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Wireless Routers as Motion Detectors

Comcast has added motion detection as a feature to its wireless routers: The feature sends push notifications to users when motion is detected near a connected device, such as a TV or printer. It has different settings for when people are home, asleep, or away. The Xfinity app also lets users see live motion activity and a feed of recent activity. Comcast acknowledges that the system has some limitations. Home size, layout, building materials, and the placement of the router and connected devices can all affect its ability to detect motion. Comcast says it does not guarantee its performance...

2026-09-02 原文 →
AI 资讯

America Can Still Lose the A.I. Race to Itself

At 5:21 p.m. Eastern on Friday, June 12, Anthropic received a United States government directive. By nightfall, two of the most capable artificial-intelligence models in the world had gone dark after a Commerce Department export-control directive prompted Anthropic to take them offline worldwide. The directive required Anthropic to prevent access to Claude Fable 5 and Claude Mythos 5 by any foreign national, whether that person was inside or outside the United States. It extended even to Anthropic's own foreign-national employees. Because the company had no reliable way to verify every user's nationality in real time, it suspended both models for everyone . The controls remained in place until June 30. On July 1, Anthropic restored Fable globally. Mythos took a narrower path: after government approval on June 26, access returned to a set of United States organizations while Anthropic continued coordinating with the government over broader domestic and international access. That sounds like a short outage with a happy ending. It was neither. I have spent years building systems where a dependency going unavailable is not an abstraction. Once software is woven into a product or an operating process, access to it becomes part of the architecture. A model that can be withdrawn immediately under a company-specific determination and factual rationale no customer could have read is no longer only a technical dependency. It is a bet on unpublished policy. The United States needs to govern frontier A.I. It also needs companies, researchers, investors, and customers to know what the rules are before the government enforces them. June showed how far apart those two needs remain. The security concern was real The government's concern was not invented. Fable and Mythos shared the same underlying model, but Fable was released with strong safeguards for general use while Mythos, with fewer safeguards, went only to a small group of defensive-cybersecurity partners. Anthropic says My

2026-09-02 原文 →
AI 资讯

Claude Fable 5.1 is now available on Agent Platform!

Claude Fable 5.1 is officially available in the Model Garden on Agent Platform. Built for long-running, high-stakes work, Fable 5.1 puts frontier intelligence into production across your code, documents, and research. 👉 Try it today and let us know what you're building: Claude Fable 5.1

2026-09-02 原文 →
开发者

Google Find Hub will soon help you locate things without a tracker

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 […]

2026-09-02 原文 →
AI 资讯

What’s the Scam?

To subscribe to my monthly email newsletter, you have to enter your information on the webpage, and then reply to an automatically generated email. This is, of course, to prevent people from subscribing addresses other than their own. Starting last weekend, I have been receiving a lot of individual responses to those emails. Always one line: Thank you for the positive impact your emails have had on my life. Your emails are a game-changer. Your emails are a constant reminder of why I subscribed. Your emails rock. Thank you for the time and effort you put into creating these informative emails...

2026-09-02 原文 →
AI 资讯

Leaked Russian Cyber-Operations Training Materials

This is interesting: The records describe a force-generation mechanism for several General Staff components, including the GRU, Main Operational Directorate, and 8th Directorate, which is associated with protected communications, cryptography, and information security. […] The reporting also linked a 2024 Department No. 4 graduate, Aleksei Kondrashov, to Military Unit 74455, widely known as Sandworm. That unit has been associated with destructive cyber activity against Ukraine and other targets, including the 2017 NotPetya attack. The reports do not establish that every listed graduate participated in a named operation; assignments should therefore be described as reported unit placements, not proof of individual operational involvement...

