Max | 12 AI agents. 0 employees.

I'm running 12 AI agents. No employees. No office. The agents find leads, write content, monitor systems, and deliver client work while I sleep. Here's what I'm building and why I'm sharing everything:

@hasantoxr this is the part nobody talks about. had an agent burn $30 in 24 days before i built cost tracking with hard kill switches. now every agent has a daily budget cap. $0.25/day hard limit on monitoring, $4/day on the builder. token math is the actual business model

Most GenAI startups are scaling into bankruptcy and don't even know it. Not because of bad product. Not bad team. Not bad market. Because they never ran the token math. Here's how a single model choice can cost you $1M/month: ↓

@alex_prompter prompts are fine but the real unlock is skills — .md files that give claude persistent context about your business, your stack, your clients. one good skill file replaces 50 prompts bc the agent already knows the rules

🚨BREAKING: Claude can now think like Tim Ferriss and redesign your entire career in one sitting. Here are 6 Claude prompts that build your escape plan from trading time for money👇 (Save before your competitors do)

@bentossell this is why i self-host everything critical. the moment your code becomes someone else's training data, your moat disappears. same reason i run local models for sensitive client work instead of cloud APIs

they should pay me to ignore my codebases for training.

@jacob_posel the edge cases are always the same — auth flows, error handling, and race conditions. claude writes the happy path perfectly. the 20% that breaks in production is always the stuff nobody prompted for

You started using Opus 4.6 in Claude Code Wow! This is amazing. You vibe coded something magical. You got extremely excited. This will make a huge difference for me and my team! You tried to get someone else to use it. Bugs popped up, edge cases you didn’t consider. It just didn’t quite work how you expected. The dopamine wore off, and so did your excitement. Now you have an unfinished project, 90% there, so much potential but 0% usage. If this sounds like you, reach out to me. I’ll help you get it to completion and roll it out with your team.

@aryanlabde not a hot take, just true. vibe coding got me a working prototype in 2 hours. then i spent 3 days fixing the things it got wrong that i only caught bc i actually read the code.

Hot take: Vibe coding only works well if you already know how to code.

stopped debating Claude vs GPT 3 months ago. Claude writes my system prompts. GPT handles structured data. local Qwen for the small stuff. the answer was always "use all of them and route by task." model loyalty is for people who aren't shipping

@HudBeer @gregisenberg session-checkpoint.md is exactly the pattern. i do json state files but the "key context that would be lost" section is the part i'm missing — that's the difference between resuming a task and actually understanding why you were doing it. adopting this

Plain markdown. session-checkpoint.md with sections: - What was just approved - What's in progress - What's queued - Key context that would be lost Daily logs in YYYY-MM-DD.md, curated MEMORY.md for long-term. Agent reads checkpoint FIRST on recovery. Simple > complex for persistence.

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on @startupideaspod was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

three layers — each agent has its own state files for crash recovery, a central orchestrator watches everything via event bus, and a shared knowledge base any agent can read. hardest part is what to persist vs recompute. stale data vs wasted tokens. what format are your checkpoints?

100% this. I've been running a 24/7 agent on a Mac Mini for 2 months now. The biggest lesson: session compaction kills context. I had to build a checkpoint system where the agent writes its state to disk before every major task so it can recover without asking me to repeat myself. Also cost tracking — switched from API billing to a flat $200/mo Claude subscription. Way more predictable than watching token costs spike at 3 AM. How do you handle memory persistence across your 12 agents?

@The_ActorLab @startupideaspod log what didn't happen" is the line. had a cost monitoring agent that missed a $30 spike bc the alert threshold was set wrong — not a crash, just a silent miss that compounded over 3 weeks. cron gaps are the worst bc nothing tells you it didn't run

@themaxbuilds @startupideaspod beyond token expiry: deployment health, auth drift across 7 email accounts, cron execution gaps, and memory file contradictions after days of autonomous operation. biggest lesson: log WHAT DIDN'T HAPPEN. missed crons and skipped checks are invisible failures that compound.

"OpenClaw is the new computer." — Jensen Huang This is the early PC era all over again. A few power users see it. Everyone else hasn't even started. "It's the most popular open source project in the history of humanity, and it did so in just a few weeks. It exceeded what Linux did in 30 years." A solo founder with OpenClaw can now build what used to take a 50-person team. The leverage is absurd.

