Capstone: Full-Stack AI App
Learn capstone: full-stack ai app through RunLocalAI's practical lens: capstone, fullstack, app and deployment, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
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Why this course matters
Capstone: Full-Stack AI App is for builders turning local models into working tools, agents and retrieval systems. It connects capstone, fullstack, app, deployment and testing to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?
What you will be able to do
By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.
How to use this course
Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Capstone Scope, Architecture Design, Model Serving Setup and API Gateway and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.
- 01Capstone ScopeScope creep kills projects—define boundaries early and resist adding features until the core loop works in production.10 min
- 02Architecture DesignDraw the data flow first, then design each service interface around that flow, not the other way around.15 min
- 03Model Serving SetupModel serving is the bottleneck in most AI applications—design autoscaling around inference latency, not request throughput.15 min
- 04API GatewayThe gateway is the enforcement point for security policies—do not let requests reach backend services without validation.15 min
- 05Frontend FrameworkStreaming UI requires careful state management—buffer chunks locally and batch renders to avoid excessive re-renders.20 min
- 06UI/UX DesignAI interfaces need more loading states than traditional apps because inference takes time—design every state, not just success and error.15 min
- 07Integration TestingIntegration tests catch the bugs that unit tests miss—specifically, the interaction failures between services and the async timing issues in streaming code.20 min
- 08Performance TestingPerformance testing must run continuously in CI—catch regressions before they reach production.15 min
- 09Security AuditSecurity is not a one-time checkbox—it requires continuous scanning, dependency updates, and code review discipline.20 min
- 10Docker Compose DeploymentDocker Compose is configuration as code—treat the compose file like production infrastructure and version it accordingly.15 min
- 11CI/CD PipelineA failing pipeline should be treated as a production incident—developers must fix CI failures before merging new code.15 min
- 12Monitoring SetupMonitor what you would debug in an incident—latency, errors, and throughput for every critical path.20 min
- 13DocumentationDocumentation rot happens fast—automate documentation generation where possible and review docs during code review.20 min
- 14User GuideUsers read documentation when something goes wrong—write troubleshooting sections with specific error messages and solutions.15 min
- 15API DocumentationThe API documentation is the contract between frontend and backend—keep it accurate and version it with the code.20 min
- 16Deployment RunbookRunbooks should be boring—routine procedures with clear steps prevent mistakes under pressure.25 min
- 17Launch ChecklistA launch checklist prevents embarrassing production incidents—treating it as ceremonial undermines its purpose.15 min
- 18Full-Stack AI App ProjectThe capstone is not the end—it is the beginning. Production systems are never finished; they are maintained, improved, and extended by teams who understand the full stack.15 min