Build-in-public. 133 steps. One real system.

You have finished the tutorials and you ship features. Then someone asks you to design the system and defend the trade-offs, and you freeze.

Go from an empty machine to a system you can defend in a design review.

ArchitectPath is a 133-step path where you build one real app with an AI coding agent. Each step adds about one new thing and explains the decision behind it, so you learn to reason like a Solution Architect instead of copying commands.

Not live yet. Join the waitlist and be first in when it launches. Read the syllabus

// what it is

One app. 133 steps. The full stack, in order.

You build FlowMart, one real application, from an empty machine to a multi-cloud, Zero-Trust platform. The ramp is gentle: Static HTML, then JS, Next.js, Docker, Kubernetes, app features, data, observability, messaging, then cloud. Each step reads as the next chapter of one long build, and an AI agent does the typing while you own the structure and the decisions.

  • 133 steps, 00 to 132
  • Plain English, every term explained
  • Tests every code step
  • Real SLOs you measure against

// who it's for

For the developer stuck between "can code" and "can architect".

You already have

  • Some React and a backend language
  • Comfort with Git and the command line
  • Features shipped, tutorials finished

You want to grow into

  • Full-stack plus DevOps plus architecture
  • Tech-lead and Solution-Architect work
  • Owning decisions, not just writing code

// how it works

Start from an empty folder. Add one thing at a time.

Step 00 is plain static HTML on an empty machine. Nothing is set up for you yet. From there each step adds about one new piece and fits in a single AI-agent chat, so the ramp is gentle and there are no big jumps. By the end you have taken one app from a single HTML file to a distributed, multi-cloud, Zero-Trust system, and you can explain every decision that got it there.

The way you learn is the point. You read the goal for a step, make the architecture call yourself, and the AI agent does the typing. Most steps also carry a short trade-offs note and a build-by-hand task, so the reasoning sticks instead of scrolling past. You own the structure and the decisions; the agent is the fast pair of hands.

The ramp, stage by stage:

  1. 00FoundationsStatic HTML, JavaScript, Next.js, then Docker, Kubernetes and Helm.
  2. 08The productCatalog, auth, cart, admin, and the databases behind them.
  3. 21Running itA hardened VPS, metrics, logs, traces, alerts, and the SLOs you measure against.
  4. 27DistributedA second service, load balancers and an API gateway, RabbitMQ, Kafka, sagas, autoscaling, load tests, then features in depth.
  5. 59PlatformsAI and MCP, Spark and Cassandra and ClickHouse, failover you measure, CI/CD, Terraform, multi-cloud and regions, Zero Trust.
  6. 107War RoomTwenty-six real big-company outages, reproduced in miniature and worked like an on-call shift.

Capacity-aware by design: from step 22 the heavy services run on a cheap rented VPS while the app stays on your laptop, because a 16 GB machine cannot hold the whole stack. Later it moves to IaC, then the cloud.

See all 133 steps

// syllabus

Every step, in order.

Thirteen blocks, 133 steps, one app. Open a block to see the exact steps it contains. Nothing here is a topic list you read: each step is a build with a definition of done, tests, and a trade-off note explaining why that tool and not the obvious alternative.

