标签：#Go

Upgrading Kubernetes clusters across a large enterprise fleet is often a balancing act between staying current with security patches and avoiding outages. By default, Google Kubernetes Engine (GKE) rolls out automatic upgrades progressively according to Google Cloud regional timelines. While regional rollout works well for standalone clusters, it does not understand your organization's business topology. If you run staging clusters in us-central1 and critical production clusters in us-east1 , a standard regional rollout could upgrade your production environment before your pre-production validation completes. The General Availability (GA) release of GKE rollout sequencing with custom stages solves this challenge. It provides platform teams with declarative control to sequence cluster upgrades across fleets, environments, and even distinct Google Cloud organizations according to business criticality rather than cloud geography. How rollout sequencing works Rollout sequencing builds on GKE fleet management. Fleets serve as logical boundaries for environments such as development, staging, and production. With rollout sequencing, you define an ordered pipeline of upgrade stages managed by a central resource called RolloutSequence . When GKE publishes a new automatic upgrade target for a release channel, or when you explicitly trigger a target version, the system creates a Rollout object. This rollout progresses through your defined stages sequentially: Control plane upgrades start in the first stage. Once all control planes in that stage reach the target version, a stage soak timer begins. Node upgrades run in parallel with control plane upgrades, respecting node pool upgrade strategies such as surge or blue-green. When both control planes and nodes complete their upgrade and satisfy the configured soak duration, the rollout advances to the next stage in the sequence. If an individual stage contains clusters that take longer than 30 days to finish upgrading—due to restr

The historic settlement reignites ongoing concern around how age-verification technology puts privacy at risk.

Ask any Go developer what a goroutine costs and you'll get the same answer with the exact byte count:...

The AI that powers Gboard's Rambler is coming to more Google products, including Chrome.

Link to the Source Code As a data engineer, I spend my days designing clean, optimized data structures. But at home, I face a much tougher crowd: my family. We manage our shared finances together to optimize our budget, and because of where we live and work, we have to do this in several different currencies (like USD, EUR, CZK, and UAH) Like any developer, I first tried to find a ready-made app to solve this. But I ran into a classic problem: they were either bloated with a million features we didn’t care about, or they were missing the exact features we actually needed. So, we did what any desperate family does: we opened a Google Sheet . We tracked our money there for a while, not because it was perfect, but because it helped us figure out what we actually needed from a real application. It was our "living schema design" before I wrote a single line of code. In this article, I want to show you how I looked at this problem from two sides—as a frustrated user who just wants to log expenses, and as a data engineer obsessed with clean database design. Here is the story of how I built our custom home accounting server. Part 1: Django, a Star Schema, and the Framework Battle By 2025, I was ready to replace our Google Sheet. My main programming language is Python, so I had three realistic choices: FastAPI, Flask, or Django. FastAPI is the cool kid on the block for high-speed APIs, but we didn’t expect millions of requests (unless my family suddenly grew by a factor of a million). We also needed a friendly web UI, which FastAPI isn't naturally built for. I had just used Flask for my previous project, but I wanted to challenge myself and learn something new. Django felt like an old friend I hadn't seen in years. It has amazing built-in tools (like the admin panel and great translation support), and using it was the perfect way to refresh my skills and grow as a developer. The Database: Why a "Star Schema" Actually Makes Sense As a data engineer, I didn’t want a messy data

Google says Gemini 3.5 Transcribe will soon let you use speech-to-text in any web field in Chrome.

Google has updated Gemini Audio with new transcription capabilities that automatically detect specialized jargon and more than 85 languages. Gemini 3.5 Transcribe is a new addition to the Gemini family that follows the launch of 3.5 Live Translate, and comes as we're still waiting for Google to release the Gemini 3.5 Pro model that it […]

"We're living in this world of scarcity with the Moon."

The New York Times has a long article ( alt link ) on surveillance systems aimed at babies. They are increasingly using AI. Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.

