Tag: AI Agents

  • What 3 months building a 99-agent Claude Code system taught me about context

    What 3 months building a 99-agent Claude Code system taught me about context

    I went quiet for seven weeks. I was building something called RennOS, a Claude Code setup with 99 specialized agents across 20 departments that runs my whole brand and a chunk of my personal life. For most of those seven weeks, it was broken in a specific, frustrating way: the agents would start sharp and get dumber the longer I used them.

    Then something clicked. I stopped writing CLAUDE.md for myself. I started writing it for a version of Claude that has zero memory of this project, zero knowledge of what I tried yesterday, zero sense of what the file it just opened is supposed to do. That one mindset shift is when 99 agents actually started cooperating.

    This is what I learned about context, and why the fix is almost never a better prompt.

    Why your sessions get dumber

    There’s a post on r/ClaudeAI from a couple of months ago that stuck with me: “You accidentally say ‘Hello’ to Claude and it consumes 4% of your session limit.” 6,200 upvotes. The replies were full of people describing the same thing, a session starts fine, then halfway through the model starts forgetting what you asked twenty messages ago, or blending two unrelated tasks together, or confidently hallucinating a file path that doesn’t exist.

    The instinct is to blame the prompt. Or blame the model. Or wait for a bigger context window. I did all three for about a month.

    What’s actually happening is simpler. Context inside a session is like a desk. Every message adds another sheet of paper. Eventually there’s no room left to think, and the model starts reaching for whatever’s on top. Joshua Chakmanho put it cleanly on Twitter recently: “the more the context accumulates, the more dumb the model is.” That matched my experience exactly.

    The fix isn’t a bigger desk. It’s a different workflow. Here are the four patterns that actually moved the needle for me.

    1. Treat every session like a cold start

    This is the mindset shift. Everything else follows from it.

    Clean wooden desk in warm morning light with a blank open notebook, sharpened pencil, and steaming tea, representing a fresh Claude Code session cold start.

    Stop thinking of your chat history as memory. It isn’t memory. It’s scratch paper that gets thrown away when the session ends, and it poisons the model’s attention long before that. If you’re relying on “I told Claude about this earlier in the conversation,” you’re building on sand.

    The first thing I did inside RennOS was make the CEO agent, the top-level orchestrator, explicitly state in its own instructions that every session is a cold start. Not as a reminder to me. As a reminder to itself. The CEO agent opens every session by reading a set of files in .claude/ceo-memory/, because it genuinely does not remember what it decided yesterday. It has to re-read.

    That sounds inefficient. It isn’t. A session that rebuilds its context from durable files is always sharper than a session that’s been stewing in its own output for two hours.

    2. Keep memory in markdown, not in the session

    Once you accept the cold start, the next question is obvious. Where does the state live?

    Wooden library card catalog with blank brass label holders and one drawer open revealing blank index cards, representing markdown memory files Claude Code agents read on demand.

    For me it lives in markdown files on disk. Nothing fancier than that. Andrej Karpathy has been talking about a pattern he calls WikiLLM. The idea is you give the model a wiki of text files and let it read what it needs instead of cramming everything into the prompt. Community implementations of the pattern have collectively pulled in thousands of GitHub stars over the past week, which suggests the rest of the ecosystem is landing on the same answer.

    RennOS uses this everywhere. The CEO agent has .claude/ceo-memory/ with files like org-chart.md, workflows.md, active_projects.md, and decisions.md. Each of the 99 specialized agents has its own .claude/agent-memory/<agent-name>/MEMORY.md, which gets loaded the moment that agent is spawned. Shared knowledge like brand identity and the content calendar lives in data/, where any agent can read or write it.

    The Twitter workflow alone has its own eight-file brain under data/social/twitter/brain/: voice, patterns, posts, viral, analytics, growth-strategy, daily-activity, and a MEMORY file. When the short-form writer gets spawned to draft a reply, it reads those files fresh. No session state required. No “remember what we talked about on Tuesday.” Just files.

    The payoff is that the system survives a cold start perfectly. Close the laptop, open it tomorrow, and every agent boots up with the same brain it had yesterday. Better, actually, because the files have been edited to be clearer.

    3. Split the roles

    This one took me the longest to accept, because it felt wasteful at first.

    Warmly lit workshop with three separate craft stations for writing, editing, and labeling, representing Claude Code's split-role multi-agent architecture.

    The instinct when you’re using Claude Code is to let one session do everything: research the problem, plan the approach, write the code, review the code, commit the code. It feels faster. It isn’t. By the time the model is reviewing its own code, its context is half-full of the exact reasoning it used to write it, which is the worst possible state for catching its own bugs.

    Inside RennOS, every agent has exactly one job. There is a long-form-writer for blog posts. There’s a content-editor that reviews drafts and never writes them from scratch. There’s an seo-specialist that adds metadata and never touches the prose. There’s a research agent that reads the web and dumps findings into data/, and a strategy agent that reads those findings and makes recommendations. They talk to each other through files, not through a shared session.

    You don’t need 99 agents to get this benefit. You can get most of it with two. Open one Claude Code session to research and plan. Close it. Open a fresh one to implement. Close it. Open a third to review. The review session has no attachment to the implementation because it genuinely didn’t write it. It’s the closest thing to a real second opinion you’ll get out of a single model.

    If you try only one thing from this post, try that.

    4. Write every file for a future amnesiac

    This is the pattern the thesis tweet is about, and it’s the one that changed the most things at once.

