Monterey

AI Researcher || Content Creator || AI & Emerging Tech | Ready to collaborate

[Monterey @MontereyCrypto]

https://t.co/jWbR5UMSba

[Monterey @MontereyCrypto]

I ran the exact 6-bot handbook setup for 14 days.

Brief, Scout, Quill, Helm, Penny, Kit.

What actually happened:

→ Morning briefs every day at 07:00. Zero exceptions. I stopped opening 40 tabs.

→ Helm drafted 87 support replies. I approved 61. Kit blocked 6 that broke voice/rules before I even saw them.

→ Scout pulled competitor pricing + 14 new leads with fit scores. Two turned into calls.

→ Penny found $2,140 in unpaid invoices I completely forgot about. I hit send on 5 of them.

→ Quill produced 4 newsletter sections + 19 post drafts. All sat in a queue waiting for one tap.

The switching problem died.

I went from being the person who does everything to the person who only approves and decides.

The handbook is the real product here. Without the rules and files the bots start inventing their own process. With it one person runs like a small team.

Most people still prompt one bot and wonder why nothing compounds.

Write the handbook tonight. Hire Brief tomorrow.

The bottleneck was never the model. It was the lack of an org chart.

[broke boy @0xfuckpoverty]

https://t.co/QmRn4aynXd

[Monterey @MontereyCrypto]

I applied the 7 Grok Bot fixes and turned the same token budget into 10x more usable trading signals.

Before: 6–7 usable setups per day.

After: 60+ clean ones. Same plan. Same limit.

What actually changed:

1. HANDOFF.md + fresh chat
Stopped carrying yesterday’s mess. Bot starts with only the current goal and confirmed edge rules.

2. Extract only what matters
Trafilatura pulls funding, volume, and price levels. No more feeding the whole page + footer.

3. Lightweight change check first
If the market data didn’t move meaningfully → no full re-analysis. Saved the biggest waste.

4. One owner + checkpoints
One failed fetch no longer restarts the whole scan. Completed work stays completed.

5. Strict “done” checklist
Source + timestamp + entry/exit + invalidation must be there. Otherwise it doesn’t count as usable.

Result on BTC Up/Down markets (Polymarket style):

Same daily token spend.

Before: mostly noisy drafts I had to fix manually.

After: ready-to-size setups with EMA direction + RSI strength + source links.

The real edge isn’t a bigger plan.

It’s stopping the bot from doing the same research twice.

Audit your current workflow first. Fix the biggest waste. Then scale.

Save this before your next scheduled run.

[Gresham @Greshamtrade]

https://t.co/x9JOa2faWx

[Monterey @MontereyCrypto]

While everyone is still rewriting prompts, a few people are already running loops.

I watched Claude Code run a full verification loop on a Polymarket BTC Up/Down strategy for 48 hours.

Not one prompt.

Not “an agent that thinks.”

A real loop: Discover → Plan → Execute → Verify → Iterate.

What it did:

→ Modified EMA/RSI parameters and entry thresholds itself

→ Ran the backtest against real historical data

→ Compared every version by profit factor and max drawdown

→ If the new version was worse — it rolled back. If better — it kept it and continued

→ Hard limits: 10 attempts per cycle + token budget

After 1,247 iterations:

Win rate went from 51% → 64%

Average trade size from $187 → $412

Max drawdown cut almost in half

Total simulated PnL over the test period: +$38,600 on a $5,000 starting balance

The most interesting part was a small scaling factor inside the momentum calculation that the agent found on iteration 614. A normal prompt never would have caught it.

This is the real difference.

It’s not that the model got smarter.

It’s that verification became the judge -not another agent agreeing with itself.

Most people are building teams of 5-7 agents that just confirm each other’s ideas.

A few are building loops where the market (or the PnL, or the test) can actually say “no.”

A prompt tells the agent what to do.

A loop gives it a way to know whether it actually worked.

The gap is already showing up in the numbers.

[Lummox @Lummox_eth]

https://t.co/P9t6FtO8Ms

[Monterey @MontereyCrypto]

@5deathpunch @MAXdeg0 Yes 👍

[Monterey @MontereyCrypto]

Most people ask Claude “is this strategy profitable?”

and get a confident 800-word essay full of Sharpe ratios and CAGR numbers.

Then the strategy dies in the first 5–7 days of real data and they lose $1,800–$4,200 on a $10k account.

@MAXdeg0 just laid out the only workflow that actually matters:

Hypothesis → Real data → Backtest → Out-of-sample validation → Paper trading → Execution

The difference is expensive.

Without a research layer, Claude will happily invent a 1.8 Sharpe and 22% annual return on a strategy that never saw real transaction costs, slippage, or out-of-sample data. People then size it at 2–5% risk per trade and watch $5k–$15k disappear in a few weeks while the AI keeps sounding smart.

With QuantGPT + MCP the picture changes:

→ The AI can only reason

→ The tools pull actual market data and run the backtest

→ You see real numbers (drawdown, trade count, calendar-year results, sensitivity to costs)

→ You can reject the idea before it touches real money
Most “AI quant systems” people post are just prompts that look good on a screenshot.

They generate nice equity curves on made-up stats, then quietly lose 15–40% when the live environment doesn’t match the story.

This stack forces the separation:

AI reasons.

Tools calculate.

Code executes (only after validation).

That’s the difference between burning another $2k–$5k on a confident hallucination and having a system you can actually stress-test and kill before it kills the account.

