AI Tinkerers - "One-Shot"

Steve Yegge, an experienced software engineer known for his work at Amazon and Google, discusses his innovative Beads framework, which enhances AI coding agents with session memory and task management. He explains how Beads revolutionizes developer workflows, allowing AI to manage complex projects like a pro. The conversation goes into the future of engineering roles, emphasizing a shift from coding to supervising AI. With insights on multimodal tools, automated validation, and enabling junior developers, Steve reveals how to harness the power of AI in coding.

What if your home robot didn’t just clean, but felt alive — learning, adapting, and becoming part of your family?In this episode of AI Tinkerers One-Shot, Joe talks with Axel Peytavin, Co-founder & CEO of Innate, about his mission to create robots that aren’t just functional, but truly responsive companions. From his early start coding at age 11 to building one of the first GPT-4 Vision-powered robots, Axel shares how his team is creating an open-source robotics kit and one of the first agentic frameworks for robots — giving developers the tools to teach, customize, and build the next generation of embodied AI.What you’ll learn:- Why Axel believes “robots that feel alive” are the future — beyond flashy demos of backflips and kung fu.- How Innate is making robotics accessible with an open-source hardware and SDK platform.- The breakthroughs (and roadblocks) in fine motor manipulation, autonomy, and real-time learning.- How teleoperation, deep learning, and reinforcement learning are shaping the next era of household robots.- Axel’s vision for robots as companions: cleaning, tidying, assisting — and even calling for help in emergencies.Whether you’re a tinkerer, developer, or just curious about how soon robots will fold your laundry, this deep dive shows what’s possible now — and what’s coming next.💡 Resources:- Innate Robotics – https://innate.bot/- Axel Peytavin’s Twitter – https://x.com/ax_pey/- AI Tinkerers – https://aitinkerers.orgSubscribe for more conversations with the builders shaping the future of AI and robotics!0:00 Axel’s mission — building robots that feel alive00:57 The open-source kit that lets any tinkerer train new behaviors05:00 Why applied mathematics is the foundation for AI + robotics08:17 Early projects: Minecraft plugins with 200K+ downloads11:04 Innate’s vision for teachable household robots12:01 Why fine-motor manipulation is the real breakthrough, not backflips15:19 How deep learning is driving rapid robotics progress17:11 Teleoperation as the engine for data collection and training23:21 Why tidying up, laundry, and dishes are the killer apps for home robots32:24 Live teleoperation demo of Maurice in action36:08 Breaking down the system architecture — Wi-Fi, WebSockets, Python SDK41:40 Maurice shows delicate fine-motor skills with object pickup43:53 How Innate built one of the first agentic frameworks for robots49:50 The rise of an open-source robotics community around Maurice57:03 Viral GPT-4 Vision robot demo — and what it revealed about the future

Learn how to demystify large language models by building GPT-2 from scratch — in a spreadsheet. In this episode, MIT engineer Ishan Anand breaks down the inner workings of transformers in a way that’s visual, interactive, and beginner-friendly, yet deeply technical for experienced builders.What you’ll learn:• How GPT-2 became the architectural foundation for modern LLMs like ChatGPT, Claude, Gemini, and LLaMA.• The three major innovations since GPT-2 — mixture of experts, RoPE (rotary position embeddings), and advances in training — and how they changed AI performance.• A clear explanation of tokenization, attention, and transformer blocks that you can see and manipulate in real time.• How to implement GPT-2’s core in ~600 lines of code and why that understanding makes you a better AI builder.• The role of temperature, top-k, and top-p in controlling model behavior — and how RLHF reshaped the LLM landscape.• Why hands-on experimentation beats theory when learning cutting-edge AI systems.Ishan Anand is an engineer, MIT alum, and prolific AI tinkerer who built a fully functional GPT-2 inside a spreadsheet — making it one of the most accessible ways to learn how LLMs work. His work bridges deep technical insight with practical learning tools for the AI community.Key topics covered:• Step-by-step breakdown of GPT-2 architecture.• Transformer math and attention mechanics explained visually.• How modern LLMs evolved from GPT-2’s original design.• Practical insights for training and fine-tuning models.• Why understanding the “old” models makes you better at using the new ones.This episode of AI Tinkerers One-Shot goes deep under the hood with Ishan to show how LLMs really work — and how you can start building your own.💡 Resources:• Ishan Anand LinkedIn – https://www.linkedin.com/in/ishananand/• AI Tinkerers – https://aitinkerers.org• One-Shot Podcast – https://one-shot.aitinkerers.org/👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!

