๐ค What does it take to build coding models for millions of developers?
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Are you language enthusiast or a techie wondering how a real-time AI language tutor and companion works? then check out my recent posts and documentation on Vingo AI, a real-time AI voice language tutor.
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50 System Design Topics โ Simple to Complex
Perfect Learning Roadmap for 2026. Save this list.
1. Design a Rate Limiter
2. Design a URL Shortener
3. Design Pastebin
4. Design a Unique ID Generator
5. Design Consistent Hashing
6. Design a Load Balancer
7. Design an API Gateway
8. Design a Basic Key-Value Store
9. Design a Caching System (e.g., LRU Cache)
10. Design a Notification System
11. Design a Typeahead/Autocomplete System
12. Design a Web Crawler
13. Design a Message Queue
14. Design a 1:1 Chat System
15. Design a Group Chat System
16. Design a News Feed System
17. Design a Proximity Service (e.g., nearby friends)
18. Design Instagram (photo/video sharing + feed)
19. Design Twitter/X (timeline + posts)
20. Design WhatsApp (real-time messaging)
21. Design Dropbox (file storage & sync)
22. Design a Ticket Booking System
23. Design an E-commerce Platform (catalog + checkout)
24. Design a Recommendation System
25. Design a Distributed Cache
26. Design Uber (ride-sharing + matching)
27. Design Netflix (video streaming platform)
28. Design YouTube (video upload + streaming)
29. Design TikTok (short-video platform)
30. Design Facebook-like Social Network News Feed
31. Design Google Docs (real-time collaborative editing)
32. Design a Content Delivery Network (CDN)
33. Design a Search Engine (indexing + querying)
34. Design Google Maps (routing + location services)
35. Design a Distributed Database
36. Design a Real-time Analytics System
37. Design an Ad Serving & Tracking System
38. Design a Fraud Detection System
39. Design a Stock Trading/Exchange System
40. Design a Distributed Job Scheduler
41. Design Event Sourcing + CQRS Architecture
42. Design a Multi-tenant SaaS Platform
43. Design Live Video Streaming at Scale
44. Design a Highly Scalable NoSQL Database
45. Design a Real-time Multiplayer Game Backend
46. Design Machine Learning Model Serving Infrastructure
47. Design a Geo-distributed Low-Latency System
48. Design a Strongly Consistent Global Database
49. Design a High-Frequency Trading Platform
50. Design a Planet-Scale Distributed System (billions of users, multi-region HA)
Want to add anything in this list?
Understanding and being able to apply ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ผ๐บ๐ฝ๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป will distinguish you as a standout ๐๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ. Here is why ๐
Small Language Models will be the cornerstone of modern Agentic Systems. When you deploy ML models to production you need to take into account several operational metrics that are in general not ML related:
๐ ๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐๐ฒ๐ป๐ฐ๐: How long does it take for your Model to compute inference result and return it.
๐ ๐ ๐ผ๐ฑ๐ฒ๐น ๐ฆ๐ถ๐๐ฒ: How much memory does your model occupy when itโs loaded for serving inference results.
Both of these are important when considering operational performance and feasibility of your model deployment.
๐ Large models might not fit on a device if you are considering edge deployments.
๐ Latency of retrieving inference results might make business case non feasible. E.g. Recommendation Engines require latencies in milliseconds as ranking has to be applied as the user browses your website or app in real time. Bad news for LLM fans!
๐ โฆ
You can influence both latency and size by applying different Model Compression methods, some of them are:
โก๏ธ ๐ฃ๐ฟ๐๐ป๐ถ๐ป๐ด: this method is mostly used in tree-based and Neural Network algorithms. In tree-based ones we prune leaves or branches from decision trees. In Neural Networks we remove nodes and synapses (weights) while trying to retain ML performance metrics.
โ In both cases the output is a reduction in the number of Model Parameters and model size.
โก๏ธ ๐๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐๐ถ๐๐๐ถ๐น๐น๐ฎ๐๐ถ๐ผ๐ป: this type of compression is achieved by:
๐ Training an original large model which is called the Teacher model.
๐ Training a smaller model to mimic the Teacher model by transferring knowledge from it, this model is called the Student model. Knowledge in this context can be extracted from the outputs, internal hidden state (feature representations) or a combination of both.
๐ We then use the โStudentโ model in production.
โก๏ธ ๐ค๐๐ฎ๐ป๐๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: a most commonly used method that doesnโt have much to do with Machine Learning. This approach uses fewer bits to represent model parameters.
