Dev Systems
Show HN: We dropped Go for Rust in our real-time telephony AI media plane
In building Vivik, an execution-grade telephony AI engine, we faced a brutal constraint: the human conversational loop.In psychoacoustics, a delay under 250 ms feels instantaneous. At 500 ms, users notice lag. Beyond 800 ms, conversations start feeling strained, and by 1.5 seconds, the illusion of real-time interaction collapses.That creates an extremely tight latency budget for voice AI:• Network RTT: 50–200 ms
• LLM inference: 200–800 ms
• TTS synthesis: 100–400 ms
• ASR processing: 100–300 ms
Show HN: A timeline of recent open source CVE intensity and volume
I was curious what it would look like if I plotted the intensity and volume of software supply chain CVEs over time, given what seemed like a flood of compromises lately.It looked exactly as I expected, and I expect it to get worse before it gets better.Yes, an LLM was used but because I wanted the simplest possible architecture, I steered away from using any back end at all. Instead it's just GitHub pages with a static json document as the source of data, updated daily by a GitHub action w
Cloud Native Platforms: Build
Audience: Cloud architects, platform engineers, engineering leaders making design decisionsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 1 of 3.Most engineering teams can build systems.Few can scale them without rebuilding them.As platforms grow, complexity does not increase linearly. It multiplies across users, services, tenants, regions, and integrations. The systems that struggle and the systems that scale are rarely separated by which cloud they run
Cloud Native Platforms: Run
Audience: SREs (Site Reliability Engineers), platform engineers, engineering managers running production systemsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 2 of 3.Most systems are designed thoughtfully.Most operations are inherited reactively.The systems that survive are not the ones built with the most care. They are the ones operated with the most discipline. Production has a way of revealing every shortcut taken during design and every assumption le
Cloud Native Platforms: Evolve
Audience: Engineering leaders, platform architects, senior developers exploring how to operationalise AI in their teamsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 3 of 3.Cloud helped us scale infrastructure.AI is starting to do the same thing for the work around the code: the planning, the testing, the release communication, the incident triage, the writing that surrounds writing software.The conversation about AI in software has narrowed too quickly t
WAR, Azure Advisor, and Us (Azure Arch Diagram Builder): Three Ways to Score an Azure Architecture
Author: Arturo Quiroga, Azure AI services Engineer - Senior Partner Solutions Architect — MicrosoftA few days ago I published From Prompt to Production: Building Azure Architecture Diagrams with AI, introducing the open-source Azure Architecture Diagram Builder. One feature got more follow-up questions than any other: the Well-Architected Framework (WAF) validation. Architects from partners and customers — many of whom already use Azure Advisor and the Well-Architected Review — wanted to know ex
Cyber resilience on AWS: A reference approach for recovery from ransomware and destructive events
Cyber resilience is the ability to recover workloads to a known-good state after an adversary has affected the environment. Prevention works to keep threat actors out and detection works to find them quickly. Cyber resilience focuses on recovery: restoring a trustworthy environment when backups, credentials, or parts of the infrastructure can no longer be assumed to be safe. For organizations running critical workloads on AWS, ransomware, data extortion, and other destructive events are increasi
A new experiment brings better group meetings to Google Beam
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Screenshot_2026-05-15_at_4.21.2.max-600x600.format-webp.webp">See and hear your colleagues in true-to-life size and sound, making hybrid meetings feel more inclusive and connected.
I created a 126K line Android app with AI – the workflow that worked for me
I really wanted to see how far I can go. Can I create a meaningful and complex application, big enough, but without knowing the language.I have 18+ years of experience as software developer. But I have no experience with Kotlin. And to learn Kotlin, to learn the Android libraries, it is not an easy job. I may need at year of active learning and trying things, before having the confidence to start doing something.So, I asked myself, how far can I go with AI tools? And I went far!I created https:&
Ask HN: What AI coding workflows have stuck for you?
There’s a lot of discussion around AI-assisted development, but I’m curious which workflows have actually remained useful beyond the initial novelty phase.
01. Introduction to Distributed Systems | Distributed Systems | Generated by NotebookLM
Disclaimer: This video is intended as a supplementary resource designed to complement textbooks, lectures, and official course ...
Making it easier to understand how content was created and edited
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/FINAL_SOCIAL_HERO_laWfMt0.max-600x600.format-webp.webp">We're expanding our tools to help you understand how content was created and edited across the web.
I/O 2026: Welcome to the agentic Gemini era
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/SundarKeynote-hero.max-600x600.format-webp.webp">The latest from Google I/O: See how we’re helping you get more done with Gemini.
New ways to create and get things done in Google Workspace
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/GoogleWorkspace-IO.max-600x600.format-webp.webp">Announcing new voice capabilities in Gmail, Docs and Keep, a new design tool called Google Pics and updates to AI Inbox.
From Prompt to Production: Building Azure Architecture Diagrams with AI
Author: Arturo Quiroga, Senior Partner Solutions Architect — MicrosoftCloud architects spend significant time translating ideas into architecture diagrams. They toggle between Visio, draw.io, pricing calculators, and documentation. According to the 2024 Stack Overflow Developer Survey, 61% of developers spend more than 30 minutes a day searching for answers or solutions, time lost to context-switching rather than design. What if you could describe your architecture in plain English and get
How ALS GeoAnalytics LITHOLENS ™ revolutionizes core logging through machine learning with Amazon EKS
In the mining industry, accurate geological analysis is required for improving mine design and development. Traditionally, this involved labor-intensive and time-consuming on-site inspections of drill core samples, often conducted in remote and challenging environments. ALS GeoAnalytics has streamlined this process through its LITHOLENS platform, a machine learning (ML)-powered system that uses deep learning and machine vision to automate core logging. LITHOLENS significantly enhances data con
How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances
Synthesia, an enterprise-focused AI video platform, has transformed content creation, helping everyone to create video content without cameras or microphones. To achieve this, Synthesia allows its users to create video avatars that synthesize the likeness and voice of real people. Synthesia achieves this through a series of in-house developed models based on various architectures, including latent diffusion video generation models. Customers like Synthesia often choose to host their models on Am
GenAI-based development platform - part 4: The coding agent sandwich pattern
This article describes the “coding agent sandwich” — an architecture pattern consisting of a tasty filling of LLM invocations sandwiched between two slices of plain old code (POC).This article is part of a series about the GenAI-based development platform (a.k.a. harness) that I’ve been developing to make GenAI-based coding agents like Claude Code more productive, more secure and less frustrating.The complete list of articles in the series is as follows: Part 0 - My GenAI development workflow:
Blackstone will create a new TPU cloud in a joint venture with Google.
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Blackstone_Google_logo_lockup.max-600x600.format-webp.webp">Blackstone announced a joint venture with Google to create a new TPU cloud.
Some Business Ideas
Infrastructure side:1. A service connector that can be attached to any AI Assistant to authenticate users and connect services easily, similar to MCP but more universal.
2. CDN service connectors that can store assets and retrieve them very fast from anywhere in the world.
3. Database-as-a-service connectors that are highly available and very simple to connect or disconnect from any provider without managing infrastructure.
4. Object storage service connectors that can be attached to personal as