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John Lindquist is an educator and developer who created egghead.io and now runs mega.dev, a hands-on program for developers building with AI agents. In this episode, he walks through eight Jev demos, including a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, and a live presentation coach. He also explains why Jev is better understood as a fast, inexpensive decision engine than a chatbot, how layering multiple classifications improves accuracy, and when a traditional LLM is still the better tool.
**Jev outputs decisions instead of text, and that constraint is its defining feature.**It turns unstructured input into structured results such as scores, classifications, probabilities, and function calls. Once developers stop treating it like a chatbot and identify the decisions their applications need to make, many previously impractical use cases become obvious.**Jev’s speed and cost unlock a different class of applications.**Across eight demos, John shows what becomes possible when decisions cost almost nothing and return nearly instantly. He spent just 73 cents across 23 development runs, while Claire processed five gigabytes of JSON for 40 cents.**The right mental model for Jev is an if/else statement, not prompt engineering.**John recommends using it wherever a traditional program would contain a condition, switch statement, or branching decision. Developers define the available paths, and Jev uses natural language to select the right one.**Routing is one of Jev’s most immediately useful patterns.**A single natural-language input can identify the right tool, infer the intended action, and trigger the appropriate function. John’s to-do and omnibar demos show the kind of fast, flexible command layer that voice assistants have long promised but rarely delivered.**Layered classification often works better than relying on one perfect decision.**John frequently runs one classification, feeds its result into another, and combines the steps later as real usage data accumulates. Because each call costs so little, adding another pass can meaningfully improve accuracy without making the workflow expensive.**The chess benchmark makes Jev’s advantage concrete.**It analyzed a full game in under a second, running ten times faster and costing four times less than the low-reasoning LLM without sacrificing accuracy. When the possible actions are finite, a decision model can outperform a generative model on the metrics that matter.**Jev makes “efficient inefficiency” a viable strategy.**Evaluating every chess move, mapping every route through Wikipedia, or checking every agent’s path for collisions may sound wasteful. But when each decision is fast and inexpensive, brute-force analysis can be simpler and more effective than carefully narrowing the search space.**Knowing when not to use Jev matters just as much.**Jev works best when the action space is defined and natural language must be routed into it. Creative reasoning, open-ended analysis, brainstorming, and image interpretation still belong to LLMs. The biggest gains come from intentionally combining both types of models.
John Lindquist’s 6 Jev Workflows for Apps, Data, and AI Agents: https://www.chatprd.ai/how-i-ai/john-lindquist-jev-workflows
↳ How to Build a Real-Time Voice-Controlled To-Do List App with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-voice-controlled-todo-app
↳ How to Deduplicate and Clean Large Datasets Using AI Decision Models: https://www.chatprd.ai/how-i-ai/workflows/jev-data-deduplication
↳ How to Build a Smart Command Bar and App Router with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-smart-command-bar-app-router
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Kath Korevec is a member of the Product staff at OpenAI who has been building and using ChatGPT Sites internally for more than a year. In this episode, she demonstrates how OpenAI uses Sites to turn live Slack, Notion, and calendar data into personalized internal tools, including an incident command center that adapts to each visitor. She also shows how she automated music discovery with Reddit and Spotify and built a community dungeon crawler through shared skills, and explains why fast models are changing how people create personal software.
