Open Source Tools

⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

Jun 21, 2026 · 33:58

Ronak Malde, CEO of Trajectory.ai, recounts his journey from building AI coding agents at Windsurf (acquired by Google DeepMind in a deal involving Demis Hassabis and Sergey Brin) to launching a platform for continual learning in enterprise AI. He argues that every future product must be a 'living system' that learns from real-world user interactions, not static models. The episode details Trajectory's technical innovations, including Self-Distillation Policy Optimization (SDPO) for learning from corrections, continuous LoRA for parallel training, and open-sourcing a training stack with SkyRL. It covers partnerships with Harvey and NVIDIA to train NeMoTron 3 Super for legal workflows, improving metrics like issue spotting and citation accuracy while cutting costs. Malde explains data curation strategies that capture nuanced user edits beyond binary signals, and outlines Trajectory's roadmap from AI-native companies like Clay and Decagon to Fortune 500 enterprises. He also reveals that the idea for Trajectory emerged after giving up his acquisition equity to pursue this vision.

⚡️Making DeepSeek v4 outperform Opus 4.7 with Taste — @AhmadAwais , CommandCode.ai

Jun 6, 2026 · 40:41

Ahmad Awais, CEO of CommandCodeAI, explains how his team's 'tool-input repair layer' lets open models like DeepSeek V4 Pro outperform premium models like Opus 4.7 in tool-calling reliability. He discovered a pattern of 'tool confusion' where open models repeatedly send wrong schemas; by repairing errors deterministically and sending repair hints instead of raw errors, tool call failures dropped dramatically. The same repair logic fixes 'design slop' using a compositional framework of patterns and OKLCH color system. Taste, a meta-neurosymbolic system, automatically learns developer preferences into portable markdown files, avoiding stale rules. Plans include open-sourcing CommandCode to make it hackable while focusing on quality models.

[State of Research Funding] Beyond NSF, Slingshots, Open Frontiers — Andy Konwinski, Laude Institute

Dec 31, 2025 · 22:34

Andy Konwinski, co-founder of Databricks and Perplexity, launches Laude Institute—a dual venture fund and nonprofit to accelerate the path from open research to breakout companies. At NeurIPS, he explains how Laude's Slingshot program funds projects like DSPy, Terminal Bench, and LMArena, and why the NSF's $1B/year for CS is insufficient, needing $10-100B for frontier AI. He argues Chinese labs (Moonshot, DeepSeek) now outpublish US because OpenAI and others stopped sharing; Open Frontiers, a live-streamed conference in SF, aims to unite top open researchers (Yann LeCun, François Chollet, Jan Leike) to reclaim global leadership. The episode also spotlights the emerging 'post-post-training' layer—prompt optimization, context management, RAG—and why research teams with multiple co-founders (like Databricks' eight) are the new gold standard for AI startups.

⚡ Open Model Pretraining Masterclass — Elie Bakouch, HuggingFace SmolLM 3, FineWeb, FinePDF

Oct 20, 2025 · 1:03:40

Elie Bakouch, Hugging Face's pre-training lead and architect behind SmolLM, presents a five-pillar framework for model training spanning data quality, architecture, information extraction, gradient quality, and stability, while breaking down recent innovations in optimizers, Mixture of Experts, and data rephrasing. He explains that the field of optimizers is moving beyond AdamW with Muon and Shampoo, noting that speed-ups are often exaggerated due to undertuned baselines. He deep-dives into MoE architecture, showing that expert specialization requires load balancing at the global batch level, as demonstrated by Qwen's findings. He discusses the rephrasing revolution, where converting low-quality web data into QA format yields non-random MMLU performance even for small models. Elie also highlights Hugging Face's open science contributions: FinePDF (new PDF dataset), FineWeb-Edu2, and tools like Nanotron, Datahub, and Liteval.

⚡️The Future of Notebooks - with Akshay Agrawal of Marimo

Jul 18, 2025 · 28:14

Akshay Agrawal introduces Marimo, an open-source reactive Python notebook built from scratch for AI and data work, emphasizing its lack of Jupyter dependencies and features like reactive execution, built-in UI elements, and pure Python storage for Git versioning. Marimo has surpassed 300k monthly PyPI downloads and more GitHub stars than Jupyter Notebook, used at OpenAI, Hugging Face, and Cloudflare. Agrawal demos interactive data exploration, including a PS5 controller for data annotation, and AI-native code generation with context-aware cell completion. He explains Marimo's UV package integration for inline dependency management and the upcoming MoLab cloud-hosted service on Modal, free for community use, addressing demand for a Colab-like experience. Marimo notebooks can be run as scripts or deployed as data apps, with a fork exploring agent cells for generative analysis.

