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"We can quash the subpoenas, or you could withdraw the subpoenas,” judge told US.

Originally published on nejckorasa.github.io . When a team moves from a monolith into microservices and event-driven, asynchronous systems, it inherits a class of problems that used to be someone else's: work that fails halfway through, steps that must not run twice, calls that return before the work is done. Temporal is a durable execution engine that handles a lot of this - you define a multi-step process, and it guarantees the process runs to completion even when workers crash in the middle. I've spent the better part of a decade building distributed systems in the money-movement core of banks - ledgers, payments, credit cards - a lot of it on Temporal, from short request-triggered workflows to ones that stayed open for weeks. This is the high-level guide I'd give a team making that jump: the principles worth internalising before you ship, not a full tutorial. Most of them aren't really about Temporal. They're the habits the async shift demands - Temporal just punishes you quickly when you skip one. Durable Execution: The Problem It Solves Distributed work fails in the middle. You call service A, it succeeds. You call B, it times out. The pod dies before C. Now you have half-finished work and no memory of how far you got. The usual fix is a pile of status columns, a cron job to find stuck rows, and retry logic hand-rolled for every step. Temporal's promise is that any process you start runs to the end. The runtime picture: there's a Temporal service (its own cluster), and your app runs worker processes that poll it and execute your code. As a workflow runs, Temporal records every step to an event history . If a worker dies, another picks the workflow up and replays that history to rebuild state, then carries on from where it left off, retrying anything that failed. The history is the source of truth, and it survives the crash. Most of the rules below fall out of that one fact. The Golden Rule: Workflows Decide, Activities Do There are two kinds of code in Tempora

ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.

If you’re tired of sponsored and AI-summarized search results, Kagi gives you a curated—and human-centric—way to find what you’re looking for.

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Here are the best delivery services and meal kits in 2026, including Martha Stewart’s meal service, a GLP-1–supportive plan, and a budget-friendly option.

ServiceNow's investment gives BusinessNext a strategic partner to expand its AI-powered banking software globally.

SoundCloud has acquired decentralized music platform Nina Protocol, months after the startup announced it would shut down. The deal brings Nina’s artists, editorial archive, and music discovery tools to SoundCloud as the company continues expanding its platform for independent musicians.

A configuration change in AWS's bill computation system showed customers estimated bills in the billions and trillions of dollars for over 24 hours. AWS's own alarms detected the anomalies but failed to halt bill generation or page engineers; customer escalations alerted the company 4.5 hours later. Budget and cost anomaly alerts were disabled platform-wide during mitigation. By Steef-Jan Wiggers

It didn’t keep my coworkers at bay, but Flipper’s expensive productivity tool did help me focus on the task at hand.

Voice agents do not usually fail because the model is “not smart enough.” They fail in the awkward half-second where the user pauses, breathes, corrects themselves, or interrupts while the AI is still talking. That tiny moment decides whether the product feels useful or robotic. If your live AI call cuts people off, talks over them, ignores barge-in, or waits so long that users repeat themselves, no prompt will save the experience. The fix is not one magic model. It is a turn-taking system: audio signals, semantic checks, interruption rules, streaming, and metrics that work together. This guide walks through a practical voice agent turn-taking design you can ship in a real product. Why turn-taking is the real voice agent bottleneck Text chat is forgiving. A user types. The model answers. If the response takes two seconds, the user may still wait. Voice is different. Humans expect conversation to move quickly. A delay feels like confusion. An early response feels rude. Talking over the user feels broken. A production voice agent has to answer three questions again and again: Is the user still speaking? Is the user finished enough for the agent to respond? If the user interrupts, should the agent stop, listen, or continue? Most teams start with a simple pipeline: Microphone -> Speech-to-text -> LLM -> Text-to-speech -> Speaker That is enough for a demo. It is not enough for a live workflow where the caller changes their mind, uses filler words, speaks in a noisy room, or interrupts because the AI misunderstood them. The practical goal is not “lowest latency at any cost.” The goal is comfortable turn timing : fast enough to feel alive, patient enough to avoid cutting people off, and interruptible enough to recover when the user takes control. Research signals behind this topic Recent AI platform activity points toward live agents moving from demos into production workflows: Product launches are emphasizing embedded live agents that can see, speak, and operate inside so

