GeezerVison is showing teaql.io — Model-Driven Development with Type-Safe Business APIs | TeaQL TeaQL / Harness Engineering Infrastructure The evolving harnessthat compounds certainty.Generate type-safe business query and mutation APIs from an evaluated domain model. Build AI-assisted applications with runtime governance and executable evidence. Learn onceMove recurring knowledge out of prompts and into the engineering system.Preserve permanentlyEncode what matters in models, generators, runtimes, tests, and evidence.Improve everywherePropagate each accepted capability across the software stack. Start a task Don’t just read about TeaQL.
Put it to work.Build a verified application in your own coding agent with the TeaQL harness engineering workflow. 01 / The unstable path A larger Skill is not a stronger harness.Skills guide the next attempt. But when every failure becomes another paragraph, the system grows larger without becoming more certain. TeaQL preserves what the engineering system has learned. - 01More instructions compete for limited context
- 02Rules still depend on probabilistic recall
- 03The same lesson is repeated in every project
- 04Behavior drifts across stacks and providers
- 05Failures enlarge prompts instead of infrastructure
02 / Durable evolution Don’t patch the prompt.
Evolve the harness.Put each lesson in the layer that can preserve and enforce it: the model, generator, runtime, provider, policy, test, or evidence system. Engineering lesson↓Correct harness layer↓Durable implementation↓Verification↓Shared capability Every repeated failure should become a durable capability. 03 / The persistence layer for learning Guidance fades.
Infrastructure endures.A naming mistake becomes a generator rule. A governance gap becomes a runtime boundary. A provider difference becomes a tested implementation. The next project inherits the improvement without asking the model to remember the same lesson again. 04 / Where the learning lives Four durable layers.
One evolving harness.01World Model- Business meaning
- Relationships
- Constraints
- Reachable states
02Generator- Stable naming
- Typed APIs
- Deterministic artifacts
- Cross-stack propagation
03Runtime- Trusted context
- Purpose gates
- Audit boundaries
- Execution policy
04Evidence- Compilation
- Real databases
- Trace and audit
- Reproducible matrices
05 / A different growth model Accumulate capabilities.
Not instructions.Skill-heavy growthTeaQL harness evolution Add another instructionAdd an executable capability Expand the contextStrengthen the infrastructure Depend on recallEnforce at the boundary Repeat across projectsPropagate through generation Drift across stacksPreserve one semantic model Across the software stack Languages, databases, caches, cloud services, and protocols.One accepted improvement can strengthen them all. Java · Rust · TypeScript · Swift · Python · .NET · GoThe stacks differ. The business meaning endures.
Practical data access Start with a development question.Compare approaches, understand the tradeoffs, then follow a native example. Compare generated business APIs with mapper XML, DTO assembly, relation loading and counts. Understand why an absent value and an unselected field need different handling. Choose an authoring layer for domain graphs, reusable filters and database-specific reports. Choose sequential exports or random page access, and keep exact counts and cache ownership explicit. Connect an AI SDK agent through typed business tools, with server-owned context and a SQLite demonstration. Follow the same Order model through native tutorials, API usage and troubleshooting. Build an engineering foundation Learn once.
Preserve permanently.
Improve everywhere.Start with one model. Grow a harness that becomes more capable, more durable, and more certain with every verified lesson. A coding agent can start from one prompt; review proposed tool installations before allowing them to run. |