Midjourney

tldraw.computer - Steve Ruiz, tldraw

Jul 21, 2025 · 18:45

Steve Ruiz, founder and CEO of tldraw, demonstrates the company's AI experiments on their infinite canvas, including Make Real—which turns hand-drawn wireframes into working web apps using vision models—and tldraw computer, a visual programming environment where arrows and LLMs power a graph of connected nodes that can execute multi-step prompts, generate images and speech, and even run loops indefinitely. He also shows Draw Fast for real-time image generation and Teach, where Claude can draw and edit shapes on the canvas. The episode explains how tldraw's SDK (tldraw.dev) enables others to build custom canvas applications, and highlights the company's philosophy of 'shitty but amazing' rapid prototyping.

Breaking AI's 1-GHz Barrier: Sunny Madra (Groq)

Oct 10, 2024 · 20:11

Sunny Madra (Groq) argues that LLM inference speed is undergoing a transformation akin to the 1-GHz microprocessor breakthrough, with Groq increasing LLaMA 3 8B speed by over 50% in just two months. He draws parallels to the industrial revolution: just as mass production replaced bespoke manufacturing, AI now enables 1,000 outputs in a minute where previously one designer produced one per day. Specific applications like Globe.Engineer plan a trip in seconds by processing 10,000 input tokens per second. Madra envisions LLMs as future OS cores, enabling instantaneous decision-making, universal natural-language interfaces, and multi-agent collaboration that allows smaller models to compete with larger ones. Edge AI projects like hyperspace.ai distribute unused GPU compute, and personalized education (citing Khan's two-sigma effect) becomes practical with cheap inference. He also highlights automated data science agents (Pioneer) that run endlessly, discovering correlations like productivity decline after certain performance reviews. The episode underscores that speed unlocks predictive analytics, context-aware systems, and enhanced security against AI-powered scams.

[Workshop] AI Engineering 101

Nov 6, 2023 · 3:02:24

In this hands-on workshop, AI engineer Noah Hein teaches the basics of AI engineering by building five projects with OpenAI's GPT-3, GPT-4, Dall-E, Whisper, and Telegram. Participants create a Telegram bot that uses GPT-3 for chat, implements retrieval-augmented generation (RAG) with embeddings and cosine similarity on MDN docs, generates code with GPT-4 using few-shot prompting, creates images via Dall-E 2, and transcribes voice with Whisper. The session explains core concepts like tokens, chunking, context windows, temperature, and top-p, emphasizing that embeddings and prompt engineering are key to performance. Hein demonstrates that these tools are cheap and accessible—the entire workshop costs about $0.05 in API fees—and shows how AI engineers can integrate multiple models into a single application.

The Intelligent Interface: Sam Whitmore & Jason Yuan of New Computer

Nov 2, 2023 · 18:56

Sam Whitmore and Jason Yuan of New Computer advocate for AI interfaces that adapt to human context by using implicit signals like proximity, gesture, and tone, rather than forcing users to adapt to rigid input modes. They demonstrate a pose-detection system that switches between keyboard and voice input based on user distance from the screen. They explore social gestures for rejecting calls and propose new physics metaphors like blending images by shaking an iPad. They argue that probabilistic materials like generative AI require familiar metaphors from nature and human behavior, and that mixing input and output modalities in real time yields more natural experiences. The demos, all speculative fictions, point toward future hardware where context-aware reasoning becomes the norm.

Building Blocks for LLM Systems & Products: Eugene Yan

Nov 2, 2023 · 17:24

Eugene Yan presents practical patterns for building LLM systems and products, emphasizing evals as the foundation. He argues that automated evals—starting with as few as 40 domain-specific questions—enable faster iteration and safer deployment, while academic benchmarks like MMLU may not fit real tasks. For retrieval-augmented generation, he warns that LLMs perform worse when the answer is in the middle of retrieved documents, and even with perfect retrieval, accuracy tops out at 75%. Guardrails against hallucination can be built via natural language inference at the sentence level or by sampling multiple summaries to check consistency. Finally, he highlights UX design—like Copilot’s accept/reject or Midjourney’s upscale/vary—as a way to collect implicit feedback that builds a data flywheel for evals and fine-tuning.

See, Hear, Speak, Draw: Logan Kilpatrick & Simón Fishman

Oct 24, 2023 · 18:43

OpenAI's Simón Fishman and Logan Kilpatrick argue that 2024 will be the year of multimodal models, showcasing how text can currently bridge vision, audio, and image generation. They demo a DALL-E vision loop where GPT-4 with vision describes an image, DALL-E 3 generates a synthetic version, then the models compare and iterate to improve accuracy—all in roughly 50 lines of code. Another demo extracts frames from a video, uses GPT-4 with vision to describe them, combines with Whisper's transcript, and generates a blog post with matching DALL-E images, making video content more accessible. They emphasize that while today's multimodal capabilities are separate, future unified models will directly reason across modalities, eliminating the need for manual integration.

The 1,000x AI Engineer: Swyx

Oct 23, 2023 · 9:27

Swyx argues that AI engineers are just in time for a 1,000x opportunity, drawing on historical tech cycles and compute scaling laws. He cites Carlota Perez's tech revolution cycles, placing the AI revolution's start at AlexNet in 2012. With compute growing 600x by decade's end, GPT-3 took one person-year of compute, GPT-4 took 100, and GPT-10 will exceed all human compute ever. He defines three AI engineer types: AI Enhanced (Copilot-like), AI Product (Midjourney), and AI Agent (Auto-GPT). To achieve 1,000x, he advises scaling knowledge from O(n) (attending talks) to O(n²) (teaching others) to O(2^n) (building networks).