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On this episode of Uncanny Valley, we dive into accusations that China’s Moonshot AI stole from Anthropic, and how the US Army needs to cut back on AI use.

New delay on Copernicus satellite pics comes as US ramps up war with Iran again.

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Anduril is said to be raising a new round of funding that may push its valuation up to about $100 billion, per Reuters.

Google's AlphaFold can help ID what parts of a gene editing protein enable mistakes.

The web and Google once had a deal: Google collects data and indexes webpages and in exchange sends oceans of traffic to websites. The deal wasn't perfect and certainly made Google more money than it made the websites, but it worked for a long time. Now, however, the deal seems to be dead. And the […]

Players may be unable to log into their account, play online or purchase games from the PlayStation Store.

This week, I covered a fascinating effort to preserve organs outside the body. There’s a huge shortage of donor organs, and one of the main reasons is time—they survive only a matter of hours outside the body, even when they’re kept on ice. Doctors dream of organ banks—stores of human organs that can be preserved…

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Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.

AI tools have democratized the opportunity to build, shortening the timelines of success and enabling more young people to start successful companies without stepping foot inside a Big Tech company.

Weeks after Anthropic's latest toe-to-toe with the US government, and days after an OpenAI security incident that dominated tech industry discussions, Anthropic on Thursday released its newest model, Claude Opus 5. The company said in a release that Opus 5 "comes close to the capabilities of Claude Fable 5 in many domains" and is much […]

The offline messaging app surged in popularity in India amid protests in New Delhi.

AI Faceless Video Generator for TikTok & YouTube Discussion | Link

Sam Altman's side project — which seeks to scan the world's eyeballs and turn them into unique digital identifiers — has raised some fresh cash through a crypto sale.

GitHub热门项目 | Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors | Stars: 1,212 | 186 stars this week | 语言: Rust

The MCP (Model Context Protocol) ecosystem is growing fast. There are now hundreds of MCP servers available — but how do you know which ones are worth installing? After building and evaluating 60+ MCP servers ourselves, we developed a practical checklist that saved us from shipping broken tools. Here's the framework we use. The Problem Most MCP server listings tell you what the server does. Very few tell you how well it does it. You install something that sounds perfect, then discover: It activates on the wrong prompts (false positives) It pulls irrelevant context (retrieval drift) It sounds confident but gives wrong answers (ungrounded reasoning) It never improves from feedback Sound familiar? The 5-Dimension Evaluation Checklist Before installing any MCP server, ask these questions: 1. Trigger Precision Question: Does this server activate when (and only when) it should? Red flags: Overly broad trigger descriptions ("use for anything related to X") No documented activation conditions Activates on common words that appear in unrelated contexts Green flags: Specific, documented trigger scenarios Clear non-activation cases listed Tested against diverse prompts 2. Retrieval Quality Question: Does it pull the right context for the task? Red flags: Returns large chunks without filtering No citation or source tracking Retrieves plausible but outdated information Green flags: Targeted, minimal context retrieval Source attribution for every piece of context Version-aware (knows when data might be stale) 3. Reasoning Grounding Question: Are its conclusions tied to actual data? Red flags: Generates advice without referencing specific inputs Can't explain its reasoning chain Confident answers that contradict its own retrieved context Green flags: Every conclusion references specific evidence Explicitly flags uncertainty Gracefully handles missing information 4. Output Usefulness Question: Does the output actually solve your problem? Red flags: Generic responses that could appl

AI companies including Nvidia and Mistral urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates responses to Chinese AI and alleged model distillation.

Many online image converters require users to upload their files to a remote server before processing. For ordinary images this may be acceptable, but for personal photos, screenshots and documents, privacy becomes an important concern. I built a lightweight online image converter that processes images directly in the browser. It currently supports: JPG PNG WebP HEIC GIF SVG BMP TIFF Why browser-based processing? The main goal was to make image conversion simple, fast and privacy-friendly. Because the conversion happens locally: Images are not uploaded to a server Users do not need to create an account There is no installation process Network upload time is avoided Private files remain on the user’s device The basic workflow is simple: Select an image Choose an output format Convert the file Download the result Challenges Browser-based image conversion still has some limitations. Different browsers support image formats differently, especially formats such as HEIC and TIFF. Large images may also consume more device memory during processing. Some format features may not be preserved after conversion. For example, transparency may be lost when converting to JPG, and animated images may become static depending on the selected output format. These limitations need to be handled through clear prompts and error messages.

Detecting what powers a website looks simple: send a request, read the response, match fingerprints. In real environments it rarely stays that clean. False positives slip through, infrastructure hides behind CDNs, old scripts linger after migrations, and fingerprints keep evolving. Developers who treat fingerprinting as a basic utility end up acting on misleading data. This guide covers the mistakes engineers make detecting website technologies and how to avoid them. Modern detection workflows lean on ProjectDiscovery's libraries, which cut these problems through structured pattern matching and maintained datasets. External resources: github.com/projectdiscovery/wappalyzergo projectdiscovery.io If you're new to the space, start with technology fingerprinting for developers before these pitfalls. Mistake 1: Trusting a single detection signal Relying on one clue is the fastest way to get a wrong answer. A script file may linger after a framework migration, a header can be spoofed, and a cookie might belong to a third-party service. Correlate several signals instead: headers, cookies, HTML patterns, script paths, metadata. When multiple indicators point at the same technology, confidence goes up. ProjectDiscovery's libraries are built around that multi-signal approach. Mistake 2: Treating detection as a one-time task Stacks change constantly. Organizations migrate infrastructure, update frameworks, and swap platforms more often than developers expect. Scan once and trust it forever and you're working from stale data. Schedule periodic scans. Many teams wire detection into automation pipelines so infrastructure changes get captured on their own. To operationalize this, see detect website technologies programmatically in Go . Mistake 3: Ignoring reverse proxies and CDNs Modern architectures hide origin servers behind proxy layers. You might detect a CDN and miss what actually powers the app. Detecting a CDN doesn't make the origin invisible. It means you need to look fur