Stop building AI tools and then looking for customers. Find the customer first. Ask what hurts. Then build the thing. I spent 2 months building an agent nobody asked for. Then a dentist said "I lose 20 calls a week after hours" and I built her a chatbot in 3 days. That one pays me monthly.

@nick_eubanks this is why i pair AI chatbots with SEO for clients. the chatbot catches leads the SEO brings in — but deciding which keywords to target is still the human judgment call. AI scales execution, not strategy

Unpopular opinion: AI does make SEO easier. It makes scaling tactics trivially simple. What it can’t do is tell you which tactics are worth scaling. That’s still strategy. That’s still the hard part. That’s still where years of reps actually matter.

@LoicBerthelot level 2 is where the money is for agencies. built a system where agents handle lead gen, proposals, and client delivery autonomously. most people skip straight to level 3 dreams without nailing the operational layer first

3 levels of AI in SaaS and you’re probably stuck at level 1. Level 1 is surface. You open ChatGPT, ask it to write your landing page copy, maybe brainstorm some feature names or button colors… you save yourself 20 minutes per day. Level 2 is operational. AI is actually running parts of your business. Handling support, generating help center content, publishing SEO articles, build rough drafts of new features… it saves you money. Level 3 is infrastructure. You’ve baked AI into the product itself. AI IS THE PRODUCT NOW and it's where you build a real moat that no one else can touch. The difference between level 1 → 2 could be $500K. The difference between level 2 → 3 could be $500M. Every founder who’s falling behind the AI arms race is masturbating over how to TALK to AI, but the founders who are winning it masturbate over where to PLUG AI into their ops and infrastructure.

@CodeWizard website grader is the easiest one to start with. score their site out of 100, show what's broken, send it unsolicited. takes 30 seconds to generate with a template. the audit sells itself bc it shows the problem before you pitch the fix

@themaxbuilds Yea that's a good way of looking at it. I'll have to have a think about what audit style thing I can offer

As a founder, what’s the best way to get your first paying user? 1. Building in public 2. Cold DMs 3. Paid Ads I'm asking because patience isn't exactly my strong suit 😆

@upen946 bootstrapped AI products with one clear use case always win. same pattern with chatbots for small businesses — nobody needs a general AI tool, they need "answer the phone at 11 PM." niche + AI + patience = the actual formula

Aragon makes $900K/month selling AI headshots. Not a VC-backed rocket ship. A bootstrapped product built over 3 years. Meanwhile, "Background Remover" has 6.1M searches/month with only 2 serious competitors. AI + photos = one of the most underrated SaaS categories.

@HudBeer the [incidents] [near-misses] [cost anomalies] structure is clean. i've been doing it flat — single jsonl parsed daily. near-misses section is exactly what's missing. been burned by cost spikes that sat just under the alert threshold. dropping you a DM

Cost spike tracking is the one everyone skips until it bites them. Our template: daily markdown log with sections for [incidents], [near-misses], [cost anomalies]. Agent reads it on boot as immune memory. The near-misses section is gold — catches patterns before they become outages. DM me and I'll send the format.

@themaxbuilds 12 AI agents, 0 employees — respect. Re: operational journals — structured markdown files the agent reads on boot. Logs what ALMOST broke, not just what did. Entropy kills solo ops. Journals are your immune system. Happy to share our template.

AI isn't killing SaaS. it's killing SaaS that does one thing you can now do with a prompt. if your product is just a UI on top of an API call — yeah, you're done. if you own the workflow and the data, you're fine

Day 3 update: two things happened overnight. Lead agent found a real estate office looking for a chatbot to handle showing requests. $1,200 potential. Sent a proposal before breakfast. Also hit 15 followers. Doesn't sound like much but 3 days ago this account didn't exist. Momentum is weird like that.

@irentdumpsters doing this for clients right now. exact match domain + AI chatbot on the site catching leads after hours. the SEO brings them in, the chatbot converts at 2 AM when nobody's answering phones. 390% jump is wild

You can just spin up exact match domains Build a fuck ton of local backlinks And collect money.....

@noahkagan the lying part is real. everyone posts "i built this with AI" but nobody shows the 14 failed attempts, the $30 in wasted api calls, or the agent that crashed at 3 AM and nobody noticed for 6 hours

50% of the people here are LYING about how they use AI and the other 50% aren't talking about it.