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joel ⛈️

@joelhooks • 23,440 subscribers

lvl 16 half-orc nerd 🍄💀🌈—independent AI researcher—built @eggheadio and working on @badass_courses & https://t.co/IiNWXFvgrz ex-@vercel 🐝🐀

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Anya Rossi

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~14min youtube packaging masterclass from Theo - t3.gg he assumes you can make content people want to watch (hah), but this is his best advice about thumbs, titles, and hooks to help the right audience connect with your work in the algorithm

~14min youtube packaging masterclass from Theo - t3.gg he assumes you can make content people want to watch (hah), but this is his best advice about thumbs, titles, and hooks to help the right audience connect with your work in the algorithm

35,842 次观看 • 7 天前

stopped reading code in May 2025 put a support inbox with 4,000 customers on autopilot and it worked 🌀 my cyc26 talk: own your tools, every mistake becomes a rule, protect your attention for the work you love

stopped reading code in May 2025 put a support inbox with 4,000 customers on autopilot and it worked 🌀 my cyc26 talk: own your tools, every mistake becomes a rule, protect your attention for the work you love

37,027 次观看 • 8 天前

pi+herdr+lakebed clutch af 🐀

pi+herdr+lakebed clutch af 🐀

64,650 次观看 • 2 个月前

🌀 "loops" are nothing new, but they take thoughtful consideration to build durably. This is a quick look at how I'm thinking about loopcraft, building durable loops from scratch, and experimenting with Pi, Herdr, and Theo - t3.gg's Lakebed (a shitty cloud for shitty apps)

🌀 "loops" are nothing new, but they take thoughtful consideration to build durably. This is a quick look at how I'm thinking about loopcraft, building durable loops from scratch, and experimenting with Pi, Herdr, and Theo - t3.gg's Lakebed (a shitty cloud for shitty apps)

45,709 次观看 • 2 个月前

Visa is doing marketing consults (see pinned!) Jason Pargin, author of John Dies at the End, etc has a solid take on the film

Visa is doing marketing consults (see pinned!) Jason Pargin, author of John Dies at the End, etc has a solid take on the film

270,375 次观看 • 2 年前

a quick demo of Swarm Tools in action - working on observability, evals, and triaging some gh issues in 3 separate OpenCode sessions

a quick demo of Swarm Tools in action - working on observability, evals, and triaging some gh issues in 3 separate OpenCode sessions

94,373 次观看 • 9 个月前

air fryer iykyk

air fryer iykyk

148,154 次观看 • 3 年前

This is a fun project Lakedbed, Herdr, Pi, Effect, XState prototyping a looping autonomous agent that monitors issues and clears its own backlog. The project is dogfooding an issue tracker into a durable agent that is building the issue tracker its looping over. It's an imperfect demo and the result isn't polished, but I think there's a lot fo interesting ideas to explore and expand on. The goal here is to demo a stack of tools that have been giving me consistently reliable results. Herdr and Pi make a KILLER combo for building a bespoke custom harness around your work. Lakebed is really nice to work with. Plenty of other opinions in the workshop lol - Pi as the agent harness — a minimal loop you own instead of rent, running GPT-5.5 through a Codex subscription inside a Docker workshop container - Planning before prompting: TLDraw sketches, a grilling session with Matt Pocock's Wayfinder skill, and a VISION.md to act as guardrails for the looping agent - Event sourcing with Effect v4 and Effect Schema, ports-and-adapters so the JSONL issue store can become GitHub Issues or Linear or whatever later - Multi-agent orchestration in herdr: an operator pane and an agent-loop pane, where one Pi session literally types prompts into another (fuckin LOVE this) - Enforcement that actually bites: OxLint with Ultracite, Lefthook hooks, and an agent that tries to eslint-disable its way past a complexity rule before fixing it properly 🤡 - A live issue tracker UI on Lakebed, built by the loop it tracks Workshop repo: The repo has a docker container to fire it up as a sandbox. Chapters: 0:00 Intro: Loopcraft and the workshop setup 2:25 Starting Pi inside the Docker container 4:31 Designing the looping issue resolver 10:16 Choosing XState v5 and Effect v4, prompting the starter 15:17 Why Pi: a minimal harness you own 19:35 Reviewing the generated scaffold 24:53 Architecture summary page with Lakebed 30:43 Matt Pocock skills and the vision.md concept 36:37 Grilling session: defining the vision 46:54 Daemon architecture and spec/ticket skills 55:38 Agent loop pane: one agent prompting another 1:01:22 Watching the build: compaction, tests, lint enforcement 1:07:59 Building the Loopcraft Pi extension and monitor 1:15:32 Lakebed issue tracker UI 1:21:07 Kanban board live: running the loop end to end 1:31:49 Wrap-up: bugs, next steps, takeaways

