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AI tooling & engineering workflows

Crucite

Personal engineering platform for AI integrations, automation, and governed software delivery

Crucite is my personal engineering and operations platform, built with TypeScript, React, and Cloudflare. It connects project work, knowledge, AI integrations, and security tooling through a shared entity model. Methodologies is central to how I develop it: versioned standards, reusable playbooks, agent guidance, and recorded delivery evidence support a workflow spanning implementation, review, remote validation, and operations.

CruciteProject overview
  • GuidanceMethodologiesStandards · playbooks · adoption
  • Tool accessGoverned MCPOAuth · capability checks
  • PlatformCloud servicesAI integrations · storage · audit
Engineering guidance, bounded AI tools, and cloud services in one platform.
My role
Founding Engineer
Period
Dec 2025 – Present
Platforms
Web
Status
active

Tools & technologies

ReactTypeScriptViteTailwind CSSTipTapZustandCloudflare WorkersDurable Objects

FULL_STACK (41)

ReactTypeScriptViteTailwind CSSTipTapZustandCloudflare WorkersDurable ObjectsHonoKyselyMCP SDKWorkers AIVectorizeAI GatewayMicrosoft Graph APID3RechartsThree.jsReact Three FiberDreiXYFlowMermaidFramer MotionKonvaReact KonvaMapLibre GLGoVitestPlaywrightsemgrepDevSecOpsSystems IntegrationAutomationAI IntegrationsCloud SecurityCI/CDGitHub ActionsCloudflareCloudflare AccessSQLiteMicrosoft 365 / Entra ID

Impact & Results

  • Connected project work, reference material, and engineering guidance in the same platform I use to develop software.
  • Reduced repeated integration work by applying shared entity schemas and generated infrastructure across modules.
  • Made AI tool permissions and selected confirmation boundaries explicit at command dispatch.
  • Created traceable development records linking decisions, implementation scope, checks, and delivery outcomes.
  • Added audit and operational signals that support investigating failures across integrated services.

Overview

Crucite is my personal engineering and operations platform: a shared workspace for projects, knowledge, AI integrations, and security tooling. I built it to connect information and workflows that otherwise live in separate applications, and I use it to organize the work of developing and maintaining software.

The Methodologies module is central to that process. It brings versioned engineering standards, playbooks, project-specific guidance, and adoption tracking into the workspace. Agents can retrieve the relevant methodology before a build or review, while proposals and build records keep decisions, scope, and delivery evidence connected to the work.

I own the architecture, implementation, integrations, and delivery process. The platform combines a React and TypeScript client with Cloudflare Workers and per-user Durable Object SQLite storage. Shared entity schemas generate common storage and API infrastructure, reducing repeated implementation across modules.

AI clients connect through OAuth-protected MCP tools with command capability checks, bounded responses, and confirmation requirements for selected actions. Application AI features use shared model helpers with rate controls, sensitivity checks where supplied, and AI Gateway routing when configured. These controls make the authority and data handling of an integration explicit.

Crucite is also where I develop operational practices: automated checks, independent review, scoped remote validation, audit verification, and release evidence. It is a personal platform and an ongoing engineering project; its value as a case study is the connection between the software, the controls around it, and the process used to change it.

Role Summary

  • Sole builder responsible for product direction, architecture, application development, integrations, and platform operations.
  • Designed the shared entity model and generation pipeline used across storage, services, dispatcher commands, and typed clients.
  • Built the Methodologies workflow and agent-facing guidance used during development and review.
  • Own the review and release decisions around AI-assisted implementation, including validation scope and evidence.
  • Investigate integration and operational failures across application, identity, storage, and delivery boundaries.

Non-Technical Summary

Crucite brings projects, notes, personal information, and connected tools into one workspace. Instead of maintaining isolated features, I built a common structure so information can be linked, searched, and used across the application.

It is also part of how I build software. The Methodologies module keeps standards and repeatable processes close to the work, and gives AI coding assistants a way to retrieve the guidance relevant to a task. Proposals and build records help preserve what was decided, what changed, and how the change was checked.

The engineering challenge is making integrations useful while keeping access and failures understandable. AI tools have permission boundaries, model requests have configurable controls, and audit and delivery records provide information for investigation. My role spans building those features and maintaining the process around them.

Highlights

  • Designed and built a cloud-hosted personal platform with a shared entity model, typed APIs, and per-user Durable Object SQLite storage.
  • Made Methodologies part of the development workflow through versioned standards, playbooks, project guidance, adoption tracking, and agent bootstrap.
  • Built OAuth-protected MCP integrations with capability-tier authorization, bounded output, confirmation gates, and blocking of destructive commands on the AI transport.
  • Integrated Workers AI and external model services through shared request helpers supporting rate budgets, sensitivity-aware egress, and configurable AI Gateway routing.
  • Established an AI-assisted delivery process linking scoped proposals, code review, remote CI gates, and recorded release evidence.
  • Implemented operational visibility through command audit metadata, tamper-evident audit verification, and scheduled detection paths.

