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At some point every growing Terraform project hits a wall. Plans that used to finish in seconds now take minutes. Applies feel risky because hundreds of resources share a single blast radius. Colleagues avoid running terraform plan because it hammers cloud APIs hard enough to trigger throttling. The state file itself becomes a liability — large, slow to lock, and one bad write away from corruption. This guide covers the symptoms of an oversized state, the band-aids teams reach for, and the structural fix that actually works. How Terraform state works under the hood Every terraform plan does two things: Refresh — for every resource in state, Terraform calls the provider's API to read the current real-world status. A state with 500 resources means 500+ API calls, often more when resources have nested data sources. Diff — compare the refreshed state against the desired configuration and produce a change set. The refresh phase is the bottleneck. It's sequential per provider (parallelism helps across providers, not within one), and every resource pays the cost whether you changed it or not. Adding ten resources to a 500-resource state doesn't make plans 2% slower — it makes the refresh 2% slower on every single plan, for every engineer, forever. Symptoms of a state that's too large Slow plans The most visible symptom. Plan time scales with resource count because every resource is refreshed on every plan, regardless of whether its configuration changed. The exact speed depends on provider — AWS resources with complex nested structures (IAM policies, security group rules) are slower to refresh than simple ones, and Azure resources that require multiple API calls per refresh are worse still. These aren't edge cases — users regularly report 2,900-resource states taking 20–25 minutes to plan and 1,600-resource states taking 8+ minutes . Even starting Terraform with a large state can take minutes before a single API call is made . There's a long-standing proposal for terraform

Elastic open-sourced Atlas, a system built on Elasticsearch that maintains three categories of memory for agents. Atlas integrates with agents via MCP and maintains per-user isolation of memories. When evaluated on question-answering capability, it scored 0.89 Recall@10. By Anthony Alford

SokoFlow Build Log — Month 1 of 4 Last semester I set out on a new strategic plan to level up my software development skills through deliberate, project-based learning. That work produced one of the most ambitious things I've built so far: Sim-Pesa , a local-first transactional appliance that lets developers working in the M-Pesa ecosystem test and simulate STK Push workflows entirely on their own machines, without depending on the Daraja sandbox. I documented that build in 16 weekly posts, which you can find here . This semester, the focus shifts — from fintech foundations to cloud-native integration and real-world systems. The flagship project is SokoFlow , a conversational ERP for small Kenyan shopkeepers to track inventory and record sales entirely through WhatsApp chat. No app to download, no training session required — just natural language. Where Sim-Pesa lived in a controlled, predictable transactional world, SokoFlow steps into the mess of cloud-native reality: third-party API failures, webhook signature verification, the statelessness of HTTP, and container orchestration. The target audience shifts too — Kenyan SMEs operating on infrastructure that is often unreliable by design, not by exception. It's an ambitious project, but the goal was always to learn as much as possible from it. With the plan in place, I got to work. 1. The Vision of a Headless ERP The first real question I had to answer before writing a line of code: what does "headless" actually mean? Headless architecture decouples the frontend — the "head," or user interface — from the backend, the "body" that holds the data and business logic. A conventional ERP bundles both: backend plus a dashboard or UI on top. A headless ERP, by contrast, is just the engine. The brain. There's no built-in screen. So how do users interact with a system that has no interface of its own? SokoFlow doesn't actually care. It could be: WhatsApp SMS A web app A mobile app A voice assistant In this case, the "frontend

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.