This is a fun project Lakedbed, Herdr, Pi, Effect, XState prototyping a looping autonomous agent that monitors issues and clears its own backlog. The project is dogfooding an issue tracker into a durable agent that is building the issue tracker its looping over. It's an imperfect demo and the result isn't polished, but I think there's a lot fo interesting ideas to explore and expand on. The goal here is to demo a stack of tools that have been giving me consistently reliable results. Herdr and Pi make a KILLER combo for building a bespoke custom harness around your work. Lakebed is really nice to work with. Plenty of other opinions in the workshop lol - Pi as the agent harness — a minimal loop you own instead of rent, running GPT-5.5 through a Codex subscription inside a Docker workshop container - Planning before prompting: TLDraw sketches, a grilling session with Matt Pocock's Wayfinder skill, and a VISION.md to act as guardrails for the looping agent - Event sourcing with Effect v4 and Effect Schema, ports-and-adapters so the JSONL issue store can become GitHub Issues or Linear or whatever later - Multi-agent orchestration in herdr: an operator pane and an agent-loop pane, where one Pi session literally types prompts into another (fuckin LOVE this) - Enforcement that actually bites: OxLint with Ultracite, Lefthook hooks, and an agent that tries to eslint-disable its way past a complexity rule before fixing it properly 🤡 - A live issue tracker UI on Lakebed, built by the loop it tracks Workshop repo: The repo has a docker container to fire it up as a sandbox. Chapters: 0:00 Intro: Loopcraft and the workshop setup 2:25 Starting Pi inside the Docker container 4:31 Designing the looping issue resolver 10:16 Choosing XState v5 and Effect v4, prompting the starter 15:17 Why Pi: a minimal harness you own 19:35 Reviewing the generated scaffold 24:53 Architecture summary page with Lakebed 30:43 Matt Pocock skills and the vision.md concept 36:37 Grilling session: defining the vision 46:54 Daemon architecture and spec/ticket skills 55:38 Agent loop pane: one agent prompting another 1:01:22 Watching the build: compaction, tests, lint enforcement 1:07:59 Building the Loopcraft Pi extension and monitor 1:15:32 Lakebed issue tracker UI 1:21:07 Kanban board live: running the loop end to end 1:31:49 Wrap-up: bugs, next steps, takeaways

15,610 次观看 • 2 个月前

event driven user-defined workflow is powerful af Vercel for serverless functions Inngest for workflow, queue, and events Sanity for a clean ez no-code workflow authoring UI watch the video and tell me this pattern doesn't give you a truckton of ideas! gonna expand this into a robust bespoke marketing automation system that can be hyper-customized for your specific domain and business needs this is a huge piece of that

event driven user-defined workflow is powerful af Vercel for serverless functions Inngest for workflow, queue, and events Sanity for a clean ez no-code workflow authoring UI watch the video and tell me this pattern doesn't give you a truckton of ideas! gonna expand this into a robust bespoke marketing automation system that can be hyper-customized for your specific domain and business needs this is a huge piece of that

30,620 次观看 • 3 年前

been really enjoying these bite sized design studies from MDS learn something literally every time

been really enjoying these bite sized design studies from MDS learn something literally every time

12,161 次观看 • 1 年前

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