Quick Highlights

  • One workspace connects project planning, knowledge, AI tools, and operational information.
  • Methodologies puts engineering standards and reusable workflows alongside the work they guide.
  • AI assistants receive bounded access to platform commands rather than unrestricted authority.
  • Per-user databases separate account data; shared services and external integrations have their own access boundaries.
  • Review, automated checks, and delivery records support changes that can be traced and investigated.

Technical Breakdown

  • Platform and data. Cloudflare Workers route application requests to per-user Durable Objects backed by SQLite. Shared services use D1, R2, and KV where appropriate. Account isolation reduces reliance on tenant filters within user-owned tables; shared metadata, authorization, and routing still require their own controls.

  • Schema-driven development. A common entity layer and typed body tables support cross-cutting links, search, and audit. Schema definitions generate storage and service infrastructure, dispatcher commands, and typed clients, reducing repeated work when adding a domain.

  • Methodologies. Versioned playbooks and standards form a maintained source corpus, with a freshness-checked application projection. Agent bootstrap resolves project guidance and adoption information. Adoption is version-specific; documented guidance and automated enforcement are distinct.

  • AI integrations. Shared helpers support Workers AI and external Anthropic requests, common rate budgets, untrusted-content handling, and optional sensitivity-egress context. AI Gateway routing depends on configuration; direct external routing remains a supported fallback.

  • MCP authorization. OAuth 2.1 with S256 PKCE protects the AI integration transport. Dispatch resolves command capabilities, blocks undeclared and destructive operations, applies response budgets, and supports confirmation requirements. Batch execution retains per-command checks.

  • Delivery and operations. GitHub Actions, required checks, review procedures, and remote CI policy support controlled changes. MCP audit metadata captures outcomes and timing without recording command arguments. Durable Object alarm paths support audit verification and detection; these mechanisms do not imply guaranteed audit delivery or independently verified uptime.

Systems Used

  • Application: React, TypeScript, Vite, Tailwind CSS, Zustand, and TipTap.
  • Cloud and storage: Cloudflare Workers, Durable Objects with SQLite, D1, R2, KV, Queues, and Workflows.
  • AI and integration: MCP SDK, OAuth 2.1 with PKCE, Workers AI, Vectorize, AI Gateway, and Microsoft Graph API.
  • Development process: Methodologies, versioned playbooks, project-specific standards, proposals, and build records.
  • Delivery and verification: GitHub Actions, Vitest, Playwright, Semgrep, code review, and scoped remote validation.
  • Extended compute: a Go station daemon for workloads on user-controlled hardware.

Deep Dive

Why combine the product and the development process? Crucite began as a way to connect information across personal and engineering workflows. As it grew, the process of building it became another integration problem: standards, agent instructions, proposals, and verification evidence were distributed across tools. Methodologies gives that process a maintained, versioned reference that agents and people can retrieve in context.

Guidance is not the same as enforcement. A playbook can describe the intended process without proving it happened. The methodology system therefore distinguishes versions and adoption, while build records and CI results carry evidence about individual changes. This distinction matters when several agents or worktrees contribute to one release.

Bounded AI authority. MCP gives assistants access to useful application commands, but a small tool surface is only a usability choice. The security boundary is the authorization and capability check on each dispatched command, including batched calls. Destructive commands are blocked on this transport; selected actions require confirmation, and returned data is bounded.

Model routing and data handling. Shared AI helpers provide a common place for budgets, prompt framing, and egress policy. Model destinations still matter: local platform inference and external providers have different data paths. AI Gateway use is configuration-dependent, and the presence of an egress API does not by itself establish coverage of every caller.

Isolation and observability. Per-user Durable Object databases separate user-owned storage, while shared authentication, routing, and collaboration require additional controls. Audit verification and detection paths provide investigative signals. Command audit writes can fail without failing the originating request, so an audit design should not be described as a guarantee that every action was durably recorded.

What this demonstrates. Crucite connects application development with systems integration and DevSecOps: define boundaries, automate repeatable work, review changes, preserve evidence, and investigate failures. I use it as a personal engineering platform and continue refining both the product and the process.


KEYBOARD_SHORTCUTS

GO_TO

  • g h TERMINAL
  • g p PROJECT_INDEX
  • g e EXPERIMENTS
  • g s SKILL_MATRIX
  • g a ABOUT_ME
  • g r EXPERIENCE

GLOBAL

  • ⌘K COMMAND_PALETTE
  • / COMMAND_PALETTE
  • ` TERMINAL_MODE
  • ? SHORTCUT_PANEL
  • ESC CLOSE_OVERLAY

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