Currently building autonomous AI systems that actually survive production

Currently building autonomous AI systems that actually survive production — not just the demo. Mo...Currently building autonomous AI systems that actually survive production — not just the demo. Mo...

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Michael Limisi

• 1d

Currently building autonomous AI systems that actually survive production — not just the demo.

Most AI projects work beautifully in a controlled test and then quietly fall apart on real data, edge cases, and concurrency. I focus on the unglamorous engineering that closes that gap: data validation pipelines, boundary checks, error handling, and knowing exactly where deterministic logic should own the decision instead of the model.

Recent work includes agentic workflows built with Python and FastAPI, an LLM reasoning layer for the genuinely ambiguous decisions, and automation orchestrated through Make with MCP. My background is technical support and operations, which means I design for the failure case first — I've spent years watching systems break in ways their builders never anticipated.

If you have an AI pilot that impressed everyone in the demo and then stalled on the way to something dependable, that gap is exactly what I build for.

Open to Applied AI / AI automation projects.

Python AI Automation AI Agent Development

Haris Sheikh

• 34m

Keep it up!

Gabriel Gabriel

• Aug 1

Many businesses want to build powerful AI agents and LLM applications, but they struggle to connect AI systems with private data, APIs, databases, and internal tools securely. Without a reliable connection layer, AI models cannot access real-time information, automate workflows, or interact with business systems effectively.

I build custom Model Context Protocol (MCP) servers that connect your data sources, APIs, databases, and AI tools into a secure, scalable environment. Your MCP server enables AI agents to retrieve information, execute actions, and communicate with your systems through structured tools and resources.

I use advanced technologies including TypeScript, Node.js, Python, MCP SDK, FastAPI, Express.js, Pydantic, PostgreSQL, Supabase, MongoDB, Redis, LangChain, LlamaIndex, OpenAI API, Anthropic Claude API, Google Gemini API, Docker, Docker Compose, Postman, GitHub Actions, AWS, Azure, Google Cloud, and DigitalOcean.

My services include MCP architecture design, API integration, database connectivity, authentication, security validation, testing, deployment, and monitoring using tools like Langfuse, Helicone, and Sentry. I help businesses create production-ready AI agents, RAG applications, SaaS platforms, and automation systems connected to the data and tools they need.

Gabriel Gabriel

• Aug 1

Many companies are building AI applications with LLMs but struggle to understand what is happening behind the scenes. Without proper monitoring and tracing, teams cannot identify slow responses, expensive API calls, failed generations, prompt issues, or areas where AI performance can be improved.

I will integrate professional LLM observability solutions like Langfuse and Helicone to help you monitor, trace, debug, and optimize your AI applications. I will set up complete visibility into your LLM workflows, including prompt tracking, token usage analysis, latency monitoring, cost optimization, evaluation pipelines, and production performance insights.

I use modern AI monitoring and development tools including Langfuse, Helicone, LangSmith, Arize Phoenix, OpenLLMetry, OpenTelemetry, Weights & Biases, PromptLayer, Traceloop, and Grafana to build a complete LLM observability system.

My process includes SDK integration, API configuration, trace setup, prompt management, cost tracking, dashboard creation, evaluation workflows, error monitoring, and performance optimization. Whether you are building AI agents, RAG systems, chatbots, or enterprise LLM applications, I will help you reduce AI costs, improve reliability, and gain full visibility into your AI operations.

Elijah okai

• Aug 1

My tech stack looks completely different today than when I first started, and rightly so.

This is largely influenced by industry demands and better tools.

Two years ago, Claude wasn't doing half of what it does now. Today, it’s a core part of how so many of us work.

Even as tools change, the core principles stay the same: ⭐ Personalize everything to the team's real workflow. ⭐ Keep your data clean and structured early. ⭐ Design for edge cases before you launch. ⭐ Choose simple, efficient setups over complex ones. ⭐ Always focus on delivering the actual result.

Businesses will always adopt new tools as they grow, but real value will always come down to efficiency.

#AI #Automation #CRM