The latest AI news we announced in January
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/LatestAI_v5.max-600x600.format-webp.webp">Google AI announcements from January
Dev Systems
Mastering millisecond latency and millions of events: The event-driven architecture behind the Amazon Key Suite
Background Amazon Key empowers customers to securely manage access to their homes and businesses through innovative solutions. Through a suite of consumer and business products, the Amazon Key team is transforming how customers receive deliveries and manage access to their spaces. Our In-Garage Delivery service offers a secure and convenient solution for receiving Amazon packages and groceries directly inside customers’ garages. For property managers and building owners, Amazon Key provides
Show HN: Stanislavski Protocol – Real-Time Emotional Logic Framework
[0.Initializing:The Magic If (Sandbox Bootloader)] Traditional cognitive methods fail due to a lack of Organic Fidelity. We use the Magic If as a System Bootloader: "I am not currently an Authority, but If I were to initialize the King.sys environment, how would I route my resources?" This creates a believing Sandbox where the brain can execute commands without the friction of "objective reality." The goal is to establish Organic Truth—a self-consistent internal state that
Show HN: VPC Principle - Why AI coding fails at scale
Hello Hackers,I’m working on a draft about AI-native software engineering.The core idea is simple but uncomfortable: most failures in AI-assisted coding are not capability problems, but governance problems. We routinely allow AI systems to make high-impact architectural decisions without bearing responsibility for their consequences.The VPC Principle proposes a governance model: - Verdict: high-responsibility decisions that only humans can make - Permission: explicit decision boundaries delegate
Code is getting cheaper. Building is not
There is a heavy sentiment floating around lately. “Everything will be built automatically” “My craft is obsolete” “In six months, a tool will read my mind and produce whatever I imagine”I want to challenge this. Code is getting cheaper. Building is not.We are confusing capability with intelligence. LLMs are not AGI. They are not minds that can discover new physics on demand. They are high‑fidelity simulators of a semantic space: compressing patterns, language, and human intent into a queryable
Architecting an Azure AI Hub-and-Spoke Landing Zone for Multi-Tenant Enterprises
A large enterprise customer adopting AI at scale typically needs three non‑negotiables in its AI foundation:End‑to‑end tenant isolation across network, identity, compute, and dataSecure, governed traffic flow from users to AI servicesTransparent chargeback/showback for shared AI and platform servicesAt the same time, the platform must enable rapid onboarding of new tenants or applications and scale cleanly from proof‑of‑concept to production.This article proposes an Azure Landing Zone–aligned ar
Reference Architecture for Highly Available Multi-Region Azure Kubernetes Service (AKS)
IntroductionCloud-native applications often support critical business functions and are expected to stay available even when parts of the platform fail. Azure Kubernetes Service (AKS) already provides strong availability features within a single region, such as availability zones and a managed control plane. However, a regional outage is still a scenario that architects must plan for when running important workloads.This article walks through a reference architecture for running AKS across multi
Show HN: Quorum-free replicated state machine built atop S3
Hi HN,I’m sharing the alpha release of S2C, a state machine replication system built atop S3.The goal is to enable a distributed application to maintain consistent state without needing a quorum of nodes for availability or consistency.The idea came from a side project that was using S3 and where I needed strongly consistent distributed state but wanted to avoid adding a separate consensus dependency. I initially tried to use S3 directly for coordination, but it became messy. Eventually, I reali
Show HN: WebGPU vs. CPU Benchmark – see how much faster WebGPU is for you
I'm interested in adding WebGPU support for a distributed ML project, so I wondered how much faster it is than CPU. I asked Claude to build this, then add export functionality. You can use it to see how much faster inference can be using WebGPU on your system. You can download the results as a .csv file if you'd like to save them.
