标签：#ast

They’re much more recyclable than the current plastic ones, and they will still probably be locked away behind that anti-theft plexiglass.

Infowars’ would-be creative director talks Sandy Hook, comedy’s MAGA turn, and why the future of satire may look more like a streaming startup than a late-night show.

Few-shot learning with large language models is one of the most practical ways to steer model behavior without updating weights. By embedding task-specific examples directly into the prompt, developers can turn a general-purpose foundation model into a domain-specific classifier, parser, or reasoning engine. The technique relies on in-context learning, where the model infers patterns from exemplars rather than from gradient updates. Because it requires no training pipeline, few-shot prompting is ideal for rapid prototyping and production tasks where data volumes are too small for fine-tuning or where model weights must remain frozen. The Mechanics of In-Context Learning In-context learning is an emergent capability of transformer-based language models. During inference, the model attends to the full context window, using the provided examples as a dynamic prior. Each example adjusts the hidden-state activations for subsequent tokens, effectively conditioning the output distribution without any parameter change. Research suggests that the model locates latent task representations within its pretrained weight space and uses the few-shot examples to activate the appropriate subspace. The result is a flexible interface: change the examples, and the model adapts its behavior immediately. Zero-Shot, One-Shot, and Few-Shot Prompting These three patterns describe how much guidance you provide before the actual task input. Zero-shot: You describe the task in natural language with no examples. This works best for simple, well-known tasks that the model has seen frequently during pretraining. One-shot: You prepend a single example. This is often enough to communicate output format or tone. Few-shot: You prepend three to ten examples, sometimes more for complex schema extraction or multi-label classification. The marginal gain from each additional example typically diminishes, but for tasks with rigid output schemas, a larger set of exemplars can substantially improve consisten

I run a hosting company. I'm also a BCA student. These two things coexist somehow. GigaNodes started in 2022 as a game server hosting brand — Minecraft, FiveM, ARK, the usual. Over time it grew into a proper VPS and dedicated server operation under GigaNode Technologies Private Limited, with AMD EPYC 7C13 hardware co-located at Yotta DC Noida. Earlier this year we did something I haven't seen any other Indian hosting provider do: we deployed Cloudflare Magic Transit across our entire network. What that actually means Magic Transit is not Cloudflare CDN. It is not a proxy. It is Cloudflare's enterprise network product where your IP prefixes get announced via BGP into Cloudflare's global backbone. All traffic destined for your servers enters Cloudflare's network first, gets scrubbed for attack traffic, and clean packets get forwarded to your data center via GRE tunnel. To deploy it, your infrastructure partner needs to have BGP-level integration with Cloudflare. Individual companies can't just sign up for it. We made it work through our partnership with Advika Datacenters Private Limited (AS135682) at Yotta DC Noida. The result: DDoS traffic never reaches our hardware. Our servers don't see the attack at all. Why no other Indian provider had done this Most Indian hosting providers use blackholing. When an attack comes in, they null-route your IP. Server goes offline. Attack stops eventually. Server comes back. That is the standard. That is what "DDoS protection included" usually means in India. The difference with Magic Transit is that legitimate traffic keeps flowing while attack traffic gets dropped. Your server stays online. Players stay connected. Trades don't get interrupted. We found out pretty quickly this actually works. We took a 1.7 Tbps attack after deployment. The servers didn't notice. A 1.7 Tbps volumetric attack hit our network in May 2026. Cloudflare absorbed it at the edge. No downtime. No support tickets from customers. We found out from the Cloudfla

These clever lights—each one an exemplar of innovation in materials, design, and function—will beautify your home.

Paul Klein discusses the distributed systems challenges of scaling cloud-hosted browser infra for AI agents. He explains how to manage bursty, stateful multi-tenancy and secure Chromium environments against remote code execution using Firecracker. He also shares how to leverage the Model Context Protocol (MCP) to turn complex websites into accessible agentic tools. By Paul Klein

Comcast is launching a same-day Xfinity Wi-Fi offering that spares you from waiting for your new router. Starting today, new Xfinity customers in almost 20 markets - including Atlanta, Chicago, Denver, Houston, Nashville, Philadelphia, San Francisco, and more - can receive the Xfinity Gateway equipment they need to get online the same day they order […]

Will the Sun roast Earth’s plants or starve them?

