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Originally published at vinpatel.com What is actually inside the Department of Energy's new Genesis Open Models Initiative? Right now, the honest answer is: a name, a URL, and a lab crest. The claim, as DOE has made it by putting the initiative live at genesisopenmodels.anl.gov, is that the federal government is now in the open-model business — training or releasing AI systems the way Meta releases Llama or Mistral releases its weights, except under a federal national lab's letterhead instead of a Silicon Valley one. The .anl.gov domain ties it directly to Argonne National Laboratory, a DOE lab that runs some of the country's largest public research compute. Here is what is measurable today: a hostname registered under a DOE national laboratory's domain, and a title. That is the entirety of what's publicly documented about this launch right now. No model card. No parameter count. No benchmark table. No license terms. No dataset provenance. If you went looking for the thing the name promises — an open model you can download, fine-tune, or audit — you'd come away with a URL and nothing to point a training run or a procurement memo at yet. That gap is not evidence of a bluff. It's what federal AI launches look like structurally. National labs run compute clusters under export-control review, multi-agency sign-off, and clearance processes that have nothing to do with how fast a model can actually train. A private lab ships a checkpoint the day it clears internal review. A DOE initiative clears communications, legal, and interagency review before it clears a single line of a model card — and the announcement is often the artifact that exists first, because it's the cheapest one to produce. The site can go live in an afternoon. The weights cannot. What would actually close this gap is specific and checkable: a published model card with parameter counts and training data provenance, an open license attached to real downloadable weights, and benchmark numbers outside resear

With rising temperatures and drier conditions, fires are more likely to explode. And with more people living next to forests, the results can be catastrophic.

Fitted with telescopes, the plane did our longest imaging of the Sun's corona.

Some of the biggest names on Google's AI team got new jobs this week. In some cases, including for legendary Googler Jeff Dean, those jobs are no longer at Google. Given that Google's models seem to be behind the best of what's coming out of anthropic and OpenAI, is this a sign of Google in […]

The Quest Begins (The "Why") Honestly, I still remember the first time I tried to build an autocomplete widget for a side‑project. I had a list of 200 k product names, a simple filter that ran on every keystroke, and the UI felt like wading through molasses. Each keypress triggered a full scan of the list, and with a few users typing at once the browser would start to lag. I was stuck in a loop that felt like the infamous “boss fight” where you keep hitting the same pattern over and over, hoping for a different outcome. I kept asking myself: There has to be a smarter way. Why am I re‑checking the same prefixes again and again? If ten users type “tea”, why do I walk through the whole dictionary ten separate times? That question turned into a mini‑quest, and the treasure at the end was the trie data structure. The Revelation (The Insight) Look, the magic of a trie isn’t that it’s some exotic tree; it’s that it stores words by their shared prefixes . Imagine you have the words “cat”, “car”, “cart”, and “dog”. In a trie you’d have a root node, then a c branch that splits into a → t (for “cat”) and a → r → t (for “cart”), while “dog” lives on its own d → o → g path. Every common prefix is stored once , and you can walk down the tree following the characters of a query to land exactly at the node that represents all words with that prefix. Why does this give us O(L + K) time for autocomplete, where L is the length of the prefix and K is the number of results? Walking the trie follows the prefix character‑by‑character → O(L). From that node we just need to collect all words in its subtree. If we keep a list of words at each node (or run a DFS), we touch each result once → O(K). No extra work for words that don’t share the prefix. Contrast that with the naive filter approach: O(N × L) where N is the total dictionary size. For a large N, the trie is a game‑changer—it’s like switching from swinging a blunt sword to wielding a lightsaber that cuts through the prefix forest in

We expected them to be there, but they had previously been too small to see.

Introduction: Cloud computing has transformed the way individuals, businesses, educational institutions, and governments store, manage, and access data and applications. Rather than relying solely on physical servers and local infrastructure, cloud computing allows users to access computing resources over the internet on demand. Popular cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform offer scalable, secure, and cost-effective solutions that support everything from email services to artificial intelligence and big data analytics. Although cloud computing offers numerous benefits, it also presents certain challenges that organizations should consider before adopting cloud-based solutions. Understanding both the advantages and disadvantages helps businesses make informed decisions that align with their operational and strategic goals. What is Cloud Computing? Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and artificial intelligence—over the internet ("the cloud"). Instead of purchasing and maintaining expensive hardware, users pay only for the resources they consume, making cloud computing flexible and cost-efficient. Advantages of Cloud Computing: Cost Savings One of the greatest advantages of cloud computing is its ability to reduce IT costs. Organizations no longer need to invest heavily in purchasing servers, networking equipment, and data centers. Cloud providers also handle hardware maintenance and software updates, reducing operational expenses. Scalability and Flexibility Cloud computing enables organizations to scale resources up or down depending on demand. Businesses experiencing seasonal spikes can quickly allocate additional computing resources without purchasing new hardware. High Availability and Reliability Leading cloud providers maintain multiple geographically distributed data centers. This redundancy ensures high avai

Today, I’m talking with Gaby Del Valle, a policy reporter here at The Verge, about the growing backlash against AI data centers. Gaby recently reported a fantastic piece about Hernando County, Florida, where last month the county commission unanimously approved a yearlong moratorium on data center construction. She attended a protest there organized by a […]

Here’s when some of this year’s most anticipated astronomical events will happen and the best way to observe them.

