AI Tinkerers - "One-Shot"

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 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

Learn how to integrate AI agents directly into your existing applications and unlock new levels of user experience and developer productivity with Atai Barkai, CEO of CopilotKit. Discover how CopilotKit provides the essential infrastructure to bridge advanced AI models with your current software stack, making your applications smarter and more intuitive.What you’ll learn: • How CopilotKit’s CoAgents and AGUI protocol simplify the integration of AI agents into any application, regardless of framework. • The practical benefits of implementing ‘SaaS Copilots’ to reduce learning curves and enhance user interaction in complex software. • Real-world strategies for driving significant internal efficiency gains within large enterprises using AI agents. • Why the ‘human-plus-AI’ mental model is crucial for the foreseeable future of intelligent systems.Atai Barkai is the founder and CEO of CopilotKit, a leading open-source framework for building production-ready AI copilots. With extensive experience in AI agent user experience, Atai shares insights from working with indie developers to Fortune 100 companies, offering a unique perspective on the evolving AI landscape.Key topics covered: • Bridging AI agents with existing application UIs for enhanced functionality. • Understanding the AGUI (Agent User Interaction Protocol) for seamless agent-user communication. • Implementing intent-based interfaces for complex SaaS applications. • Achieving ‘industrial evolution level productivity gains’ with AI co-agents. • The open-source model of CopilotKit and its ease of self-hosting.This episode of AI Tinkerers One-Shot goes under the hood with Atai Barkai to share practical learnings for the community.💡 Resources: • CopilotKit Website - https://copilotkit.ai • Atai Barkai LinkedIn - https://www.linkedin.com/in/atai-barkai • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://aitinkerers.org/podcastSocial Media: @AITinkerers @copilotkit @ataiiam👍 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 Introduction00:01:20 Welcome to One-Shot00:01:55 How Joe Met Atai00:03:29 Getting Started with CopilotKit00:03:52 CopilotKit Components: Standard Agent & CoAgents00:04:50 AGUI Protocol & Events Explained00:08:05 CopilotKit UI & Shared State Demo00:11:34 Open Source vs. Cloud Model00:13:18 Integrating CoAgents into Your App00:14:38 Why Bring Agents into Applications?00:15:39 Practical Agent Adoption & Use Cases00:17:42 Learning More About CopilotKit00:18:15 What Atai is Tinkering With00:21:26 Craziest & Most Impactful Use Cases

Learn how to integrate AI agents directly into your existing applications and unlock new levels of user experience and developer productivity with Atai Barkai, CEO of CopilotKit. Discover how CopilotKit provides the essential infrastructure to bridge advanced AI models with your current software stack, making your applications smarter and more intuitive.What you’ll learn: • How CopilotKit’s CoAgents and AGUI protocol simplify the integration of AI agents into any application, regardless of framework. • The practical benefits of implementing ‘SaaS Copilots’ to reduce learning curves and enhance user interaction in complex software. • Real-world strategies for driving significant internal efficiency gains within large enterprises using AI agents. • Why the ‘human-plus-AI’ mental model is crucial for the foreseeable future of intelligent systems.Atai Barkai is the founder and CEO of CopilotKit, a leading open-source framework for building production-ready AI copilots. With extensive experience in AI agent user experience, Atai shares insights from working with indie developers to Fortune 100 companies, offering a unique perspective on the evolving AI landscape.Key topics covered: • Bridging AI agents with existing application UIs for enhanced functionality. • Understanding the AGUI (Agent User Interaction Protocol) for seamless agent-user communication. • Implementing intent-based interfaces for complex SaaS applications. • Achieving ‘industrial evolution level productivity gains’ with AI co-agents. • The open-source model of CopilotKit and its ease of self-hosting.This episode of AI Tinkerers One-Shot goes under the hood with Atai Barkai to share practical learnings for the community.💡 Resources: • CopilotKit Website - https://copilotkit.ai • Atai Barkai LinkedIn - https://www.linkedin.com/in/atai-barkai • AI Tinkerers - https://aitinkerers.org • One-Shot Podcast - https://aitinkerers.org/podcastSocial Media: @AITinkerers @copilotkit @ataiiam👍 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 Introduction00:01:20 Welcome to One-Shot00:01:55 How Joe Met Atai00:03:29 Getting Started with CopilotKit00:03:52 CopilotKit Components: Standard Agent & CoAgents00:04:50 AGUI Protocol & Events Explained00:08:05 CopilotKit UI & Shared State Demo00:11:34 Open Source vs. Cloud Model00:13:18 Integrating CoAgents into Your App00:14:38 Why Bring Agents into Applications?00:15:39 Practical Agent Adoption & Use Cases00:17:42 Learning More About CopilotKit00:18:15 What Atai is Tinkering With00:21:26 Craziest & Most Impactful Use Cases

What if you could skip half of your LLM’s computations—and still get the same output?In this episode of One-Shot, we sit down with Tomasz Kolinko, the Warsaw-based founder of Effort Engine—a new AI inference algorithm that dynamically adjusts precision in real time.This isn’t quantization. It’s something weirder—and maybe more useful.Tomasz walks us through how he:- Built a custom algorithm that runs 2–3x faster on MacBooks- Developed a system that can skip 50%+ of model computations dynamically- Created heatmaps to visualize token-level divergence- Benchmarked everything himself… and shared the codeYou’ll also see:- Live demos of inference tuning from 100% to 5%- Why AI models still work (sometimes better!) with just 30% effort- How a DIY hacker space in a car shop led to one of the most creative AI projects in EuropeIf you’re building with LLMs, pushing inference limits, or just obsessed with optimization — this episode will change how you think about AI computation.