Show HN: Workflow – A Git-native, local-first DAG orchestrator in Go
I built workflow because I was tired of the "infrastructure tax" required to run simple task pipelines. Most orchestrators assume you want a distributed system with a web UI and a database server.I wanted something simple and reliable, but with the features of a modern data orchestrator:- Stateful: It uses a local SQLite DB to track every run and retry. If a task fails, you can fix it and resume.- Deterministic: Strict topological ordering based on depends_on logic.- Static: A single G
I realized "Thinking", it WORKS
Today, we perform calculations in the way, that is easiest for the human mind. Maximum number we use, is the number of our fingers. When I examined how computers actually calculate, I realized what we call “thinking”. Computers perform only 0-1 calculations, but we can use it’s fast memory: to remember results of many simple calculations, and we sum them up. Then, computer „remembers” results of much more digits operations and sums them up correctly. See, whenever a computer performs a calculati
Show HN: Redress – failure policy for Python services
Hi HN,I've been working on redress, a failure-policy library for Python services. It treats retries, circuit breakers, and stop conditions as coordinated responses to classified failure, rather than independent wrappers. The goal is to make failure behavior explicit, bounded, and observable across a codebase.I kept running into the same problem in distributed systems and data pipelines I worked on. Retry logic grows organically at call sites and circuit breakers live somewhere else entirely
10 ways to plan your 2026 budget with Gemini
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Budget_with_Gemini_hero.max-600x600.format-webp.webp">Learn how to use simple Gemini prompts to create a 2026 budget, find hidden savings and organize your spending.
Show HN: Terminal MCP – A sandboxed terminal interface for LLMs and beyond
Hi HN — I built Terminal MCP, a utility that exposes terminal sessions over MCP, primarily to help LLMs interact with CLIs and TUIs during development and debugging.It’s also useful outside of AI: it provides a general way to run terminal workflows through a controlled interface, with better isolation and observability than a raw shell.Recent addition: Sandbox mode, based on Anthropic’s work on safe tool execution. It lets you run sessions with explicit controls over what the process can access
Show HN: Lok – Treating LLMs more like infrastructure, not chatbots
Hi HN,I've been building a small open-source CLI called Lok for orchestrating multiple LLMs in a single workflow.Instead of a single "agent", Lok treats models as interchangeable tools: one can plan, another can execute or critique, and others can verify output. The focus is on inspectable, composable workflows rather than autonomous behavior.It's local-first, CLI-driven, and intentionally small. I've been dogfooding it for real development work and documenting what work
Show HN: Cwt – Sandbox AI coding agents using Git Worktrees
My primary workflow now involves coordinating multiple, independent Claude instances to handle different parts of a codebase simultaneously.The biggest friction point I found was context switching. I tried manual git worktree management, custom Claude skills, and various wrappers, but they all felt too heavy, slow, or restrictive. I wanted something that solved the overhead of worktree management without trying to "own" my entire development process.I built cwt to bridge that gap. It i
Show HN: Nod – Pre-code compliance validation for agentic coding workflows
AI coding agents read specs and start building — but nothing in the CI pipeline validates that those specs are compliant, complete, or that requirements survive the iteration process.nod is a lightweight GitHub Action that scans project documentation against community-maintained compliance rule sets. It catches missing controls, regulatory anti-patterns, and requirement drift before or during agentic development. It also includes an Agentic Action Security pack for validating agent guardrails an
Ask HN: How do you handle auth when AI dev agents spin up short-lived apps?
Hi HN,I’m working on AI agents used for software development. These agents automatically spin up short-lived app instances – for example per pull request, per task, or per experiment – each with its own temporary URL.Auth is handled in the standard way:- OAuth2 / OIDC- external identity provider- redirect URLs must be registered in advance and be staticThis clashes badly with short-lived apps:- URLs are dynamic and unpredictable- redirect URLs can’t realistically be pre-registered- auth bec
Show HN: I acquired ExtraDock, rebuilt it, now it's the macOS app of my dreams
About a year ago, I was building DockFlow (a macOS app for managing macOS dock presets) When I bumped into ExtraDock on Reddit. The original creator had built a cool tool: create multiple floating docks on your Mac, position them on different monitors. But the app was hard to maintain, and the developer was looking to move on.I loved the concept. I saw how ExtraDock and DockFlow could work beautifully together. DockFlow manages your dock configurations, ExtraDock gives you multiple docks pe
Show HN: Gemini Workspace Framework – Sustainable AI-Assisted Development
AI coding tools excel at generation but fail at organization. Most demos built in 10 minutes become unmaintainable in 6 months. This framework addresses that gap. It provides: • Tiered complexity model (Lite/Standard/Enterprise) - match structure to project scale • Skills + Workflows architecture - reusable automation across projects • AI-optimized documentation (GEMINI.md) - reduce iteration cycles • Consistent patterns - same structure across all projects The focus isn't "