TL;DR: Grafana Agent reached End-of-Life on November 1, 2025 and has been replaced by Grafana Alloy. Alloy consolidates Agent's Static mode, Flow mode, and Kubernetes Operator into a single collector built on the OpenTelemetry Collector while maintaining native support for Prometheus and Loki. If you're using Flow mode, migration is relatively straightforward. If you're using Static mode, the migration process will involve reviewing and testing the converted configuration. Before switching over, verify relabeling rules, recheck resource usage, and confirm that Prometheus and Loki are receiving the same data and labels as before. If you're still running Promtail, it's worth migrating both to Alloy at the same time since Promtail is also End-of-Life. If you deployed Grafana Agent a couple of years ago, there's a good chance you haven't thought about it since. It quietly collects metrics, ships logs, and generally stays out of the way. What you may not realize is that Grafana Agent reached End-of-Life on November 1, 2025. That includes Static mode, Flow mode, and the Kubernetes Operator. Grafana Labs has stopped creating bug fixes, security patches, and official support. If you're still running it, your collection layer is probably still performing normally, but is now unsupported. That doesn't necessarily mean it will stop working tomorrow, plenty of unsupported software continues running for years. It does mean you're taking on the risk yourself, especially as the rest of your monitoring stack continues to evolve. This article covers why Grafana Labs replaced Agent with Alloy, what actually changes during the migration, and where people tend to run into problems. Why Grafana Agent was deprecated One of the biggest issues with Grafana Agent is that it was essentially three agents, not one product: Static mode, which used YAML and looked similar to Prometheus. Flow mode, which introduced a component-based configuration using River. The Kubernetes Operator, which manage

Your AI bill tripled last quarter. Your CTO forwarded you an article about companies saving 70% by switching to open models. Now someone is asking you to figure out what that would actually look like. I spent the last few weeks digging into this. The numbers, the hardware, the real trade-offs. Here's what I found, with enough specifics that you can actually make a decision rather than just nodding along to another "open source is the future" think piece. What "Open Models" Actually Means When someone says "open model" they mean an AI model where the weights (the learned parameters that make the model work) are publicly downloadable. You grab the file, run it on your hardware, and you don't pay anyone per request. The big names right now: Meta's Llama 4, DeepSeek V4, Zhipu's GLM-5.2, Moonshot's Kimi K3, Alibaba's Qwen 3.5, and Google's Gemma 4. These aren't toys. Some of them genuinely compete with the frontier models on real benchmarks. Chinese open models now handle over 30% of enterprise traffic on OpenRouter, up from 4.5% in early 2025. That's a massive shift in barely a year. The Architecture: What You Actually Need You want your team to use an open model. Here's the stack, from bottom to top. Hardware (The Expensive Part) A model is a giant file. We're talking anywhere from 4 GB (a small 7B model, quantized) to 1.5 TB (Kimi K3, full weights). That entire file needs to sit in GPU memory to run fast. Why GPU memory specifically? Because generating each word in a response requires billions of multiply-and-add operations. GPUs do thousands of these in parallel. A CPU does them one at a time. The practical difference: a 7B model on a CPU generates 2-5 tokens per second (painfully slow for interactive use). The same model on a GPU generates 30-80 tokens per second (feels instant). For one person on a CPU, it might be tolerable. For a team of 10 all hitting the same endpoint? Unusable. Requests queue up and everyone waits 30-60 seconds for responses. Think of it like

Governor who touted Texas as AI “epicenter” pauses data center grid connections.

Attribution is preliminary , and so far it seems no real damage. And it seems like this is a campaign that has targeted at least seven states . And, because this is where the US is right now, Trump doesn’t believe it’s Iran and that Minnesota…I guess…hacked itself. “I think I blame it on Minnesota because they’re grossly incompetent,” Trump said. “I would blame it on Minnesota and the governor, the corrupt governor of Minnesota. They like to say, ‘Oh, it’s Iran.’ Iran should be so lucky. Iran’s got bigger problems than worrying about Minnesota.”...

Puck’s Matthew Belloni says AI has quietly become part of everyday filmmaking. The battle now isn’t whether Hollywood will use the technology—it’s who controls what’ll come next.

Also: Cyborg diving suits for cockroaches, why sleepy sperm whales blow bubbles, Betelgeuse's companion star.

Today, I’m talking with Toni Schneider, who is the brand new CEO of the social platform Bluesky — he formally took over after a short stint as interim CEO. This is one of my favorite kinds of interviews to do on Decoder, because a couple years ago, we had Bluesky’s prior CEO, Jay Graber, on […]

Andrea Peruffo discusses the evolution of WebAssembly beyond the browser and its growing role on the server-side JVM. He covers performance advancements in Wasm runtimes, moving from interpreters to efficient JIT compilation, and explores real-world production use cases ranging from edge computing platforms to modular plugin architectures. By Andrea Peruffo

From how galaxies form to the beginnings of the universe, the images from the space telescope are giving cosmologists new data to explore—and theories to build out.

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

Last week I was doing a routine check on a CyberPanel server and noticed something that didn't add...