AI Tinkerers - "One-Shot"

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

While most people are still asking ChatGPT to write code snippets, Kevin Leneway is building full-stack products using nothing but prompts.In this One-Shot episode, he reveals the exact system he’s used to launch over 40 startups at Pioneer Square Labs.We break down:- How he writes BRDs and PRDs that don’t suck- Why vibe coding fails and how to actually use AI agents- The markdown checklist that replaces a product team- How to go from idea to working app with zero context switching- His open-source starter kit that makes Cursor and Claude 3.5 feel like magicIf you’re a builder, this will change how you work.No gimmicks. Just a ruthless focus on speed, clarity, and shipping.Watch now. Learn the system. Steal it.Check out more information like this at Kevin's YouTube: https://www.youtube.com/@kevinleneway2290Also, check out Kevin's Github:http://github.com/kleneway/next-ai-starterNextAI Starter Repo: https://github.com/kleneway/next-ai-starterBRD/PRD/Checklist prompts: https://chatgpt.com/share/67c5ee78-0b94-800d-b467-ceecdbf6ce70Agent Tasklist example: https://gist.github.com/kleneway/07432638aeaf6210316ebbc32dfbe643Storybook Link: https://storybook.js.org/UX Rubric Example: https://github.com/kleneway/pastemax/blob/main/docs/ux-rubric.mdPasteMax Open Source Repo: https://github.com/kleneway/pastemax/

While most people are still asking ChatGPT to write code snippets, Kevin Leneway is building full-stack products using nothing but prompts.In this One-Shot episode, he reveals the exact system he’s used to launch over 40 startups at Pioneer Square Labs.We break down:- How he writes BRDs and PRDs that don’t suck- Why vibe coding fails and how to actually use AI agents- The markdown checklist that replaces a product team- How to go from idea to working app with zero context switching- His open-source starter kit that makes Cursor and Claude 3.5 feel like magicIf you’re a builder, this will change how you work.No gimmicks. Just a ruthless focus on speed, clarity, and shipping.Watch now. Learn the system. Steal it.Check out more information like this at Kevin's YouTube: https://www.youtube.com/@kevinleneway2290Also, check out Kevin's Github:http://github.com/kleneway/next-ai-starterNextAI Starter Repo: https://github.com/kleneway/next-ai-starterBRD/PRD/Checklist prompts: https://chatgpt.com/share/67c5ee78-0b94-800d-b467-ceecdbf6ce70Agent Tasklist example: https://gist.github.com/kleneway/07432638aeaf6210316ebbc32dfbe643Storybook Link: https://storybook.js.org/UX Rubric Example: https://github.com/kleneway/pastemax/blob/main/docs/ux-rubric.mdPasteMax Open Source Repo: https://github.com/kleneway/pastemax/

In this episode of the “New AI Tinkerers Podcast,” Joe sits down with Tanya Verma—co-founder of Tinfoil and fresh admit to Y Combinator—to explore how confidential computing is changing the game for AI privacy and security. Tanya explains the concept of “secure enclaves,” hardware-based encryption that keeps your data safe even from the cloud provider. Tanya also discuss real-world use cases for private AI, how open-source models like DeepSpeed Chat stack up, and why privacy is crucial for large-scale adoption of generative AI.Whether you’re building a high-stakes internal chatbot or just want to keep your personal data secure, this conversation will help you understand how Tinfoil’s technology ensures no one can peek under the hood—not even Tinfoil itself. Don’t miss their deep dive into the future of confidential computing, agent-based automation, and the challenges of scaling secure AI solutions.Highlights & Takeaways• What confidential computing really is—and why it matters• How Tinfoil is bringing secure enclaves to generative AI• The trade-offs between on-prem vs. cloud-based AI solutions• Why open-source models and privacy go hand in hand• Practical steps for deploying private AI applications at scaleSubscribe for more interviews with builders and visionaries in AI—and get an insider’s look at the next generation of secure and private computing!

Vik from Moondream AI joins Joe to demo a vision-language model that runs locally—on your laptop, your phone, even a Raspberry Pi.From visual question answering to gaze detection and UI automation, Vik shows how Moondream is redefining edge computer vision—no cloud required.Whether you're into robotics, home automation, or lightweight AI, this “One-Shot” is packed with insights for builders.Try it yourself at moondream.ai 🚀00:00 - Intro to Moondream’s compression tech for 2B parameter models 00:22 - Joe welcomes Vik from Moondream 01:53 - Shift from traditional CV to promptable vision-language models 03:23 - Playground demo: Visual Question Answering (VQA) 04:42 - VQA demo results: speed, structure, and accuracy 05:03 - Object detection, pointing, and captioning demos 07:57 - Prompts that push reasoning: uniform detection 10:07 - Cross-task benefits: gaze detection improves directional reasoning 11:02 - Comparing Moondream’s VQA to Quinn’s visual reasoning model 13:21 - Why edge deployment still matters in vision 15:21 - 0.5B model runs on Raspberry Pi using 816MB with int4 16:15 - HAL 2000 setup: Moondream + Tiny LLaMA + Coqui TTS 20:55 - Texas rancher uses drone and Moondream for cow detection 21:53 - Commercial use: air-gapped environments like retail, safety 23:09 - UI automation and button detection with pointing feature 29:41 - Vision for ambient agents and local inference 30:27 - Power efficiency: 10x less energy than 7B/20B cloud models 31:01 - Moondream API & Hugging Face transformers integration 36:11 - Vik’s background: From AWS to machine learning 40:56 - Discovering AI Tinkerers and global meetups #AITinkerers #MoondreamAI #EdgeAI #ComputerVision #LLM #OpenSource #OneShot