    For the first month of RennOS, I wrote CLAUDE.md the way I’d write a note to myself. Short, gestural, full of phrases like “the usual workflow” and “the main agents.” I knew what I meant. The problem is Claude didn’t. Every cold-start session would open that file, hit a phrase like “the usual workflow,” and just… guess. Sometimes it would guess well. Often it wouldn’t.

    The shift was writing every file for a hypothetical fresh session that has never seen this project. That means:

    • Name the specific thing, not the category. Not “the main agents,” but “the CEO agent at .claude/CLAUDE.md, which delegates to 99 specialists listed in ceo-memory/org-chart.md.”
    • State the assumptions. If a workflow expects Asana to be connected, say so in the file, not in your head.
    • Leave no implicit context. If a decision has a reason, write the reason down. A future session will not remember why.
    • Define acronyms the first time. My own ADHD brain was happy to write “UAT gate” without defining User Acceptance Testing. A cold-start Claude was not.

    This is also where the 230+ skill playbook files in .claude/skills/ stopped being a mess and started being useful. Each skill is a markdown recipe another agent can follow, written for a reader who has never done this task before. They work because they assume nothing.

    The rule is simple. If a file only makes sense to someone who already has the context in their head, it will fail every cold-start session that opens it. Which is all of them.

    What to do Monday morning

    You don’t need to build RennOS to get the benefit of any of this. Here’s the smallest useful thing you can do.

    Pick one project where your Claude Code sessions keep going sideways. Open the project folder and create a CLAUDE.md file. Write it for a version of Claude that has never seen this project before. Name the specific files that matter. State the assumptions. Write down the two or three decisions that have a reason you’d otherwise have to explain out loud. Keep it short, but keep it specific.

    Then next time you open a session, let it read that file first and nothing else. Ask your question. See what happens.

    That single file changed more for me than any prompt tweak I’ve ever made. It’s a small habit with a big shape, and once you feel the difference you start writing everything this way.

    I’m going to keep sharing what worked and what didn’t from the RennOS build in future posts. No hype. No fear. Just what I tested.

  • How to Build and Sell AI Agents

    How to Build and Sell AI Agents

    AI agents are everywhere.

    And guess what? 

    They’re not just for big tech companies anymore.

    You can build one. You can sell one. And it’s way easier than you think.

    No PhD. No coding wizardry.

    Just a simple plan, some smart tools, and a market that’s ready to throw cash at anything that saves them time.

    Here’s how to make your first ai agent

    Step 1: Find a Profitable AI Agent Idea

    The first mistake? People build agents without knowing if anyone even wants them.

    You don’t need to guess. 

    You need to spot problems that AI can fix.

    Two ways to find winning ideas:

    Use AI to analyze workflows

    • Take a process you know (like content creation, marketing, or customer support).
    • Ask AI: “Where can I automate this?”
    • Example: A YouTuber’s workflow. AI spots automation gaps: scriptwriting, video editing, comments, content ideas, engagement.
    • Boom. Now you have a list of agent ideas.

    Look at what’s already working

    • Go to AI agent marketplaces (like FuturePedia, Agent.ai)
    • Check what’s selling.
    • Look for gaps and opportunities.
    • Find agents with high demand, weak competition, or bad UX.
    • Improve on them. Offer something better.

    Once you have an idea, you move to step 2.

    Step 2: Build Your AI Agent 

    Most people overcomplicate this.

    You don’t need to build some crazy system.

    You just need a simple agent that does one job well.

    Keep it simple:

    • Identify the exact process your AI agent will handle.
    • Break it down into tiny steps.
    • Use AI to build the agent for you.

    Example: A YouTube comment-handling agent.

    1. AI pulls comments from your channel.
    2. AI classifies them (questions, feedback, spam, etc.).
    3. AI generates replies based on your style.
    4. You approve or tweak before posting.

    That’s it. One job. Done well.

    Now, how do you build it?

    The Fastest Way to Code an AI Agent

    If you know coding: 

    Use VS Code + AI extensions like Cline or Roo Code

    You can also try Cursor AI or Windsurf or Bolt.new

    If you don’t know coding: 

    Use no-code platforms like bubble, make.com, n8n, etc, or hire someone cheap.

    Here’s a real example:

    • An AI that summarizes YouTube videos in bullet points.
    • Uses SearchAPI to get transcripts.
    • Uses OpenAI to summarize.
    • Outputs a clean summary in seconds.

    Simple. Fast. Useful.

    Step 3: Sell Your AI Agent and Make Money

    You have two ways to make money with AI agents:

    1. Sell as a service

    • Offer your AI agent as a freelance service (Upwork, Fiverr, Twitter DMs).
    • Example: “I’ll automate your YouTube comments so you never have to reply manually again.”
    • Charge a one-time fee or a monthly retainer.

    2. Turn it into a product

    Package your agent as a tool or SaaS.

    Add a simple UI on WordPress or Webflow.

    Sell it as a micro-SaaS.

    Example: YouTubeDigest (turns YouTube videos into articles, summaries, and tweets).

    But how to sell it? 

    Here’s what works:

    • Social listening: Find people complaining about the problem your AI solves (Reddit, Twitter, Quora). Engage, offer your agent.
    • Cold DMs + outreach: Message businesses and content creators who need automation.
    • Build an audience: Share case studies and demos on Twitter, LinkedIn, Medium, and YouTube.
    • Launch on AI directories like Product Hunt and FuturePedia.

    That’s it.

    AI agents is not some mystery tech but a tool to solve real-world problems.

    And right now, the demand is insane.

    People don’t want AI.

    They want results.

    Build something that saves time, makes money, or removes frustration.

    They’ll pay. A lot.