In this episode of AI Tinkerers Global Stage, we go deep with Steve Krenzel, founder of LogicLoop and ex-CTO office at Brex. Steve shows us how his company turns standard operating procedures (SOPs) into fully functioning APIs—complete with schema generation, test cases, structured outputs, and backtesting—within seconds.We break down:1. Why Steve avoids agentic frameworks2. How Logic automates 100K+ tasks/month for real customers3. The power of structured output for reasoning and reliability4. How prompt caching and append-only templates unlock scale5. His open-source coding agent that builds software from scratch6. How they achieved less than 2% error rates beating human teams7. His famous Prompt Engineering Guide that went viral in 2023If you’re building with LLMs, designing autonomous workflows, or just want to see what the future of developer productivity looks like—this is a must-watch.

Discover how Robert Lukoszko, CEO of Stormy AI, is building the future of AI-powered marketing by automating influencer outreach end-to-end. This interview goes deep into his journey from viral AI demos to Y Combinator, revealing critical insights for AI builders and founders.You’ll learn: • The surprising challenges and limitations of building AI applications that deeply integrate with operating systems. • Why local AI models, despite their appeal, often struggle to compete with cloud-based solutions for real-world business cases. • Robert’s unique approach to AI-assisted development, leveraging tools like Claude 3.7 for rapid prototyping and efficient coding. • How Stormy AI uses advanced AI to find niche influencers, analyze engagement, and automate outreach, transforming traditional marketing. • The strategic importance of distribution and market fit over pure technological innovation for venture-scale AI companies.Robert Lukoszko, previously co-founder of Fixkey AI (acquired) and an alumnus of Y Combinator (S24 with Stormy AI, W22 with ngrow.ai), shares his extensive experience in applying AI to new modalities and building high-growth startups.This episode of AI Tinkerers One-Shot offers a practical look at the technical and entrepreneurial realities of building in the generative AI space.💡 Resources: • Stormy AI - https://stormy.ai • Robert Lukoszko’s LinkedIn - linkedin.com/in/robert-lukoszko • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://one-shot.aitinkerers.org/Social Media: @AITinkerers @stormy_hq@Karmedge👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!00:00 – Introduction & Background02:38 – Visual AI, Demos & Startup Idea06:27 – Local vs. Cloud Models10:07 – Desktop AI App & Context Importance14:11 – Building the App & OS Integration23:13 – Ambient AI & Contextual Vision32:17 – Stormy AI Pivot & Demo38:35 – AI Mindset & Content Creation43:57 – AI Model Comparison & Cost

Kyle Corbett, founder of OpenPipe, shares insights on enhancing AI agents through fine-tuning and reinforcement learning. He reveals how RL can cut error rates by 60% and reduce latency, making AI agents more reliable. Listeners learn about building an effective email search agent that surpasses GPT-3.5, using the Enron dataset for realistic training. Kyle also discusses the importance of designing nuanced reward functions and highlights ideal use cases for RL fine-tuning, including real-time voice assistants and high-volume applications.