The correlation notice fired. Three sites, same anomaly type, inside the time window. The orchestrator caught it and logged it, live, against the deployed service. Clean, first try. Then I asked myself a question I almost didn't bother asking, because the thing had just worked: why did it work? The answer wasn't "because the logic is correct." It was "because Cloud Run happened to route both requests to the same running instance." Well, shit. My orchestrator was holding its list of recent risk events in a plain Python list, in process memory. Worked in local testing because there's only one process. Worked live because Cloud Run, under light traffic, often reuses the same instance instead of spinning up a second one. Neither one's a guarantee. The moment traffic patterns shifted and two readings landed on two different instances, the second instance wouldn't have a clue the first one existed. A correlation that should fire would just silently not. A bug that passes its own demo is the hardest kind to catch. There's no error to chase. There's just a checkmark. What I was building VES Fleet is a network of independent site-agents (Bori, Choba, Etche, three real survey sites in the Niger Delta). Each one reads an underground electrical survey, send current into the ground, measure how it flows back, a real physical signal of what's down there, and calibrates its own contamination-risk threshold from its own site's real history. Not a number copied from anywhere else. An orchestrator watches for the same risk signature showing up at more than one site inside a time window. It's my submission to the Fortified Enterprise Fleet track of Google's All Things Agentic Hackathon. Architectural discipline is 30% of the score there. Proving it actually runs on Google Cloud is a separate 30%. So a bug that only looked fixed was never going to survive someone actually reading the state-management story. Checking the thing that already worked Once I understood the actual failure mod

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Hoi hoi! I’m @nyaomaru, a frontend engineer who struggles to make game sounds. 😿 Have you used DSA...

When you combine the absolute safety and elegance of a purely functional language with the raw execution speed of a modern low-level language, it's like discovering that space and time are inextricably linked: a whole new dimension opens up to you. In my previous article , I introduced phpurs , a compiler backend that brings the absolute mathematical safety of PureScript to the 70% of the web that still runs on PHP. The goal was to prove that we do not have to sacrifice modern safety and ergonomics just because we target a runtime that is, at first glance, not the most natural. Today, we are looking at the exact opposite end of the spectrum. If PHP was about ultimate portability and legacy compatibility, what happens when we want pure, raw metal speed ? What happens when we want true multi-core concurrency, static native binaries, and a garbage collector designed for extreme high-throughput? Say hello to one of my recent projects: gopurs , a super-optimized PureScript-to-Go compiler. And let me spoil the end of the story right away: by combining the high-level semantic purity of PureScript with the raw execution force of modern Go, the final compiled code matches Chez Scheme speed on pure computational benchmarks. Yes, Chez Scheme, one of the absolute historical champions of functional AOT compilation (i.e., decades of compiler optimizations). This will be a shorter article in the series, but here is a quick version of the story... Breaking the AOT ceiling: how TAST changed everything Historically, compiling highly polymorphic functional languages (like PureScript or Haskell) to statically typed languages like Go has often been a nightmare. Older attempts often relied on mapping everything to Go's interface{} (or any ). It works, but it's a performance sacrifice. Every primitive value you assign to an interface{} is boxed and escapes to the heap , generating massive pressure on Go's Garbage Collector. To solve this, gopurs initially used a flat Value struct (a tagge

During the original US missions to the moon in the 1960s and ’70s, astronauts relied on radio-­frequency systems to communicate, sending grainy, low-quality images and videos to Earth. But on the Artemis II Orion mission in April, spacecraft transmitted crystal-clear footage at speeds on par with those of home internet connections. The livestreams so many…

Many people find AI-based chatbots helpful in keeping up with news, but a study by Pattie Maes and her colleagues at the MIT Media Lab points to a big problem with this strategy. Participants who evaluated paired news headlines and images over the course of four weeks were initially 21% percent more accurate at telling…

When remotely operated vehicles settle on the seafloor or dig through a sand bed, they can kick up clouds of sediment that onboard cameras struggle to see through. Often, the only thing to do is wait until the dust settles. But a new system developed by Amy Phung, SM ’23, PhD ’26, and her advisor…

Oral and forehead thermometers may not accurately capture a person’s core body temperature, and the few ingestible temperature sensors on the market are so big they are hard to swallow and risk obstructing the GI tract. But MIT engineers created one that can send continuous temperature updates at a size of just six by four…

Vaccines that turn the body’s immune system against tumors have shown promise in clinical trials, and a handful have been FDA approved for certain cancers. In many patients, however, these vaccines don’t stimulate enough of a response, and the approach some researchers have taken to strengthening it—delivering the vaccine along with immune-stimulating molecules called cytokines—can…

When Don Yansen ’63 arrived at the MIT AgeLab for a study on technology in care­giving for older adults, he didn’t plan to launch another company. But when he heard participants talk about how hard modern devices can be to use, he decided to develop an alternative. Yansen, a serial entrepreneur who retired to care for…

For Rupert Young ’95, SM ’95, his career in data science and cybersecurity began when his grandfather gifted him thousands of stamps: He built intricate databases to catalogue them, displaying the “precise eye” for detail and nuance that his MIT application essay said would make him a good engineer. Young is now chief product officer…