SRE Jr. Enthusiast for HPC and Low-cost Post-Moore device
Colombia
Joined September 2019
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Pablo J RY retweeted
Backend Engineering is more than just writing APIs.
You need to understand how databases scale when millions of users start accessing your application.
Master these 6 concepts:
1. Leader-Follower Replication
2. Multi-Leader Replication
3. Leaderless Replication
4. Consistent Hashing & Virtual Nodes
5. Partitioning & Sharding
6. Hot Partitions & Rebalancing
Also understand replication lag, cross-shard queries, distributed transactions, and 2PC.
Practical challenge: Design a database capable of handling 500 million users with 3 replicas per shard.
Grab system design ebook: codewithdhanian.gumroad.com/…
Save this visual guide for your System Design journey.
Which concept do you find the most challenging?
#BackendEngineering #SystemDesign #DatabaseEngineering
Pablo J RY retweeted
Linux Learning Roadmap
|-- Linux Fundamentals
| |-- What is Linux? (Kernel, Distributions, Open Source)
| |-- Linux Architecture
| |-- Popular Distributions (Ubuntu, Debian, Fedora, Arch)
| |-- Installing Linux (Virtual Machines, WSL, Dual Boot)
| |-- Linux Desktop vs Server Environments
|-- Linux Command Line Basics
| |-- Working with the Terminal and Shell
| |-- Navigation Commands (pwd, cd, ls)
| |-- File Commands (touch, cp, mv, rm)
| |-- Directory Management (mkdir, rmdir)
| |-- Viewing Files (cat, less, head, tail)
| |-- Command History and Tab Completion
|-- File System & Directory Structure
| |-- Linux Filesystem Hierarchy (/bin, /etc, /home, /var)
| |-- Absolute vs Relative Paths
| |-- File Types and Inodes
| |-- File Permissions (Read, Write, Execute)
| |-- Ownership (chown, chgrp)
| |-- Symbolic Links and Hard Links
|-- Users & Permissions Management
| |-- Creating and Managing Users
| |-- Groups and Group Membership
| |-- User Privileges and sudo
| |-- chmod, chown and umask
| |-- SSH Keys and Authentication
| |-- Access Control Lists (ACLs)
|-- Process & System Management
| |-- Understanding Linux Processes
| |-- Process Commands (ps, top, htop)
| |-- Managing Processes (kill, pkill, nice)
| |-- System Services (systemctl, service)
| |-- Boot Process and System Targets
| |-- Managing Background and Foreground Jobs
|-- Shell Scripting & Automation
| |-- Bash Scripting Fundamentals
| |-- Variables and Environment Variables
| |-- Conditional Statements and Loops
| |-- Functions and Script Arguments
| |-- Pipes, Redirection and Command Substitution
| |-- Cron Jobs and Task Scheduling
|-- Package Management
| |-- APT Package Manager
| |-- DNF and YUM
| |-- Pacman
| |-- Installing and Removing Packages
| |-- Repository Management
| |-- Software Updates and Dependency Management
|-- Networking in Linux
| |-- TCP/IP and Network Fundamentals
| |-- Network Configuration
| |-- DNS and Hostname Management
| |-- SSH and Remote Connections
| |-- Network Troubleshooting
| |-- Firewall Configuration
|-- Storage & Disk Management
| |-- Disk Partitioning
| |-- Filesystems
| |-- Mounting and Unmounting Disks
| |-- Disk Usage
| |-- LVM and RAID Fundamentals
| |-- Backup and Recovery Strategies
|-- Linux Security & Hardening
| |-- SSH Security Best Practices
| |-- Firewall Rules and Network Protection
| |-- User Privileges and Least Privilege
| |-- SELinux and AppArmor
| |-- Security Updates and Patch Management
| |-- Auditing and System Logs
|-- Monitoring & Troubleshooting
| |-- System Logs
| |-- CPU, Memory and Disk Monitoring
| |-- Performance Troubleshooting
| |-- Resource Management
| |-- Diagnosing Boot and Service Failures
| |-- Monitoring Tools
|-- Linux Administration & DevOps
| |-- Web Servers
| |-- Docker and Container Management
| |-- CI/CD Automation with Linux
| |-- Managing Cloud Linux Servers
| |-- Configuration Management
| |-- Infrastructure Automation
|-- Advanced Linux Concepts
| |-- Kernel Modules and System Calls
| |-- Kernel Compilation and Configuration
| |-- Virtualization and KVM
| |-- Namespaces and Cgroups
| |-- Linux Networking Internals
| |-- System Performance Tuning
|-- Real-World Linux Projects
| |-- Build a Linux Server from Scratch
| |-- Automate Tasks Using Bash Scripts
| |-- Configure a Secure SSH Server
| |-- Deploy a Web Application with Nginx
| |-- Set Up Linux Monitoring and Logging
| |-- Build a Docker-Based Linux Environment
|-- Continuous Learning & Growth
| |-- Read Linux Man Pages and Documentation
| |-- Practice Linux Commands Daily
| |-- Explore Open Source Linux Projects
| |-- Learn Linux System Administration
| |-- Practice in Linux Labs and Virtual Machines
| |-- Prepare for Linux Certifications
Get the Linux Development & Mastery Ebook here:
codewithdhanian.gumroad.com/…
This ebook helps you master Linux through practical exercises, hands-on projects, command-line techniques, system administration, shell scripting, and real-world DevOps workflows.
