标签：#an

"Black hole stars," making cookies from plastic, tiny sound-powered drones, and more.

OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features. By Daniel Dominguez

An argument about a digital file is almost never lost over what the file says. It is lost one question earlier: How do we know that is the file you received, and not the one you edited last night? If the answer is "trust me", you have already lost. However right you are on the substance. This problem is not exclusive to a courtroom. The auditor receiving a log dump has it. So does the team documenting an incident, or anyone keeping a copy of a contract signed over email. In every case the need is the same: being able to prove that a set of bytes has not changed since a given moment — and having that proved by someone who is not you . That is why I wrote Tunjo : a Rust tool that walks material read-only, computes its fingerprint, and signs a record anyone can verify. Why a tree and not a hash The obvious approach would be to concatenate everything and take one SHA-256. It works, and it is useless in practice. When someone disputes one file — a specific email out of four thousand — a single hash leaves you two options: hand over the complete set so it can be recomputed, or ask to be believed. The first exposes material that has no business being exposed; the second is not evidence. A Merkle tree solves exactly that. Each file is a leaf, each pair of nodes combines upward, and a root remains. To prove a leaf belongs to that root, you only need to show that leaf and the path of hashes to the top: a few kilobytes. The rest of the set is never touched. Two details of the tree that are not optional: // Domain separation: a leaf can never pass itself off as an internal node. h .update ([ 0x00 ]); // leaf h .update ([ 0x01 ]); // internal node // And the root binds the number of leaves. h .update ([ 0x02 ]); h .update ( n .to_be_bytes ()); Without the first, a leaf hash could be presented as if it were a node of the tree. Without the second you get the classic ambiguity of trees with an odd number of leaves: two different sets can produce the same root. It is an old, well-kn

Historically, state health departments determine cases and deaths, not the CDC.

Disclaimer: This is a side project, not a production story. The slow-query problem is real, but the database is synthetic data I generated to make it show up on demand. I have no connection to PgCache. Everything here is in a repo you can clone and run. I tested version 0.6.2. A handful of dashboard queries on one of my projects were fine for a year and then weren't: count users by tier, revenue grouped by country, best-selling products per category. Nothing exotic, just aggregates and joins over tables that had gotten big. The usual fixes didn't sit right with me. A materialized view means picking a refresh interval and serving slightly stale numbers in between. Redis in front of Postgres means writing and maintaining code that knows which cache entries to throw away on every write. A read replica just runs the same slow query on another machine. PgCache offers a different trade. It's a proxy that talks the Postgres wire protocol, so your app connects to it as if it were the database. It caches reads. And instead of expiring entries on a timer, it follows Postgres's replication stream and refreshes a cached result when the rows behind it change. That stream is the same feed Postgres uses to copy data to a standby server , a running log of every insert, update, and delete. The "no timers, no manual invalidation" part is the interesting claim. Here's how it held up. A database big enough to be slow First I needed a database where "slow" was real and not a rounding error. I wrote a seed script for a small e-commerce schema and filled it to about 16 million rows: Table Rows Notes users 1,000,000 10 countries; tiers 50% free / 33% pro / 17% enterprise products 2,000 10 categories orders 5,000,000 four statuses, random totals, spread over two years order_items 10,000,000 about two per order I added indexes on every foreign key and on every column the test queries filter or group by. That was on purpose. I wanted to compare PgCache against a Postgres that had been tuned p

New features are rolling out across the Android ecosystem starting today.

Double-check before you accidentally remove a protective layer of your screen.

Google announced new features coming to Android devices as part of this month's feature drop update. Among the highlights are the introduction of Motion Assist - Android's version of Apple's Motion Cues feature, which tries to reduce motion sickness by overlaying dots on your screen that move in response to a vehicle's movements - as […]

With Google Pics, Google is pushing deeper into the creative software market dominated by Canva and Adobe, but with a distinctly AI-first approach.

Waymo argued that fully autonomous vehicles aren't possible without using a mix of sensors and warned that pure end-to-end AI systems aren't safe enough.

Waymo robotaxi rides are now open to the public in Denver, San Diego and Tampa.

GoPro is merging with a company that builds AI infrastructure, and will continue supporting existing consumer products.

