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The Large-Language Model Glossary

Must-Know Network Protocol Dependencies

25 Papers That Completely Transformed the Computer World. 1. Dynamo - Amazon’s Highly Available Key Value Store 2. Google File System: Insights into a highly scalable file system 3. Scaling Memcached at Facebook: A look at the complexities of Caching 4. BigTable: The design principles behind a distributed storage system 5. Borg - Large Scale Cluster Management at Google 6. Cassandra: A look at the design and architecture of a distributed NoSQL database 7. Attention Is All You Need: Into a new deep learning architecture known as the transformer 8. Kafka: Internals of the distributed messaging platform 9. FoundationDB: A look at how a distributed database 10. Amazon Aurora: To learn how Amazon provides high-availability and performance 11. Spanner: Design and architecture of Google’s globally distributed databas 12. MapReduce: A detailed look at how MapReduce enables parallel processing of massive volumes of data 13. Shard Manager: Understanding the generic shard management framework 14. Dapper: Insights into Google’s distributed systems tracing infrastructure 15. Flink: A detailed look at the unified architecture of stream and batch processing 16. A Comprehensive Survey on Vector Databases 17. Zanzibar: A look at the design, implementation and deployment of a global system for managing access control lists at Google 18. Monarch: Architecture of Google’s in-memory time series database 19. Thrift: Explore the design choices behind Facebook’s code-generation tool 20. Bitcoin: The ground-breaking introduction to the peer-to-peer electronic cash system 21. WTF - Who to Follow Service at Twitter: Twitter’s (now X) user recommendation system 22. MyRocks: LSM-Tree Database Storage Engine 23. GoTo Considered Harmful 24. Raft Consensus Algorithm: To learn about the more understandable consensus algorithm 25. Time Clocks and Ordering of Events: The extremely important paper that explains the concept of time and event ordering in a distributed system Over to you: I’m sure we missed many important papers. Which ones do you think should be included? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/bbg-social

Top AI Coding Tools for Developers You Can Use in 2025 What’s your favorite? Which other AI Coding Tool will you add to the list?

How to Learn Backend Development?

𝗔𝗣𝗜 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Whether you're a beginner or an experienced developer looking to learn about API, this comprehensive API learning roadmap will guide you through the key concepts and technologies you need to master. Read more in the thread: 🧵 #softwareengineering #programming #api

Copy & Paste these ChatGPT Prompts to learn anything 10X Faster👇: (Save it for later):

A clear understanding of some basic concepts can make a huge difference in the way you tackle any system design problem. After analyzing 30 key system design concepts, I’ve put together this one-page cheat sheet to help you: 1. Scale like a pro – Auto-scaling, horizontal scaling, database sharding, and CDN strategies. 2. Manage data efficiently – Data partitioning, NoSQL, SQL transactions, and indexing best practices. 3. Ensure reliability & fault tolerance – Load balancing, redundancy, heartbeat mechanisms, and event-driven architecture. 4. Master caching strategies – Read-through vs. write-through caching, Denormalise Databases, and Distributed Caching. 5. Design flexible architectures – Microservices, async tasks, and avoiding over-engineering. 6. Define problems before jumping into solutions – System constraints, trade-offs, WebSockets, and security. This is the ultimate cheat sheet I wish I had before my system design interviews. Save this post for your next interview prep. Share it with a friend who’s preparing for system design interviews. What’s the hardest part of system design interviews for you? Let’s discuss in the comments.

18 Key Design Patterns Every Developer Should Know Patterns are reusable solutions to common design problems, resulting in a smoother, more efficient development process. They serve as blueprints for building better software structures. These are some of the most popular patterns: - Abstract Factory: Family Creator - Makes groups of related items. - Builder: Lego Master - Builds objects step by step, keeping creation and appearance - Prototype: Clone Maker - Creates copies of fully prepared examples. - Singleton: One and Only - A special class with just one instance. - Adapter: Universal Plug - Connects things with different interfaces. - Bridge: Function Connector - Links how an object works to what it does. - Composite: Tree Builder - Forms tree-like structures of simple and complex parts. - Decorator: Customizer - Adds features to objects without changing their core. - Facade: One-Stop-Shop - Represents a whole system with a single, simplified interface. - Flyweight: Space Saver - Shares small, reusable items efficiently. - Proxy: Stand-In Actor - Represents another object, controlling access or actions. - Chain of Responsibility: Request Relay - Passes a request through a chain of objects until handled. - Command: Task Wrapper - Turns a request into an object, ready for action. - Iterator: Collection Explorer - Accesses elements in a collection one by one. - Mediator: Communication Hub - Simplifies interactions between different classes. - Memento: Time Capsule - Captures and restores an object's state. - Observer: News Broadcaster - Notifies classes about changes in other objects. - Visitor: Skillful Guest - Adds new operations to a class without altering it. -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/bbg-social

