What happens when you have free desk space, A mac and PC who share screens, and then a desk arm turns up as id by magic. Now I have 3 monitors on the PC, 2 Monitors on the Mac Mini, and 2 Monitors for the MacBook Pro. Im sure I can find more somewhere. Now to get a blue tooth keyboard and mouse for the MacPro and im off to the races :)

Hey guys! I just got to sit down with Smitha Kullan and Vlad Kolesnikov from Google on their Agent Factory podcast—and the timing couldn't have been better. Google released Gemini 3 Pro literally hours before we recorded. So we got to dive deep into building production-ready AI agents, I did a live demo of my market research AI employee workflow, and we nerded out about everything from Gemini CLI to building entire AI workforces. If you've been wondering how to actually build AI agents that do real work for you, this episode is packed with actionable strategies. You can watch the full episode below and make sure to subscribe to their channel! Here's what we covered: ✅ Live demo: How I use Gemini CLI to build market research AI employees that search across entire markets in parallel ✅ My "AI-first" philosophy—how I've built AI employees to handle everything from customer research to email ghostwriting ✅ The SOP (Standard Operating Procedure) framework for creating AI employees that follow instructions at scale ✅ How I never write code anymore—I just review it while AI does the heavy lifting ✅ Real examples from my EMS startup showing AI doing market research across multiple cities simultaneously ✅ Why Gemini 3 Pro is crushing it for high-level reasoning and agentic operations Plus, Vlad showed off an insane AI video creation agent using Google's Agent Development Kit (ADK) that generates educational videos with a talking capybara. You have to see it to believe it. Whether you're just getting started with AI agents or you're looking to automate more of your business operations, this conversation is exactly what you need right now. P.S. Black Friday is here—and for the first time ever, Shipkit is offering our biggest discount of the year. If you want to start building production-ready AI applications with pre-built templates, Claude Code mastery modules, and everything you need to ship AI Chat, RAG, ADK agents, and more—now's the time. Skip the boilerplate, learn the workflows, and start shipping real AI applications in days instead of months.

My little weekend project that I called NeuroHelix. NeuroHelix is a fully automated AI research lab that wakes up before you do. Every morning a LaunchD-scheduled pipeline fires off 22 context-aware prompts via Gemini CLI, logs every run at the prompt level, and aggregates the answers into a polished intelligence report. The system isn’t just a batch of shell scripts—it’s a complete ops stack. Research outputs land in data/outputs/daily/, telemetry writes JSON ledgers plus human-readable logs, and the aggregators synthesize cross-domain themes, strategic recommendations, and execution summaries with zero manual touch. A Bash orchestrator enforces idempotency, git cleanliness, and pipeline locking, while maintenance flags let operators reprocess a single day or nuke the workspace with auditable trails. Publishing is equally modern. NeuroHelix exports structured payloads, source manifests, and vector-ready metadata, mirrors raw artifacts into an Astro site, and deploys to Cloudflare Pages with bundle metrics logged for every build. The front end embraces an IDE aesthetic: dual “Processed vs Source” views, repo-style navigation, command palette search that scans both final narratives and raw prompts, and accessibility baked into every interaction. Optional notifications—email or Discord—announce when the daily dashboard is ready, and bash-based smoke tests cover telemetry and prompt execution edge cases. In short, it’s an opinionated blueprint for AI-assisted research automation end to end. https://neurohelix.patchoutech.com/ and I made it public for anyone who wants to try : https://github.com/hopchouinard/NeuroHelix Notice : it is a personal fun project still very much in development but feel free to try it if you want.

as an AI developer, what kind of hardware will I need? I am thinking a mac mini m4 16gb Apple Mac mini MU9D3LL/A (Late 2024) Desktop Computer Apple M4 10-Core CPU; 16GB Unified Memory; 256GB Solid State Drive; 10-Core GPU/16-Core Neural Engine $500 + tax will do

Has anyone experimented with using a Discord MCP inside Cursor (or any other AI-assisted dev tool)? I’ve been wondering if it’s possible to use the content of specific Discord servers—like community Q&A threads, bug-report channels, or feature discussions—as contextual data for development projects. So many open-source and indie tools rely on Discord as their main knowledge hub. There’s a ton of practical insight buried in those chats: solutions to obscure errors, implementation workarounds, early warnings about breaking changes, you name it. Imagine asking your AI coding assistant a question, and it automatically draws on those server discussions for context—essentially merging real-time community intelligence with your codebase context. Has anyone tried hooking that up through MCP yet? Or built a pipeline that surfaces relevant Discord threads as part of a project’s knowledge layer? I feel like that could be a goldmine for accelerating debugging and implementation. Curious if anyone’s explored this territory yet—or hit limitations doing so.