% in February, fueled by open-source models like Hunyuan and partnerships with closed labs such as Play.ht and Google DeepMind. They argue advertising is the killer application for generative media and that image/video RL, specialized data pipelines, and cheaper conversational video models are underexplored startup opportunities.

DeepSeek V3, SGLang, and the state of Open Model Inference in 2025 (Quantization, MoEs, Pricing)

Jan 19, 2025 · 57:18

Amir Haghighat and Yineng Zhang from Baseten explain how they serve DeepSeek V3, a 671-billion-parameter MoE model requiring H200 clusters with FP8 support, making it the top open-weights model. They detail Baseten’s dedicated inference model (no shared endpoints) and its reliance on SGLang for performance gains like Radix prefix caching and MLA attention support. Yineng highlights SGLang’s edge over vLLM and TensorRT-LLM in usability and customization, especially for large models. Amir outlines three pillars for mission-critical inference: model-level performance (via frameworks like SGLang), horizontal scaling across regions and clouds, and low-latency multi-model workflows. The episode also covers quantization trends, MoE architecture, and speculative decoding as key enablers for open model deployment.

Why Compound AI + Open Source will beat Closed AI — with Lin Qiao, CEO of Fireworks AI

Nov 25, 2024 · 55:49

Lin Qiao, CEO of Fireworks AI, argues that compound AI systems combining multiple open-source models will outperform closed monolithic models like OpenAI's. She explains Fireworks' evolution from a PyTorch platform to a full-stack inference and customization engine serving 40+ customers including Cursor. Qiao details their distributed inference engine, Fire Optimizer for quality-latency-cost tradeoffs, and upcoming o1-like model built on open-source foundations. She defends their quantization approach after public criticism from rivals, emphasizes specialization over general intelligence, and invites developers to test their free LoRA adapter hosting.

A Brief History of the Open Source AI Hacker - with Ben Firshman of Replicate

Feb 28, 2024 · 1:21:09

Ben Firshman, CEO of Replicate, explains how the inference platform grew from a research reproducibility tool into a 2M-user API business by embracing the generative image community and treating open-source AI as a hacker-friendly ecosystem. They accidentally discovered their API when a user reverse-engineered their web form, leading to their first $1k/month customer. Cog, their container standard for ML models, was born from lessons at Docker and the need to make models tinkerable. Ben argues that fine-tuning's low cost makes open-source models sustainable, and that AI engineers (orders of magnitude more than ML engineers) just need to start playing with models. He also discusses GPU scarcity, preferring sustainable pricing over price wars, and reveals that demand is not outpacing supply thanks to aggregating demand.

Truly Serverless Infra for AI Engineers - with Erik Bernhardsson of Modal

Feb 19, 2024 · 1:08:58

Erik Bernhardsson, creator of Annoy and Luigi, founded Modal to build a 'postmodern data stack' with a self-revisioning runtime that eliminates container cold starts and lets developers attach GPUs with a Python decorator. His serverless platform can fan out to thousands of GPUs within seconds, becoming a natural fit for GenAI workloads like Stable Diffusion and fine-tuning (e.g., RAMP fine-tuning 100 models in parallel). Modal differentiates from Replicate by targeting custom models and workflows, and from Modular by offering a managed cloud service rather than licensed software. Erik argues that buying hardware is inefficient for startups and that infrastructure founders must be willing to spend years on load balancing, page faults, and DNS. He shares how his IOI Gold Medal background shapes Modal's talent culture, hiring competitive programmers for complex scheduling and bin-packing problems.

The Four Wars of the AI Stack - Dec 2023 Recap

Jan 26, 2024 · 1:20:58

Swyx and Alessio recap December 2023 by framing the AI landscape as four wars: the Data War (NYT lawsuit demanding destruction of GPTs, OpenAI's data partnerships, synthetic data from DeepMind); the GPU/Inference War (Mixtral price dropping 90% to $0.27/million tokens, benchmark drama between Anyscale and Together, alternative architectures like Mamba); the Multimodality War (Midjourney reaching $200M ARR, ElevenLabs hitting unicorn status, OpenAI and Google building god models); and the RAG/Ops War (LangChain vs LlamaIndex, Pinecone's $750M valuation, Qdrant serving OpenAI and Anthropic internally). They argue agents and open source are not active wars, and predict 2024 will see AI finally reach production, with hardware like Rabbit R1 and Tab capturing unique context.

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.