Session, Cookie, JWT, Token, SSO, and OAuth 2.0 Explained in One Diagram

50 Important Abbreviations in CyberSecurity *CIA - Confidentiality, Integrity, Availability *IDS - Intrusion Detection System *IPS - Intrusion Prevention System *WAF - Web Application Firewall *PII - Personal Identifiable Information *DoS - Denial of Service *DDoS - Distributed Denial of Service *DNS - Domain Name System *ZTA - Zero Trust Architecture *NAT - Network Address Translation *CTF - Capture the Flag *ACL - Access Control List *CDN - Content Delivery Network *CVE - Common Vulnerabilities and Exposures *RAT - Remote Access Trojan *APT - Advanced Persistent Threat *ATP - Advanced Threat Protection *SSO - Single Sign-on *URL - Uniform Resource Locator *TLS - Transport Layer Security *ARP - Address Resolution Protocol *RDP - Remote Desktop Protocol *FTP - File Transfer Protocol *SFTP - Secure File Transfer Protocol *HTTP - Hypertext Transfer Protocol *HTTPS - Hypertext Transfer Protocol Secure *LDAP - Lightweight Directory Access Protocol *MFA - Multi-factor Authentication *IAM - Identity and Access Management *SIEM - Security Information and Event Management *SAM - Security Account Manager *MDM - Mobile Device Management *XXS - Cross Site Scripting *XSRF - Cross Site Request Forgery *DRaaS - Disaster Recovery as a Service *DLP - Data Loss Prevention *TCP - Transmission Control Protocol *SNMP - Simple Network Management Protocol *L2TP - Layer 2 Tunneling Protocol *SOC - Security Operations Center *EDR - Endpoint Detection and Response *MDR - Managed Detection and Response *KMS - Key Management Service *TOR - The Onion Router *UEBA - User and Entity Behavior Analytics *UEFI - Unified Extensible Firmware Interface *RFI - Remote File Inclusion *SSID - Service Set Identifier *LAN - Local Area Network *WAN - Wide Area Network *VLAN - Virtual Local Area Network *PGP - Pretty Good Privacy *MiTM - Man in the Middle Attack *CA - Certificate Authority *MAC - Mandatory Access Control *PUA - Potential Unwanted Application *ECDH - Elliptic Curve Deffie-Hellman *BYOD - Bring Your Own Device *GDPR - General Data Protection Regulation *ADFS - Active Directory Federation Service *EPP - Endpoint Protection Platform *DMARC - Domain Based Message Authentication, Reporting and Conformance *UAC - User Account Control *CLI - Command Line Interface

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful! In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment. As coding becomes easier, more people should code, not fewer! Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step. I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively. One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that. When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result. Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do. [Original text: deeplearning.ai/the-batch/issu… ]

What is MCP? Why is everyone talking about it? Let’s take a closer look. Model Context Protocol (MCP) is a new system introduced by Anthropic to make AI models more powerful.

