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Engineering contributions

The work behind
the products.

89 contributions across 5 product and engineering areas. Explore the changes, the decisions behind them, and the stack I used.

Integration records code history. Deployment is identified separately where verified.

One mark. One contribution.

7 contributions of 89

Contribution results

ClevahMobile accountsGoogle, Apple and guest sign-in with validated onboardingImplemented mobile-learning authentication that supports guest entry, Google/Apple sign-in and account onboarding.FlutterDartFirebase AuthGoogle Sign-InApple Sign-In+2 in detailBuilt · integrated sourceMay 2026

What I changed

  • Implemented anonymous Firebase sessions and account-linking paths.
  • Added Google Sign-In and the Firebase Apple provider.
  • Added live signup-form validation and verification refresh.
  • Centralized onboarding navigation and captured per-subject exam dates.
Decision
Keep account linking and onboarding progression separate so guest users can acquire credentials without silently losing their learning identity.
Result
The mobile client offers multiple credential paths through one repository/auth data source and keeps onboarding state in typed Cubits.

Stack used

  • Flutter
  • Dart
  • Firebase Auth
  • Google Sign-In
  • Apple Sign-In
  • Bloc/Cubit
  • Firestore
Scope & evidence

Implementation is verified. Production usage and business impact were not independently measured.

ClevahMobile commerceIn-app subscription purchase, verification and restoreAdded the mobile pro-subscription purchase flow, account binding and subscription-management paths.FlutterDartStoreKitBloc/CubitFirestore+1 in detailBuilt · integrated sourceMay 2026 – Jun 2026

What I changed

  • Loaded monthly store offers and routed purchase-stream updates.
  • Bound purchase verification to the learner account.
  • Added purchase restoration and pending-purchase handling.
  • Added an iOS StoreKit development configuration and subscription-management URI tests.
Decision
Verify store purchases against the account-bound backend entitlement before presenting subscribed access.
Result
The client exposes purchase and restoration flows with backend entitlement verification, rather than unlocking pro access only from a local purchase event.

Stack used

  • Flutter
  • Dart
  • StoreKit
  • Bloc/Cubit
  • Firestore
  • Dio
Scope & evidence

Implementation and a StoreKit development configuration were inspected. Public store approval, real purchase revenue and subscriber counts were not verified.

ClevahMobile API platformDedicated mobile API and worker deployment boundaryEstablished mobile backend deployment assets and an explicit API root, with later workload-isolation revisions recorded separately.PythonFastAPIGitHub ActionsHelmKubernetes+1 in detailIntegrated + historical workMay 2026

What I changed

  • Integrated: mobile backend deployment assets and environment release callers.
  • Integrated: Cloud Build image configuration.
  • Integrated: the mobile FastAPI root-path boundary.
  • Historical source: a per-service deployment application and isolated mobile worker Helm resources.
Decision
Give the mobile workload explicit deployment ownership instead of depending on the shared backend process.
Result
The mobile backend has integrated deployment assets and a defined API prefix. Later per-service and worker-resource isolation remains recorded historical work, without deployment proof here.

Stack used

  • Python
  • FastAPI
  • GitHub Actions
  • Helm
  • Kubernetes
  • Celery
Scope & evidence

Deployment assets, build configuration and the API prefix were integrated. Later service and worker isolation remained historical development work without verified integration or production rollout.

ClevahMobile release integrationPinned optimizer releases as the mobile backend moved to the edgeCoordinated mobile-backend pins for edge-optimizer routing, production environment support and consolidated-state changes.Git submodulesPythonCloudflare WorkersFirestoreHistorical implementationJun 2026

What I changed

  • Pinned optimizer source for develop edge deployment.
  • Advanced the mobile route configuration and production environment source.
  • Updated exact optimizer revisions for shared-core, recycled-state and sharding changes.
Decision
Coordinate consumer integration through exact dependency revisions while keeping the edge implementation in its owning service.
Result
The mobile orchestration repository records which optimizer revision is consumed for each integration change.