Discover how AI is bridging the communication gap between humans and dogs, unlocking deeper insights into canine emotions, intentions, and even health. In this One-Shot interview, Praful Mathur, founder of Sarama, shares his groundbreaking work on a full-stack AI system designed to interpret dog vocalizations and body language. Praful, an experienced builder in AI, reveals how his innovative approach could transform our understanding of our furry companions.What you’ll learn: • The surprising history and potential of human-animal communication through AI. • How to build custom AI models for complex, real-world data like dog vocalizations. • Praful’s unique strategy for collecting and annotating large datasets from pet owners. • The practical applications of AI in detecting subtle health issues in dogs. • How AI tools can accelerate product development, from industrial design to strategic planning.Key topics covered: • The sophistication of dog understanding vs. current AI models. • Leveraging dogs as ‘biological peripherals’ for detection. • Training original AI models on novel datasets (SVMs, KNNs, transformers). • Hardware and software architecture for real-time animal data collection. • Using AI for rapid industrial design and company building tasks.Join Joe from AI Tinkerers One-Shot as he takes a deep dive with Praful Mathur, an innovator pushing the boundaries of AI to create meaningful connections between humans and animals. This conversation explores the technical challenges and profound implications of building AI that truly understands our pets.💡 Resources: • Sarama - https://www.sarama.ai/• Sarama on IG - https://instagram.com/withsarama• Praful Mathur’s LinkedIn - linkedin.com/in/praful-mathur • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://one-shot.aitinkerers.org/Social Media: @AITinkerers @PrafulMathur👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!00:00 Introduction00:15 Joe’s Reflection on the Interview01:32 Introducing Praful Mathur & Sarama00:01:55 Understanding Dog Communication00:03:39 Beyond Words: Dog Communication00:04:37 Dogs as AI Peripherals00:05:27 History of Animal Communication Tech00:06:47 Sarama: What They’ve Built Today00:07:47 Sarama Hardware & Data Collection00:09:31 Sarama App & Cloud Processing00:11:30 Understanding Dog Behavior & Emotion00:12:57 Training Original AI Models for Dogs00:16:32 Multimodal Data & Sensors00:18:49 Confidence & Data Needs for Dog AI00:20:19 ML Stack & Training Approaches00:22:06 Live Demo: Dog Bark Analysis00:28:20 Dog’s Vocabulary & Alerts00:30:13 AI for Early Health Detection00:32:13 AI in Meta Process & Design00:35:04 Open Source Strategy & Data Collection00:37:18 Communicating AI Insights to Humans00:38:36 Agentic Coding Workflow00:44:19 Model Comparison: Gemini vs. Claude00:46:39 How Praful Found AI Tinkerers00:47:48 Conclusion

Discover a groundbreaking approach to optimizing Large Language Models with Tomasz Kolinko, a true OG tinkerer and entrepreneur. In this One-Shot interview, Tomasz unveils his 'Effort Engine,' a novel algorithm that dynamically selects which computations are performed during LLM inference, allowing for significant speed improvements while maintaining surprising output quality. Learn how this method goes beyond traditional quantization by dynamically managing computations and even enabling partial model loading to save VRAM.Tomasz shares his unique benchmarking techniques, including the use of Kullback-Leibler divergence and heat maps, offering a new lens to understand how models behave under reduced 'effort.' This conversation provides practical insights into the underlying mechanics of AI models and offers a fully open-source project for practitioners to experiment with.💡 Resources:• Tomasz Kalinko's GitHub - https://kolinko.github.io/effort/about.html• The Basics - https://kolinko.github.io/effort/equations.html• AI Tinkerers - https://aitinkerers.org• One-Shot Podcast - https://one-shot.aitinkerers.org/Social Media Tags: @AITinkerers @kolinko👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!00:00 Introduction00:01:07 Welcome Tomasz Kalinko00:02:11 Introducing Effort Engine00:03:10 Dynamic Inference Explained00:05:56 How the Algorithm Works00:08:07 Speed vs. Quality Trade-offs00:11:37 Dynamic Weight Loading & VRAM00:15:24 Effort Engine Demo00:26:01 Model Breakdown Observations00:29:49 Architecture & Benchmarks00:32:17 Kullback-Leibler Divergence00:39:22 Heat Map Visualization00:41:07 Community & Future Work

