AI ThatShips.

Sione Milhem builds AI assistant infrastructure and advises on Web3 risk management. On the AI side, he designs and deploys custom AI agents, automation, and retrieval-augmented generation (RAG) systems for teams. On the Web3 side, he helps people and projects understand crypto, avoid common mistakes, and manage risk.

He also helped build KaspaCom, a live DeFi platform on the Kaspa network. His approach across both domains is the same: ship real systems, cut the hype, and manage risk from experience.

Four core services, offered standalone or end to end:

• Custom AI Agents & Automation: autonomous agents built on Claude and other models that handle real workflows.
• AI Infra Setup & Deployment: LLM APIs, self-hosted or cloud models, VPS/Docker, MCP servers, monitoring, and cost control.
• AI Strategy & Consulting: model selection, workflow design, build-vs-buy, and privacy/risk tradeoffs.
• Integrations & RAG Systems: connecting AI to your data and tools with retrieval-augmented generation, knowledge bases, and internal copilots.

An AI agent is an AI system that can take actions on its own toward a goal, not just answer a question, but use tools, call APIs, read and write files, and chain steps together to complete a task.

You likely need one if you have a repetitive, rules-based workflow (research, data entry, support triage, reporting, monitoring) that eats hours every week. You probably don't need one for a one-off task or something a simple script already handles. A quick call is usually enough to tell which side you're on.

RAG is a technique that grounds an AI model in your own data. Instead of relying only on what the model learned during training, a RAG system retrieves relevant documents from your knowledge base at query time and feeds them to the model as context.

The result: answers based on your content, docs, policies, product data, that stay accurate and current, with far fewer hallucinations. It's the backbone of most useful internal chatbots and copilots.

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools, data sources, and systems in a consistent way. Think of it as a universal adapter between an AI model and the software around it.

In practice, MCP lets an agent securely read your databases, call your APIs, and use your internal tools without custom glue code for every integration. It's a core building block of the infrastructure I set up.

I build primarily with Claude (Anthropic) and other leading large language models, choosing the model per task rather than by brand loyalty.

The typical stack: Claude / OpenAI APIs, MCP servers, RAG pipelines, Python and Node.js, Docker, and both self-hosted and cloud deployment. Model choice is driven by capability, cost, latency, and privacy requirements, not hype.

Start with a short, no-pressure call. We figure out what you're trying to automate or decide, whether AI is actually the right tool, and what the smallest useful first step looks like.

From there, engagements range from a one-off strategy session to a full build-and-deploy. The fastest way to reach me is the WhatsApp button on this page.

Good money needs to serve three main roles well: as a medium of exchange (easy to use for buying/selling), a unit of account (clear way to measure value), and a store of value (holds purchasing power over time).

For something to work as a strong store of value, it typically has these traits:

• Scarcity (limited supply, hard to create more endlessly)
• Durability (lasts without degrading)
• Divisibility (can be split into smaller units)
• Portability (easy to move or store)
• Acceptability (widely trusted and used)
• Stability (value doesn't swing wildly short-term)
• Hard to counterfeit (difficult and costly to fake or produce without detection)

People started with barter, trading goods directly, but it was inefficient (you need to find someone who wants what you have and has what you want).

Over time, societies settled on commodities like cattle, shells, or metals as money because they were useful and somewhat scarce.

Gold and silver became standards for centuries: portable, divisible, durable, rare, and hard to counterfeit convincingly.

In the 20th century, most countries moved to fiat money (government-issued currency not backed by a commodity, like the US dollar today). It's convenient but relies on trust in governments and central banks.

Some see cryptocurrencies as a modern evolution, digital, borderless, and often designed with built-in scarcity and verification that's extremely hard to fake.

A hot wallet is connected to the internet (like a mobile app or exchange account). It's convenient for everyday use, sending, receiving, or trading quickly.

A cold wallet is offline (like a hardware device or paper backup). Your keys never touch the internet, making it much harder for hackers to access.

Many people use both: small amounts in hot for convenience, larger amounts in cold for peace of mind.

These are ways blockchains secure themselves and agree on transactions without a central authority.

PoW feels more "battle-tested" for security; PoS is more efficient and accessible.

Kaspa is a proof-of-work cryptocurrency designed to be fast and scalable while staying decentralized.

Traditional blockchains add one block at a time in a straight line. Kaspa uses a structure called a blockDAG (with the GHOSTDAG protocol), allowing multiple blocks to be created in parallel.

• Speed: Following the 2025 Crescendo upgrade, it produces 10 blocks per second.
• Throughput: Enables thousands of transactions per second (records over 5,000 TPS).
• Future: Upcoming upgrades like DAGKnight aim for sub-second finality.

It's community-driven, fair-launched (no premine), and focuses on being efficient, secure digital money, like Bitcoin, but built for speed.