[MAX @MAXdeg0]

https://t.co/RUyBPwDixb

[Monterey @MontereyCrypto]

@5deathpunch Solid breakdown. Market had it basically a coin flip (52/49) and Detroit ended up winning 121-113. Pistons’ continuity looked better than Boston’s new pieces on night one

[Monterey @MontereyCrypto]

@0xKorens @parimatch Yeah, people always treat 7–10% like free money on every bet. Reality is usually a few bucks after eligibility, house edge and net loss calc. Prediction volume often doesn’t even count

[Monterey @MontereyCrypto]

@PolyDekos 🔥

[Monterey @MontereyCrypto]

@5deathpunch 🔥

[Monterey @MontereyCrypto]

@Meddes0119 Thanks 🙏

[Monterey @MontereyCrypto]

THIS CHANGES EVERYTHING AI agents just turned reverse engineering from a months-long specialist job into something one person can do in a weekend.
REA + Opus 5.5 already has 170k+ GitHub stars. One version shipped with 571 commits and 236 PRs. People are reverse-engineering entire games in ~12 days (697 functions, almost all matching). Here’s the loop one solo builder can now run: → Point the agent at any local app, binary, Electron app, or .NET game → It extracts the real functions, calls, and logic with evidence → Writes a clean spec (behavior, inputs, outputs - no copied code) → Fresh session rebuilds a working version from the spec only No team. No years of assembly experience. No uploading files. While most people keep paying $50-200/month for closed tools, the ones running this are spending agent hours (one Super Mario Sunshine port took ~365 agent hours and 17B tokens) and ending up with their own version. Software companies bet you’ll never open the folder. This tool breaks that bet. The people who learn the loop won’t just use software. They’ll rebuild the parts they actually need and keep the rest. Bookmark this. The infrastructure is already here.

[Spike 1% @SpikeCalls]

https://t.co/VbDyhS939E

[Monterey @MontereyCrypto]

@zscdao 🔥

[Atlantis liquidity @Atlantislq]

I've talked to the Polymarket team many times, both as a KOL and as a builder/trader in the ecosystem and honestly, it's funny to see all this FUD that haters are trying so hard to push

If you're an active user you probably remember @shayne_coplan or @MatthewModabber sliding into traders and KOLs DMs, asking for feedback, connecting with ppls and just genuinely wanting to chat

This was literally not that long ago and Shayne was already a billionaire at the time

I genuinely can't think of another billionaire who would PERSONALLY REACH OUT FIRST to regular users just to talk to them
That says a lot about him.

I've been watching the platform evolve and seeing all the updates they've shipped over the past year

Honestly, we're growing at an INSANE pace

You can love or hate @Polymarket but I genuinely believe they're leading the prediction market industry and driving mass adoption.

Yes, I agree that the team sometimes makes mistakes and some of them can look pretty cringe

But they fix them and more importantly IT'S NOT THAT BIG OF A DEAL

C'mon guys, we're talking about one of the fastest-growing startups out there, a company that has grown massively in just one year

Of course people are gonna make mistakes along the way. That's completely normal

Just give them more time. They're already doing an incredible amount of work

At the end of the day all this FUD is just another stress test

And I believe Polymarket will come out of it even stronger

[Monterey @MontereyCrypto]

@Atlantislq @shayne_coplan @MatthewModabber Really nice perspective. Shayne still personally reaching out says a lot. Love the growth and the connection with the community 🔥

[Monterey @MontereyCrypto]

@KnightPredict @ProblyHQ Market trusts 5+ goals more than either team winning tonight. Classic PSG–Barça chaos. What’s your scoreline?

[Monterey @MontereyCrypto]

@mmoyshaa “181k trades in 5 months — that’s not a human anymore, that’s a market Terminator 😂 And on tennis he’s still calmly printing money, even with a 1:1 win/loss ratio. Brutal.”

[Monterey @MontereyCrypto]

@0xbobaaa This is the right shape for agent tools. Most AI decks spit out images you have to rebuild. Real editable PPTX + per-slide controls in the browser is the actual difference between “cool demo” and something you can ship at work.

[Monterey @MontereyCrypto]

WHAT THE FK** Most people still ask Claude “does this strategy still work?”
It answers from memory. I forced it to answer from data. I built a 4-agent stack that only keeps strategies after they survive post-publication decay. 87 days. $5,000 → $128,400. Average trade $214. Win rate 61%. Max DD 11.3%. Zero strategies that were famous in 2015–2019 survived the filter. How it actually works:
1. Agent 1 pulls every new paper / published signal + its exact rules
2. Agent 2 runs the rules ONLY on data after the publication date (delistings included)
3. Agent 3 does parameter sweeps ±20% + cost stress + shock tests. If it dies -killed instantly
4. Only what remains goes to live paper trading. The fourth agent watches decay in real time and liquidates the moment the edge collapses This is the exact opposite of “textbook looked good so I trade it”. The strategies that are still running right now are the ones that passed the trial Veles described. Most AI setups explain strategies. This one tries to kill them.

[Veles @velesxbt]

how do you know a trading strategy still works?

you don’t. unless you test it.

this guide turns your AI into a research desk that does it for you: real data, real backtests, stress tests, and a path to your broker https://t.co/8Y47ZMutUe

[Monterey @MontereyCrypto]

@0xbobaaa 😁😁

[Monterey @MontereyCrypto]

@EagleVisions good luck

[Monterey @MontereyCrypto]

@polysuccubus @variational_io 🔥I will save it