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For decades, the senator has argued that concentrated wealth threatened American democracy. Now he’s betting that frustration with Big Tech, billionaires, and unchecked AI is reaching a tipping point.
IndexCache: Killing the Indexer's O(NL²) Bottleneck in DeepSeek Sparse Attention Notes from my notebook on GLM-5.2 / DeepSeek Sparse Attention (DSA), reconstructed from the IndexCache paper (Bai, Dong et al., Tsinghua + Z.ai, 2026) — the mechanism behind GLM-5.2's "IndexShare." 1. Why this exists — the bottleneck nobody talks about DSA's whole pitch is: don't do full O(L²) attention, instead let a cheap lightning indexer look at all preceding tokens and pick the top-k (k=2048) that actually matter, then do real attention only on those. That drops core attention from O(L²) → O(Lk). Great — except I missed this the first time I read DSA: the indexer itself is still O(L²) . It has to score every preceding token against the query to decide who's in the top-k. So across N layers you've traded one O(L²) cost for N separate O(L²) costs — total O(NL²). At long context this indexer becomes the dominant cost, not the attention it was supposed to fix. Adding the indexer is "DSA on steroids" because it kills DSA's one real bottleneck (full attention) — but in doing so, it grows its own. The indexer is cheap per-FLOP (few heads, low-rank, FP8) but it still runs at every single layer. The fix the paper proposes isn't a smarter indexer — it's don't run it every layer at all. 2. The core insight: adjacent layers pick almost the same tokens If you measure pairwise overlap between the top-k token sets selected by each layer's indexer, adjacent layers share 70–100% of their picks. The heatmap even shows block structure — clusters of layers (e.g. layers 3–5, 17–30, etc.) that all converge on roughly the same "important" tokens. So most of the O(NL²) indexer cost is redundant computation of the same answer. This motivates IndexCache : split the N layers into two roles — F (Full) layers — run their own indexer, compute fresh top-k, cache it. S (Shared) layers — skip the indexer entirely, just reuse the nearest preceding F layer's cached top-k. The first layer is always F (has to seed the
My "serverless" database was billing me like it never slept. Neon has this great feature called scale-to-zero. Your Postgres compute suspends when it's idle, so you only pay for the queries you actually run. For a pre-revenue product, that should cost a few cents a month. Mine ran 24/7. The compute never once scaled to zero. The culprit wasn't my app logic. It was my database driver. I was using postgres-js, which holds a persistent connection open to the database. From Neon's side, an open connection looks like activity, so it never suspended. It just stayed awake and kept quietly billing me. The fix was basically one conceptual change: switch to @neondatabase/serverless, the HTTP driver. Instead of a long-lived connection, every query becomes a stateless one-shot HTTP request. The query fires, the connection closes, and the compute is free to suspend. Scale-to-zero finally worked. The lesson I keep relearning as a solo founder: "serverless" is a property of your whole stack, not a checkbox on one service. One persistent connection upstream and the whole cost model breaks.
By mid-afternoon, you can know where your floor will close by end of day — accurately enough to make the remaining hours a decision, not a guess. Here's how intraday performance forecasting works and what it takes to build it. The Problem With Yesterday's Numbers Most contact centers have end-of-day metrics. Dials, connects, conversion rate against target. Those numbers are accurate, useful for trend analysis, and arrive the next morning. By the time you see them, the day is already over. The decisions that drive outcomes happen during the day — in real time, when hours remain to influence the result. Do you push harder in the final stretch? Adjust campaign priority? Pull a server that's underperforming? Those decisions get made in the afternoon with one question underneath all of them: where are we going to close? If you're answering that question with yesterday's data and experienced intuition, you're working with an information deficit that compounds every day it stays open. How Intraday Forecasting Works The system records dial conversion rates at regular intervals throughout the business day. Not a snapshot at end of day. A continuous read of how the floor is performing as it performs. Every morning, before the floor opens, the model retrains. It processes the intraday conversion patterns from previous days — how conversion tends to develop through the morning, when it typically accelerates, when it softens, how afternoon performance differs from morning — and calibrates to the current operation's historical data. As the day runs, the forecast updates on a regular schedule. Each update incorporates actual conversion data that's come in, narrowing the prediction window. By mid-afternoon, with hours remaining, the model's error range has compressed enough that the closing metric is predictable within an actionable range. Not a rough estimate. A forecast with a documented accuracy track. What this changes in practice: Before the forecasting system, the afternoon c
It’s time for our annual Fourth of July grill episode here at Decoder. This is when we invite the CEOs of outdoor cooking companies onto the show to explain just how their businesses kind of look like every other business. And this is a very special edition. Today I’m talking to Roger Dahle, the CEO […]
NBCUniversal and Sky will be spun off into separate companies.
