Demo showcase#
Work through one module at a time: change the inputs, inspect what responds, and connect the result to your own project. Repeat a scenario until you can explain the trade-off without relying on the interface.
- Modules
- 26
- Runtime
- Browser
- Mode
- Self-guided
Scalable development
Implementation discovery labImplementation discovery lab
Find a direction worth building three times
The behavior is shared. Your interpretation, stack, architecture, and experiment are yours to choose.
Scalable development
Constraint feedback loopConstraint learning loop
Turn evidence into project memory
- 01Constrainstate what must remain true
- 02Generateproduce a bounded candidate
- 03Validatecompare evidence with intent
- 04Revisemake the learning explicit
Candidate + evidence
No candidate generated
- —Browser testCan clicking Export create a download?
- —Changed-files checkDid the patch stay inside export.ts and export.test.ts?
- —CSV assertionAre commas and quotation marks escaped?
Generation is not evidence. Generate a candidate, then run the checks.
Scalable development
Deterministic boundary labDeterministic boundary lab
Give the agent a map
What is scalability?
Scalability dimensions labScalability dimensions lab
Choose what must scale first
Architecture
Boundary traceArchitecture boundary trace
Trace one event request
Architecture
Cache decision labHTTP reuse control room
Where does the next response come from?
Every case begins after one successful response. Change its policy and age, then trace the next request without changing the URL.
Architecture
Web architecture lensTwo architectural decisions
Compose a web request
SSR, ISR, and CSR describe where and when a view is produced. MPA, HDA, and SPA describe how the browser moves to later application states.
Architecture
Client activation lensBrowser activation cutaway
What wakes up before the click?
The server returns the same useful event page in every case. Change only how browser behavior becomes available, then trace the first attempt to record an event.
Architecture
API contract lensContract topology lab
Where does the client learn what it can do?
Hold the domain intent constant. Move between contracts to see which facts live in client code, which arrive from the server, and where change creates coordination.
Benchmarking
Tail latency simulatorTail latency simulator
An average can hide slow requests
POST /events
requests. It does not contact a server.
Benchmarking
Browser performance workbenchBrowser performance workbench
Repeat one interaction before trusting it
02 / Inspect the runs
Time until the next painted frame
Run the same interaction five times to reveal its distribution.
- Median total
- —
- Fastest
- —
- Slowest
- —
- Median longest slice
- —
One run is an observation. Repeated runs show whether that observation was typical.
Benchmarking
Queue balance labLittle’s Law · L = λW
Check whether the queue tells one story
- Expected L
- 10.0
- Observed L
- 10
- Gap
- 0.0
The three averages are mutually consistent for a stable observation window.
Benchmarking
Model evaluation labChallenge eval lab
Compare two model attempts on the same task
Benchmarking
Observability signal pickerObservability signal picker
Choose evidence for one symptom
Benchmarking
OpenTelemetry trace explorerOpenTelemetry trace explorer
Follow one request, span by span
The rows below are spans from one trace. Select a row to inspect the record the SDK could include in a batched OTLP export.
Trace
POST /events · 286 ms
4bf92f3577b34da6a3ce929d0e0e4736
Select any span row to inspect its telemetry record.
Friction
Workflow loopWorkflow loop lab
Trace a human-agent change loop
Friction
Friction auditFriction audit lab
Find the slow part of a small change
Reality
Changed assumptions mapReality pressure map
Map changed assumptions to failures
Agent fleets
Agent fleet delegationCompare delegation lab
Plan a small agent fleet
Agent fleets
Sequential work boundAmdahl’s Law · S(N) = 1 / ((1 − p) + p/N)
Find the work that more agents cannot remove
- Ideal speedup
- 2.50×
- Relative time
- 40.0%
- Efficiency
- 62.5%
- Infinite-agent ceiling
- 5.00×
Four agents divide the parallel work, but the sequential 20% remains.
Scaling
Scaling pressure plannerCompare scaling lab
Choose scaling moves for one pressure
Scaling
Saturation boundary labCapacity envelope
Find the first binding limit
This is a teaching model, not a capacity benchmark. It compares one effective demand rate with declared stage limits so the movement of a bottleneck is visible.
Scaling
Contention curve labUniversal Scalability Law · C(N)
See when adding concurrency starts taking capacity away
- Selected capacity
- 5.57×
- Peak at
- 22
- Peak capacity
- 7.33×
- Next worker
- helps
At eight workers, added concurrency still increases modeled capacity.
Scaling
Orchestration control loopBrowser cluster lab · Webernetes
Watch the control loop close
Debrief the mechanism and its limits
Name the desired state, the observed state, and the controller action after each experiment. Then identify what remains outside this model.
- Replicas add service instances; they do not remove database limits.
- Replacement demonstrates recovery, not uninterrupted availability.
- This simulator omits real images, resource limits, persistent volumes, and production networking.
Scaling
Latency budget mapLatency budget lab
Count distance and database waits
Recap
Final reflection packageFinal reflection package