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This week’s system design refresher:
AI Agent versus MCP
How does HTTPS work?
How to Learn Kubernetes?
Top 6 most commonly used Server Types
Amazon Key Architecture with Third Party Integration
Hiring Now: Top AI Startups and AI Roles
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AI Agent versus MCP
An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.
Key characteristics of AI agents are as follows:
An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control.
Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions.
Agents must be able to perceive and process the information available from their environment.
Model Context Protocol (MCP) is a new system introduced by Anthropic to make AI models more powerful.
It is an open standard that allows AI models (like Claude) to connect to databases, APIs, file systems, and other tools without needing custom code for each new integration.
MCP follows a client-server model with 3 key components:
Host: AI applications like Claude
MCP Client: Component inside an AI model (like Claude) that allows it to communicate with MCP servers
MCP Server: Middleman that connects an AI model to an external system
Over to you: Have you used AI Agents or MCP?
Stop Software Erosion with Smarter Static Code Analysis (Sponsored)
This webinar, "Using Static Code Analysis Correctly", explores how static code analysis can be a powerful ally in maintaining code quality, preventing software erosion, and ensuring long-term maintainability.
Whether you're working in automotive, medical, embedded systems, or other safety-critical industries, you'll gain practical insights into spotting design drift, managing architectural consistency, and avoiding common pitfalls in C and C++ codebases.
In this 40-minute webinar, we break down real-world challenges and demonstrate how thoughtful use of static analysis can help you build more robust, reliable systems from the inside out.
How does HTTPS work?
Hypertext Transfer Protocol Secure (HTTPS) is an extension of the Hypertext Transfer Protocol (HTTP.) HTTPS transmits encrypted data using Transport Layer Security (TLS.) If the data is hijacked online, all the hijacker gets is binary code.
How is the data encrypted and decrypted?
Step 1 - The client (browser) and the server establish a TCP connection.
Step 2 - The client sends a “client hello” to the server. The message contains a set of necessary encryption algorithms (cipher suites) and the latest TLS version it can support. The server responds with a “server hello” so the browser knows whether it can support the algorithms and TLS version.
The server then sends the SSL certificate to the client. The certificate contains the public key, host name, expiry dates, etc. The client validates the certificate.
Step 3 - After validating the SSL certificate, the client generates a session key and encrypts it using the public key. The server receives the encrypted session key and decrypts it with the private key.
Step 4 - Now that both the client and the server hold the same session key (symmetric encryption), the encrypted data is transmitted in a secure bi-directional channel.
Why does HTTPS switch to symmetric encryption during data transmission? There are two main reasons:
Security: The asymmetric encryption goes only one way. This means that if the server tries to send the encrypted data back to the client, anyone can decrypt the data using the public key.
Server resources: The asymmetric encryption adds quite a lot of mathematical overhead. It is not suitable for data transmissions in long sessions.
Over to you: how much performance overhead does HTTPS add, compared to HTTP?
How to Learn Kubernetes?
Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications.
Core Concepts and Architecture
This includes topics like What is Kubernetes, Cluster, Node, Pod, Control Plane, and Worker Node.
Workloads and Controllers
This includes topics like Pod, ReplicaSet, Deployment, StatefulSet, Job, CronJob, Labels, Selectors, and Autoscalers (HPA, VPA, and Cluster Autoscaler).
Networking and Service Management
This involves topics like Cluster Networking Model, Services (ClusterIP, NodePort, LoadBalancer), Ingress, and Network Policies.
Storage and Configuration
This includes topics like Volumes, PersistentVolume, PersistentVolumeClaim, Storage Classes, ConfigMap, Secret, and Stateful Applications.
Security & Access Control
This involves topics like RBAC, Service Accounts, Secrets Management, Admission Controllers, Pod Security, TLS, and API Access.
Tools, Observability & Ecosystem
This includes topics like kubectl, YAML files, Helm Charts, CI/CD Integration, GitOps, Logging, Monitoring, and EKS.
Over to you: What else will you add to the list for learning Kubernetes?
Top 6 most commonly used Server Types
Web Server:
Hosts websites and delivers web content to clients over the internet
Mail Server:
Handles the sending, receiving, and routing of emails across networks
DNS Server:
Translates domain names (like bytebytego. com) into IP addresses, enabling users to access websites by their human-readable names.
Proxy Server:
An intermediary server that acts as a gateway between clients and other servers, providing additional security, performance optimization, and anonymity.
FTP Server:
Facilitates the transfer of files between clients and servers over a network
Origin Server:
Hosts central source of content that is cached and distributed to edge servers for faster delivery to end users.
Over to you: Which type of server do you find most crucial in your online experience?
Amazon Key Architecture with Third Party Integration
Amazon Key is a REALLY convenient way to have your packages securely delivered right inside your garage or multifamily property.
We sat down with Kaushik Mani and Vijayakrishnan Nagarajan to learn how a small side project evolved into a global platform: now powering over 100 million secure door unlocks every year.
This story is packed with valuable lessons on:
Scaling IoT infrastructure across thousands of locations
Building resilient, field-ready hardware for real-world environments
Overcoming cold starts and connectivity challenges
Designing a microservices architecture for global expansion
Creating a secure, partner-ready platform to support third-party integrations
And many more
Dive into the full newsletter here.
Hiring Now: Top AI Startups and AI Roles
This Week’s High-Impact Roles at Fast-Growing AI Startups
Software Engineer, AI Platform at Pano AI
Senior Software Engineer (AI Products) at Mutiny
Senior Software Engineer, Financial Infrastructure at Vercel
Senior Software Engineer at Otter.ai
Senior Software Engineer, ML Infrastructure, Predictive Planner at Waymo
Senior Full Stack Engineer at Gatik
Senior / Staff Software Engineer, Simulation Platform at Waabi
High Salary SWE Roles this week
Engineering Manager, ML Performance and Scaling at Anthropic
Director of Engineering, Data Platform at WorkAsPro
Engineering Manager - Trust and Safety at TikTok
Senior Full Stack Software Engineer at Netflix
VP, GTM Engineering at Linkedin
Staff Backend Engineer, Invoicing Products at Stripe
VP, Sales Engineering, Northeast Division at Comcast
Senior Staff Software Engineer, iOS at Salesforce
Today’s latest ML positions - hiring now!
Principal Machine Learning Services Engineer at Adobe
Staff Software Engineer, Machine Learning at Tinder
Senior Engineering Manager - Machine Learning at Uber
Senior Machine Learning Software Engineer at Waymo
Staff Machine Learning Engineer (ML Portfolio) at Affirm
Machine Learning Engineer at Amazon
Senior Machine Learning Engineer at Yahoo
Principal Data Scientist - Machine Learning Engineering at Atlassian
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