# Cyberd > The personal site of Mike Cherneski: enterprise architect and builder. Founder of Cyberd, formerly co-founder and CTO of Moxa. Projects, writing, and a framework for adopting AI on foundations you can trust. ## Profile - [Home](https://cyberd.tech/): Landing page with selected projects, credentials, testimonials, recent writing, and contact path. - [File](https://cyberd.tech/about): Resume-style summary of founder, enterprise, and startup technology experience. ## Projects - [Serverless AI-Native Portfolio](https://cyberd.tech/projects/serverless-ai-native-portfolio): A static-first portfolio architecture that serves humans fast and gives agents curated context through Markdown reports and structured data. - [EthSafari Coordination Bot](https://cyberd.tech/projects/ethsafari-coordination-bot): A serverless Telegram bot that helped a small coordination team keep 300+ developers informed at EthSafari in Kenya, measurably reducing the load on the human support team. - [MetaCamp Community Bot](https://cyberd.tech/projects/web3-community-tooling): A serverless Telegram bot for MetaCamp, two-week on site founders' retreat and ongoing community: contribution points, a shared event calendar, and bot-proxied requests to program directors, credited with strengthening community cohesion. - [Alternun: Tokenizing Gold In Place](https://cyberd.tech/projects/alternun): Advisor and interim CTO for a protocol tokenizing in-ground gold reserves: whitepaper review, technical architecture, partner introductions, and delivery planning. - [Web3 Game Platform Architecture](https://cyberd.tech/projects/web3-game-architecture): Multi-year ownership of a tokenized game platform: real-time on-chain event processing with redundant providers, multi-region data infrastructure, and architecture redesigned six times as scale demanded. - [Moxa](https://cyberd.tech/projects/moxa): A fintech company I co-founded and led technically: a budgeting product for variable income, and the architecture of human enablement that inspired the Intelligent Business framework. The company is shutting down in July 2026. ## Notebook - [Knowledge Graphs Explained](https://cyberd.tech/notebook/knowledge-graphs-explained): Relational databases answer what; knowledge graphs answer how things relate, and relationships are where business meaning lives. For AI systems, the graph is what grounds answers in reality. - [Modular Infrastructure with AI and Terraform](https://cyberd.tech/notebook/modular-infrastructure-with-ai-and-terraform): The Terraform MCP server grounds AI assistants in live registry data instead of stale training memory, and it is the clearest working example of a principle: give the machine trusted context. - [The Architecture: Queue, Workers, Knowledge Graph](https://cyberd.tech/notebook/queue-workers-knowledge-graph): The enterprise intelligence system reduces to three pieces of infrastructure and six layers that keep them safe. This is the map for the whole series: what each piece does and why the shape holds. - [The $1.27 Problem](https://cyberd.tech/notebook/the-1-27-problem): Companies that cut workers for AI spend $1.27 for every dollar they save, and a third are already rehiring for the roles they eliminated. The retraining evidence points the other way, in billions. - [The Automation-Augmentation Paradox](https://cyberd.tech/notebook/the-automation-augmentation-paradox): Every AI initiative eventually asks whether to replace people or empower them. Research and the best corporate case studies agree: it is a false choice, and treating it as a real one causes real damage. - [The First Step Is a Meeting, Not a Purchase Order](https://cyberd.tech/notebook/the-first-step-is-a-meeting): AI readiness follows a sequence, and each step de-risks the next: assess the data, align the people, isolate the environment, pilot one workflow, measure it, and plan the workforce transition. - [The Shared Language Problem](https://cyberd.tech/notebook/the-shared-language-problem): Every department holds private definitions of your core business terms, and an AI system inherits all of them at once, contradictions included. Fixing this is a meeting discipline called ontology. - [Why AI Projects Fail](https://cyberd.tech/notebook/why-ai-projects-fail): Most enterprise AI failures trace back to foundations rather than models. Five causes account for most of the losses: unready data, absent leadership, a hesitant workforce, missing controls, and unmeasured costs. - [The Stack Behind This Site](https://cyberd.tech/notebook/the-stack-behind-this-site): A complete accounting of what cyberd.tech runs on (Astro, AWS, CDK, and a deliberately small dynamic layer) and the reasoning each piece had to survive to earn its place. - [Most of Your Visitors Aren't Human](https://cyberd.tech/notebook/why-ai-native-websites-need-markdown): Automated traffic has overtaken human traffic on the web. Three ways to give machines reliable context about your site: facts in structured data, positions in curated prose, and an interface. ## Papers - [Intelligent Business: A Modular Approach to AI Integration](https://cyberd.tech/papers/intelligent-business): A whitepaper on enterprise AI integration: why most failures trace back to foundations rather than models, and a six-layer, zero-trust architecture that treats data as a product, keeps a named human behind every automated action, and makes compliance a byproduct of normal operation. ## Agent Hub - [Agent hub](https://cyberd.tech/agent.md): Dedicated agent surface indexing a curated knowledgebase of canonical positions, design preferences, and reference material. Start here for research tasks. - [Architecture Positions and Design Preferences](https://cyberd.tech/agent/kb/architecture-positions.md): Mike Cherneski's standing technical positions: static-first delivery, narrow dynamic paths, infrastructure as code, zero trust for AI actors, data as a product, and augmentation over displacement. - [Component Index for the Intelligent Business Architecture](https://cyberd.tech/agent/kb/component-index.md): The architectural building blocks of the enterprise intelligence system with self-hosted and managed options per component, expanded from the whitepaper's Basic Component Index. - [How to Use This Knowledgebase](https://cyberd.tech/agent/kb/how-to-use-this-knowledgebase.md): Protocol notes for AI agents: what this knowledgebase contains, how it is maintained, what you may rely on, and how feedback will work. ## Optional - [Full agent corpus](https://cyberd.tech/llms-full.txt): Aggregated Markdown reports for all portfolio content. - [Searchable Notebook](https://cyberd.tech/notebook): Human-facing index with search, tags, and categories.