System Design.
From back-of-the-envelope to global scale.
System design is the difference between software that merely works and software that scales. It's the skill that separates senior engineers from everyone else.
Every product that runs at scale, every feed, marketplace, payment system, and chat app, is a system-design problem underneath. This live weekend cohort builds the distributed-systems toolkit from storage engines to consensus, then breaks down the real systems behind the modern internet: Uber, online travel booking, Google Docs, and more.
A request, from the edge to your pods.
One log abstraction, many engines.
Choosing a store is a trade-off, not a religion.
Glad to be part of this cohort. What stands out is the practical depth, not just tools, but how AI, system design, and agentic patterns come together for real-world engineering. Looking forward to learning more.
What I appreciate most is the depth of learning. Instead of just covering the “what,” the cohort dives into the “how” and “why” behind AI concepts. Great experience so far!
Aseem's masterclass finally made AI click for me beyond just writing prompts. He goes deep into how the models and agents actually work under the hood, the attention math, the agent loops, the evals, exactly the kind of depth you need as an engineer who wants to build with AI, not just use it. Genuinely one of the most useful technical programs I've done in years.
Joining this cohort was one of the best decisions I made for my AI learning journey. Before this, I was unsure where to start and overwhelmed by the noise around AI. Aseem's sessions gave me clarity, strong fundamentals, and the confidence to build my own agents. The focus on basic principles and real-world systems makes all the difference.
I learned a lot about the internal workings of AI, which is helping me use AI far more effectively for technical and complex problem-solving tasks.
The pace is intense and the depth is real. We built things from scratch instead of gluing libraries together, and that completely changes how you think about the stack. Easily the best technical cohort I've taken.
I would highly recommend this cohort to anyone who wants to understand Agentic AI beyond the hype and surface-level tutorials. What makes this program stand out is the way it combines fundamentals, system design, and real-world implementation thinking. The cohort does not just focus on tools or quick demos. It helps you understand how AI systems are actually designed, how LLMs and agents fit into modern product architectures, and how to reason about them as an engineer. If you want depth instead of buzzwords, this is the cohort to join.
Genuinely one of the best learning experiences I've had as an engineer. Aseem takes dense AI and systems topics and turns them into something you can actually build with, every session moves you from theory to working code. It's the rare cohort that respects your time and assumes you want real depth. Highly recommend it to anyone serious about going beyond the surface.
As a staff engineer, what I value most is depth and first-principles thinking, and this cohort delivers both. Aseem connects the math, the systems, and the production reality in a way I haven't seen in any other program. It's rare to find teaching that is this rigorous and this practical at the same time. I came in to fill gaps and left with a genuinely stronger mental model of the whole stack.
I've done plenty of online courses that stay at the surface. This one goes all the way down, tokenization, attention, agent loops, evals, and then back up to production. I finally feel like I understand AI instead of just using it.
As someone coming from a backend and system-design background, this cohort has helped me connect traditional engineering principles with modern AI systems. Every session leaves me with a long list of things to explore and apply. Great learning experience so far.
I'm attending this weekend cohort on AI agents, and now I finally understand how AI and agents actually work. Earlier, AI was just magic to me, now I understand the machinery behind it. Thanks Aseem for these sessions.
Coming from a distributed-systems background, I expected the AI parts to feel hand-wavy. They didn't. Every concept is grounded in how you'd actually design, ship, and operate it, latency, failure modes, evals, the works. This is the most engineering-honest AI course I've come across.
Glad to be part of this cohort. What stands out is the practical depth, not just tools, but how AI, system design, and agentic patterns come together for real-world engineering. Looking forward to learning more.
What I appreciate most is the depth of learning. Instead of just covering the “what,” the cohort dives into the “how” and “why” behind AI concepts. Great experience so far!
Aseem's masterclass finally made AI click for me beyond just writing prompts. He goes deep into how the models and agents actually work under the hood, the attention math, the agent loops, the evals, exactly the kind of depth you need as an engineer who wants to build with AI, not just use it. Genuinely one of the most useful technical programs I've done in years.
Joining this cohort was one of the best decisions I made for my AI learning journey. Before this, I was unsure where to start and overwhelmed by the noise around AI. Aseem's sessions gave me clarity, strong fundamentals, and the confidence to build my own agents. The focus on basic principles and real-world systems makes all the difference.
I learned a lot about the internal workings of AI, which is helping me use AI far more effectively for technical and complex problem-solving tasks.
The pace is intense and the depth is real. We built things from scratch instead of gluing libraries together, and that completely changes how you think about the stack. Easily the best technical cohort I've taken.
I would highly recommend this cohort to anyone who wants to understand Agentic AI beyond the hype and surface-level tutorials. What makes this program stand out is the way it combines fundamentals, system design, and real-world implementation thinking. The cohort does not just focus on tools or quick demos. It helps you understand how AI systems are actually designed, how LLMs and agents fit into modern product architectures, and how to reason about them as an engineer. If you want depth instead of buzzwords, this is the cohort to join.
Genuinely one of the best learning experiences I've had as an engineer. Aseem takes dense AI and systems topics and turns them into something you can actually build with, every session moves you from theory to working code. It's the rare cohort that respects your time and assumes you want real depth. Highly recommend it to anyone serious about going beyond the surface.
