Start from a rough need, observation, or workflow pain point worth turning into a system.
Raw input / sparkAI Capability Showcase
From idea to shipped AI-assisted systems.
A hands-on showcase of how SeanSiao-Agent uses AI-DLC to turn rough ideas into requirements, specs, working applications, tests, deployments, and learning loops.
LifeOS Concept
A personal AI operating system with memory, tools, evidence, and learning loops.
Memory, reusable skills, tools, delivery evidence, and learning loops are treated as one system instead of scattered prompts and notes.
02 / How I Build
AI-DLC is the delivery framework behind the agent system.
AI-assisted Delivery Lifecycle
I use AI-DLC to build SeanSiao-Agent as a repeatable system, not a one-off prompt experiment. Each app starts from intent, moves through a reviewable build path, and leaves evidence that can be tested, deployed, documented, and improved.
IdeaSignal / pain pointRequirementRequirement / Data / SolutionSpecAcceptance criteriaBuildAI-assisted implementationTestType / build / visual QAHuman ReviewScope, risk, evidence checkCI/CDBranch, checks, deploy pathDeployRelease and rollback trailChangelogWhat changed and whyLearnMemory, skill, next loop
Separate the user requirement, required data, and proposed solution before building.
Requirement / data / solutionTurn intent into acceptance criteria, flows, edge cases, and review points.
Spec / task planUse AI-assisted implementation while keeping diffs, context, and decisions inspectable.
Code / UI / automationRun type checks, builds, rendered QA, and targeted evidence checks.
QA notes / screenshotsReview the result, risk, scope drift, and evidence before it moves toward release.
Manual review gateUse a traceable branch, checks, build output, and deployment path before shipping.
Checks / deployment pipelineShip through a tracked path with branch, build, deployment, and rollback awareness.
Deployment trailRecord what changed, why it changed, and what should be learned next.
Notion / memory loopTurn delivery evidence into reusable memory, rules, skills, and the next iteration.
Learning loop03 / Capabilities
Skill set is shown as operating capabilities.
Agentic workflow design
Memory, routing rules, and reusable AI-assisted patterns become operating workflows.
Agent orchestration
Tools, agents, dashboards, and follow-up actions are coordinated around a clear task path.
RAG / knowledge operations
Notion, source records, memory, and retrieval context keep answers grounded.
Information structuring
Raw notes, job posts, school data, and ideas become comparable fields.
Decision tooling
Messy real-world data becomes searchable, filterable, and comparable.
Delivery discipline
Git, QA, screenshots, deployment, changelog, and learning loops stay traceable.
04 / Selected Systems
Demo apps that prove the framework through working systems.
01 / AI Assistant Operating System
SeanSiao-Agent LifeOS
A memory-backed AI assistant system that turns repeated work into reusable workflows, tracked decisions, and reviewable delivery evidence.
Memory and feedback loopReusable skills and tool routingNotion dashboard and changelog
02 / Opportunity Intelligence System
Jobs Tracker
AI-assisted job pipeline that turns scattered job posts into structured, comparable, and trackable opportunities.
Job ingest and structured JD fieldsRole category and priority tagsPipeline status dashboard
03 / Research-to-Decision Tool
Kindergarten Finder
A decision-support app that turns scattered school information into a structured, searchable, mappable, and comparable view.
64-school structured datasetNatural-language to structured filter searchMap, dashboard, and comparison workflow
04 / Idea Nutrition Loop
Spark Inbox
An AI-powered idea capture and digestion system that turns fleeting thoughts into research, workflows, skills, or the next better idea.
Instant idea captureAI digest and value scoringExpansion analysis