Comcast has announced plans to separate itself into two publicly traded companies, spinning off its NBCUniversal and Sky broadcasting arms. The shake up aims to protect the media conglomerate's profitable broadband and wireless brand, which will retain the "Comcast" company name, as its media and entertainment business - now collectively named "NBCUniversal" - faces increasing […]
In this episode, Heroku co-founder and Ink & Switch founder Adam Wiggins argues for a 'local-first' architecture that reconciles cloud-based collaboration with the performance and data ownership of local software. He explores the role of CRDTs and version control primitives in non-code domains, and examines how a hybrid AI future might leverage local models for core productivity tasks. By Adam Wiggins
I am committed to understand how systems actually work. I'm working on a multi-node lab to follow the complete path of a request from Python APIs to Linux processes, through Docker containers, networking and observability. The idea is simple: build a system that observes another system to understand the abstraction layers behind modern infrastructure. This project is about learning by building, experimenting and understanding what happens under the hood. Link: [ https://github.com/daniloprandi/devops-network-automation-lab ] DevOps #Linux #Python #Docker #Networking #Observability #Infrastructure
The founding story of Nest is pretty much a perfect tech myth. A legendary product maker (in this case, Tony Fadell) helps create one of the most successful products ever (the iPhone) and then rides off into the sunset to enjoy the rest of his life, only to have an experience that drags him back […]
The Euclid space telescope's stunning photo of our galaxy's “crowded heart” captures more than 60 million stars.
Factors like the short interval between the two powerful quakes and different types of soil led to some structures collapsing while others stayed standing.
The asteroid will be visible for several nights from different parts of the world. We’ll tell you when and where to look, and what equipment you’ll need to spot it.
Meta is, by and large, a company built on other companies' ideas. It has almost perfected the strategy: wait for a new platform or social mechanic to take off, then either buy or clone it, put it next to Meta's unmatched user base and advertising engine, and watch the money pile up. Well, the next […]
Amazon-owned MGM Studios’ decision to drop the OpenAI movie is just part of AI and film industries becoming increasingly intertwined. On Uncanny Valley, we take a look at where this is all headed.
Picture this: You just spent weeks building an awesome new feature. It's fully tested and ready to go. But when you hit "Deploy," your entire application goes down for 5 minutes, and your users are met with a blank loading screen. Not a good look. In the world of DevOps and Cloud Infrastructure, how you roll out updates matters just as much as the code itself. AWS Elastic Beanstalk gives us 5 distinct deployment policies to handle this smoothly. Let's break them down from simplest to most robust so you know exactly which one to pick for your next project. 1. All at Once (The Speed Demon) This is the simplest method. Elastic Beanstalk takes all your existing servers, shuts them down, deploys the new code, and boots them back up simultaneously. [ Old App ] ─> SHUTDOWN ALL ─> DEPLOY NEW ─> [ New App ] The Good : It’s incredibly fast. The Bad : Your app goes completely offline during the deploy. When to use it : Only in development environments where downtime doesn't matter. Never use this in production! 2. Rolling (The Line Worker) Instead of updating everything at once, Beanstalk splits your servers into batches (e.g., 2 at a time). It takes the first batch offline, updates them, brings them back online, and then moves to the next batch. Batch 1: [ Updating... ] Batch 2: [ Running Old App ] Batch 1: [ Running New App ] Batch 2: [ Updating... ] The Good : No total downtime! Your app stays online. The Bad : While a batch is updating, your overall server capacity drops. Plus, users might experience a "mixed state" where refreshing switches them between the old and new version. When to use it : Production environments that can handle a temporary dip in bandwidth. 3. Rolling with Additional Batch (The Safe Substitution) To fix the capacity problem of standard rolling, this policy launches a brand new batch of instances first. Once those new servers are healthy and running the updated code, Beanstalk starts rolling the update through the old servers. [Old Servers: 100% Capa
At the right point of the orbit and stellar cycle, the star's chromosphere brightens.