Today, I’m talking with Adam Bry, who is CEO of Skydio, the leading US maker of autonomous drones. Before we recorded this episode, I actually got to remotely operate one of Skydio’s drones in the Bay Area from Adam’s laptop in our podcast studio in New York and fly an indoor drone around our office. […]

Martin Kleppmann, an associate professor at Cambridge and author of Designing Data-Intensive Applications, discusses the evolution of data systems over the last decade, mainly the shift from monolithic databases to modular building blocks. Kleppmann underlines the importance of moving from cloud-centric data storage systems to decentralised data storage similar to Bluesky’s AT protocol. By Martin Kleppmann

Simple Intro Your company gets one big cloud bill. It says $30,000. But which team spent it? Which app? Nobody knows. Kubernetes makes this worse because 100 small apps share the same computers. It’s like 10 families sharing one electricity bill. Let’s fix this in 5 easy steps Step 1: Put Nametags on Everything In Kubernetes, you can add "labels" to your apps. Example: team=sales , app=website , owner=pooja If you don’t add name tags, you can never track who spent what. It’s the most important step. Step 2: Check the Big Cost - Computers 70% of your bill is for CPU and RAM. That’s the “brain” and “memory” your apps use. The problem: Most people book a big computer but only use 20% of it. You pay for 100%, use 20%. You waste 80% money. Easy fix: Every month, check “How much did I book vs How much did I use?” Then book smaller next time. Step 3: Don’t Forget Hidden Costs Two things people forget: Storage: Like a hard disk. You deleted the app but forgot to delete the disk. It still charges you every month. Network: Moving data between countries or zones costs money. Check for old disks and big data transfers once a month Step 4: Share the Common Bill Fairly Some costs are for everyone. Like the main Kubernetes system or empty computers waiting for work. How to split it? Easy. If Team A uses 60% of the total computer power, they pay 60% of the common bill. Fair for everyone. Step 5: Use a Tool, Not Excel Doing all this in Excel will make you cry. It’s too much data. Use a tool that does it automatically. It connects to your Kubernetes, reads all the name tags, and tells each team: “You spent $2,340 this week.” Final Tip You can’t save money if you don’t know where it’s going. First, make the costs clear to everyone. Then the savings happen automatically. FAQ - In Simple Words Q1. Why can’t I just see costs in AWS bill? Because AWS only tells you “EC2 cost $10k”. It doesn’t tell you which of your 50 apps used that EC2. Kubernetes hides the details. Q2. What is the first

Why This Matters: The 2 AM Problem It's 2 AM. Your phone rings. Your production database is down. Customers can't log in. Revenue is dropping by the second. You call the Ops team. They restart the server. Downtime: 45 minutes. Cost: $100K in lost sales. Root cause? Unknown. This happens thousands of times a week at companies worldwide. The question isn't "Will your system break?" It's " When it breaks, are you ready? " That's where SRE comes in. What is SRE? (The Real Definition) SRE (Site Reliability Engineering) = Applying software engineering principles to build reliable, scalable infrastructure and systems. It's not just about keeping servers running. It's about: Reliability : Systems that don't break unexpectedly Scalability : Systems that handle growth without collapsing Infrastructure : Automating how systems are built, deployed, and monitored Measurability : Knowing exactly how your system is performing at any moment Traditional operations manages infrastructure reactively — when something breaks, you fix it. SRE manages infrastructure proactively — you engineer it so it rarely breaks, and when it does, it heals itself. The Key Insight: Reliability is Engineered, Not Hoped For Here's the critical shift in thinking: Old mindset : "Let's build this system and hope it doesn't break." SRE mindset : "Let's measure what 'reliable' means, design the system to achieve that, and automate the monitoring and recovery." But reliability isn't just uptime. It includes: Uptime : Is the system available? Latency : How fast does it respond? (A slow system is effectively broken) Error rate : What percentage of requests fail? Throughput : Can it handle the traffic? User experience : Does the system meet user expectations? All of these are engineered and measured. A Simple Analogy: The Bridge Imagine you're managing a bridge. Traditional approach : Engineers patrol daily, react to problems, work around the clock fixing issues SRE approach : Engineers design monitoring that aler

University of Leicester historian thinks Eilmer of Malmesbury saw two different comets: in 1018 and 1066

