Olamide Oso

Backend & AI Engineer | LangChain + RAG | Node.js, Python, FastAPI, RestAPI, Apache Airflow.

[Visual Studio Code @code]

🤖 What does it take to build coding models for millions of developers?

Go behind the scenes with Microsoft AI and learn how GitHub Copilot models are trained, evaluated, and optimized for real world coding workflows.

https://t.co/FP7iEynRkf

[Olamide Oso @Olamide_Oso65]

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.

https://t.co/cJA2JZ5LV1

https://t.co/4TyxwjzbSC

[Swapna Kumar Panda @swapnakpanda]

50 System Design Topics – 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?

[Aurimas Griciūnas @Aurimas_Gr]

Understanding and being able to apply 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 will distinguish you as a standout 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. Here is why 👇

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:

👉 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗟𝗮𝘁𝗲𝗻𝗰𝘆: How long does it take for your Model to compute inference result and return it.
👉 𝗠𝗼𝗱𝗲𝗹 𝗦𝗶𝘇𝗲: How much memory does your model occupy when it’s loaded for serving inference results.

Both of these are important when considering operational performance and feasibility of your model deployment.

👉 Large models might not fit on a device if you are considering edge deployments.
👉 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!
👉 …

You can influence both latency and size by applying different Model Compression methods, some of them are:

➡️ 𝗣𝗿𝘂𝗻𝗶𝗻𝗴: 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.

✅ In both cases the output is a reduction in the number of Model Parameters and model size.

➡️ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻: this type of compression is achieved by:

👉 Training an original large model which is called the Teacher model.
👉 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.
👉 We then use the “Student” model in production.

➡️ 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻: a most commonly used method that doesn’t have much to do with Machine Learning. This approach uses fewer bits to represent model parameters.

👉 You can apply quantization techniques both during the training and after the models has been already trained.
👉 In regular Neural Networks what is quantized are Model Weights, Biases and Activation Functions.
👉 Most usual quantization is from float to integer (32 bits to 8 bits. There are talks about 1 bit LLMs nowadays 😅).

➡️ …

❗️ 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!

[Swapna Kumar Panda @swapnakpanda]

If I need to pick ONLY FIVE courses to learn AI & ML from scratch, I'll pick:

❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - DL
❯ CS234 - RL
❯ CS336 - LLM

These courses could cost $100K. But Stanford is offering for FREE on YouTube:

[Swapna Kumar Panda @swapnakpanda]

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

[Swapna Kumar Panda @swapnakpanda]

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

[Tech with Mak @techNmak]

Build LLMs from Scratch 🚀

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:
→ Transformer architecture
→ GPT internals
→ Tokenization (BPE)
→ Attention mechanisms
→ 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.

🔗 [Playlist link in comments]

[techcori @techcori]

At techcori, we help brands, startups, SMEs design scalable digital products, optimize workflows and leverage ai driven innovation to grow and scale efficiently

[techcori @techcori]

A simple but interactive web interface for a client to help their team manage issues, collaborate seamlessly and ship high quality software faster

[techcori @techcori]

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.

[Olamide Oso @Olamide_Oso65]

@AIR_News01 ? Nmjmfj6? N nyjj
I 56
K6
I nok TVv
6 7yhv..
N
Gyv k
! Gf n.. Nnu55g v j,! TN mv
I
., I 5yh j. Mm, Mm h6 is yh
8, i5yhh
B. L

N. N. T5d
is n 5l,!6g
6rd.,!I my mk. is. I k
7
. Arf. 5 4 4 b n
I go 5tv

[LangChain @LangChain]

📊 Learn how to observe & evaluate agents on LangChain Academy 📊

Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t 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 😵‍💫

To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement 🚀

In our quickstart course, LangSmith Essentials, you’ll learn how to observe, evaluate, and deploy your agent in less than 30 minutes.

Enroll for free ➡️ https://t.co/ix8DTbFgPS

[Olamide Oso @Olamide_Oso65]

@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

[LangChain @LangChain]

🤖 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

[Olamide Oso @Olamide_Oso65]

Check out my latest article: Solving RAG Retrieval Noise with HyDE (Hypothetical Document Embeddings) https://t.co/1DgdNTDAEV via @LinkedIn

[LangChain @LangChain]

🔐 OpenAgent Framework

Made by the LangChain Community

OpenAgent is an AI agent framework for Web3 with verifiable compute—enabling cryptographic proof of execution for DeFAI and DeSci applications requiring trust and precision.

🔗 GitHub: https://t.co/XBbjxNMKre
📚 Docs: https://t.co/1xBpfTYXBF

[Jay Bisen @JayBisen473370]

120 Mind-Blowing AI Tools;

1. Ideas

- YOU
- Claude
- ChatGPT
- Perplexity
- Bing Chat

2. Presentation

- Prezi
- Pitch
- PopAi
- Slides AI
- Slidebean

3. Website

- Dora
- Wegic
- 10Web
- Framer
- Durable

4. Writing

- Rytr
- Jasper
- Copy AI
- Textblaze
- Writesonic

5. AI Models

- RenderNet
- Glambase App
- Luma AI
- Sora (OpenAI)
- Leonardo AI

6. Meeting

- Tldv
- Krisp
- Otter
- Avoma
- Fireflies

7. Chatbots

- Poe
- Claude
- Gemini
- ChatGPT
- HuggingChat

7. Automation

- ClickUp
- Drift
- Outreach
- Emplifi
- Phrasee

8. UI/UX

- Uizard
- Visily
- Khroma
- Galileo AI
- VisualEyes

9. Image

- Stylar
- Freepik
- Phygital+
- StockIMG
- Bing Create

10. Video

- Pictory
- HeyGen
- Nullface
- Decohere
- Synthesia

11. Design

- Looka
- Clipdrop
- Autodraw
- Vance AI
- Designs AI

12. Marketing

- AdCopy
- Predis AI
- Howler AI
- Bardeen AI
- AdCreative

13. Twitter

- Typefully
- Postwise
- Metricool
- Tribescaler
- TweetHunter

Follow @JayBisen473370 for more AI content.

[Olamide Oso @Olamide_Oso65]

@SomanathDi48172 Congratulations @SomanathDi48172

[Olamide Oso @Olamide_Oso65]

@Om_Codes_ Hi