OpenClaw user builds 10-automation operations stack with sports picks, lead generation, and digital fulfillment

A developer documented their two-month experience building a practical AI operations stack on OpenClaw, resulting in 10 working automations that run autonomously.
Working automations built
- Daily sports picks pipeline running at 10AM CT that generates picks from ESPN data using a custom confidence model, formats them into subscriber SMS cards, and delivers via email + Twilio
- Nightly pick grader that wakes at 1AM to look up final scores and update W/L records
- Prospect builder that scrapes Google Maps every weekday morning for local business leads
- Stripe pollers running every 5 minutes to deliver digital products to buyers automatically
- Session briefing email that fires every time a new session starts so the agent knows exactly where things were left off
- Daily ops report at 6AM covering social stats, pick record, credential status, and open items
What didn't work
The developer also built a full AI video production pipeline with automated renders, QA checks, and ElevenLabs voiceover, but killed it due to zero revenue, constant maintenance, and a QA system that once approved "a video of a player standing in a parking lot giving an interview." They describe this as "built for ego, not for customers."
Documentation approach
The developer packaged all 10 working automations into a playbook described as "not a tutorial — a field manual." Each automation includes:
- What it does
- How it works
- What burned me so it doesn't burn you
The playbook also includes:
- All 10 automations with architecture notes
- The MEMORY.md discipline that makes the agent actually remember things across sessions
- Full ASCII diagram of how it all connects
- A straight-talk section on ego-driven products (using the video pipeline as a case study)
- A pointer to Volume 2 covering digital product fulfillment stack
The developer notes that "most of it took longer than I expected to get right" and that the entire system "runs while I sleep."
📖 Read the full source: r/openclaw
👀 See Also

Using Claude Cowork to Automate Gift Card Extraction from Gmail
A developer used Claude Cowork to extract 48 gift card numbers from Gmail by connecting to their account, searching emails with specific subjects, and running JavaScript scripts to automate website interaction after Python scripts triggered bot detection.

Local Qwen3-0.6B INT8 as Embedding Backbone for AI Memory System
A developer implemented Qwen3-0.6B quantized to INT8 via ONNX Runtime as a local embedding model for an AI memory lifecycle system, achieving 12ms batch inference on CPU with 1024-dimensional vectors and cosine similarity thresholds of 0.75 for semantic relatedness.

OpenClaw Configurations That Last: Less Complexity, More Reliability
Analysis of 40-50 OpenClaw setups shows that sustainable configurations use 1 agent, 3-5 skills, Sonnet model, and focus on mundane tasks like calendar management and email triage, while complex multi-agent systems with 20+ skills typically fail within 3 weeks.

RunLobster AI Agent Integrates Business Data for Operational Insights
A developer gave RunLobster root access to their business systems including Stripe, CRM, email, and call transcripts. The agent autonomously monitors operations, flags anomalies, and provides detailed briefings based on integrated data analysis.