๐ You can apply quantization techniques both during the training and after the models has been already trained.
๐ In regular Neural Networks what is quantized are Model Weights, Biases and Activation Functions.
๐ Most usual quantization is from float to integer (32 bits to 8 bits. There are talks about 1 bit LLMsย nowadays ๐ ).
โก๏ธ โฆ
โ๏ธ While the above methods do reduce the size of the models, allowing them to be deployed in production scenarios, there is almost always a reduction in accuracy so be careful and evaluate it accordingly.
What methods for reducing model size have you used? What were the main challenges? Let me know in the comment section!
If I need to pick ONLY FIVE courses to learn AI & ML from scratch, I'll pick:
โฏ CS221 - Artificial Intelligence
โฏ CS229 - Machine Learning
โฏ CS230 - DL
โฏ CS234 - RL
โฏ CS336 - LLM
These courses could cost $100K. But Stanford is offering for FREE on YouTube:
When it comes to Mathematics,
These are the TOP TWO channels. They share absolutely beginner friendly tutorials.
1. ProfRobBob
https://t.co/3OeCSSoUZk
2. Professor Leonard
https://t.co/7ESIjk6Apu
Learn Mathematics before AI & ML.
BEST YouTube Courses for ABSOLUTELY BEGINNERS:
1. Prealgebra
https://t.co/007XeqKTKy
2. Algebra
https://t.co/O8GyQVrxR5
3. Linear Algebra
https://t.co/j9X7IgqYSb
4. Linear Algebra for ML
https://t.co/r66cezkbCb
5. Precalculus
https://t.co/rpGvb6ElT0
6. Calculus
https://t.co/stRgVE8MdA
7. Calculus for ML
https://t.co/brhX4EkmXF
8. Statistics
https://t.co/4eJ1yg1S32
9. Probability
https://t.co/FgAjdlOcr8
10. Probability & Statistics
https://t.co/MuglrjDsKT
11. Limits
https://t.co/nShaPQeQz3
12. Derivatives
https://t.co/0wMuBI2OSI
13. Integrals
https://t.co/5QszLH6Mfo
Build LLMs from Scratchย ๐
Found this gem by Vizuara Technologies, a 43-lecture series that actually delivers on its promise: building Large Language Models from the ground up.
What's inside:
โ Transformer architecture
โ GPT internals
โ Tokenization (BPE)
โ Attention mechanisms
โ Complete Python implementations
Perfect for ML engineers and developers who want to understand what's really happening under the hood of ChatGPT, Claude, and similar models.
๐ [Playlist link in comments]
At techcori, we help brands, startups, SMEs design scalable digital products, optimize workflows and leverage ai driven innovation to grow and scale efficiently
Business owners: stop forcing one chatbot to run your entire company.
This Enterprise Agents Platform helps you run operations smarter by using a team of specialized AI agents, each focused on one job: inventory, analytics, compliance, and actions.
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๐ Learn how to observe & evaluate agents on LangChain Academy ๐
Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic โ you canโt always predict how they will behave.
Add multi-turn interactions and tool-calling agents, and testing agents becomes even more complex than traditional software testing ๐ตโ๐ซ
To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement ๐
In our quickstart course, LangSmith Essentials, youโll learn how to observe, evaluate, and deploy your agent in less than 30 minutes.
Enroll for free โก๏ธ https://t.co/ix8DTbFgPS
@Aurimas_Gr Tracing is an essential concept in AI Engineering/Agentic AI systems. I usually use OpenTelementry to track the execution progression/flow to ensure the entire system trajectory(from query to output, including tools called and invoked parameters) is captured
๐ค Build Your Own RAG Chatbot with JavaScript
Made by the LangChain Community
freeCodeCamp just released a course teaching JS devs to build RAG chatbots overcoming LLM knowledge cutoffs. LangChain.js orchestrates DataStax, OpenAI, and Next.js for real-time retrieval.
Watch: https://t.co/WeBJxxXbuU
๐ OpenAgent Framework
Made by the LangChain Community
OpenAgent is an AI agent framework for Web3 with verifiable computeโenabling cryptographic proof of execution for DeFAI and DeSci applications requiring trust and precision.
๐ GitHub: https://t.co/XBbjxNMKre
๐ Docs: https://t.co/1xBpfTYXBF