Stack used

  • Git submodules
  • Python
  • Cloudflare Workers
  • Firestore
Scope & evidence

Historical dependency-integration work. Integration into the main product and successful production deployment were not verified. The optimizer implementation is covered separately.

ClevahAdaptive-learning deliveryAuthenticated Python optimizer on Cloudflare WorkersImplemented an authenticated mobile optimization endpoint on Cloudflare Workers, with later shared-core and production-route extensions recorded separately.PythonCloudflare WorkersFirebase AuthFirestoreFetch APIIntegrated + historical workJun 2026

What I changed

  • Integrated: Python Worker request handling and environment route bindings.
  • Integrated: Firebase token verification with cached certificates and WebCrypto.
  • Integrated: Firestore REST and GCP OAuth handling for the edge runtime, without a Celery request hop or Firebase Admin SDK.
  • Historical source: a dependency-free optimizer core and production-environment route extension.
Decision
Adapt request transport, authentication, and storage to the edge runtime. Later work extracts shared recommendation logic rather than duplicating the algorithm.
Result
An integrated edge-compatible request, authentication, and storage path exists. The shared-core extraction and production-environment extension are historical authored work without independently established deployment here.

Stack used

  • Python
  • Cloudflare Workers
  • Firebase Auth
  • Firestore
  • Fetch API
Scope & evidence

The edge request handler and environment routing were integrated. Integration of later shared-core and production-environment changes was not verified. No request-volume or latency gain is claimed.

ClevahAdaptive-learning stateSharded optimizer state with append/roll persistenceReworked optimizer persistence to consolidate state and support sharded append/roll updates without storing every recommendation in one growing document.PythonFirestoreFetch APICloudflare WorkersHistorical implementationJun 2026

What I changed

  • Consolidated optimizer-state loading and recycling behavior.
  • Added shard-document generation for state records.
  • Loaded state shards through batch Firestore REST reads.
  • Committed stack/recommendation updates through the edge storage adapter.
Decision
Separate logical optimizer state from its physical document layout so record growth can be handled without duplicating the optimizer algorithm.
Result
The adapter represents optimizer state as shard documents and exposes batch-load/commit paths for continued recommendation growth.

Stack used

  • Python
  • Firestore
  • Fetch API
  • Cloudflare Workers
Scope & evidence

Historical persistence-design work. Integration into production, supported load and CPU or cost improvements were not verified.

ClevahEdge performance measurementProfiled edge optimizer CPU across empty and large question poolsBuilt a seeded development workload and CPU-monitoring tools for the mobile edge optimizer, keeping compute time separate from response latency and storage cost.PythonCloudflare WorkersCloudflare KVFirestoreGraphQL+1 in detailRecorded development measurementJun 2026

What I changed

  • Added a development seeder with five learned topics and a 10,000-question index.
  • Added a Cloudflare analytics monitor for CPU percentiles, wall time, request errors, and subrequest counts.
  • Recorded warm cache-hit and cold cache-miss CPU measurements against the large seeded pool.
  • Modeled question-pool and served-history growth separately to identify repeated pool traversal and storage reads as distinct optimization targets.
Decision
Measure runtime CPU and Firestore document operations separately. Cache conditions and question-pool size must accompany the numbers because they change the work performed per request.
Result
The archived development report records approximately 220 ms warm CPU p50 and 655 ms cold CPU p99 at 10,000 questions. These are CPU-time observations, not end-to-end response latency or a before-and-after production gain.

Stack used

  • Python
  • Cloudflare Workers
  • Cloudflare KV
  • Firestore
  • GraphQL
  • Bash
Scope & evidence

Recorded on 10 June 2026 for a historical mobile-edge implementation: warm isolate with a KV cache hit versus cold execution with a KV miss. The report and measurement tooling were recovered, but raw telemetry responses, underlying sample counts, and a reproduced run were not. The source branch and pull request do not establish production integration.