Paid career tracks
Courses
20 subscription courses across 6 tracks. One plan unlocks every course at or below your tier.
0-to-1 SaaS Engineering
Learn the core multi-tenant SaaS engineering patterns by fixing real, working bugs: Postgres row-level security as a database-enforced tenant boundary, JWT authentication vs. tenant-scoped authorization, idempotent billing webhook handling with proration math, per-tenant rate limiting, and fair multi-tenant background job queues -- every vulnerable/fixed pair demonstrated with live before/after results, not just description.
AI & LLM Security
Learn to find and fix the vulnerability classes unique to LLM-integrated systems: direct and indirect prompt injection, insecure output handling that reintroduces classic injection one layer removed, training-data memorization and leakage, and building guardrail pipelines whose effectiveness is measured with a red-team suite instead of assumed.
AI Product Management
Learn to product-manage AI/ML features specifically: precision/coverage tradeoffs instead of pass/fail correctness, AI-aware PRDs with named failure modes and model-tier cost analysis, evaluation with inter-rater reliability, feedback-loop risk, and production guardrails/monitoring that catch a quality regression the day it ships.
AI Agent Engineering
Design and ship production multi-agent systems — tool use, memory, orchestration, and the guardrails that keep autonomous agents safe to deploy.
Applied RAG & Enterprise Search
Learn to build production-grade retrieval-augmented generation systems for enterprise use: deliberate chunking strategy, hybrid sparse+dense retrieval with re-ranking, multi-tenant access control, retrieval evaluation, and scaling to enterprise data volumes.
Blockchain & Smart Contract Security
Learn to find, exploit, and fix real smart contract vulnerabilities: the EVM execution model, live reentrancy attacks and the checks-effects-interactions fix, tx.origin authorization bugs and integer math pitfalls, oracle price manipulation, and automated static analysis with Slither -- all demonstrated with working exploits against deployed contracts, not just description.
Cloud Security Engineering
Learn to secure cloud infrastructure systematically: IAM policy evaluation with correct explicit-deny semantics, automated network security-group scanning for overly permissive rules, regex-based secrets detection, cloud security posture management across a resource fleet, and anomalous access-pattern detection using per-account statistical baselines.
DevRel & Technical Content Engineering
Learn to treat developer relations content as an engineered system: model developer activation as a funnel, apply the Diataxis framework to write tutorials developers actually finish, build a doc-testing pipeline that catches broken code samples automatically, measure which content actually drives activation, and monitor support-ticket signal to catch content breakage the day it happens.
Distributed Systems Design (Staff-Level)
The deep systems knowledge staff and principal engineers are expected to have.
Embedded / Edge AI & Robotics Basics
Learn to design AI and control systems for resource-constrained edge and robotics hardware: model quantization and the real memory/accuracy tradeoff, real-time latency budgets against sensor sampling rates, PID control loop fundamentals, sensor fusion that combines noisy and drifting signals into a better estimate than either alone, and power-budget duty cycling that trades responsiveness for battery life.
FinOps & Cloud Cost Engineering
Learn to make cloud spend visible, fair, and accountable: cost allocation and tagging, showback/chargeback, unit economics and anomaly detection, commitment-discount math, and Kubernetes/multi-cloud cost allocation -- the FinOps practice that turns a shared cloud bill into numbers teams can actually act on.
LLM Infrastructure & MLOps
Learn to size, serve, train, monitor, and cost real LLM deployments: GPU memory and KV-cache capacity planning, continuous batching and quantization tradeoffs, distributed training with verified gradient accumulation, production drift monitoring, and utilization-aware cost economics.
Multi-Cloud Solutions Architect
Learn to design real multi-cloud architectures: dependency-aware cost placement that accounts for data gravity, genuinely cross-provider failover routing, migration-cost-weighted vendor lock-in scoring, and data-residency compliance as a hard constraint over cost and latency decisions.
Offensive Security (OSCP Track)
Learn the systematic methodology behind authorized security assessments: structured reconnaissance and port scanning, identifying injection vulnerabilities through code review, quantifying password strength as entropy, auditing hosts for the specific misconfigurations that enable privilege escalation, and scoring and reporting findings the way a real engagement deliverable does. Every technique is framed around authorized testing of systems you own or have explicit permission to assess.
Quantum Computing Fundamentals
Learn the practical fundamentals of quantum computing: qubits and superposition, multi-qubit gates and circuits, entanglement, the two canonical quantum algorithms (Deutsch-Jozsa and Grover's search), and the real-world noise that separates an ideal simulator from today's NISQ hardware.
Real-Time & Streaming Systems
Learn the core patterns behind reliable real-time data pipelines: partition-key strategies for ordering guarantees, event-time vs. processing-time windowing with watermarks, idempotent consumers that survive at-least-once delivery, backpressure and deliberate load-shedding under bursty load, and schema registry compatibility gates that catch breaking changes before they reach production.
SRE at Scale
Learn to run Site Reliability Engineering practices across a large, interdependent service fleet: measuring and capping toil, rolling up composite error budgets across dependency chains, deriving incident metrics correctly at scale, planning capacity against nonlinear queueing behavior, and running guarded chaos experiments.
Analytics Engineering
Learn to build reliable, tested data models that power analytics at scale: dimensional modeling, production dbt projects, semantic layers, data quality testing, and where it all fits in the modern data stack.
Platform Engineering
Learn to build internal developer platforms that make compliance automatic rather than aspirational: golden-path scaffolding with a measurable compliance gap, infrastructure-as-code drift detection, policy-as-code deployment gates, fleet-wide service scorecards, and platform-level golden-signals monitoring that catches a shared-infrastructure regression the day it starts.
Application Security Engineering (AppSec)
Learn to build security into the SDLC rather than bolting it on after the fact: STRIDE threat modeling that catches systematic category gaps, taint-aware static analysis that distinguishes real injection risk from superficially similar safe code, continuous dependency/supply-chain scanning against known CVEs, automated security-review flagging for discrepancies a human reviewer needs to see, and a severity-weighted merge gate that combines every finding source into one consistent, auditable decision.