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The ports look the same, but one is much faster than the other.

OpenAI's Hugging Face breach has reignited debate over AI alignment and control, exposing competing views on whether increasingly capable AI should be better aligned, better contained, or both.

Amazon's Leo subsidiary has launched more than 390 satellites so far and plans to launch thousands more.

Ad-supported Peacock will be available for free to YouTube Premium subscribers early next year.

Meta on Monday said it is rolling out its Meta AI chatbot within Threads' DMs, giving users a way to chat with the AI assistant.

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Have you tried turning it off and on again?

YouTube's ad-free Premium subscription is getting another perk: access to Peacock. In an announcement on Monday, NBCUniversal says the multi-year agreement will allow Premium subscribers to stream Peacock's ad-supported shows, movies, and live sports directly through the YouTube app in 2027. YouTube will bundle Peacock in its standard subscription, which increased to $15.99 / month […]

Meta AI is coming to Threads DMs, following availability inside Facebook, Instagram, and WhatsApp DMs.

New to the GitHub Copilot app? Learn how to start projects, work with AI agents, explore canvases, and streamline your development workflow. The post GitHub Copilot app for Beginners: Getting started appeared first on The GitHub Blog .

The government says destroying his own data during an airport interrogation was illegal.

This series follows a fictional conversation between an experienced engineer and his nephew. Every episode explores one stage of how software moves from an idea to production. 👦 Nephew: Uncle, requirements are clear. I checked the codebase — there's already a FavoritesService I can extend for Wishlist. Now can I open VS Code? 👨‍🦳 Uncle: Almost. Tell me — what do you think HLD even is ? You've heard the term in every interview. What do you think it actually means? 👦 Nephew: Some kind of... diagram? Boxes connected with lines, before you start coding? 👨‍🦳 Uncle: That's what it looks like. That's not what it's for . Let me ask differently. Why do you think experienced engineers insist on drawing this before touching code, when they could just start building? 👦 Nephew: ...to plan the work? 👨‍🦳 Uncle: Closer, but still not it. Here's the real answer: HLD exists to decide, in advance, where the walls go — so that six months from now, when someone adds a new feature, they know exactly which room to build it in, without knocking down a wall that was holding up the ceiling. 👦 Nephew: That's a strange way to describe a diagram. 👨‍🦳 Uncle: Then let me show you, instead of describing it. That's the only way this actually lands. What Talks to What 👨‍🦳 Uncle: Suppose we're building this at Flipkart. Not a college project — a company with hundreds of live services, where breaking one thing can affect ten others you've never even heard of. Here's the simplest picture for Wishlist. Frontend ↓ Wishlist API ↓ Wishlist Service ↓ Database 👦 Nephew: That's it? Four boxes? 👨‍🦳 Uncle: That's it. HLD answers exactly one question, and nothing more — what talks to what. Not how the button looks. Not what fields the database table has. Just: which component calls which, and in what direction. 👦 Nephew: Then why does everyone treat it like it's such a big deal? This took ten seconds to draw. 👨‍🦳 Uncle: Because the value isn't in the ten seconds you spend drawing it today. The value is in what i

SQL is quite flexible, making it easy to write a single query that works for two situations: one without a parameter and a WHERE clause, and another with a parameter for filtering, all in the same SQL query. For example, I came across a benchmark comparing MongoDB and PostgreSQL that shows how to handle pagination effectively—by avoiding OFFSET and instead using the last value to fetch the next set of results. The first page includes a WHERE clause along with ORDER BY and LIMIT, while the following pages add an extra WHERE condition. In the MongoDB version of this benchmark, the filter is handled within the application, which leads to two separate queries for these scenarios. export async function getOrders ( cursor ) { const match = cursor ? { timestamp : { $gt : new Date ( cursor ) } } : {}; const rows = await orders . aggregate ([ { $match : match }, { $sort : { timestamp : 1 } }, { $limit : PAGE_SIZE }, ]) We can do the same in PostgreSQL using a single prepared statement. SQL is such a powerful language that it often feels tempting to write it this way: SELECT * FROM orders WHERE $ 1 :: timestamptz IS NULL OR "timestamp" > $ 1 ORDER BY "timestamp" ASC LIMIT $ { PAGE_SIZE } If $1 is NULL, it skips the second condition in the OR clause and retrieves all rows without filters, resulting in a broad fetch. When $1 has a value, it filters the results using that specific value, enabling a more targeted search. However, using a generic query can sometimes lead to a less-than-ideal execution plan that's not perfectly tailored for each specific situation. I gave it a try: drop table if exists orders ; create table orders ( order_id text primary key , "timestamp" timestamptz not null ); create index idx_orders_timestamp on orders ( "timestamp" ); insert into orders select 'ORD-' || g , '2025-01-01' :: timestamptz + g * interval '1 minute' from generate_series ( 1 , 5000000 ) as g ; analyze orders ; prepare getorders ( timestamptz , int ) as select * from orders where $ 1 :