As a staff engineer, what I value most is depth and first-principles thinking, and this cohort delivers both. Aseem connects the math, the systems, and the production reality in a way I haven't seen in any other program. It's rare to find teaching that is this rigorous and this practical at the same time. I came in to fill gaps and left with a genuinely stronger mental model of the whole stack.
I've done plenty of online courses that stay at the surface. This one goes all the way down, tokenization, attention, agent loops, evals, and then back up to production. I finally feel like I understand AI instead of just using it.
As someone coming from a backend and system-design background, this cohort has helped me connect traditional engineering principles with modern AI systems. Every session leaves me with a long list of things to explore and apply. Great learning experience so far.
I'm attending this weekend cohort on AI agents, and now I finally understand how AI and agents actually work. Earlier, AI was just magic to me, now I understand the machinery behind it. Thanks Aseem for these sessions.
Coming from a distributed-systems background, I expected the AI parts to feel hand-wavy. They didn't. Every concept is grounded in how you'd actually design, ship, and operate it, latency, failure modes, evals, the works. This is the most engineering-honest AI course I've come across.
Twenty-three modules. Every one earns its place.
Six levels, from estimation through the building blocks to a studio that breaks down real systems end-to-end: Uber, online travel booking, Google Docs, video streaming, and payments.
Foundations & Mental Models
Thinking in Systems: Latency, Throughput & Estimation
Senior engineers don't guess, they estimate. This module builds the numerical intuition to size any system on a whiteboard, and the mental models to decompose ambiguity into components.
A capacity-planning calculator that, given DAU and access patterns, outputs QPS, storage growth/year, bandwidth, and server count for a Twitter-scale workload.
The Laws of Distributed Systems: CAP, Consistency & Failure
Distributed systems fail in ways single machines never do. Internalise the impossibility results and consistency spectrum that dictate what you can't have, so your designs stay honest.
A simulated quorum-based replicated register (configurable N/R/W) that demonstrates stale reads, write conflicts, and the consistency/availability trade-off.
The Network & The Edge
Networking Foundations: DNS to TLS
You can't design a system whose request path you don't understand. Trace a request from the browser to your backend and back, demystifying the protocols underneath every architecture.
A real-time online/offline presence indicator using WebSockets, with heartbeat/timeout logic and a benchmark of polling vs. push.
Load Balancing, Proxies, CDNs & API Gateways
The edge layer is where scalability, security, and routing converge. Learn to distribute traffic without single points of failure and to push content close to users.
A configurable L7 reverse proxy / load balancer routing across backend pools with health checks and pluggable balancing strategies.
Data: Storage, Databases & Caching
Storage Engines from the Ground Up: B-Trees vs. LSM-Trees
Every database is a storage engine in a trench coat. Build one yourself to understand why your DB behaves the way it does under read- vs. write-heavy load.
A persistent log-structured key-value store with a memtable, SSTable flush, bloom filter, and compaction, benchmarked against a B-tree variant.
Databases: SQL, NoSQL & Indexing
Choosing a datastore is a trade-off, not a religion. Map workloads to the right model and understand the indexing and transaction guarantees behind each.
A schema + index design for a social-graph feature with EXPLAIN-driven query optimization, plus a key-value store layered on Postgres.
Replication, Partitioning & Sharding
Scaling data past one machine is the central problem of distributed data. Learn how to copy and split data while keeping it consistent and balanced.
A consistent-hashing ring with virtual nodes that distributes keys across nodes and demonstrates minimal key movement on node add/remove.
Caching: Patterns, Eviction & Redis
Caching is the cheapest performance win and the easiest correctness footgun. Learn the patterns, the invalidation traps, and how to run a cache at scale.
An in-process O(1) LRU/LFU cache, then a distributed cache using consistent hashing across nodes, with stampede protection.
Distributed Patterns & Coordination
Asynchronous Processing: Message Queues & Pub/Sub
Decoupling producers from consumers is how systems absorb spikes and stay responsive. Learn queue semantics and the delivery guarantees you can actually rely on.
A SQL-backed message broker with at-least-once delivery, visibility timeout, and synchronized competing consumers.
Event Streaming & Log-Based Architecture
The append-only log is a foundational abstraction for streaming, replication, and event sourcing. Master Kafka's model and the architectures it enables.
An event-ingestion pipeline that consumes a high-volume stream and computes real-time windowed aggregates (impressions / click counts).
Consensus, Coordination & Leader Election
When nodes must agree, you need consensus. Understand the algorithms behind every coordination service so you know when to reach for one, and when not to.
A simplified Raft leader-election + log-replication simulation across N nodes that survives leader crashes and network partitions.
The Distributed Systems Patterns Toolkit
A grab-bag of the high-leverage patterns that recur in every staff-level design. Internalise these and most “Design X” problems become composition.
A Snowflake-style distributed unique ID generator, plus a Count-Min Sketch / HyperLogLog “top-K & unique-visitor” counting service.