The Shape of Compute (Chris Lattner of Modular)

Jun 13, 2025 · 1:18:18

Chris Lattner of Modular explains how his company is breaking the CUDA monopoly with Mojo and MAX, matching NVIDIA's best inference performance on AMD and NVIDIA GPUs. After three years in R&D, Modular open-sourced its stack and now delivers state-of-the-art Llama 3 serving at over 800 tokens per second. Mojo, a Python-family language, runs kernels faster than Rust and can extend Python without bindings, while MAX provides a full inference framework with automatic kernel fusion and cluster management. Unlike VLLM or SGLang, Modular's container is just a gigabyte, fully open source, and not reliant on proprietary CUDA blobs. DeepSeek's low-level PTX work validated the approach, but Lattner emphasizes that Mojo's portability avoids rewriting kernels for each new GPU architecture. He also details his daily routine, using Cursor for coding, and hiring "elite nerds" to grow the team.

Voice AI Masterclass — Kwindla Hultman Kramer and swyx

May 6, 2025 · 20:51

Shawn Wang and Kwindla Hultman Kramer announce a voice AI masterclass course, diving into the landscape of models like Dia and Parakeet, production deployment challenges, and future trends such as speech-to-speech and real-time video. Kwindla explains that telephony (Twilio) drives 99% of current monetizable voice AI, while open source frameworks like Pipecat enable low-latency multi-modal apps. The course covers turn detection, context management, and evals, with 28 sessions featuring partners like OpenAI, Google, and NVIDIA. The goal is to jumpstart builders from prototype to production, with real-time video expected to hit its inflection point by year-end.

Why every AI Engineer needs an AI Gateway (ft Portkey.ai CEO)

Feb 5, 2025 · 31:18

Rohit Agarwal, CEO of Portkey, explains why every AI engineer needs an AI gateway: an operational platform that connects to LLMs more efficiently, handling routing, observability, guardrails, and cost management. He notes that 90% of production use cases don't use automatic routing, but reasoning models and agents are driving new routing demands. Portkey's open-source gateway minimizes latency with a 21 MB memory footprint using JSON transformers. Key jobs include routing, observability (monitoring price, performance, accuracy), guardrails (e.g., regex-based, empty output detection, PII redaction), and human feedback tied to business metrics like video download rates. Rohit also highlights MCP adoption for simplifying agent-to-service connections, with bidirectional communication via RPC.

AI Engineering for Art - with comfyanonymous

Jan 4, 2025 · 52:09

Comfy Anonymous, the anonymous creator of ComfyUI, details in his first-ever podcast interview how his node-based image generation tool overtook Automatic1111 through superior memory management and early support for SDXL, becoming the de facto interface for advanced diffusion workflows. He started coding on January 1, 2023, and released the first version on January 16, driven by a desire to chain models and experiment with custom samplers. His work at Stability AI from June 2023 ensured ComfyUI efficiently handled SDXL, which leaked via early access and forced users from less powerful GPUs to adopt his tool. He discusses model preferences: Flux for consistency, SD 3.5 for creativity, and SD1.5 remaining popular. For video, he highlights Mochi as a 'true' video model with 3D latents, already implemented in Comfy. Now with a core team, ComfyUI is targeting a v1 release with an easy installer on Windows and Mac, while planning monetization through cloud and enterprise features—but keeping the open-source core free.

llm.c's Origin and the Future of LLM Compilers - Andrej Karpathy at CUDA MODE

Sep 21, 2024 · 23:29

Andrej Karpathy explains llm.c, his from-scratch C implementation of GPT-2 training that outperforms PyTorch by 20% in speed and 30% in memory, arguing that LLMs will eventually act as compilers for custom code. He recounts starting the project out of frustration with PyTorch compile errors, writing all forward and backward passes manually in C, then porting to CUDA with kernel optimizations. A community of contributors (Eric, Arun, Alex R) helped achieve 50% MFU on a single H100 node, training GPT-2 1.6B in 24 hours for $600. Ongoing work adds Llama 3.1 and FP8 support. Karpathy sees llm.c as proof that LLMs can automate such low-level optimization, making Python and PyTorch a temporary crutch.

