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AI concepts developers should know:
1. RAG (Retrieval-Augmented Generation)
โณ Retrieves relevant external data to ground model responses.
2. MCP (Model Context Protocol)
โณ A standard for connecting models to external tools, data, and context.
3. Model Routing
โณ Dynamically selecting the best model for a given task.
4. Embeddings
โณ Turning data into vectors so models can search and compare meaning.
5. Context Windows
โณ The amount of information a model can process at once.
6. Evals
โณ Measuring the quality, reliability, and behavior of AI systems.
7. Multi-Agent Systems
โณ Multiple agents collaborating to solve complex tasks.
8. A2A (Agent-to-Agent)
โณ How agents communicate and coordinate with each other.
9. Memory & State Management
โณ Persisting and retrieving context across interactions.
Understanding these concepts is one thing. Seeing them work together is where it clicks.
Oracle has a great guide that walks you through building a scalable multi-agent RAG system โ https://t.co/icEylLlR45
Their free DeepLearning course on building memory-aware agents also puts these concepts into action: https://t.co/DBrwzmvStK
What else should be on the list?
โโ
โป๏ธ Repost to help others learn AI.
๐ Thanks to @Oracle for sponsoring this post.
โ Follow me ( Nikki Siapno ) to improve at AI engineering.
Kafka is often described as a message queue.
Itโs not...
Kafka is a distributed commit log that lets you replay, scale, and process data streams in parallel.
That difference matters.
Hereโs the mental model:
โณ Producers write events to a ๐๐ผ๐ฝ๐ถ๐ฐ
โณ Topics are split into ๐ฝ๐ฎ๐ฟ๐๐ถ๐๐ถ๐ผ๐ป๐
โณ ๐๐ฟ๐ผ๐ธ๐ฒ๐ฟ๐ replicate partitions for durability
โณ ๐๐ผ๐ป๐๐๐บ๐ฒ๐ฟ๐ read at their own pace using ๐ผ๐ณ๐ณ๐๐ฒ๐๐
The key idea:
Kafka doesnโt just move messages, it ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ ๐ผ๐ฟ๐ฑ๐ฒ๐ฟ๐ฒ๐ฑ ๐ฒ๐๐ฒ๐ป๐ ๐๐๐ฟ๐ฒ๐ฎ๐บ๐.
Thatโs why it works for:
โ Event-driven systems
โ Real-time analytics
โ Log aggregation
โ Data lake ingestion
Hereโs a simple decision rule:
If you need:
โข Replayable history
โข Horizontal scalability
โข Parallel consumption
โข Durable event storage
โ Kafka fits.
If you just need:
โข Simple async jobs
โข Low operational overhead
โข Basic task queues
โ Itโs probably overkill.
Kafka is powerful.
But it earns its complexity.
What else would you add?
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Theyโre bringing ๐๐ฒ๐น๐น-๐ธ๐ป๐ผ๐๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐น๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐ together to discuss building better software in the AI era.
๐๐ณ ๐๐ผ๐ ๐๐ฎ๐ป๐ ๐๐ผ ๐๐๐ฎ๐ ๐๐ต๐ฎ๐ฟ๐ฝ in the current landscape, ๐๐ฎ๐๐ฐ๐ต the ๐๐ถ๐ฟ๐๐๐ฎ๐น event here (๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ): https://t.co/L3D4BEIqLd
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โป๏ธ Repost to help others learn Kafka.
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๐๐บ๐ฎ๐ด๐ถ๐ป๐ฒ ๐ฌ๐ผ๐'๐ฟ๐ฒ ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐๐ผ ๐ฎ ๐ญ๐ฌ-๐ฌ๐ฒ๐ฎ๐ฟ-๐ข๐น๐ฑ...
"Why does YouTube never crash even when millions are watching videos at the same time?"
"How does Google find answers so fast?"
"Why does my online game not lag even when thousands are playing?"
If you've ever struggled to explain these to someone new in tech, you're not alone. System Design can feel like a complex puzzle - until you break it down into simple, relatable ideas.
Thatโs exactly what this post does!
From Load Balancing (spreading work like distributing pizzas to multiple chefs ๐) to Database Sharding (cutting a giant cake into smaller slices ๐), these 20 key concepts make System Design feel less intimidating and more like everyday logic.
Want to understand fault tolerance, caching, and microservices in a way that even a kid could grasp? Going through this visual breakdown might just change how you see backend systems forever! ๐
Whatโs the one system design concept that took you the longest to understand? Drop it in the comments! โฌ๏ธ
Follow @goyalshaliniuk for more such information !