Marketplace

Design AI code review systems: PR analysis, finding prioritization, false positive suppression, and developer feedback.

Design AI content moderation pipelines: multi-layer classification, appeal workflows, edge case handling, and trust safety.

Design AI-powered search experiences: semantic search, conversational interfaces, result ranking, and relevance tuning.

Design AI writing assistant features: tone control, suggestion timing, feedback loops, and human-in-the-loop editing.

Design canary and progressive deployment strategies: traffic splitting, health metrics, automatic rollback, and analysis windows.

Design chaos engineering experiments: hypothesis formation, blast radius control, steady-state metrics, and game day execution.

Audit cloud infrastructure for cost optimization: right-sizing, reserved capacity, storage tiers, and waste elimination.

Review cryptographic implementations: algorithm selection, key management, random number generation, and protocol-level flaws.

Systematically clean a messy dataset with profiling, ordered fixes, validation, and a provenance log; use before analyzing or merging untrusted data.

Design robust data pipelines: ingestion patterns, transformation stages, schema evolution, and data quality monitoring.

Design data quality monitoring: anomaly detection, schema validation, freshness checks, and data contracts between teams.

Design data warehouse schemas: star/snowflake schemas, slowly changing dimensions, materialized views, and query optimization.

Use when a failure's cause is unknown: a hypothesis-driven protocol from symptom to confirmed root cause, with evidence trail and regression test.

Use when a Dockerfile builds slowly, produces oversized images, or needs hardening; rewrites it for smaller, faster, and more secure builds.

Design document processing pipelines: OCR, layout analysis, information extraction, classification, and human review.

Choose embedding models and strategies: dimensionality, domain-specific models, multi-modal embeddings, and evaluation.

Design and scaffold idempotent, observable extract-transform-load pipelines with safe retries, backfills, and quality gates; use when building data pipelines.

Drive headless browsers (Playwright/Puppeteer) to navigate pages, auto-screenshot at each step, fill forms, click elements, and capture visual evidence of workflows.

Design reusable Infrastructure as Code modules: Terraform, Pulumi, and CloudFormation patterns for maintainable infrastructure.

Design least-privilege IAM policies for AWS, GCP, and Azure: role design, permission boundaries, and access auditing.

Use after an incident is resolved to produce a blameless postmortem with a reconstructed timeline, contributing factors, and owned action items.

Design incident response playbooks: detection triggers, severity classification, containment, eradication, and communication.

Optimize model inference: quantization, distillation, batching, speculative decoding, and serving infrastructure tuning.

Use when reviewing or writing Kubernetes manifests; audits workloads for reliability, security, and cost issues and produces ready-to-apply fixes.