Discover how to build powerful AI agents that integrate with your personal communication, as Luke Harris, Head of Growth at ElevenLabs, shares his journey and groundbreaking projects. Luke, a true tinkerer, reveals how he built the most popular WhatsApp MTP server and an ingenious iPhone shortcut for superior voice transcription.What you’ll learn: • The surprising gap in consumer AI and how to build solutions for it. • Practical insights into building secure AI agent systems and managing data privacy. • How ElevenLabs’ new speech-to-text (Scribe) and conversational AI APIs enable advanced voice agents. • The importance of launching personal projects and the unexpected opportunities they create. • Emerging growth areas for AI in education and niche industries.Luke Harris leads growth at ElevenLabs, a leading company in generative voice AI. With a background spanning software engineering, biotech ML, and entrepreneurship, Luke is a prolific builder and a Y Combinator alum.Key topics covered:• Building the WhatsApp MTP server and its technical challenges. • The power of Apple Shortcuts for AI-powered mobile workflows. • ElevenLabs’ Scribe model for highly accurate speech-to-text and diarization. • Designing conversational AI agents with low latency and emotional expressiveness. • AI-first workflows for product development, content creation, and sales intelligence.This episode of AI Tinkerers One-Shot goes deep under the hood with a builder at the forefront of voice AI.💡 Resources: • ElevenLabs - https://elevenlabs.io • Luke Harris’s Blog - https://harrys.co • Luke Harris’s LinkedIn - linkedin.com/in/luke-harris-ai • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://aitinkerers.org/podcast👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!00:00 Introduction to Luke Harris00:01:51 Luke’s Background and Growth at ElevenLabs00:04:59 The WhatsApp MTP Server Project00:11:17 Data Privacy and Security in AI Agents00:16:15 Automation vs. Human-in-the-Loop00:27:17 ElevenLabs Speech-to-Text and iPhone Shortcut00:31:34 ElevenLabs Scribe: Advanced Transcription Features00:35:03 Conversational AI and Voice Agents00:44:08 Building Voice Agents with ElevenLabs00:51:41 Unexpected Growth Drivers for Voice AI01:00:15 Luke’s AI-First Workflow and Resources01:05:22 Keeping Up with AI & AI Tinkerers Community

Discover how to build powerful AI agents that integrate with your personal communication, as Luke Harris, Head of Growth at ElevenLabs, shares his journey and groundbreaking projects. Luke, a true tinkerer, reveals how he built the most popular WhatsApp MTP server and an ingenious iPhone shortcut for superior voice transcription.What you’ll learn: • The surprising gap in consumer AI and how to build solutions for it. • Practical insights into building secure AI agent systems and managing data privacy. • How ElevenLabs’ new speech-to-text (Scribe) and conversational AI APIs enable advanced voice agents. • The importance of launching personal projects and the unexpected opportunities they create. • Emerging growth areas for AI in education and niche industries.Luke Harris leads growth at ElevenLabs, a leading company in generative voice AI. With a background spanning software engineering, biotech ML, and entrepreneurship, Luke is a prolific builder and a Y Combinator alum.Key topics covered:• Building the WhatsApp MTP server and its technical challenges. • The power of Apple Shortcuts for AI-powered mobile workflows. • ElevenLabs’ Scribe model for highly accurate speech-to-text and diarization. • Designing conversational AI agents with low latency and emotional expressiveness. • AI-first workflows for product development, content creation, and sales intelligence.This episode of AI Tinkerers One-Shot goes deep under the hood with a builder at the forefront of voice AI.💡 Resources: • ElevenLabs - https://elevenlabs.io • Luke Harris’s Blog - https://harrys.co • Luke Harris’s LinkedIn - linkedin.com/in/luke-harris-ai • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://aitinkerers.org/podcast👍 Like this video if you found it valuable, and subscribe to AI Tinkerers One-Shot for more conversations with innovators building the future of AI!00:00 Introduction to Luke Harris00:01:51 Luke’s Background and Growth at ElevenLabs00:04:59 The WhatsApp MTP Server Project00:11:17 Data Privacy and Security in AI Agents00:16:15 Automation vs. Human-in-the-Loop00:27:17 ElevenLabs Speech-to-Text and iPhone Shortcut00:31:34 ElevenLabs Scribe: Advanced Transcription Features00:35:03 Conversational AI and Voice Agents00:44:08 Building Voice Agents with ElevenLabs00:51:41 Unexpected Growth Drivers for Voice AI01:00:15 Luke’s AI-First Workflow and Resources01:05:22 Keeping Up with AI & AI Tinkerers Community