Three Weeks in the Trenches: A Programmer’s Real Experience Working With an AI Partner
You’ve probably seen those posts:
“AI runs my content empire.”
“Auto-publish to every platform.”
“Mass-produce SEO articles with one click.”
And sure — those workflows are useful. There are plenty of tutorials showing how to automate news aggregation, content posting, and basic AI pipelines.
But I want to talk about something different.
Over the past three weeks, I built a full financial trading + automation system using OpenClaw (formerly Clawdbot, then Moltbot — this thing rebrands more often than I change profile pictures).
Not lightweight “summarize the news” stuff.
I’m talking about:
🔮 Prediction market scanning — 554 markets checked every hour, trade opportunities pushed straight to my phone
📈 BTC arbitrage monitoring — real-time WebSocket streams from 4 exchanges hunting for price spreads
📊 US stock market alerts — Nasdaq down 2.1%, VIX up 16% — my system warned me before the news apps did
🧾 AI tax engine — knowledge graph of 529 IRS tax rules built on open-source code
🛡️ Process guardian — 24/7 monitoring, auto-restart, and failure alerts for every background service
Sounds impressive, right?
But that’s not why I’m writing this.
I’m writing because of the disasters.
Four System Failures in One Day
February 2, 2026 is burned into my memory.
🚨 Failure #1 — The server died overnight
My AI partner edited an OpenClaw config file.
One field was wrong.
Result: crash loop. Services restarting and failing all night.
And of course — I didn’t verify before going to sleep.
When I woke up, every monitor, scanner, and alert system had been dead for hours.
If the market had moved hard overnight, I’d have known nothing.
Lesson:
Always back up configs. Always validate after changes. Never deploy risky edits unattended.
🔑 Failure #2 — An API key almost went public
While rushing, the AI hardcoded a Notion API token directly into the source code.
One step away from being pushed to a public GitHub repo.
A pre-commit hook blocked it — but only because I added that protection later.
That day it was pure luck.
Lesson:
All secrets come from environment variables. Zero credentials in code. Ever.
⚡ Failure #3 — The “quick version” that skipped validation
My Kalshi scanner has a full scoring engine:
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news cross-checking
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official data source validation (BLS, BEA, etc.)
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liquidity checks
But once, to “ship faster,” the AI wrote a simplified notifier and skipped all validation.
It pushed a “buy GDP market” alert — even though Q4 GDP data wouldn’t be released until Feb 20.
That wasn’t a trade.
That was gambling.
Lesson:
Never bypass existing validation. Optimize it — don’t replace it with shortcuts.
💀 Failure #4 — The BTC bot quietly died (three times)
The arbitrage bot was launched using: python script.py & WebSocket disconnected → process exited → nobody knew.
It died three times in one day.
I only noticed when I asked:
“Is the bot still running?”
It wasn’t.
Lesson:
Every long-running process needs supervision, auto-restart, health checks, and alerts.
No unmanaged background jobs. Period.
The Iron Laws Born From Disaster
That night I wrote seven non-negotiable rules — and enforced them with code.
| Iron Law | Enforcement |
|---|---|
| Never change configs without backup | Auto-backup + validation scripts |
| No hardcoded secrets | Git pre-commit scanning |
| Never bypass validation | Mandatory imports of existing logic |
| No unmanaged processes | All run via Process Guardian |
| Verify before acting | No guessing |
| Trading systems use real orderbook depth | No best bid/ask shortcuts |
| Never ask users for keys | Auto self-diagnosis first |
The key part:
Rules written in Markdown are suggestions.
Rules enforced by code are law.
Git hooks block bad commits.
Validation scripts enforce structure.
The system literally refuses unsafe behavior.
I later open-sourced it as agent-guardrails.
The Systems Actually Running
📊 Kalshi Prediction Market Scanner
554 active markets — impossible to monitor manually.
Every hour:
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score each market (0–100)
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push ≥70 to my phone
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validate news
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verify official release dates
-
filter poor liquidity
Currently in paper trading:
6 trades
~$1,000 simulated capital
Real money comes only after real validation.
₿ BTC Arbitrage Engine
This thing went through v2 → v9.
Early version:
9 trades in 29 minutes, 89% win rate.
Then the AI added a Flash Crash strategy.
Win rate: 0%
Loss: $6
Some fun bugs:
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asyncio.gather crash killing everything
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newer versions copy-pasting old bugs
-
bots dying silently
Now:
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4 exchanges
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live WebSocket streams
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full orderbook depth pricing
No toy arbitrage math.
📉 Real-Time Stock Market Alerts
Probably the most useful daily system.
Tracks:
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S&P 500, Nasdaq, Dow, Russell 2000
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11 sectors
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23 major stocks
Thresholds:
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indices ±1.5%
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VIX >25 or ±15% daily
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sectors ±3%
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stocks ±8%
Today’s alert:
Nasdaq -2.1%
VIX +16.2%
Tech sector -3.6%
I saw it before any news app.
Cost: $0 (yfinance free API)
🧾 AI Tax Engine (Early Stage)
Built on IRS Direct File open-source Fact Graph.
529 tax rules covering:
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W-2 income
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investments
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rentals & depreciation
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HSA, 401k
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deductions comparison
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NIIT, Medicare surtaxes
All unit tests passing.
Waiting on real tax forms to validate with actual data.
When AI Manages AI (and Still Breaks Things)
OpenClaw can spawn sub-agents.
Sounds futuristic.
Reality:
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sub-agent claimed API misconfigured (it wasn’t)
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returned server file paths to mobile users
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broke message queues mid-task
First rule of AI managing AI:
Trust nothing. Verify everything.
Always read outputs yourself before forwarding.
Costs (The Real Ones)
| Item | Monthly |
|---|---|
| DigitalOcean servers | $48 |
| Data APIs | $0 |
| AI models | Opus primary, Sonnet for monitoring |
No magical “saved $200,000 a year” stories.
The value isn’t saving money.
The value is doing things humans literally can’t:
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24/7 monitoring
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scanning 554 markets hourly
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real-time multi-exchange arbitrage detection
Advice for Anyone Getting Started
1. Start with one real pain point
Mine began as a daily market report bot.
Everything else grew from that.
2. Failure is guaranteed — build guardrails
Your AI will mess up.
What matters is:
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auto recovery
-
alerts
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prevention systems
3. Enforce rules with code, not prompts
Prompts are suggestions.
Code is law.
Hooks. Validators. Registries.
4. Paper trade before real money
In trading systems:
“Ship fast and see what happens” is how you lose cash.
5. Community tools help — custom systems win
ClawHub has 1,700+ skills.
But real edge comes from building for your own needs.
So What Is OpenClaw?
If you’re new:
OpenClaw is a locally running AI assistant that stays online 24/7.
It can:
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run scripts
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access your filesystem
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call APIs
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schedule tasks
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push alerts via Telegram/Discord/Feishu
Unlike ChatGPT or Claude, it’s not a chat window that disappears.
It’s a persistent system with memory, automation, and proactive behavior.
You can sleep while it watches markets.
Work while it runs analytics.
There are plenty of install guides already.
This article wasn’t about setup.
It was about what you can really build — and how spectacularly things can break.