In this episode of AI Tinkerers "One-Shot", we go deep with Steve Krenzel, founder of Logic, on how his company turns standard operating procedures (SOPs) into fully functioning APIs. Dive deep with us on schema generation, test cases, structured outputs, and backtesting.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.Relevant Links:Follow Steve: https://www.linkedin.com/in/stevekrenzel/Follow Logic: https://www.linkedin.com/company/with-logic From the episode:- http://github.com/stevekrenzel/pick-ems- http://app.staging.logic.inc/

What if building complex AI agents felt as natural as composing React components—and they could even rewrite their own code? 🤯In this episode of One Shot / AI Tinkerers, host Joe sits down with Evan Boyle, founder of GenSX, to explore a radically new way to design, run, and ship long-running agent workflows:🔑 Key takeaways- React-inspired component model for agents – why JSX-style, type-safe functions beat static graphs for scalability and code reuse.- Traces, telemetry & evals baked-in – see every prompt, variable, and LLM call in real time.- $4 self-modifying coding agent – Evan demos an agent that checks out its own repo, refactors 3 K lines, runs tests, and pushes to GitHub… iteratively.- Real-world production use cases – from million-document legal discovery to inbox-wide entity extraction and analytics.- Durable execution & infra shift – why 5-second latencies and massive parallelism are forcing a rethink of serverless, queues, and caching.- Developer experience first – faster dev loops with component-level caching, cursor rules, and LLM “rubber-duck” debugging tricks.🛠️ Tools & frameworks mentionedGenSX, React/JSX, OpenAI & Anthropic models, Temporal, Pulumi, Cursor, LangChain, LlamaIndex, Crew AI…and more.🔗 Try GenSX → https://www.gensx.com💬 Join the community → https://github.com/gensx-inc/gensx🐦 Follow Evan on X/Twitter → https://x.com/_Evan_Boyle🙌 Enjoyed the conversation?👍 Like, 🔔 subscribe, and drop your questions or aha moments in the comments. It helps more builders discover the pod!📍 Chapters00:00 Intro & Evan’s background04:28 Why existing agent frameworks break at scale12:55 Inside the React-style component model23:10 Live demo: Hacker News Analyzer (1,000 LLM calls in parallel)32:45 Tracing, telemetry, and evals38:20 The self-modifying code agent ($4/iteration)50:40 Real production agent use cases59:05 Dev-tooling tips: caching, logging-only debug loops1:08:30 The future of AI infrastructure & closing thoughts#GenSX #AIAgents #DeveloperExperience #React #SelfModifyingCode #AIWorkflow #OneShotPodcast

What if building complex AI agents felt as natural as composing React components—and they could even rewrite their own code? 🤯In this episode of One Shot / AI Tinkerers, host Joe sits down with Evan Boyle, founder of GenSX, to explore a radically new way to design, run, and ship long-running agent workflows:🔑 Key takeaways- React-inspired component model for agents – why JSX-style, type-safe functions beat static graphs for scalability and code reuse.- Traces, telemetry & evals baked-in – see every prompt, variable, and LLM call in real time.- $4 self-modifying coding agent – Evan demos an agent that checks out its own repo, refactors 3 K lines, runs tests, and pushes to GitHub… iteratively.- Real-world production use cases – from million-document legal discovery to inbox-wide entity extraction and analytics.- Durable execution & infra shift – why 5-second latencies and massive parallelism are forcing a rethink of serverless, queues, and caching.- Developer experience first – faster dev loops with component-level caching, cursor rules, and LLM “rubber-duck” debugging tricks.🛠️ Tools & frameworks mentionedGenSX, React/JSX, OpenAI & Anthropic models, Temporal, Pulumi, Cursor, LangChain, LlamaIndex, Crew AI…and more.🔗 Try GenSX → https://www.gensx.com💬 Join the community → https://github.com/gensx-inc/gensx🐦 Follow Evan on X/Twitter → https://x.com/_Evan_Boyle🙌 Enjoyed the conversation?👍 Like, 🔔 subscribe, and drop your questions or aha moments in the comments. It helps more builders discover the pod!📍 Chapters00:00 Intro & Evan’s background04:28 Why existing agent frameworks break at scale12:55 Inside the React-style component model23:10 Live demo: Hacker News Analyzer (1,000 LLM calls in parallel)32:45 Tracing, telemetry, and evals38:20 The self-modifying code agent ($4/iteration)50:40 Real production agent use cases59:05 Dev-tooling tips: caching, logging-only debug loops1:08:30 The future of AI infrastructure & closing thoughts#GenSX #AIAgents #DeveloperExperience #React #SelfModifyingCode #AIWorkflow #OneShotPodcast