And by viral I mean from $0 to $31. Umami told me Clew Directive got 14 visits last month. AWS told me I owed $31 for it. That works out to $2.21 a visitor, which would make it the most expensive free learning-path tool in California. Spoiler alert: 14 visitors, $31, and not a single one of them was the reason. Something was off. Here is how Amazon Q, Claude, and a few hours of reading my own code untangled it. The app turned out to be innocent. What Clew Directive is, quickly A free, stateless tool that builds you a personalized AI learning-path PDF. You take a 60-second Vibe Check, four questions about your goals and how you learn, and it maps you to free, verified resources and hands you a briefing. No accounts, no database, no paywall, nothing stored about you. It runs on Amazon Nova, which is why it costs close to nothing to operate, which is also why a $31 bill made no sense. The name is the Theseus kind of clew. A ball of thread to find your way out of the maze. Less hype, more direction. Live at clewdirective.com . The number that didn't add up Twelve visitors, 14 visits, 93% bounce, average session about a minute. Referrers from Bing, Google, Yahoo, GitHub. Visitors from the US, India, Netherlands, Egypt, Ethiopia, Singapore. Mostly crawlers stopping by to say hello. A few curious humans and a parade of bots is not a $31 month. So either every visit was doing something enormous, or the bill was never about visits at all. The dashboard lied, politely. An Amazon Q Story My cost tracker said Clew Directive was running on Claude Sonnet. Sonnet is the expensive one. Case closed, right? I opened the repo. Clew Directive does not run Sonnet. The Navigator agent runs Amazon Nova 2 Lite. Scout and Curator run Nova Micro. The IAM policy is scoped to Nova ARNs only, so a Sonnet call from these functions would come back AccessDenied. The app physically cannot bill Sonnet. The math agreed. A full learning-path generation on Nova costs about two-tenths of a cent. Fourtee
devtools #infrastructureascode #cdk #aws Index TL;DR Major Features Bedrock AgentCore — From Alpha to Stable Fn::GetStackOutput & Weak Cross-Stack References Validations Framework Performance Improvements CloudWatch PromQL Alarms CLI Improvements New L2 Constructs Service Enhancements Community Highlights Community Content & Resources How Can You Be Involved Hey CDK community! Here's an update covering everything that shipped in April and May 2026. TL;DR Bedrock AgentCore graduated to stable — production-ready AI agent infrastructure with semver guarantees. Cross-region references got a major upgrade with native Fn::GetStackOutput support and weak cross-stack references. The new Validations framework replaces policyValidationBeta1 with a richer plugin system. And file fingerprinting is ~33% faster with persistent asset caching. These features are available in aws-cdk-lib v2.247.0 through v2.257.0 and aws-cdk CLI v2.1116.0 through v2.1125.0. Full changelogs on GitHub Releases ( Library | CLI ). Major Features Bedrock AgentCore — From Alpha to Stable The @aws-cdk/aws-bedrock-agentcore-alpha module has graduated to aws-cdk-lib/aws-bedrockagentcore — stable APIs, semver guarantees, production-ready. If you've been building AI agents with Bedrock but held off on CDK because of the alpha label, it's time to upgrade. ( #37876 ) AgentCore provides the core infrastructure for building AI agents: runtimes, gateways, identity management, observability, and online evaluation. The Policy submodule remains in alpha as it continues to evolve rapidly. ┌─────────────────────────────────────────────────────┐ │ Bedrock AgentCore (Stable) │ ├─────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ │ │ Runtime │ │ Gateway │ │ Identity │ │ │ │ (L2) │ │ (L2) │ │ (L2) │ │ │ └────┬─────┘ └────┬─────┘ └────────┬─────────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ │ │Observa- │ │Online │ │ Policy Engine │ │ │
Our infrastructure "documentation" was a Google Sheet. Anyone on the team could edit it. Nobody...