In this episode of AI Tinkerers One-Shot, Joe sits down with Steve Yegge—engineer and creator of the Beads framework—to explore how open source tools are transforming the way we build with AI. Steve shares the story behind Beads, a new framework that gives coding agents memory and task management, enabling them to work longer, smarter, and more autonomously. From his days at Amazon and Google to leading engineering at Sourcegraph, Steve reveals how Beads is already reshaping developer workflows and why it’s gaining hundreds of contributors in just weeks.

What you’ll learn:

- How Beads gives coding agents “session memory” and lets them manage complex, multi-step projects.

- Why Steve believes the future of engineering is about guiding and supervising AI—rather than just writing code.

- The evolution from chaotic markdown files to structured, issue-based workflows.

- Techniques for multimodal prompting, automated screenshot validation, and “landing the plane” for session cleanup.

- The challenges and breakthroughs in deploying AI tools at scale within organizations.

- How Beads and similar frameworks are making it easier for both junior and senior developers to thrive in the age of AI.

Whether you’re a developer, tinkerer, or just curious about the next wave of AI-assisted coding, this deep dive with Steve Yegge will show you what’s possible now—and what’s coming next.

💡 Resources:

Beads – https://github.com/steveyegge/beads

Steve Yegge – https://www.linkedin.com/in/steveyegge/ & https://x.com/Steve_Yegge

AI Tinkerers – https://aitinkerers.org

Subscribe for more conversations with the builders shaping the future of AI and robotics!

00:00 - Introduction to Steve Yegge and Beads Framework

02:10 - Steve's Background and Source Graph AMP

08:00 - Building a React Game Client with AI Agents

15:36 - Multimodal Prompting and Screenshot Validation

23:16 - Code Review Techniques and Agent Confidence

32:01 - The Evolution of Beads: From Markdown Chaos to Issue Tracking

43:11 - Landing the Plane: Automated Session Cleanup

52:09 - Deploying AI Tools in Organizations

58:59 - Code Review Bottlenecks and Graphite Solution

01:02:57 - Closing Thoughts on AI-Assisted Development

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.org

Subscribe for more conversations with the builders shaping the future of AI and robotics!

0:00 Axel’s mission — building robots that feel alive

00:57 The open-source kit that lets any tinkerer train new behaviors

05:00 Why applied mathematics is the foundation for AI + robotics

08:17 Early projects: Minecraft plugins with 200K+ downloads

11:04 Innate’s vision for teachable household robots

12:01 Why fine-motor manipulation is the real breakthrough, not backflips

15:19 How deep learning is driving rapid robotics progress

17:11 Teleoperation as the engine for data collection and training

23:21 Why tidying up, laundry, and dishes are the killer apps for home robots

32:24 Live teleoperation demo of Maurice in action

36:08 Breaking down the system architecture — Wi-Fi, WebSockets, Python SDK

41:40 Maurice shows delicate fine-motor skills with object pickup

43:53 How Innate built one of the first agentic frameworks for robots

49:50 The rise of an open-source robotics community around Maurice

57:03 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/

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