High Agency Pydantic over VC Backed Frameworks — with Jason Liu of Instructor

Apr 24, 2024 · 1:02:59

Jason Liu, creator of the Instructor library, explains why structured outputs from LLMs are best handled by a simple requests-like wrapper rather than a VC-backed framework, arguing that Pydantic-defined schemas via function calling outperform JSON Mode for typed responses. He details his journey from being bearish on LLMs at StitchFix to building Instructor on a bullet train to Japan after GPT-3 proved him wrong. Liu advocates for workflow-based DAGs over reactive agent loops, recommends using rankers rather than cramming 60+ tools into an API call, and credits high agency—trying many experiments and documenting conditions for revisiting failures—as key to his success. He also critiques the MLE hiring hype, urging startups to empower motivated AI engineers instead.

Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI

Mar 6, 2024 · 1:37:38

Soumith Chintala, creator of PyTorch and engineering lead at Meta AI, argues that open source AI is essential for distributing opportunity and trust. He details PyTorch's complexity—1,000 operators needed for generality—and explains synthetic data as a vehicle for imparting symbolic knowledge where humans already have good symbolic models. He highlights a coordination problem in open source: feedback is lost because frontends like Ooba and Ollama lack feedback buttons, and proposes a centralized sinkhole to collect high-quality feedback. Beyond text, he is excited about robotics, where hardware remains a bottleneck, and Osmo's work to digitize smell, which he compares to images in the 1800s.

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.

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 Accidental AI Canvas - with Steve Ruiz of tldraw

Jan 5, 2024 · 1:27:49

Steve Ruiz, founder of tldraw, recounts how his open-source whiteboard accidentally became a premier multimodal AI canvas, enabling visual prompting with GPT-4V. Originally built as a personal project with obsessive attention to arrows and ink, tldraw gained traction as a developer SDK for infinite canvas apps. The 'Make It Real' feature, which turns wireframes into working HTML using GPT-4V, exploded with 22 million Twitter views, showcasing iterative design through spatial branching and annotation. Ruiz explains how the canvas supports multiple input layers—UI sketches, state charts, screenshots, even API endpoints—and how his fine arts background guided his taste-driven approach. He argues tldraw's moat lies in its web-native, hackable canvas infrastructure, not ephemeral AI integrations, and positions it as the battlefield for comparing multimodal models like OpenAI's and Gemini.

Powering your Copilot for Data - with Artem Keydunov from Cube.dev

Oct 27, 2023 · 43:01

Artem Keydunov, co-founder of Cube (formerly Statsbot), explains how his early text-to-SQL Slack bot in 2016 led to the creation of Cube as a semantic layer, which he argues is now essential for grounding AI models on structured data. Statsbot failed due to the lack of LLMs, relying on regex and custom models, but the underlying Cube framework—an open-source semantic layer defining metrics and dimensions—solved the context problem. Today, semantic layers index data into text descriptions, enabling agents to generate simple queries against the layer instead of complex SQL, reducing errors. Keydunov highlights that natural language querying is becoming a commodity feature in BI tools, but the real value lies in treating metric definitions as code with version control. He advises AI engineers to use a proper warehouse, a semantic layer like Cube, and tools like LangChain, while cautioning that production systems need extra Python code for math and error handling.

Ep 18: Petaflops to the People — with George Hotz of tinycorp

Jun 20, 2023 · 1:23:24

George Hotz of tinycorp argues that a simplified, open-source ML framework (tinygrad) can democratize AI compute and replace NVIDIA, Google, and AMD. Tinygrad uses 25 ops (vs. XLA's 250) and fuses kernels automatically, achieving 2x speed on Qualcomm GPUs over their library. Hotz reports AMD kernel panics fixed after emailing CEO Lisa Su, but laments AMD's open-source as 'dumped on GitHub.' For hardware, tinybox is a six-GPU desktop at 350W per GPU, aiming for 5x better price-performance than NVIDIA H100 for 90% of training. He criticizes OpenAI's secrecy, revealing GPT-4 is an 8-way mixture of 220B models, and cites the Bitter Lesson. He also outlines FLOPcoin using hardware identity to prevent cheating, and his goal of an AI girlfriend as the third company, merging humans via data rather than implants.