Every time this comes up, someone credits the same setup: Athena on Iceberg is where "the code is the spec" — where you open Git and read the whole system, catalog to transforms to schema, without logging into anything. In my experience they're not wrong. What I want to argue is that they're right for mostly the wrong reason. The reason people reach for is the engine — serverless, open format, nothing to provision. But the thing that actually keeps code as the spec, when it does, is something you could have applied to almost any engine. And the thing that breaks it, when it breaks, has nothing to do with Iceberg at all. So here's the split I've landed on, for now: whether "code is the spec" holds is about 90% discipline and 10% engine . Athena/Iceberg earns that 10% honestly — but 10% is all it earns, and I keep watching people mistake it for the whole thing. (Just where my own tinkering has led — not advice.) I should say up front that I'm still in the middle of this. What the best declarative setup for an agent actually looks like — how you turn a system into a spec it can read and act on without guessing — is something I'm actively testing, not something I've settled. Read what follows as a working idea at a particular moment, written down partly so I can find out where it's wrong. To see where the 10% actually lives, it helps to notice that a stateful system always keeps two copies of itself. The spec has two copies One copy is declared : the code you wrote, the schema you committed, the transforms in dbt, the catalog in Terraform. The other is realized : the state the engine accumulates while running — statistics, physical layout, caches, maintenance history, tuning knobs someone set at 2am. "Code is the spec" is really a claim about the distance between those two. When the declared copy explains almost everything about the realized one, you can reason about the system by reading Git. When it doesn't, you can't. Some of that state is declarable — you can pin a

8 min read · telecom provisioning platform, 30M+ subscribers Most companies avoid migrating their monolith for one reason: they imagine it as a single, terrifying event — months of a feature freeze, a weekend cutover, and a rollback plan that's really just hope. That fear is reasonable. A big-bang rewrite of a live system serving 30M+ subscribers really would be terrifying. So we didn't do that. We used a pattern that lets you migrate a monolith one slice at a time, while the system keeps running and the product team keeps shipping features — the same pattern Martin Fowler named Strangler Fig , after the vine that grows around a host tree, gradually taking over, until eventually the original tree is no longer needed. Why the "just rewrite it" instinct is usually wrong The instinct to rewrite a legacy monolith from scratch is understandable — the old code is scary, undocumented, and nobody wants to touch it. But a full rewrite has a well-known failure pattern: it takes far longer than estimated, the business can't freeze feature development for that long, and by the time the rewrite is "done," the old system has changed underneath it and the rewrite is already out of date. The alternative isn't "don't migrate." It's: migrate in slices small enough that each one is boring , and never require the business to stop shipping while you do it. The pattern: a facade, and one slice at a time Strangler Fig works by putting a routing layer — a facade or API gateway — in front of the monolith. At first, 100% of traffic passes through to the old system untouched. Then, one capability at a time: Build the new version of that one capability as an independent service. Update the facade to route just that capability's traffic to the new service. Run both in parallel long enough to trust the new one (see "shadow traffic" below). Retire that piece of the old monolith. Repeat for the next capability. At every point in this process, the system is fully functional. There's no "half-migrat

DISCLAIMER: This post was written entirely by me! I used AI for a little research, spelling, grammar, and comprehension checks. This post was originally shared on Hackernoon Entertain me for a moment, let's appreciate where we are today by understanding where we've been… or at least, where I've been. I remember when I first learned to code. I was bad, like really bad. But I was so curious! It all started by writing some VBA in an MS Access Database to create an IT Inventory app in the late 90s. Then I learned JavaScript, ASP (without the .Net), then C#, .Net ( I still have my .Net for Dummies book, see below) , jQuery, Python, Java, Angular, ReactJS, and Python again (yeah, had to relearn that one for some reason), and I'm sure there are others in there I forgot about. Learning to write code was rewarding! There were those days I'd spend hours on a bug, only to realize I didn't initialize the variable or forgot a semicolon. I learned .Net over a weekend thanks to the above book. I never became an artist of the craft, like some of my colleagues have (you know who you are: Josh, Kevin, and many others), but I knew how to build anything. I loved that ability: I could build anything. Pure joy! If you haven't had the joy of learning to code, do your best to learn it, because you can't prompt your way to being a Senior Engineer . As I progressed in my career, I became an architect and senior lead. I started off by leading a single engineering team, and now I support large programs and teams. All the while, I never let go of hands-on-code. I still love coding for work and my myriad of side projects. Then, a few years ago, this GenAI thing showed up. Put me in that group of: oh-no-there-goes-my-joy. Joy, yes, not my job. I love my job because I get to do what I love. I loved the dramatic rollercoasters: architecting a perfect solution, realizing it's wrong, getting to write every line of code, chasing impossible bugs, panicking with deadlines, late nights chasing hot fixes,

Online retailer AliExpress says it's shocked by largest DSA fine yet.

DoorDash has developed Entity Cache, a transparent proxy caching platform built on Envoy and Valkey to reduce redundant service-to-service requests across its microservices architecture. Operating within DoorDash’s service mesh, the platform serves over 1.5M requests per second with 99.99999% availability through caching, event-driven invalidation, failure handling, and performance optimizations. By Leela Kumili

From countertop kits to a bathroom bucket full of straw and beyond, I spent a year testing popular mushroom-growing methods. Here’s what fruited—and what just grew mold.

A recent update made video playback the default for songs and podcasts with a visual component. If you just want to listen to music—and save your data and battery—follow these tips.

The end of the FIFA Men’s World Cup is nigh. Here’s how to watch the final games and the first ever World Cup halftime show.

Our remote team clocked serious hours walking, working, and sometimes jogging to find the best under-desk treadmills for home offices and small spaces.