标签：#ice

Netflix has described how it redesigned the streaming pipeline behind Service Topology, its real-time service dependencies map, to support production scale. The system uses three stages to separate intermediary resolution from enrichment and persistence, propagates backpressure to Kafka rather than dropping records, and uses server-sent events instead of gRPC for high-volume internal transfers. By Eran Stiller

My first voice agent took 1,200ms to answer a spoken sentence. Then I rewrote three seams in the pipeline and it dropped to 340ms. No new hardware, no new models, no smaller LLM. The words the user says, the words the agent says back, the same. What changed was the shape of the wait. If you have ever built a voice agent that felt polite but slow, this is the part of the pipeline where the seconds hide. The 1,200ms baseline was polite and wrong Here is what my first version did, in the order it did it: Record until the user stops talking (~200ms of tail silence). Send the whole clip to Whisper. Wait for the transcript. Send the transcript to the LLM. Wait for the full response. Send the full response to Piper. Wait for the WAV. Play the WAV. Each stage was fine on its own. The pipeline was a one-lane road. Whisper could not start until recording finished. The LLM could not start until Whisper finished. Piper could not start until the LLM was done. The user waited for the sum. The car metaphor gets old fast, so I will use a real one. This is what the timeline looked like on my machine: [record]--[200ms silence]--[whisper 380ms]--[LLM 480ms]--[piper 340ms]--[playback] ^ 1,200ms Every one of those bars was blocking the next. I had built a relay race where each runner waited for the previous runner to sit down. Trick 1: Frame-based STT so Whisper starts before the user stops The first fix is to stop treating the user's speech as a single file. Feed the audio to Whisper in 20-30ms frames as it is captured. By the time the user hits the tail silence, most of the transcription is already done. You only wait for the last few frames plus a short flush. Pipecat is the reference implementation. Its whole model is frame-based: every stage processes 20-30ms chunks and hands them forward as soon as they are ready. There is no batch, no full-clip handoff, no "wait for this stage to complete." Its own docs quote sub-500ms voice-to-voice when all models are hosted on the same GPU clu

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Digital transformation has led companies to organize their infrastructure and development processes in entirely new ways. To address the challenges of modern cloud-native applications, concepts like Platform Engineering are gaining increasing importance. Especially in environments using Azure Kubernetes Services (AKS) , platform engineering plays a crucial role in efficiently managing and scaling containerized applications. What is Platform Engineering and Why is it Important? Platform engineering is the process of designing, implementing, and managing internal platforms that provide developers with a stable and efficient environment. These platforms bundle all the necessary resources and services to ensure smooth development and operation of applications. A well-developed platform engineering team ensures that recurring tasks are automated, allowing developers to focus on writing code without dealing with the underlying infrastructure. In a container environment like AKS, automation and standardization are critical. Platform engineering provides the framework to simplify these complex workflows. How Does Platform Engineering Support AKS Deployments? A key advantage of platform engineering is the ability to standardize the entire lifecycle of applications—from development to testing and deployment. When working with AKS, the main task of the platform engineering team is to create a seamless and scalable environment for container orchestration. Here are some key aspects of how platform engineering supports AKS: Standardizing and Automating Deployments Platform engineering enables the automation of Kubernetes cluster deployments in AKS using best practices and tools such as Infrastructure as Code (IaC) (e.g., Terraform or Azure Resource Manager templates). This automation reduces human errors and accelerates the time needed to deploy applications in production environments. Self-Service Platforms for Developers A well-designed platform engineering team builds self-ser

There’s no perfect way to transfer possession of your digital assets to your loved ones after you’re gone. But there are things you can do now to make the process easier for them.

There are endless ways to record and transcribe your virtual meetings with AI. Here’s an option that’s free and open source.

Multi-Repo to Monorepo: How I Automated 6 Go Microservice Releases and Then Made It 15x Faster Amandeep Singh Amandeep Singh Amandeep Singh Follow Aug 7 Multi-Repo to Monorepo: How I Automated 6 Go Microservice Releases and Then Made It 15x Faster # go # devops # automation # monorepo 6 reactions 1 comment 10 min read

Xteink’s new X4 Pro e-reader is its best yet, but the lack of an ebook store can be limiting.