Pablo J RY retweeted
What Goes Into Building a Production-Ready AI Agent?
☑ Agent orchestration
☑ Tool calling & APIs
☑ Memory & state
☑ LLMs & embeddings
☑ Retrieval & evaluation
☑ Testing & monitoring
☑ Deployment & scalability
Strong architecture makes AI agents reliable and scalable.
#AI
Pablo J RY retweeted
𝗔𝗡 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧 𝗜𝗦 𝗠𝗨𝗖𝗛 𝗠𝗢𝗥𝗘 𝗧𝗛𝗔𝗡 𝗝𝗨𝗦𝗧 𝗔𝗡 𝗟𝗟𝗠.
Most people think giving an LLM a prompt is enough to build an AI agent.
But a truly capable AI agent needs memory, tools, planning, decision-making, reflection and much more.
Here are 9 essential components of an AI Agent Architecture you should understand:
1. LLM — The Brain
The core reasoning engine that understands instructions, processes context and generates responses or decisions.
Examples: GPT, Claude, Llama.
2. Memory — Retains Context
Allows an agent to remember information and use it later.
- Short-term memory: Current conversation
- Long-term memory: User preferences and past interactions
- Episodic memory: Previous events and experiences
3. RAG — Knowledge Access
Retrieves relevant information from external knowledge sources and provides it to the LLM.
This helps agents work with private, domain-specific and updated information.
4. Tools — Action Executor
Gives agents the ability to interact with external systems.
Examples:
- Web search
- APIs
- Databases
- Calculators
- Email and messaging
5. Planning — Decides the Steps
Breaks complex goals into smaller, manageable tasks.
The agent understands the goal, creates a plan, executes steps and observes the results.
6. Reflection — Self-Improvement
Evaluates its own output, identifies mistakes and improves future attempts.
This helps make agent workflows more reliable.
7. Multi-Agent — Collaboration
Multiple specialized agents work together to complete complex tasks.
For example:
- Research Agent
- Coding Agent
- QA Agent
- Manager Agent
Each agent handles a specific responsibility.
8. Monitoring & Guardrails — Safety Layer
Keeps agent behavior controlled, reliable and aligned with defined rules.
Includes input validation, output filtering, safety checks, logging and auditing.
9. Human-in-the-Loop — Human Oversight
Humans review, approve, edit or reject important decisions before the agent proceeds.
Especially important for sensitive and high-impact tasks.
𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘:
An LLM generates intelligence.
Memory provides context.
RAG provides knowledge.
Tools enable action.
Planning creates direction.
Reflection improves results.
Multi-agent systems enable collaboration.
Guardrails provide safety.
Human oversight builds trust.
The real power of AI agents comes from how these components work together, not just from having a powerful LLM.
Save this cheat sheet for your next AI agent project.
Follow @mayaislam_ai for more AI tools & tech updates.