**Plugin Insights lets one site automatically personalize itself for every visitor.**Kath connected an incident command site to Slack and Notion, allowing it to infer someone’s team from their channel access and display only the relevant incidents and runbooks. Connector authentication handles the personalization without requiring custom logic.**Developers do not have to write the connector logic themselves.**Codex can infer the appropriate plugins when a prompt mentions collaboration, Notion, calendars, or other connected tools. Kath often uses the phrase “use Plugin Insights to build this site,” but says Codex frequently reaches for the right connectors without being explicitly told.**Sites is infrastructure, not simply a hosting layer.**It includes D1 storage, an R2 bucket, MCP plugin hosting, and support for co-editors. Kath’s team uses it for production work, including the slides used during the OpenAI DevDay keynote, while many others still treat it as a prototyping tool.**Software can now be temporary and still worth building.**Kath might create a personalized site for one business trip, use it for a week, and discard it afterward. When development overhead falls far enough, even a tool with a seven-day lifespan can deliver meaningful value.**Kath’s automated music discovery workflow shows what personal software can already do.**Her site uses computer control to find popular Reddit playlists every Monday, creates a fresh Spotify playlist, and plays it automatically. During the demo, it surfaced a song she had never heard and genuinely liked.**Skills let a community extend an application without accessing its codebase.**Kath published a dungeon-building skill that defines the room dimensions, controls, and structure. Anyone can install it, create a themed room with Astra, and add the result to her shared dungeon crawler.**Faster models can make builders more creative.**Kath and Claire both found that slow generation breaks the creative thread, while immediate results encourage continued experimentation. During one Astra ultrafast session, Claire’s kids called out requests like “jellyfish belly” and “add a chicken,” and she kept building without losing momentum.**Kath draws a clear boundary around AI acting in her name.**She uses Codex for research, thinking, and drafting, but will not let it email or message people without her approval. Her rule is simple: AI can help prepare the communication, but the send button remains hers.
Kath Korevec’s ChatGPT Sites Workflows: Dashboards, Music, and Games: https://www.chatprd.ai/how-i-ai/kath-korevec-chatgpt-sites-workflows
↳ How to Build a Real-Time Incident Command Dashboard with ChatGPT Sites: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-incident-dashboard
↳ How to Create an Automated Weekly Music Discovery Playlist with AI: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-weekly-music-playlist
↳ How to Create a Collaborative 3D Game with ChatGPT Sites and Custom Skills: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-collaborative-3d-game
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In this solo episode, Claire breaks down the biggest announcements from OpenAI DevDay and shares what happened when she tested several of them early. She explores how Dots, Spaces, and Sites could change the way people and agents work together, then uses the Decisions API to select better podcast thumbnails and Astra ultrafast to build a collaborative sketchpad and an interactive 3D world. She also shares what feels promising, what still needs work, and which new tools are worth trying first.
**Dots are more capable than they look, but the product still needs time to mature.**Claire’s Dot shopped, wrote code, and proactively noticed a conflict in her kids’ swim schedule. The results were strong, but the relationship between Dot threads, ChatGPT conversations, and Codex remains confusing, so she is waiting to understand where Dots fit before giving a final verdict.**ChatGPT Spaces may be the most underrated announcement from DevDay.**It gives humans and agents a shared workspace for documents, slides, and other artifacts, with proper permissions and data controls. Claire had recently built a similar tool called Claire’s Notebook because she believed this workflow was missing, and sees OpenAI’s version as a serious challenge to Google.**Sites with bundled plugins offers an enterprise answer to the flood of AI-generated internal tools.**Teams can package a connector such as Snowflake inside a shareable app while preserving each user’s existing data permissions. Authorized employees see the relevant information, while everyone else remains blocked automatically.**GPT-6.1 Sol is a compelling workhorse for cost-conscious teams.**It costs $2 per million input tokens and $10 per million output tokens, compared with Astra’s $10 and $50. Claire still pays the premium for Astra because she loves using it, but believes Sol deserves serious consideration for everyday work.**Adding vision to the Decisions API unlocks an entirely new category of workflows.**Claire used it to scan 100 video frames and identify usable thumbnail shots in about ten seconds. Combining fast, inexpensive classification with visual understanding turns a previously tedious production task into a largely solved problem.**Astra Ultrafast is expensive, but it gave Claire a glimpse of the future.**An interactive session with her kids cost roughly $97, yet its real-time SVG sketchpad and natural-language 3D game showed what near-instant code generation can feel like inside a product. If the price falls, this level of speed could dramatically expand what people build.**The platform primitives may matter more than the flashy demos.**Computer use in the Agents API, Codex developer tools, planned plugin monetization, and the new Pro 500 plan create infrastructure developers can build on over time. The ability to use the same foundations powering Codex is especially significant.**Claire is still using Grok Bots for daily work, and she is not rushing to switch.**She prefers assigning narrow agents to specific jobs rather than relying on one agent for everything. Her advice is to try Dots and watch for where their proactive model earns a place in the workflow, not abandon a system that already works.
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
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