New AI toolbars and prompts are showing up in Google Docs and Gmail. If you don’t want Gemini’s help in writing documents and emails, here’s how to turn that stuff off.

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In today's digital world, businesses face the challenge of developing, deploying, and scaling applications faster and more efficiently. One of the key technologies supporting this transformation is container technology. What are Containers and Why Are They Important? Containers allow applications to be packaged into lightweight, self-contained, and portable units that can run consistently in any environment—from a local development machine to a cloud platform. This reduces dependencies and significantly simplifies application deployment and scalability. Unlike virtual machines (VMs), containers share the operating system kernel, making them more resource-efficient. This leads to higher efficiency and allows businesses to run more applications on the same infrastructure. Introduction to Kubernetes: Orchestration of Containers While containers represent a revolutionary approach to developing and running applications, it’s not enough to simply have containers. Once applications consist of dozens or hundreds of containers, managing, orchestrating, and scaling them becomes critical. This is where Kubernetes comes in. Kubernetes is the world’s most widely used container orchestration platform. It enables the automatic deployment, scaling, and management of containerized applications in clusters. With Kubernetes, companies can ensure their applications are always available, automatically recover from failures, and roll out new versions without downtime. Azure Kubernetes Services (AKS): Kubernetes in the Cloud Azure Kubernetes Services (AKS) is Microsoft’s fully managed Kubernetes solution. With AKS, businesses benefit from simplified Kubernetes deployment by offloading infrastructure management to Microsoft. This means you can focus on developing and scaling your applications while AKS simplifies the management and maintenance of Kubernetes clusters. Benefits of AKS: Fully managed: AKS takes care of the management and patching of Kubernetes, allowing businesses to focus on

Flock posted videos on its YouTube channel highlighting at least four police departments whose officers have faced allegations of misusing the company’s tech.

A growing collection of pocket-size gadgets lets you easily make recordings and extract info from them. Here are our favorites.

Chris Richardson discusses leveraging Team Topologies and internal platforms to accelerate microservices delivery. He explains six key platform patterns - from security and observability to build and deployment - and shares strategies for minimizing cognitive load on stream-aligned teams while avoiding common platform engineering pitfalls. By Chris Richardson

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I tested modern locks, lights, climate controls, and more in a historic home that I very much wanted to keep historic.

Building Laravel NATS: A Modern, Production-Ready NATS Integration for Laravel When building distributed systems, one of the biggest challenges is enabling services to communicate reliably without creating tight coupling. Laravel has excellent support for queues, events, broadcasting, and jobs, but when it comes to NATS , the ecosystem has been relatively limited. That's exactly why I built Laravel NATS . Instead of being just another wrapper around an existing PHP client, Laravel NATS aims to provide a Laravel-first developer experience while exposing the full power of NATS for modern event-driven architectures. In this article I'll explain: Why I built Laravel NATS Why you should consider NATS How Laravel NATS works Features that make it production ready Code examples Real-world use cases What makes this package different from existing solutions What is NATS? NATS is a lightweight, high-performance messaging system designed for cloud-native applications. Unlike traditional queues, NATS focuses on: Extremely low latency High throughput Simple publish/subscribe messaging Request/Reply APIs JetStream persistence Horizontal scalability Instead of applications calling each other directly: Order Service │ ▼ Notification Service Applications publish events: Order Service │ ▼ NATS Server │ │ ▼ ▼ Email Analytics Every service becomes independent. Why Laravel Needed a Better NATS Package Most existing packages expose the underlying PHP client almost directly. That means developers still have to understand: client lifecycle connections serialization subscriptions queue consumers JetStream APIs Laravel developers expect something different. We are used to APIs like: Cache :: put (); Queue :: push (); Event :: dispatch (); The goal of Laravel NATS was to make NATS feel just as natural. Installing Laravel NATS Installation is straightforward. composer require zaeem2396/laravel-nats php artisan vendor:publish --tag = nats-config Then configure your environment: NATS_HOST=127.0.0

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The startup is building voice models designed to make AI phone calls pass the Turing test.