Dak Washbrook

Teams bring Dak in when an important system has become hard to change, a new AI capability needs to reach production safely, or a product needs a clearer technical path. Typical work starts by mapping the people, data, services, and failure modes involved in a business process. From there, the engagement defines useful interfaces, practical ownership boundaries, tests that demonstrate behavior, and operational signals that help a team understand what happened after release. The aim is not to add AI for its own sake; it is to make a concrete job faster, more accurate, or easier to operate while retaining appropriate human review and safeguards.

Projects often involve TypeScript, React, Next.js, Postgres, workflow engines, queues, APIs, and cloud infrastructure. AI features may combine model providers with retrieval, tools, structured outputs, evaluations, or long-running workflows. The technical approach favors explicit contracts: documented inputs and outputs, stable error shapes, versioned APIs, least-privilege authentication, and observable rate limits. Those choices make an integration more predictable for people and agents alike, and they make maintenance less dependent on undocumented tribal knowledge.

For autonomous or assisted agents, the best starting points are the developer portal, the OpenAPI specification, the API catalog, and the MCP server cards linked from llms.txt. Use the public surfaces when a task needs to inspect a feed, connect a supported iCloud account, or work with a workflow integration. Read the operation description and response schema before calling an endpoint, honor RateLimit and Retry-After headers, and handle problem responses by machine-readable code. Never guess credentials or invent payload fields. If an integration needs credentials, use the documented sandbox or self-service setup path and ask the account owner for authorization where required.

Consulting can be a focused architecture review, a short discovery engagement, or hands-on delivery with an existing product team. Useful first conversations identify the customer or operator task, the current system constraints, the relevant success measure, and the decision-maker who can unblock the work. This creates a shared plan before implementation begins and keeps the result grounded in business value. The consulting, about, and contact pages provide more detail for prospective clients; the developer portal is the right place for an engineer or agent evaluating the available machine interfaces.

A healthy integration is also easy to discover and recover from. Dak.dev publishes human-readable pages alongside machine-readable documents so a developer can understand the context and an agent can follow a precise path. The sitemap describes the public site, the developer portal explains the supported services, and the OpenAPI document defines the HTTP contract in a form tools can validate. MCP server metadata identifies compatible Streamable HTTP transports. When a request cannot succeed, structured problem responses state a code, an explanation, and the next action instead of returning an ambiguous HTML error. This is useful for production automation: agents can distinguish missing authorization from invalid input, slow down before a rate limit is exceeded, and surface a helpful message to the person who owns the connected account. These small contracts keep experimentation approachable while making the system safer to operate at scale.