Most experiments that come back "no clear winner" were unwinnable on the day they launched. The data could not resolve an effect that size, and no amount of extra runtime was going to change that. You can find this out in about two minutes, before you spend anything, with one formula and a resampling pass over your own data. Here is the check, in three steps. Step 1. Compute the smallest lift your data can see For a two-arm test on a conversion rate, the smallest lift detectable at 95% confidence and 80% power is a one-liner: from math import sqrt Z_ALPHA = 1.96 # two-sided 95% Z_BETA = 0.84 # 80% power def mde ( baseline_cvr : float , n_per_arm : int ) -> tuple [ float , float ]: """ Minimum detectable effect: absolute (pp) and relative (%). """ se = sqrt ( 2 * baseline_cvr * ( 1 - baseline_cvr ) / n_per_arm ) abs_lift = ( Z_ALPHA + Z_BETA ) * se return abs_lift * 100 , abs_lift / baseline_cvr * 100 At a 3% conversion rate: clicks per arm smallest lift you can detect 5,000 +32% relative 20,000 +16% relative 100,000 +7% relative Read the middle row twice. Twenty thousand clicks per arm is a serious amount of traffic for a mid-market account, and a real 15% improvement still lands inside the confidence interval. The report will say "inconclusive," and the team will read that as a verdict on the idea. It is a verdict on the instrument. Invert the same formula and the planning question gets easier: at 3% baseline, detecting a 10% lift needs about 51,000 clicks per arm, and detecting a 5% lift needs about 203,000. If your account produces 8,000 clicks a month, you now know the honest answer to "how long should we run this." Step 2. Stop assuming your conversions are independent The formula above treats every click as an independent coin flip with the same probability. Account data does not behave that way, and the gap is not small. In a corpus of 31 advertiser accounts I maintain for diagnostic work (9.46 million search term rows, roughly $133M of spend, September 2024

You already run Claude Code by hand: copy issues into a prompt, watch it work, check the diff, and if something breaks halfway through, you restart it. This works fine for one task at a time, but it falls apart when you have 10 tasks simultaneously. Claude Code is good at handling routine engineering tasks: bug fixes, dependency bumps, and small features, when the prompt is clear and the task is scoped. But when it comes to scaling, you need an infrastructure with isolated workspaces, retry logic, state that survives a restart, and tracker integration, not to waste time on babysitting. Sortie removes the manual work. You label an issue, Sortie picks it up, creates an isolated workspace, runs the agent, retries it if it stalls, and opens a pull request when it's done. This article describes how to set the entire process from an empty directory to a GitHub issue turning into a PR without you touching the keyboard in between. What you need A GitHub repository you control Export two environment variables: ANTHROPIC_API_KEY - authenticates Claude Code GITHUB_TOKEN - it's read by tracker.api_key: $GITHUB_TOKEN for polling/updating issues, and it's the same token gh pr create inside the after_run hook uses to open the PR, so it needs Issues: read/write, Contents: read, and Pull requests: read/write scopes on that repository, all on one fine-grained PAT. Push access to the repository over SSH. The after_create hook below clones with git@github.com:... , so git authenticates with your SSH key, not with GITHUB_TOKEN . Verify with ssh -T git@github.com . If you'd rather stay on one credential, swap the clone URL for https://${GITHUB_TOKEN}@github.com/yourorg/yourrepo.git and give the token Contents: read/write. In your repository, create the agent-ready label — you need it to exist before you can put it on an issue, and query_filter finds nothing without it. Creating in-progress , review , and done up front is also worth doing: GitHub does create a missing label when Sortie ap