Core track 00 - 90War Room 107 - 132

00 - 07 Foundations An empty laptop to a working app inside Kubernetes. 8 steps
  1. 00Environment bootstrap + Linux/vim basics
  2. 01Static storefront page
  3. 02Interactivity + first API call (JS → FastAPI)
  4. 03Next.js rebuild
  5. 04Dockerize
  6. 05Kubernetes basics
  7. 06Helm packaging
  8. 07Repo automation (Makefile task runner + robust bash scripting)
08 - 17 The shop Catalog, login, cart, admin, and the databases under them. 10 steps
  1. 08Catalog browse (read-only)
  2. 09Auth - JWT
  3. 10Cart & orders
  4. 11Admin - products
  5. 12Postgres
  6. 13MySQL schema design + normalization (1NF/2NF/3NF, then deliberate denormalization)
  7. 14MongoDB
  8. 15Redis cache
  9. 16Memcached vs Redis (when to pick which cache)
  10. 17Best-sellers leaderboard (Redis sorted sets)
18 - 20 Plan and numbers Write the spec, count the load, catch your first race condition. 3 steps
  1. 18Product spec (NFR + SLI/SLO/SLA)
  2. 19Capacity planning & system design
  3. 20Reservations & concurrency/race lab
21 - 26 Watch it run Rent a server. Add metrics, logs, traces and alerts. 6 steps
  1. 21Observability - Prometheus + Grafana
  2. 22VPS provisioning & hardening
  3. 23Logging - ELK pipeline (Filebeat/Logstash) + Loki bake-off
  4. 24Product search + typeahead (inverted index, trie)
  5. 25Distributed tracing
  6. 26Alerting - email + Telegram
27 - 42 Many services Split it up. Load balancers, an API gateway, queues, sagas, autoscaling, then break it on purpose. 16 steps
  1. 27Security hardening
  2. 28Data governance & PII
  3. 29Second service - Node/Express catalog
  4. 30Load balancing - L4 and L7
  5. 31API gateway (HelloFresh)
  6. 32Geospatial locator (geohash, "near me")
  7. 33RabbitMQ basics
  8. 34Saga + compensation
  9. 35Kafka event streaming
  10. 36Transactional outbox (reliable publish, no lost events)
  11. 37Resilience patterns
  12. 38Workflow orchestration engine (Temporal durable execution vs the hand-coded saga)
  13. 39Load testing - k6 + JMeter
  14. 40Autoscaling - HPA, KEDA and node capacity (Delivery Hero)
  15. 41eBPF profiling & flamegraphs (find the real bottleneck)
  16. 42Incident response - game day
43 - 58 Real features Payments, realtime, wallet, media and big uploads, gRPC, GraphQL, mobile and offline. 16 steps
  1. 43E2E testing
  2. 44A/B testing
  3. 45Realtime - WebSockets
  4. 46Chat data model + capacity (messages, fan-out write vs read, timelines)
  5. 47Collaborative cart (CRDT merge)
  6. 48Payments - provider integration (Stripe)
  7. 49Payments - edge cases
  8. 50Store-credit wallet (event sourcing)
  9. 51Media + background jobs (image thumbnails + video transcoding)
  10. 52Large file upload - multipart and resumable
  11. 53gRPC internal
  12. 54GraphQL API
  13. 55Frontend performance (Core Web Vitals, code splitting, images, budgets)
  14. 56Mobile API optimization + BFF (thin payloads, one call per screen, offline sync)
  15. 57PWA (service worker, offline shell, install, background sync, push)
  16. 58Brownfield migration & deprecation
59 - 63 AI inside the app An LLM layer, an agent, MCP on both sides, automation. 5 steps
  1. 59LLM provider abstraction
  2. 60AI agent + MCP
  3. 61MCP server (stdio JSON-RPC)
  4. 62MCP client (Streamable HTTP)
  5. 63n8n automation
64 - 80 Big data and stores Six more databases, a matching engine, one you write yourself, and failover that you measure. 17 steps
  1. 64Spark batch analytics
  2. 65Hadoop - HDFS
  3. 66Databricks
  4. 67Catalog ingestion crawler (price & supplier data)
  5. 68Go inventory service
  6. 69Marketplace matching engine (order book)
  7. 70Cassandra + consistent hashing
  8. 71Database sharding (shard key, routing, cross-shard reads, resharding)
  9. 72NoSQL single-table design (access-pattern-first, one table, no joins)
  10. 73LSM storage engine from scratch (memtable, WAL, SSTable, compaction)
  11. 74Consensus & coordination (leader election, split-brain, fencing)
  12. 75High availability per component - Postgres, Redis, the front door
  13. 76Deterministic simulation testing (seeded scheduler, replay a bug)
  14. 77neo4j - recommendations
  15. 78ClickHouse OLAP + probabilistic counting (HyperLogLog)
  16. 79InfluxDB - time-series
  17. 80Cross-engine migration (Mongo → Postgres, translate shape + reconcile)
81 - 89 Ship it Pipelines, Terraform, Ansible, single sign-on, two more languages. 9 steps
  1. 81CI/CD - GitHub Actions / GitLab
  2. 82Jenkins pipeline
  3. 83Progressive delivery & paved road
  4. 84Terraform IaC
  5. 85Ansible provisioning
  6. 86Ecosystem SSO - Google + SAML
  7. 87Polyglot frontends
  8. 88Nest.js service
  9. 89Migrate to cloud
90 - 94 Cloud and regions The same stack on AWS, Azure and GCP, then two live regions and global traffic steering. 5 steps
  1. 90Cloud AWS - EKS
  2. 91Cloud AWS native - Bedrock + Lambda
  3. 92Cloud Azure - AKS
  4. 93Cloud GCP - GKE
  5. 94Regional clusters and global traffic steering
95 - 104 AI in production RAG that works, evals, cost control, guardrails, fine-tuning. 10 steps
  1. 95Prompt engineering (versioned + regression-tested)
  2. 96RAG production
  3. 97Vector DB tuning (ANN index + recall)
  4. 98RAG evals + observability
  5. 99Agent eval harness (trajectory + tool-call accuracy, not just the answer)
  6. 100LLM output validation (schema + repair)
  7. 101AI observability (errors, latency, cost)
  8. 102Efficient inference - vLLM
  9. 103AI security & guardrails
  10. 104Fine-tuning - LoRA/QLoRA (optional)
105 - 106 Lock it down Zero Trust, then you attack your own app on purpose. 2 steps
  1. 105Zero Trust - mTLS/mesh
  2. 106Offensive security lab (attack your own app)
107 - 132 War Room 26 outages that really happened, rebuilt on a laptop and one server. 26 steps
  1. 107Retry storm / metastable failureMeta / Rockset
  2. 108A regex that pegged every CPU (Cloudflare 2019)Cloudflare 2019
  3. 109Gray failure (differential observability)Microsoft Azure
  4. 110Shuffle sharding (blast-radius isolation)AWS
  5. 111Cache stampede / hot keycache stampede
  6. 112Flash sale - virtual queue (Shopify)Shopify
  7. 113A region dies, move everyone (Netflix)Netflix 2012
  8. 114Online schema migration under load (GitHub/Uber)GitHub / Uber
  9. 115Zero-downtime data movement (Stripe)Stripe
  10. 116Multi-region & split-brain (Figma)Figma
  11. 117Split-brain from cross-region failover (GitHub 2018)GitHub 2018
  12. 118Idempotency key - double charge (Stripe)Stripe
  13. 119Trillion-event delivery pipeline (Spotify)Spotify
  14. 120LLM router misroutes servers (Anthropic)Anthropic
  15. 121Silent inference-quality corruption (Anthropic)Anthropic
  16. 122KV-cache economics (Character.AI)Character.AI
  17. 123One control plane over 7500 nodes (OpenAI)OpenAI
  18. 124Training fault tolerance + SDC (Meta/ByteDance)Meta / ByteDance
  19. 125Config-change blast radius (Cloudflare/Spotify)Cloudflare / Spotify
  20. 126One letter in a YAML file deletes your DNS (Zalando)Zalando 2022
  21. 127The 45-minute deploy disaster (Knight Capital)Knight Capital
  22. 128Cascading dependency failure (AWS)AWS
  23. 129One command takes out a region (AWS S3 2017)AWS S3 2017
  24. 130Locked out of your own network (Facebook BGP 2021)Facebook BGP 2021
  25. 131Automated quota takes down login for everything (Google)Google 2020
  26. 132Diagnosing a 2-year-unexplained bug (Facebook)Facebook