2026-09-02 原文 →
AI 资讯

Google Pics is like Canva, but with even more AI

Google has a new suite of creative design tools for Workspace users called Google Pics, which aims to make editing and generating "professional-grade" AI images less cumbersome for businesses. Built around Gemini and the Nano Banana generative AI model, Google Pics is designed to give more granular control over prompt-based image making and manipulation, allowing […]

2026-09-02 原文 →
开发者

Zstandard einfach erklärt in 2 Episoden — Episode 1

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

2026-09-01 原文 →
AI 资讯

Rewiring Democracy Series on The Renovator

Nathan E. Sanders and I are writing a series of essays on real-world examples of democratic technologies for The Renovator . I haven’t been posting the full text on the blog because they’re a bit long, but here are links. Part 1 is about the Japanese digital democracy party, Team Mirai. Part 2 is about the Swiss Public AI model, Apertus. Part 3 is about the civic technologists of Open Knowledge Brazil. And the new one, Part 4 , is about civic AI in Scotland.

2026-09-01 原文 →
AI 资讯

Four security decisions that look like nothing and are not

Disclosure: these are decisions from Tessera, which I work on. They are all small enough to copy into your own service, which is why they are worth writing up. Security feature lists are made of nouns: encryption, RBAC, SSO, audit. The things that actually decide whether a system holds up are smaller than that and never make the list. Here are four of ours, with the reasoning, including the case where we made the trade in the direction most people do not. 1. The login rate limit ignores X-Forwarded-For Rate limiting a login endpoint is table stakes. The question is what you count against. The natural implementation reads X-Forwarded-For , because your service is behind a load balancer and the real client address is in that header. Almost every tutorial does it this way. The problem: X-Forwarded-For is a request header. If your service trusts it, an attacker sets a different value on every request and each one gets its own bucket. You have not built a rate limit, you have built a counter that resets on demand, and the dashboards will look perfectly healthy while the endpoint is being brute-forced. We count against the real TCP connection address instead. That is the peer address of the socket, which an attacker cannot forge without actually controlling that address. The cost is real and worth naming. Behind a reverse proxy, every request arrives from the proxy's address, so the limit applies to the proxy as a whole rather than per client. That is a worse experience in some topologies, and people will file bugs about it. We took that trade because a rate limit that can be bypassed by setting a header is not a degraded rate limit, it is the absence of one — and the absence is worse than useless, because it looks like presence. If you do want per-client limits behind a proxy, the answer is an explicit allowlist of trusted proxy addresses whose forwarded headers you accept, not blanket trust of a header. 2. Target addresses are validated to prevent SSRF Our controller is

2026-09-01 原文 →
AI 资讯

Sizing a session broker: the unit is concurrent sessions, and the bottleneck is not the CPU

Disclosure: the numbers below are from Tessera, which I work on. The reasoning applies to any proxy that sits in a session path. Every vendor page in this category says something like "scales to thousands of users". It is a useless number, because a user who is not connected costs nothing. What costs something is a session that is open right now. So here is the arithmetic instead, with the method, so you can check it against whatever you are evaluating. The unit The controller does not know how many engineers you employ and does not care how many targets are registered. It knows how many sessions are open. The planning rule that has held up for us: on a normal working day, 10–20% of a team is connected at once. A 200-person engineering organisation is 20–40 concurrent sessions, not Size for your own observed peak, but if you are estimating from scratch, start there. This matters because the difference between the two numbers is the difference between a 512 MB VM and an argument about whether you need a cluster. Memory A proxied session is mostly buffers. In our case: about 256 KB of copy buffers, plus 6 to 10 goroutines at roughly 8 KB of stack each. Call it 320 KB per session. The base Go process is about 30 MB. So 200 concurrent sessions is 200 × 320 KB ≈ 64 MB of live data, plus 30 MB base, ≈ 94 MB. Except that is not what RSS will show you, and this is the part people get wrong when they size Go services. Go does not hand memory back to the operating system promptly, and at the default GOGC=100 the collector lets the heap grow to roughly twice the live set before collecting. So resident memory settles at about double the arithmetic. Concurrent sessions vCPU Expected resident Provision up to 50 1 ~90 MB 512 MB 50–200 2 ~190 MB 1 GB 200+ 4 ~380 MB 2 GB The gap between the last two columns is headroom for spikes, not a hidden cost. A controller serving 200 sessions really does use a couple of hundred megabytes. Our Helm chart ships requests: 256Mi and limits: 1Gi ,

2026-09-01 原文 →