Manually checking what technologies power a website works once or twice. After that it gets slow, repetitive, and impossible to scale. Modern developers skip the manual step and detect tech stacks in code instead. Your program reads a response, pulls out the signals, and tells you what's running. No DevTools, no guesswork. This guide shows how that detection works and how to build it in Go with the open-source tooling ProjectDiscovery maintains. External resources: github.com/projectdiscovery/wappalyzergo projectdiscovery.io If you're new to the concept, start with technology fingerprinting for developers to understand the signals behind detection. What does "programmatic detection" mean? Programmatic detection just means letting software identify technologies instead of a person doing it by hand. Your application does five things: Sends a request Reads the response Extracts signals Matches fingerprints Outputs technologies No browser, no manual inspection. The same pipeline shows up in recon platforms, developer tooling, automation pipelines, and security workflows. Read detecting website technologies using Go first if you want the foundational walkthrough. Why developers prefer automated detection Manual workflows fall apart as systems grow. Scripted detection holds up because it's fast, consistent, and drops straight into a pipeline. Speed: scan hundreds of targets in minutes. Consistency: scripts don't skip clues a tired human would. Automation: pipe results straight into the rest of your tooling. Intelligence: raw HTTP turns into something you can act on. Building a fingerprint engine yourself means reimplementing years of pattern work. A mature library like wappalyzergo saves you those hundreds of hours. How programmatic fingerprinting works Most detectors run the same four-stage pipeline. Step 1: Fetch the target Send an HTTP request and keep the headers and body. Step 2: Extract signals Look for the clues a stack leaves behind: response headers, cookies, scr

Amazon filed an FCC application on Saturday to launch a new Leo satellite constellation that will provide direct-to-device satellite service for "voice, messaging, data, and emergency services." If approved, Amazon will begin deploying the new constellation of 5,105 satellites in 2028. It says it plans to partner with mobile network operators to offer direct-to-device satellite […]

Did you know that Kiro doesn't just have a Spec-Driven Development (SDD) flow, but also a bug fix workflow that helps you resolve one issue at a time? That's one of seven features worth knowing about. If you're completely new to Kiro, it's an agentic harness for the CLI, web, IDE, iOS, and more. It helps teams and individuals do their best work while coding. I've been using it since it launched in July last year, and I keep finding features I didn't know were there. (Full disclosure: I'm a Developer Advocate at AWS, and Kiro is a part of AWS. I use it every day, and I'll be forthcoming about the parts that are still preview or experimental.) Heads up: Kiro ships fast. I've flagged the version-sensitive bits of these features inline. Check the docs if something looks different in your build. 1. Stop approving every single command After talking to a lot of people about Kiro, one of the main pieces of feedback I get is on approving commands. When Kiro asks permission to run a shell command, the default reaction is to hit yes and move on. Then it asks again for the next git command. And the next one. Press Tab instead in the CLI. This allows you to edit it and put the exact permissions you'd like. For example you can be pickier on the trust tiers: git pull --rebase # this exact command git pull * # git pull with any arguments git * # anything git * # the entire shell tool Whatever you pick persists for the session and gets stored as a regex in your agent's allowedCommands . There's also /tools trust-all , which trusts everything. It's the documented replacement for the old /acceptall , and the security docs are blunt about it: don't use it in production or with sensitive data, and you're responsible for whatever it does. One version note: on CLI v3 this moves to a permissions.yaml file, so the agent JSON advice above is v2. More on v3 in a minute. Full details: tool permissions 2. The # menu is bigger than #file in the IDE Type # in the IDE chat and you get a list of co

Accessibility has become personal for me. I am getting older, and large type is no longer an abstract preference somebody else needs. It is how I read a phone comfortably. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . I worked with accessibility experts at Sun Microsystems and learned how deep the problem goes. A label is the easy part. Real accessibility needs roles, values, ranges, actions, traversal order, live announcements, collections, focus, platform conventions, and a way to test all of it. That complexity is why full Codename One accessibility support sat dormant for a decade. We eventually added setAccessibilityText() . It was useful, but it was the poor man's version. PR #5363 replaces that single-label model with a portable semantics tree we can be proud of. Lightweight UI needs a second tree Codename One paints lightweight components into its own native surface. VoiceOver cannot inspect a Button as a UIKit button because there is no UIKit button there. TalkBack cannot walk an Android View hierarchy because most of the painted controls are not Android views. The new accessibility manager builds an immutable virtual tree beside the visual component tree. Standard controls infer their semantics. Custom controls can replace or extend them. Each port exposes that virtual tree through the platform accessibility API. The visual and semantic hierarchies can differ. A card made from five labels might need to read as one item. A chart may paint 200 points from one component, but expose each meaningful point as a virtual child. A renderer-backed list can expose stable rows even though those rows are not component instances. Standard components work without annotations Buttons, checkboxes, radio buttons, sliders, text fields, lists, tables, tabs, labels, dialogs, and containers infer their normal roles, values, states, and