On pinned steps you write the decision record yourself: you make the call, the agent writes it up. The whole roadmap exists in English and Russian, with every technical term explained the first time it appears.

WAR ROOM steps 107 - 132

Reproduce real big-company outages, in miniature.

The last track puts the system under real pressure. You run a load test that breaks an SLO on purpose and fires a Telegram alert, then work the incident like an on-call engineer: find the cause, mitigate, write it up.

// what makes it different

Built to teach the decisions behind the code.

Architect skill, from step one

Most steps carry a short trade-off note: the real alternatives, and when this tool is the wrong choice. You make the call, the agent writes it up.

Real docs on real steps

You write ADRs, C4 diagrams and design docs on the steps that deserve them, copying filled Big-Tech examples instead of blank templates.

Build it by hand

On every step you write the core mechanism yourself, or fix a bug planted on purpose. How you work a step matters more than the topic.

Tests on every step

Unit and integration tests, including a failing case, are part of what "done" means on every code step. Early on you write them yourself.

Choose tools by measuring

You pick a database or a vendor with a small bake-off and a weighted matrix, then defend the choice out loud with your own numbers.

Runs on the laptop you already have

Heavy services move to a cheap rented server, so a 16 GB laptop lasts the whole stack. A production-shaped system without a production-shaped bill.

// faq

Questions worth answering.

Do I need an AI coding agent?

Yes, the path is built around one. Cursor, Claude Code, or a similar agent does the typing while you own the structure and the decisions. Each step is written to fit in a single agent chat.

What do I actually build?

FlowMart, one real application, from an empty machine to a multi-cloud, Zero-Trust platform. The roadmap tells you the what and the order. You build the app in a separate repo.

What will I walk away with?

One real system you built end to end, from a static page to a distributed multi-cloud platform, plus the ADRs, C4 diagrams, and incident write-ups that go with it. You will be able to walk into a design review and explain your choices, the alternatives, and the trade-offs. That is the exact skill that moves you toward tech-lead and Solution-Architect work.

Is it beginner-friendly?

Yes. Every technical term is explained on first use, in plain English, so you can follow along even if a topic is new. The ramp is gentle and each step builds directly on the last, so you are never dropped into something you have not been shown.

What language is it in?

English and Russian. The same 133 steps in both, written so a beginner can follow either one.

When does it launch?

It is in progress. Join the waitlist and you will be first to hear when it opens, and first in when sign-up goes live.

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