Architecture & Low-Level Design
Architecting Services: Monolith → Microservices → Event-Driven
Architecture is about organizational and operational trade-offs as much as technical ones. Learn when to split, how services communicate, and how to keep data consistent across boundaries.
Decompose a monolithic e-commerce app into services with an event-driven order/payment flow using the saga pattern.
Observability & Production Resilience
Systems you can't observe are systems you can't operate. Build in the telemetry and failure-handling that separate prototypes from production.
A metrics monitoring & alerting system, time-series ingestion, aggregation, push vs. pull collection, and threshold alerts.
Low-Level Design I: OOD, SOLID & Design Patterns
Senior interviews and real code both demand clean object models. Translate requirements into maintainable, extensible designs using time-tested principles.
Machine-coding rounds: design and implement a parking lot and a chess / game engine with extensible rules via the Strategy and State patterns.
Low-Level Design II: Concurrency & Thread-Safety
Concurrency is where correct designs go to die. Learn to reason about shared state, locking, and lock-free structures under real contention.
A thread-safe in-memory event bus with concurrent publishers/subscribers, then a flash-sale inventory decrement correct under high concurrency.
The Case-Study Studio
Uber: Real-Time Ride-Hailing
A real-time, location-aware, two-sided marketplace. Match a rider to the nearest driver in milliseconds, price the trip dynamically, and stream its progress live, all while ingesting millions of location updates a second.
Design and prototype the dispatch service: an S2/geohash-indexed driver-location store with a nearest-driver query, a matching loop, and a live trip-tracking channel.
Online Travel Booking: An iXiGo-Style OTA
A multi-modal travel platform that searches trains, buses, and flights in one query, holds volatile third-party inventory, and books through unreliable supplier APIs without ever double-charging. The exact problem space Aseem owned at iXiGo, taught from the inside.
Design the booking pipeline: a fare-aggregation layer with caching, a saga-based booking orchestrator resilient to flaky suppliers, and an idempotent payment step that never double-charges.
Google Docs: Real-Time Collaborative Editing
Many people editing one document at once, converging in under a second, with offline support. The canonical real-time consistency problem, and the one most engineers get wrong.
Build a collaborative-editor backend: an op-log + transform engine (OT or a CRDT) that merges concurrent edits from N clients and provably converges, with live presence cursors.
WhatsApp: Chat & Messaging at Scale
Billions of messages a day, ordered, delivered, and present in real time across flaky mobile networks. A masterclass in persistent connections, fan-out, and store-and-forward.
Design the messaging core: a connection gateway + message router with per-user inbox sharding, ordered delivery, and offline store-and-forward; prototype 1:1 and small-group fan-out.
News Feed: Fan-Out at Scale (Twitter / Instagram)
Compose a personalized, ranked feed for hundreds of millions of users, balancing write-time fan-out against read-time assembly, and solving the celebrity problem that breaks naive designs.
Design a hybrid feed service: write-time fan-out for normal users, pull-and-merge for high-fan-out accounts, served as a cached, cursor-paginated, ranked timeline.
Video Streaming: YouTube & Netflix
Ingest, transcode, store, and stream petabytes of video to a global audience at adaptive bitrate. Where storage tiers, transcoding pipelines, and CDN strategy all collide.
Design the ingest→serve pipeline: a transcoding job system that produces an ABR ladder, packages it to HLS/DASH, and fronts it with a CDN for adaptive playback.
Payments, Ledgers & the Studio Capstone
The systems where a bug costs real money: correctness-first design under strong consistency and auditability. Then the grand finale, take one system to a complete, defended end-to-end design.
Studio capstone: design a payment + wallet system (idempotent API, double-entry ledger, reconciliation, saga PSP integration), then take one studio system of your choice to a full end-to-end design, estimation → HLD → critical-path LLD → a working prototype of the hardest subsystem, defended in a mock interview.
Aseem Rastogi.
Software & AI Architect · Co-Founder & CTO, Agentcord.ai
Ex-Architect, iXiGo · Ex-Staff Engineer, Synaptic · Ex-Senior Computer Scientist, Belzabar · B.Tech CSE, NIT Hamirpur (Gold Medalist)
Not a course taught from tutorials. Taught by the architect who built the systems, at iXiGo, Synaptic, and now Agentcord.ai.
Live on weekends. Supported every day.
Live weekend cohort
Saturday and Sunday sessions, with recordings of every class.
Hands-on
Weekly labs and a portfolio-grade capstone at the end of every level.
Support
Weekly office hours and capstone reviews per level.
Private Discord community
Dedicated channels per module and topic. Ask anything, any time. Every question and answer lives permanently, becoming a growing knowledge base for the cohort.
The full modern distributed systems toolkit.
Grouped by the problem it solves. Every one of these appears in at least one module or case study.
Every product that scales is a system-design problem in disguise. The engineers who can architect for scale from first principles are the ones who get to lead.
Doors open for the next cohort soon.
Questions worth asking.
Both, deliberately. You build the actual building blocks, storage engines, consistent hashing, a Raft simulation, then run interview-style “Design X” sessions on top. The depth that wins staff interviews is the same depth that ships production systems.
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Free to join. Open to anyone serious about going deep on AI.