Founding Cohort · Starts July 7

man's best
bot.

You're prompting like you're writing emails. ChatGPT, Claude, Gemini, Grok, Perplexity, whatever — needs telegrams.

Badmutt teaches you how to prompt like a telegram to train your AI to fetch more time.

Tight inputs, specific outputs, and zero wasted words fix broken workflows and AI amnesia.

Your AI tools are powerful alone. We make them work together.

Apply for the Founding Cohort $10,000 total · 3 months · 12 seats · Starts July 7, 2026
The Problem

You're writing essays.
Your AI needs telegrams.

Every time you open ChatGPT, you write three paragraphs of context. You explain the background. You add caveats. You paste in a whole document and say "summarize this and also could you maybe help me think about next steps."

Your AI reads all of it and gives you six hedged paragraphs back. Then you close the tab and do it again tomorrow - from scratch.

You're not getting bad output because the model is bad. You're getting bad output because nobody taught you how to talk to it. Prompt like a telegram. Tight. Specific. One ask, one output, no padding.

Every bloated prompt, every re-explained context, every session that starts from zero - that's token slippage. Same concept as price slippage in a trade: the gap between what you should have spent and what you actually burned. You don't see it on an invoice. You see it in the hours you're not getting back.

Prompting like a telegram fixes the skill problem. But skill alone doesn't solve the architecture problem - you also need a system that remembers, delegates, and runs without you re-explaining everything Monday morning. That's what Badmutt builds. We eliminate the slippage and install the system.

Sample Findings

What we find when we
look under the hood.

These are real patterns from Garrett's own workflow audit - the same process every cohort member goes through in Week 1.

Finding #1 - Re-prompting Loop
⚠ Critical High confidence

42 minutes/day spent re-prompting.

Garrett's workflow audit Time impact: High

Same questions, different tabs, no memory between sessions. The AI starts from zero every time because there's no persistent context layer.

✓ Fix

Persistent memory layer - session summaries and preference files that carry forward automatically. The AI knows your patterns by week 2.

Analyzed by Badmutt Source: workflow audit · Confidence: High
Finding #2 - Tool Sprawl
⚠ Critical High confidence

3 tools doing 1 job.

Garrett's workflow audit Time impact: High

A notes app, a task manager, and a chatbot - all holding pieces of the same workflow, none of them talking to each other. Consolidation to a single agent chain cut the loop from 25 minutes to 4.

✓ Fix

One agent chain with shared context. Every tool has one job. You stop being the middleware.

Analyzed by Badmutt Source: workflow audit · Confidence: High
Finding #3 - Token Slippage
⚠ Critical High confidence

Prompt length averaging 280 words when 40 would do.

Garrett's workflow audit Time impact: High

Over-explaining is the most common pattern and the biggest source of token slippage. Tighter prompts produced better output in every test - not sometimes, every time. The telegram rule applies: if you wouldn't pay per word to send it, your AI doesn't need it either.

✓ Fix

Prompt like a telegram. Tight. Specific. One ask, one output, no padding.

Analyzed by Badmutt Source: workflow audit · Confidence: High
Finding #4 - Manual Recurring Work
⚠ Critical High confidence

Zero automation on recurring tasks.

Garrett's workflow audit Time impact: High

Daily briefings, meeting prep, follow-up emails - all done manually despite being identical in structure every time. Each one is a cron job waiting to happen.

✓ Fix

Automated scheduled jobs. If it happens on a schedule and follows a pattern, it should run without you.

Analyzed by Badmutt Source: workflow audit · Confidence: High

The audit maps all of this for your specific workflow. You'll see where the hours are going and exactly which ones we're reclaiming first.

Proof of Concept

The founder built it first.
On himself. In six weeks.

Garrett Mastro doesn't teach frameworks he read about. He's a full-time options trader who built a fully automated 22-strategy trading system - profitable across every quarter it's run. Then he turned that same systematic, checklist-driven methodology on his own AI stack. What follows is exactly what he built, and how long it took.

Starting point

Six weeks ago, his AI setup was: ChatGPT, used occasionally, with zero memory and zero integration. Every conversation started from scratch. 2.6GB of documents scattered across Google Drive with no system. Newsletter written manually - hours per issue. No monitoring, no automation, no agents. Just a browser tab he opened when he needed something.