You don't really ever have to explain why a universal remote is a good idea. You have a bunch of stuff that needs controlling; this thing controls them all. Many companies have set out to build a product worthy of this idea, and one product came much closer than most. It was called the Harmony, […]

The test passed. The restore completed inside the window. The workload came online. The team signed off, closed the ticket, and filed the results. DR test: successful. And then, somewhere between the test environment and the next real incident, the recovery plan drifted out of alignment with the infrastructure it was written to protect. Not dramatically. Not all at once. Gradually — through a cloud migration, an IdP consolidation, a new SaaS dependency, a network redesign that didn't make it into the runbook. DR plan failure rarely happens where you tested. It happens at the assumptions the exercise never reached. The Test Has a Boundary. The Incident Doesn't. A DR exercise begins with a defined scope. A specific workload. A known starting state. A target environment that has been prepared in advance. The team is available, credentialed, and not managing anything else. The blast radius is controlled before the test starts. A real incident does none of that. Scope expands from the first alert. Authentication problems surface because the IdP that wasn't in exercise scope is now unreachable. Networking issues appear because the failover path assumes a routing table that was updated three months ago. A vendor the plan never named is unavailable, and the recovery sequence stalls waiting for a dependency that was never documented as a dependency. The plan was written for the conditions of the test. The incident arrives in conditions the plan never anticipated. That gap is where DR plan failure actually lives — not in the restore mechanism, but in everything the restore mechanism was assumed to be able to reach. Most DR Plans Depend on Things They Never Recover The recovery exercise validates a workload. What it rarely validates is the recovery infrastructure itself. Consider what a typical enterprise DR plan silently depends on: Assumed — Not Tested: Identity provider, backup management console, cloud account access, ticketing and incident management systems, third-party

President Donald Trump says a secret mission moved 100 million barrels of oil through the blocked Strait of Hormuz. That number is impossible to verify.

HashiCorp has announced the general availability of the Terraform MCP Server, an open-source MCP server that enables agents to integrate with Terraform Registry APIs. The company says that it can improve infrastructure teams productivity by relieving engineers of rote tasks. By Sergio De Simone

I was thinking about some tests for a service that uses language models; there are several, even prompt injection. A question came to mind: is it possible to make multiple requests asking for any text like Lorem Ipsum, generating many unnecessary tokens and incurring costs? But creating a test where there are multiple accounts making the same request to generate 10,000 Lorem Ipsum tokens simultaneously, could that cause a service outage? Because most of the infrastructure I see doesn't use any queuing method when the chat is free of tasks involving an agent or even heavier functionalities. I didn't actually generate anything, I just wanted to start a discussion on this topic.

Published June 17, 2026 by gyorgy Infrastructure used to be something you wrote separately from your application. Lately that boundary has been dissolving, and the vocabulary has not kept up. Three distinct ideas are getting blurred together, partly because they all start from the same place: your code already implies what infrastructure it needs, so why state it twice. They diverge sharply on what they do about that. Here is the short version, then the longer one. The short version Infrastructure as Code (IaC). You describe the infrastructure explicitly, in its own files. The tool turns those files into real resources. Total control, total verbosity, and your infrastructure definition lives apart from your application code. Framework-defined Infrastructure (FdI). The framework infers the infrastructure from your application code, and a managed platform provisions it for you. Almost no configuration, no drift between app and infra, but the inference only covers what the framework exposes, and the resulting infrastructure runs on the platform's rails. Infrastructure as Framework (IaF). The framework reads your applications and generates infrastructure code that you own, deployed into your own cloud accounts. The framework does the inferring, you keep the output and the account. Who writes the infra Who owns the output Where it runs Scope IaC You, by hand You Any cloud Anything you can express FdI The framework The platform The platform What the framework exposes IaF The framework You Your cloud accounts What the framework covers The rest of this is just those three rows, explained. Infrastructure as Code IaC is the established answer. You write declarations, in HCL or a general-purpose language, that spell out the resources you want: this VPC, this load balancer, this database, these IAM bindings. A tool like Terraform or Pulumi reads the declarations and reconciles your cloud to match. The strength is that nothing is hidden. Every resource is something you chose and

AWS recently announced CDK Mixins, a new AWS CDK feature that lets developers add reusable capabilities like security, monitoring, and configuration to AWS resources. Mixins work across different construct types, making infrastructure code more flexible and reusable. By Renato Losio