Fastly vs NGINX

Comcast and Fastly’s New Content Delivery Model Powers Highest Quality Experience for Peacock’s Biggest Live Events

Fastly announces a new content delivery model with Comcast to power Peacock's biggest live events.

Fastly Unveils AEDA: Autonomous Edge Security in Gemini Enterprise

Discover Fastly’s Autonomous Edge Defense Agent (AEDA) in Gemini Enterprise, designed to streamline incident response and slash MTTR.

Akamai surges after $11.6B Anthropic CPU deal

Akamai Technologies' shares surged up to 16.4% after announcing an $11.6 billion deal with AI company Anthropic for dedicated cloud computing capacity over seven years, marking the largest deal in Akamai's history. The deal focuses on CPU-based infrastructure for agentic AI workloads, with an option to expand to $20 billion. Anthropic will receive a warrant for up to 5% of Akamai's stock, vesting based on spending milestones. Akamai expects $5.5 billion in capital spending plus $1.7 billion for components, and analysts estimate this equates to about 77 megawatts of computing power. Revenue is expected to begin in 2027, with an annual run rate of $1.7 billion by end of 2028. CNBC's Jim Cramer highlighted Akamai as a buy, citing its low valuation (under 16 times expected earnings) and the Anthropic deal, while suggesting a pullback for Cloudflare (279 times earnings) and Fastly (50 times earnings). Akamai is repositioning with edge computing to handle AI workloads closer to users.

• Akamai signed an $11.6 billion deal with Anthropic for dedicated cloud computing capacity over 7 years, the largest in Akamai's history.
• The deal can be expanded to up to $20 billion and includes a warrant granting Anthropic up to 5% of Akamai's stock, vesting based on spending milestones.
• Computing capacity focuses on CPUs for agentic AI, not GPUs; Akamai expects $5.5 billion in capital spending (plus $1.7 billion for components), ~77 MW of power.

Gap Between TLS Everywhere and Actual Transport Security

This article discusses the common misconception of 'TLS everywhere' in cloud-native architectures, where TLS is often only implemented at the edge, leaving internal traffic unencrypted. It identifies four key layers where TLS is typically absent: ingress-to-pod, pod-to-pod, application-to-database, and cluster infrastructure certificates. The author details implementation strategies to close these gaps without necessarily adopting a service mesh, including re-encryption at the ingress, using cert-manager for automated certificate management, and implementing mTLS at the application level for smaller service estates. The article provides practical configuration examples, such as NGINX ingress annotations, cert-manager Certificate resources, and Prometheus alerting rules for certificate expiry. It emphasizes the importance of automating certificate rotation and monitoring time-to-expiry to prevent outages.

• TLS is often terminated only at the edge, leaving internal traffic unencrypted in many systems.
• Kubernetes does not encrypt pod-to-pod data plane traffic by default.
• Enabling TLS on a database (e.g., RDS) does not enforce its use; connection strings need to have sslmode=require or Encrypt=True.

Resilient Real-Time Systems with WebSockets & Redis Pub/Sub

This technical guide explains how to build resilient, low-latency real-time systems by combining persistent WebSocket client-server connections with Redis as a central pub/sub broadcast layer, distributed state store, and cache. It describes architectural patterns for scaling (single server, multiple WebSocket servers + single Redis, and Redis Cluster), and explains when to integrate durable queues (Kafka/RabbitMQ/AWS SQS) for persistence and guaranteed delivery. The article includes a concrete Node.js example (ws and ioredis: server.js, publisher.js, client.html) and Docker, plus client reconnection best practices (exponential backoff) and session persistence in Redis to allow resuming on another instance. Operational topics covered are Redis high availability (Sentinel/Cluster), sharding and serialization, backpressure handling, load balancing, security, idempotency, and monitoring. It also discusses operational deployment patterns, monitoring metrics, and security practices for production environments.

• Combines persistent WebSocket client connections with Redis Pub/Sub as a broadcast layer and Redis as a distributed state store and cache.
• Covers scaling patterns: single server, multiple WebSocket servers + single Redis, and multiple servers with Redis Cluster for sharding and horizontal scale.
• Provides a practical Node.js example using ws and ioredis (server.js, publisher.js, client.html) with Docker.

Mitigating HTTP Request Smuggling Attacks

The article explains HTTP Request Smuggling, an attack that leverages discrepancies in how front-end proxies (load balancers, WAFs) and back-end servers parse HTTP/1.1 request boundaries when both Content-Length and Transfer-Encoding headers are present or malformed. It describes common variants (CL.TE, TE.CL, TE.TE), concrete examples showing how smuggled requests can be interpreted differently by proxy and backend, and the resulting risks: bypassing security controls, cache poisoning, session hijacking, and credential theft. Recommended mitigations include upgrading to HTTP/2 end-to-end, normalizing/rejecting ambiguous requests at the edge (e.g., return 400 when both headers appear), using consistent server software across layers, disabling connection reuse, strict HTTP parsing (Nginx/Gunicorn settings), WAF rules, and timeouts. The article also provides testing guidance (curl, Python socket example, Burp Suite extension) and log-monitoring suggestions to detect attempted smuggling.

• HTTP Request Smuggling exploits inconsistent parsing of request boundaries between front-end proxies and back-end servers when both Content-Length and Transfer-Encoding headers are present or malformed.
• Common smuggling variants include CL.TE, TE.CL, and TE.TE; these can cause the back-end to treat leftover bytes as a new request, enabling attacks like cache poisoning and session hijacking.
• Primary mitigations: upgrade to HTTP/2 end-to-end, normalize/reject ambiguous requests at the edge (return 400 for requests containing both Content-Length and Transfer-Encoding), and use consistent parsing across layers.