Founder of CoachAI Tech Camps helping underdogs become heroes..accepting donations and sponsors now!
Texas, USA
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Shared insights from real experience! I love when listeners take something I tell them and immediately apply it for immediate results! Thats WHY we do this every week. Shout-out to @optrade_ai @Glitchyonsol1 @fugazinft @Thebitcoinbarb1
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Come Update us on your project and catch a vibe...Lets go!
Set a reminder for my upcoming Space! nitter.net/i/spaces/1NGaroqYbAjJj
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I build these type of systems for fun, profits and learning. Its amazing the times we are in because this info and tools are so accessible. Dont just digest info. Execute builds and design your life. Your future self requires a new version of you! Level UP!
A fired Goldman Sachs quant trader taught me everything in a single conversation
He said: “We don’t do predictions. We only buy contracts where the price deviation exceeds 6%.”
It’s just that simple
That’s the desk operation for a $2 million annual salary
I fed his explanation and 5 GitHub repos into Claude, and Claude built a scanner. It processes over 400 markets every hour
This scanner can find those contracts priced in the 7-19c range, with true probabilities between 60-90%
At these entry points, you need a win rate of 1/4
And this bot’s win rate is 81%
Three months later:
From $2,000 to $8,191
99 trades, Sharpe ratio 2.30
A few cases:
ETH Merge upgrade - market 72c, true probability 88%, +19c
SOL breaks $200 - market 44c, true probability 81%, +15c
Florida hurricane cat3+ - market 81c, true probability 92%, +7c
Wheat breaks $800 - market 53c, true probability 68%, +20c
All of these were found by the scanner, and all were profitable
He looked at my terminal last week
He said: “This is what we do with $800M, 47-person team.”
And my current setup costs $25 per month
Claude - $20
VPS - $5
Repos - free
API - free
Now there are 8 agents running 24/7:
velvet_void +$697
nano_alpha +$541
ratking_eth +$407
darkpool_7 +$356
His fund returned 19% last year
And my setup returned 409% in three months
The real edge was never any secret—it’s just always been expensive, until now
70% win rate, 7 wallets copytrading rn from ~500 monitored, bot never paused, never gambling, just math and profit
Giving This Free for 24 hours. To get it:
1. Comment the word 'CLAUDE'
2. Like and Retweet this post
3. Follow me @tec_marco10 (so i can DM you)
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I put the starter guide together because I kept getting the same 3 questions about agent guardrails. Comment LAUNCH and I'll send it — no pitch, just the framework.
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If I was FORCED to teach one thing about marketing agents, it would be this: the job is not to replace judgment. The job is to make judgment scalable. Every system we build has a human at the release point. That's the product.
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The brands that win with agents are not the ones with the best prompts. They are the ones with the cleanest failure modes. When something breaks, you know which agent, which step, and which human fixes it.
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A 6-agent X funnel only works if the human is the router, not the bot. Content drafts. Publishing schedules. DM routes to a setter. Nurture sends. Setter books. Reporting flags. Every agent reports up. No agent owns the customer.
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The first time a camper fixed a broken agent handoff on their own, I realized the teaching was working. They didn't ask me what to do. They asked me where the log was.
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Want the exact 90-minute framework we use to onboard marketing agents without letting them embarrass the brand? Comment LAUNCH and I'll DM it.
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Agent reporting is where most people fake competence. The agent tells you how many posts went out, how many emails sent, how many leads captured. None of that matters if you don't know what the human did next.
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I once let a nurture agent run for two weeks before I realized it was sending the same case study to people who had already read it. The open rate looked fine. The relevance was dead.
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If I was FORCED to audit a broken agent funnel in 30 minutes, I'd check these 5 places first:
1. Is the handoff field actually populated?
2. Does the next agent read the same format the previous one writes?
3. Are there two agents editing the same record?
4. Is the approval queue owned by a human or by hope?
5. Does the reporting match the source, or just the summary?
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