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AI Inference Observability Dashboard - Acts as an overlay for OpenClaw that improves analysis of autonomous agent operations. Works with local and cloud models and extracts tool chain calls with deep request/response details for logging and debugging.
7 days after installing OpenClaw, I built a multi-model LLM inference lab:
- 475 sessions / 16k messages
- 22 model tuning experiments
- 19 instrumented tools
Full cron replay, conversation tracking, token cost, telemetry, and cross-provider model benchmarking.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Just opened up capacity for new AI/ML projects.
What I build:
— Production RAG systems over large document collections
— LLM fine-tuning pipelines (LoRA / QLoRA)
— Document AI: classification, extraction, OCR
— Workflow automation with n8n
My focus is on systems that ship to production — not demos. Local-first deployments with zero cloud lock-in are my specialty.
Open to projects starting this week.
I gave an AI agent one task:
"Analyze this dataset and tell me what's wrong."
It didn't just answer.
It loaded the data.
Checked for nulls.
Flagged class imbalance.
Suggested 3 fixes.
Wrote a summary report.
No prompting after the first message.
This is what LangGraph agents actually do — they don't wait for instructions at every step. They reason, act, and loop until the job is done.
Built this inside AIRO. 6 agents running in parallel. The jump from "chatbot" to "agent" is bigger than most people think.
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