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Here's a list of the best wireless headphones you can buy right now, as reviewed by Engadget editors.

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 […]

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Originally published at https://blog.pathvector.dev/protocol-in-code-bgp-07/ — part of the free Protocol Lab series. This post is part of Protocol in Code , a free series that reads network protocols as logic — inputs, state, and branches — rather than as configuration examples. Every module points at one real Python file and asks you to read it the way you'd read any other code: what comes in, what mutates, where does control leave early. The source lives at github.com/pathvector-studio/protocol-in-code . Note: If you're newer to this and want to run things before you read things, start with Protocol Lab — the hands-on companion series that builds the muscle memory this one assumes. The question How does local import policy change or reject a path before best-path selection runs? That's the whole module in one line, and it hides a claim worth being suspicious of. Best-path selection in BGP is the famous part — the ordered tiebreaker list everyone half-remembers: highest weight, highest local_pref , shortest AS path, and so on. It's easy to treat that comparison as the decision point, as if routes arrive from peers and get ranked. They don't arrive and get ranked. They arrive, get rewritten , and then get ranked. Import policy is a function that runs between the wire and the comparison, and it has two powers: it can change the values the comparison reads, and it can make the candidate not exist at all. Which means the interesting question isn't "who won best-path" but "what did best-path actually receive." Read the code The file is src/protocol_in_code/bgp/import_policy.py . It's short enough to hold in your head all at once, which is the point — the shape is the lesson. Start with the policy object: @dataclass ( frozen = True ) class ImportPolicy : local_pref_override : int | None = None weight : int = 0 reject_next_hops : tuple [ str , ...] = () reject_invalid : bool = False Four knobs, and notice they're not four of the same thing. Two of them ( local_pref_overri

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.

Google’s AI Overviews now appear in 43% of searches, underscoring how quickly AI-generated answers are becoming the default way people discover information online.

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

TLDR, Let's Seal gives the finger to Adobe and every doc signing tool (docusign, google, etc) who pay to play with the Adobe Approved Trust List and then charge you for something that should be free. Currently even the person checking if a document/contract is sealed or code is authentic has to also be inside the same Adobe walled garden too. Verification, the part that should be free is the part everyone charges for. Thats the shape Let's Encrypt fixed for TLS, and I wanted the same thing for d

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

Spain is putting a hash chain behind every invoice, and almost everything written about it so far has been written for accountants. This is the version for whoever has to ship it. The deadlines are January 1, 2027 for companies and July 1, 2027 for sole traders. If you read something last year that said 2026, that was true until RD-ley 15/2025 moved the whole calendar back twelve months. Software vendors have been on the hook since July 2025, which is a detail worth holding on to if you sell a product that issues invoices for other people. At BeeL., we sell an API for this, so read the rest with that in mind. The requirement Each invoice your software issues has to produce a registro de facturación de alta: a record containing a defined set of fields, hashed with SHA-256, where the hash of each record folds in the hash of the one before it. One chain per issuing tax ID, growing forever, never edited. Cancelling an invoice is not a delete. It's a second record type, a registro de anulación, which goes into the same chain. Same for corrections, which come in two flavours depending on whether you're amending a difference or replacing the original document. The printed invoice carries a QR code with verification data, plus the string VERI*FACTU if you're in submitting mode. Then you either push each record to the tax agency as it happens, or you keep everything locally under stricter signing and retention rules and hand it over when asked. Written down like that, it reads like an afternoon of work. A hash function, a previous_hash column, an HTTP call. Where the estimate falls apart The chain is strictly sequential, so two workers issuing invoices for the same tax ID at the same time are racing for the same link. You need a lock per issuer, or a queue, or both, and either way concurrent issuance stops being free. Retries are worse than they look. A failed submission that you retry carelessly either duplicates a record or breaks the chain, and a broken chain isn't someth