Before → After

Feb 16, 2026

  • ✕ChatGPT with no memory - re-explained everything every session
  • ✕347 documents scattered across Drive, unsearchable
  • ✕Newsletter written manually - hours per issue
  • ✕Zero automated monitoring of markets, news, or security
  • ✕No agents, no system - just individual tools used one at a time

April 3, 2026

  • ✓5 AI agents with persistent memory - never re-explains context
  • ✓All documents organized, indexed, searchable, auto-backed-up
  • ✓Newsletter: screenshot → AI draft → edit → publish in minutes
  • ✓9 automated jobs running 24/7 - intel, security, health, backups
  • ✓7-model Board of Directors for independent decision review

By the numbers

6

weeks, start to full system

5

coordinated AI agents running 24/7

9

automated jobs, zero daily intervention

10+

hours/week reclaimed (conservative)

347

documents organized from chaos to searchable

$0

per-token cost for local LLM operations

The build

WEEKS 1-2

Established the AI agent's identity, memory system, and operating rules. Audited 2.6GB of Google Drive - 347 documents, 360K words - and built a 14-category taxonomy. Set up Drive API integration and moved 102 files into the new structure. Zero to organized in two weeks.

WEEKS 3-4

Ran a failure audit on 1,494 messages to find every breakdown. Deployed Scout (daily intel monitoring across 89 accounts) and Sentinel (twice-daily security sweeps). Built the Board of Directors - 7 AI models giving independent reviews on major decisions. Installed a local LLM for zero-cost classification at 93.1% accuracy. Added local audio transcription, automated health checks, and a monitoring dashboard.

WEEKS 5-6

Migrated everything from a laptop to a dedicated Ubuntu server. Deployed 9 automated cron jobs that run without intervention - intel, security, backups, health checks, and a supervisor that monitors the monitors. Added a knowledge base librarian bot, automated newsletter workflow, local embeddings replacing cloud APIs, and a smoke test suite with 23 automated checks. Built and launched badmutt.com.

This isn't a hypothetical. It's not a demo environment. It's the actual system running the business you're reading about right now - this website was built, reviewed, and deployed by the same AI stack described above. The methodology that built a fully automated trading system with 15 consecutive profitable quarters is the same methodology we apply to your AI workflow in the cohort.

What You Get

What you'll have
after 12 weeks.

Step 1 - AI Audit

We map every workflow where you're losing time. Not a survey. We watch how you actually work - where you context-switch, where you re-explain things to AI, where you're doing manually what a system should handle. You get a written audit document: every leak identified, every hour quantified, prioritized by impact.

After this step, you have: A complete map of where your 10+ lost hours are going and which ones we're fixing first.

Step 2 - System Design

We architect your AI stack before touching a single tool. Based on the audit, we design the system: which agents handle what, how they talk to each other, what runs automatically, what stays human. This is a blueprint - not a tool recommendation list.

After this step, you have: A technical architecture document you can hand to anyone and say "this is how my AI works."

Step 3 - Build Room

Daily calls. We build it live, together. 12 people on daily calls with Garrett, building your systems in real time. You're not watching tutorials. You're doing the work, getting feedback, and seeing how 11 other people solve similar problems differently.

After this step, you have: A working AI system - built, tested, and running on your actual workflows. Not a demo. The real thing.

Step 4 - Deployment

Everything goes live on your real work. Your system moves from "works in testing" to "runs my actual day." We troubleshoot edge cases, stress-test against your real inputs, and make sure nothing breaks when it meets the chaos of your actual schedule.

After this step, you have: An AI stack handling real work - scheduling, research, briefs, follow-ups - without you babysitting it.

Step 5 - Handoff

You own it. It runs without us. Full documentation. You know how to maintain it, extend it, and fix it when something breaks. No ongoing dependency on Badmutt. The goal was never to keep you as a client. The goal is to make you dangerous on your own.

After this step, you have: Complete ownership of an AI system built on open-source tools, documented, running, and yours. No recurring fees. No vendor lock-in.

Fit Check

Who this is for:

This is for you if
  • You earn $250K-$500K+ and your time costs more than the tools you're fumbling with
  • You already use ChatGPT, Claude, or Copilot - but nothing's connected and nothing remembers
  • You'd rather have someone build the system with you than spend 6 months figuring it out alone
  • You can commit to daily Build Room calls for 12 weeks
  • You want to own the result - no subscriptions, no lock-in, no dependency on us
This is not for you if
  • You want a course you can watch at 2x and forget
  • You think buying a tool is the same as building a system
  • You're not willing to do the daily work - this is a gym, not a seminar
  • You need enterprise-scale AI infrastructure (that's a different engagement)
Who's Behind This
Mastro
Mastro
Founder, Badmutt

Full-time SPX 0DTE options trader. 22-strategy system. 15 consecutive profitable quarters. Trading fully automated via Option Omega + tastytrade.