As an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 you should also care about regular, non LLM-based ML models, productionising them comes with its own challenges. For example, 𝗖𝗜/𝗖𝗗 process is 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 compared to regular software. The important difference that the Machine Learning aspect of the projects brings to the CI/CD process is the treatment of the Machine Learning Training pipeline as a first class citizen of the software world. ➡️ CI/CD pipeline is a separate entity from Machine Learning Training pipeline. There are frameworks and tools that provide capabilities specific to Machine Learning pipelining needs (e.g. KubeFlow Pipelines, Sagemaker Pipelines etc.). ➡️ ML Training pipeline is an artifact produced by Machine Learning project and should be treated in the CI/CD pipelines as such. What does it mean? Let’s take a closer look: Regular CI/CD pipelines will usually be composed of at-least three main steps. These are: 𝗦𝘁𝗲𝗽 𝟭: Unit Tests - you test your code so that the functions and methods produce desired results for a set of predefined inputs. 𝗦𝘁𝗲𝗽 𝟮: Integration Tests - you test specific pieces of the code for ability to integrate with systems outside the boundaries of your code (e.g. databases) and between the pieces of the code itself. 𝗦𝘁𝗲𝗽 𝟯: Delivery - you deliver the produced artifact to a pre-prod or prod environment depending on which stage of GitFlow you are in. What does it look like when ML Training pipelines are involved? 𝗦𝘁𝗲𝗽 𝟭: Unit Tests - in mature MLOps setup the steps in ML Training pipeline should be contained in their own environments and Unit Testable separately as these are just pieces of code composed of methods and functions. 𝗦𝘁𝗲𝗽 𝟮: Integration Tests - you test if ML Training pipeline can successfully integrate with outside systems, this includes connecting to a Feature Store and extracting data from it, ability to hand over the ML Model artifact to the Model Registry, ability to log metadata to ML Metadata Store etc. This CI/CD step also includes testing the integration between each of the Machine Learning Training pipeline steps, e.g. does it succeed in passing validation data from training step to evaluation step. 𝗦𝘁𝗲𝗽 𝟯: Delivery - the pipeline is delivered to a pre-prod or prod environment depending on which stage of GitFlow you are in. If it is a production environment, the pipeline is ready to be used for Continuous Training. You can trigger the training or retraining of your ML Model ad-hoc, periodically or if the deployed model starts showing signs of Feature/Concept Drift. Let me know your thoughts. 👇 #AI #LLM #LLMOps

Why you need to understand 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 as an AI Engineer? Simple naive RAG systems are rarely used in real world applications. To provide correct actions to solve the user intent, we are always adding some agency to the RAG system - it is usually just a bit of it. It is important to 𝗻𝗼𝘁 𝗴𝗲𝘁 𝗹𝗼𝘀𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗯𝘂𝘇𝘇 𝗮𝗻𝗱 𝘁𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝘆 and understand that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 to add this agency to your RAG system and you should adapt to your use case. My advice is to think in systems and engineering flows. Let’s explore some of the moving pieces in Agentic RAG: 𝟭. Analysis of the user query: we pass the original user query to a LLM based Agent for analysis. This is where: ➡️ The original query can be rewritten, sometimes multiple times to create either a single or multiple queries to be passed down the pipeline. ➡️ The agent decides if additional data sources are required to answer the query. 𝟮. If additional data is required, the Retrieval step is triggered. In Agentic RAG case, we could have a single or multiple agents responsible for figuring out what data sources should be tapped into, few examples: ➡️ Real time user data. This is a pretty cool concept as we might have some real time information like current location available for the user. ➡️ Internal documents that a user might be interested in. ➡️ Data available on the web. ➡️ … 𝟯. If there is no need for additional data, we try to compose the answer (or multiple answers or a set of actions) straight via an LLM. 𝟰. The answer gets analyzed, summarized and evaluated for correctness and relevance: ➡️ If the Agent decides that the answer is good enough, it gets returned to the user. ➡️ If the Agent decides that the answer needs improvement, we try to rewrite the user query and repeat the generation loop. ✅ Remember the Reflection pattern from my last Newsletter article? This is exactly that. 👆 The real power of Agentic RAG lies in its ability to perform additional routing pre and post generation, handle multiple distinct data sources for retrieval if it is needed and recover from failures while generating correct answers. What are your thoughts on Agentic RAG? Let me know in the comments! 👇 #RAG #LLM #AI

Another huge week in AI Agents 🧵 I summarized everything announced by LangChain, Replit, Cursor, Anthropic, Cobot, AgentOps, Triple Whale, Factory AI, Lovable, Hugging Face, you, and more. Here's everything you need to know and how to make sense out of it. (save for later)

Top 4 Forms of Authentication Mechanisms 1. SSH Keys: Cryptographic keys are used to access remote systems and servers securely 2. OAuth Tokens: Tokens that provide limited access to user data on third-party applications 3. SSL Certificates: Digital certificates ensure secure and encrypted communication between servers and clients 4. Credentials: User authentication information is used to verify and grant access to various systems and services Over to you: How do you manage those security keys? Is it a good idea to put them in a GitHub repository? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/bbg-social

How to be a great storyteller