#AI #AIAgents #ArtificialIntelligence #LLM #Automation
Pablo J RY retweeted
兄弟们，发现一个把计算机网络协议知识做成动画的宝藏网站！
以前学计网全靠书本里的文字和图片，现在数据怎么发包、怎么握手、怎么传输，直接看动画就明白了。比对着一堆文字硬啃直观太多，新手也能看懂。
面试或考试的时候不再死记硬背💯💯！！
protocol-explorer-alpha.verc…
Pablo J RY retweeted
📈 Data by itself does not generate knowledge.
The analysis process involves collecting, cleaning, exploring, visualizing, and interpreting information to extract useful evidence. Concepts such as descriptive statistics, correlation, and regression are essential tools.
Pablo J RY retweeted
Anthropic pays $250,000-750,000K a year for people who can walk this book.
Most people will save the thread and never open a chapter.
Supervised learning => shallow nets => deep nets => loss => fitting => gradients => CNNs => transformers => generative models => the system you ship.
01 Introduction.
udlbook.github.io/udlbook/
02 Supervised learning.
udlbook.github.io/udlbook/
03 Shallow neural networks.
udlbook.github.io/udlbook/
04 Deep neural networks.
udlbook.github.io/udlbook/
05 Loss functions.
udlbook.github.io/udlbook/
06 Fitting models.
udlbook.github.io/udlbook/
07 Gradients and initialization.
udlbook.github.io/udlbook/
08 Convolutional networks.
udlbook.github.io/udlbook/
09 Transformers.
udlbook.github.io/udlbook/
10 The free PDF and the notebooks.
udlbook.github.io/udlbook/
The book and the notebooks are free.
save this ⭣
Pablo J RY retweeted
Use this Super Simple Template to Learn How to Implement CI/CD in Your Agile Team.
Often, I see confusion surrounding the integration of Agile Development with a CI/CD pipeline.
To clear the fog, I've put together a straightforward, step-by-step guide that encapsulates the entire process in one easy-to-understand format.
Remember, this isn't merely a CI/CD workflow, but an agile development process harmoniously intertwined with CI/CD flow.
Also, bear in mind that this is just a baseline diagram and the tools/technology used here are examples - feel free to replace them with equivalent tools or technology that suits your team's preferences and requirements.
👉 Let the Product Owner kick-off by crafting engaging user stories.
👉 Have your development team spring into action, bringing these stories to life with tools like Jira/Rally.
Utilize Slack/Team, GitHub/Bitbucket/ Integration to:
• Promote transparency and collaboration.
• Enable continuous integration.
• Catch bugs early.
• Support frequent releases and continuous testing.
Harness the power of SonarQube and DevOps to:
• Conduct static code analysis.
• Trigger builds.
• Provide insightful feedback.
Implement Unit Tests and Code Coverage with JaCoCo and JUnit:
• Use Jenkins and Maven for build and deployment.
• Strive for 100% unit test execution success.
• Maintain at least 80% code coverage.
• Eliminate critical defects/vulnerabilities.
Master Testing and Quality Assurance by:
• Using Selenium for regression and UAT tests.
• Relying on Nexus/Artifactory for Binary Repo.
Enhance Security and Infrastructure Management:
Conduct routine security scans with Qualys.
Employ Terraform and Ansible for infrastructure management.
If you're looking for ways to improve your software development process, CI/CD is definitely worth considering.
Here are some additional tips for implementing CI/CD:
Start small. Don't try to implement CI/CD for your entire codebase all at once.
Start with a small project and then gradually expand your CI/CD pipeline as your needs grow.
Get buy-in from your team. CI/CD can be a big change for your team, so it's important to get buy-in from everyone involved.
Make sure that your team understands the benefits of CI/CD and is willing to put in the effort to make it work.
Automate as much as possible. The more you can automate, the more efficient your CI/CD pipeline will be. Look for opportunities to automate manual tasks, such as building, testing, and deploying code.
Strive to reach these key milestones of automation:
🧪 Unit Test Execution Automation
🛠️ Build Automation
📈 Code Coverage Check Automation
📊 Code Quality Check Automation
🔒 Security Scanning Automation
🚀 Automated Deployments with Gating
💬 Feedback Automation to Production Teams
💾 Binary Storage Automation into Repo Manager
🏗️ Infrastructure Setup Automation
Stay connected for more insights!
Follow me @LearnWithBrij
#DevOps #Agile #softwareengineering