He didn't come to AI from tech. He came from checklists. The same systematic, process-driven methodology that built a consistently profitable trading system is the one he applied to AI - and the one he teaches in this program. DEVISE framework. Checklist Manifesto philosophy. Routine over intuition. Systems over inspiration.

Runs The Routine Trader newsletter. Built his entire AI agent stack from scratch on OpenClaw. Writer at heart. ENTJ-A. Faith-centered.

Man's best bot isn't a gimmick. It's the thesis.

mastro@badmutt.com Telegram: @gjmastro
The Category Is Catching On

The Chief of Staff
unlock.

"omg @openclaw is sooooo good at being a Chief of Staff. What huge unlock for founders (and everyone)! It's taken me 2 weeks to refine my setup and now it's working like a dream. Biz dev, calendar management, research, task management, brainstorming and more"

- Ryan Carson, founder of Treehouse (acquired). 930K views. Unsolicited.

Ryan's a technical founder and it took him two weeks of daily refinement to get his AI stack working.

Badmutt exists because most people don't have two weeks - or the technical instinct to self-direct the build. We do it with you, systematically, in 12 weeks.

The Cohort

12 seats.
Small on purpose.

This is a hands-on implementation cohort. Twelve people. Real workflows. Live help every business day. Join when you need a decision, a fix, or a shove across the line.

12
Total Spots
$10K
3-Month Program
July 7
Starts July 7

$2,500 deposit holds your seat. Remaining $7,500 due June 23.

Add-On

Done-for-you implementation - $2,500

We deploy the entire stack ourselves - configured, tested, running in your environment. Pair it with the cohort: $12,500 total, zero heavy lifting on your end.

Add Done-for-You Implementation - $2,500
Apply

12 seats. $2,500 deposit.
Starts July 7.

Prompt like a telegram. Train your AI to fetch more time.

Something went wrong. Email mastro@badmutt.com directly.
Prefer to skip the application? Pay the $2,500 deposit directly →

Application received. We'll be in touch within 48 hours.

"We train your AI to fetch more time."
- Mastro, Badmutt
Questions

Before you ask.

What if I'm not technical?
That's who this is for. You don't need to code. You need to learn how to communicate with AI effectively - tight prompts, clear structure, specific outputs. We handle the architecture. You learn the operating system.
What's the daily time commitment?
The Build Room call is 45-60 minutes. Outside the call, you're doing the work on your actual workflows - expect another 30-60 minutes on build days. Total: about 90 minutes on active days, less as your system starts running itself.
What do I actually get for $10,000?
A working AI system you own outright - audited, designed, built, deployed, and documented. Plus 12 weeks of daily coaching, cross-cohort intelligence from 11 other people building alongside you, and the skill to maintain and extend the system yourself. No recurring fees after the program.
What's the Agent Setup add-on?
For $2,500, we handle the technical configuration - setting up agents, deploying crons, configuring your local stack. Same system, but we do the terminal work so you don't have to. Optional for people who want the outcome without the command line.
What if I already have AI tools set up?
Good. The audit will tell you what's working, what's redundant, and what's leaking time. Most people who "have tools" don't have a system. Isolated tools are the problem this program solves.
What models/tools does this work with?
The program is model-agnostic. Claude, GPT, local models, Copilot - doesn't matter. The system design works across tools. We build on OpenClaw (open-source), so there's no vendor lock-in.
Can I get a refund?
The $2,500 deposit holds your seat. If we do not move forward with the July 7 cohort by June 23, your deposit is refunded in full. Once the cohort kicks off, the deposit is non-refundable - it ensures commitment on both sides. If you do the work for 12 weeks and don't have a working system, we have a problem and we'll address it directly.
What does "prompt like a telegram" mean?
Most people write 200-word prompts when 30 words would get a better result. A telegram costs per word, so you'd write tight - subject, action, constraint, done. AI responds better to the same discipline. Less input, better output. We call the waste "token slippage" - every unnecessary word degrades your output and costs you time. Prompting like a telegram eliminates the slippage.