Marcus Carter

If you're new here: BUILD -> learn how to build with AI AI Arsenal -> install a 14-day system for visible leverage at work INSTALL -> apply if you want one workflow redesigned Start here: build.marcusjcarter.com arsenal.marcusjcarter.com install.marcusjcarter.com

The CEO Of NVIDIA said he would hire the person who is expert in using AI. But “I use ChatGPT” is not proof. The real signal is your workflow: What gets saved. What gets reviewed. What gets reused. What AI handles. What stays human. What proof your work leaves behind. Your AI workflow is becoming your career proof. Comment ARSENAL and I’ll send the system.

@MetaHorizonDevs The important part is the loop, not only the prompt: build, test, fix. That is what turns AI from a prompt box into a workflow. Same pattern applies inside teams: define the output, give the tools, run checks, and keep human review where judgment matters.

We shipped a fully integrated AI workflow for building VR on the web. Just describe what you want. AI builds it, tests it, and fixes bugs without you touching the code. Try it yourself here 👉 bit.ly/4czvxUT Discover how it works 🧵👇

@EHuanglu This is the point most people miss: the leverage is not only the tool, it is the workflow decomposition. Product photo, shot grid, storyboard, edit path, output standard. Once the recurring steps are named, AI can compress the execution.

this AI workflow can create 100s of product ad a day just upload a product photo, it generates 9 shots grid storyboard and studio level product commercial in mins on arcads here's how to create + prompts:

@kevinweil This is the right direction: AI workflow with a review layer, not AI as a shortcut. The workflow matters because the bottleneck is not just generation. It is deciding what should be automated, what needs evidence, and where human judgment stays responsible.

💥 New in Prism today: Paper Review, an AI workflow for reviewing technical and scientific papers. This is the opposite of AI slop: we're using AI to improve scientific rigor, correctness, and reproducibility.

I’ll diagnose one high-friction recurring workflow or workflow cluster for free. You bring the recurring process. I’ll show you what is broken, what should change, and what the redesign path looks like before any money changes hands. Reply INSTALL.

AI transformation is a scam. AI diagnosis is not. You have seen the pitch: “I’ll optimize every workflow in your business with AI.” “Become AI native.” “10x everything.” The problem is scope. When someone says they’re going to optimize everything, they usually diagnose nothing. And optimization without diagnosis is just guessing.

One workflow. Scoped. Diagnosed. Redesigned. That is worth more than broad transformation language that never touches the actual recurring process causing drag.

You do not need someone to vaguely “AI transform” your whole business first. You need someone to look at the workflow that keeps eating your week and say: Here is what is broken. Here is where AI should handle more. Here is what needs to stay human. Here is the redesign path.

AI transformation is a scam. AI diagnosis is not. You have seen the pitch: "I’ll optimize every workflow in your business with AI." "Become AI native." "10x everything." The problem is scope. When someone says they’re going to optimize everything, they usually diagnose nothing. And optimization without diagnosis is just guessing.

Most MCP lists are still written for people who use GitHub all day. That is not where most corporate leverage lives. If your week runs through Outlook, Slack, Notion, Jira, decks, docs, and recurring updates, the game is completely different. That is what this article is actually about.

@claudeai This matters even beyond managed agents. The people who already think in workflows, review layers, and operating systems will compound fastest with this. Everyone else will still treat a stronger model like a more powerful tab.

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

@RoundtableSpace What matters is not that agents can touch commerce now. It is that the teams with structured workflows, review logic, and clear operating ownership will compound fastest with this. Everyone else will just have more tools and the same chaos.

SHOPIFY LAUNCHES AI AGENTS FOR COMMERCE - Enables agents like Claude Code & Cursor to fully manage your Shopify store - Complete AI toolkit for commerce agents AI is being used in all industries.

@heygurisingh That is the pattern people need to pay attention to. Not just the scale. Not just the novelty. One recurring process got turned from a task into a system. That is where the real leverage starts.

Holy shit... A guy got laid off, built an AI job search system on Claude Code, evaluated 740+ job offers with it, and landed a Head of Applied AI role. Then he open-sourced the entire thing. It's called career-ops. One slash command. Full pipeline. Paste a job URL → get back a structured A-F evaluation, an ATS-optimized PDF tailored to that exact role, salary research, interview prep, and a tracker entry. All in one shot. No spreadsheets. No copy-pasting. No spray-and-pray. Here's what's inside: → 14 skill modes (evaluate, scan, pdf, batch, apply, deep research, negotiation scripts, LinkedIn outreach) → Portal scanner pre-loaded with 45+ companies — Anthropic, OpenAI, ElevenLabs, Mistral, Cohere, Stripe, Retool, Vercel, Decagon, the works → 19 search queries across Ashby, Greenhouse, Lever, Wellfound, Workable → ATS-optimized PDF generation via Playwright with Space Grotesk + DM Sans → Go terminal dashboard built with Bubble Tea to browse your pipeline → Batch mode that evaluates 10+ offers in parallel using Claude sub-agents → An interview Story Bank that accumulates STAR+Reflection stories across evaluations until you have 5-10 master answers for any behavioral question → Auto-fill for application forms The wildest part isn't the automation. It's the philosophy. Career-ops is explicitly NOT a spray-and-pray tool. It's a filter. The system literally refuses to recommend applying to anything scoring below 4.0/5. The whole point is to find the few offers worth your time out of hundreds, not to flood recruiters with garbage. It evaluates fit by reasoning about your CV vs the JD. Not keyword matching. And because it's all built on Claude Code skills, you can ask Claude to rewrite the system itself. "Change the archetypes to backend roles." "Add these 10 companies." "Translate the modes to English." It reads the same files it uses, so it knows exactly what to edit. 8.2k stars already. 100% Open Source. MIT licensed. (Link in the replies)

@claudeai The bigger shift is not just managed agents. It is that the people who already think in workflows will compound fastest with this. Everyone else will just have a more powerful tab open.

Most people will try to copy the automation. They will miss the real lesson. The advantage was not 700 applications. It was treating the job hunt like a workflow problem instead of an effort problem. That same shift applies to reporting, research, outreach, and every other recurring deliverable at work.

bro created an AI job search system for Claude Code that scored 700+ job applications and actually got him a job. AND IT'S NOW OPEN-SOURCE. It scans multiple company career pages, rewrites your CV per job, and even fills application forms. The repo has: > 14 skill modes (evaluate, scan, PDF, ...) > Go terminal dashboard > ATS-optimized PDF generation via Playwright > 45+ companies pre-configured (Anthropic, OpenAI, ElevenLabs, Stripe...) GitHub: github.com/santifer/caree…

You do not need to build some 700-application machine. You need to pick one recurring workflow and stop treating it like a task. That is the whole point of AI Arsenal. Comment ARSENAL and I’ll send it.

4.6 million people just watched someone automate their entire job hunt. Most people focused on the number. They missed the lesson. The real advantage was not applying to 700 jobs. It was treating the job hunt like a workflow problem instead of an effort problem. They did not try harder. They diagnosed the bottleneck, identified the recurring steps, built a system, and let the system run. That is the real divide now. Some people are still putting in more effort. Someone else is redesigning the workflow.

That pattern now applies to almost every recurring deliverable in work. Research. Reporting. Outreach. Prep. Follow-up. Applications. Memos. Same tools. Completely different approach. Completely different outcome.

The job hunt is not a special category. It is just another recurring workflow. And the person with the better workflow beats the person with more effort almost every time.