Episode 1: Was in einer ZST-Datei passiertEpisode 1: Was in einer ZST-Datei passiertZST-Dateien begegnen uns immer häufiger bei großen Downloads, Softwarepaketen, Backups und Serverdaten. Sie sind oft deutlich kleiner als die ursprünglichen Dateien und lassen sich trotzdem sehr schnell wieder entpacken. Doch wie funktioniert das? Warum werden Dateien komprimiert? Eine Datei besteht aus Daten. Je mehr Daten sie enthält, desto mehr Speicherplatz wird benötigt und desto länger dauert ihre Übertragung. Kompression versucht, dieselben Informationen mit weniger Daten darzustellen. Beim späteren Entpacken muss daraus wieder exakt die ursprüngliche Datei entstehen. Nach dem Entpacken ist die Datei Bit für Bit identisch mit dem Original. Es wird nichts weggelassen und nichts vereinfacht. Wiederholungen benötigen unnötig viel Platz Betrachten wir diesen Satz: Kleine Katzen kuscheln auf kleinen Kissen, junge Katzen kuscheln auf bunten Kissen und alte Katzen kuscheln auf weichen Kissen.Die folgenden Teile kommen mehrfach vor: A = Katzen kuscheln auf B = KissenWenn wir die wiederkehrenden Textteile durch die Variablen A und B ersetzen, können wir den Satz kürzer darstellen: Kleine A kleinen B, junge A bunten B und alte A weichen B.Damit ist der Text noch nicht vollständig. Zusätzlich müssen wir speichern, wofür A und B stehen: A = Katzen kuscheln auf B = KissenAus diesen Informationen lässt sich der ursprüngliche Satz wiederherstellen. Jedes A wird durch Katzen kuscheln auf und jedes B durch Kissen ersetzt. Das ist bereits die grundlegende Idee der verlustfreien Kompression: Wiederkehrende Daten werden nicht jedes Mal vollständig gespeichert. Stattdessen werden sie einmal gespeichert und anschließend durch kürzere Verweise ersetzt. ### Zstandard verwendet keine Variablen Unsere Variablen A und B dienen nur dazu, das Prinzip verständlich zu machen. Zstandard versteht weder Wörter noch Sätze. Es weiß nicht, was Katzen oder Kissen sind. Für das Programm besteht eine Datei lediglich

Every time I put a model behind an endpoint I make the same lazy decision. I pick whatever I used last time, or whatever I read about most recently, and I tell myself I'll benchmark it properly later, and later never arrives because there is always something with an actual deadline on it and comparing model latencies feels like procrastination even when it isn't. I never do it. Not once. So I built the thing that would make me do it. One prompt, fired at six models at once, streaming side by side in columns, with time to first token and cost per run underneath each one. About 390 lines of Python. Code's here , MIT, take it. Then I ran it, and three things happened that I didn't plan for. The integration is two lines, and that's the least interesting part DigitalOcean's inference endpoint speaks OpenAI, so this is the whole thing: client = OpenAI ( base_url = " https://inference.do-ai.run/v1/ " , api_key = os . environ [ " DIGITAL_OCEAN_MODEL_ACCESS_KEY " ], ) Every model below goes through that one client. Llama, DeepSeek, Mistral, Qwen, OpenAI's open-weight gpt-oss line. Only the model string changes. That is the pitch, and it's real, and I'll move past it quickly because you already knew an OpenAI-compatible endpoint would work like an OpenAI- compatible endpoint. What I didn't know is everything that follows. One footnote before you paste that snippet. The credential is a model access key , created under the Gradient AI Platform. It is not the API token from Settings, API. Different thing, different page. (Although, as I found out later, the endpoint doesn't care nearly as much about that distinction as the docs do.) Six streams, no event loop I wanted the columns to fill simultaneously. Real racing, not six sequential progress bars pretending. The tidy way to do that is one endpoint that fans out server side and multiplexes everything back down a single connection. I didn't do the tidy way. The browser opens one EventSource per model instead: GET /stream?model=<

Flock's searchable network of 130,000 license plate cameras around the U.S. have sparked bipartisan privacy and civil liberties concerns.

Newlight has raised a $9 million seed round and completed an 8,500-nautical-mile test run from Singapore to Ghana.

Sonos is cramming AI into its software because it’s “very hot these days.” The new features, which include agentic automation, are opt-in.

Waymo will begin inviting riders on a rolling basis and slowly ramp up access in the three cities, following its usual commercial playbook.

Yao Yue discusses the fundamental limitations of standard line charts for system observability. Drawing from 15 years of operating large-scale systems, she shares how engineering leaders and software architects can transform telemetry data - moving beyond simple time-series defaults - to build visualizations that directly answer critical capacity, latency, and fleet-sizing questions. By Yao Yue

The flagship headphones get up to 57 hours of battery life and have easily replaceable batteries.