AI 资讯

Google's SynthID, designed to embed imperceptible signals into AI-generated content, is adding a new Content Detection API on Google Cloud's Gemini Enterprise Agent Platform, after gaining adoption by several industry players including Nvidia and OpenAI. By Sergio De Simone

The call to action “to make 2026 the year of Java in the terminal” was quickly responded to by the launch of TamboUI. Inspired by Ratatui, the library used in Claude CLI, it promises support ranging from low-level terminal drawing to high-level APIs such as components and event handling. Currently at version 0.3.0, it has already been adopted by major projects such as Maven and Spring. By Olimpiu Pop

Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all of us knowledge workers. But before you quit your job as a software developer or financial analyst—or tech journalist—and…

Artificial intelligence has not so far produced a clean story of mass unemployment. Aggregate employment in developed countries remains broadly stable, and recent assessments have found limited evidence that AI has shifted the headline numbers. But a troubling change may be hiding beneath the surface: the quiet weakening of the first rung of the career…

submitted by /u/Efficient-Public-551 [link] [留言]

Local AI agents are having a moment right now. Developers are increasingly uncomfortable with...

Self-driving AI CRM that you can talk to Discussion | Link

Is this normal? I searched it up and last year it was only 8000. submitted by /u/NightCR_ [link] [留言]

I received an email seemingly from a business asking for a website redesign. Everything seems legit on first glance. The website is legit because it's a real company but the person doesn't work there. The sender's email is a bit suspicious as its not a domain email but rather emma.companyname@gmail.com . After you reply they will send a followup email asking you to schedule on their calendly. Here's a screenshot: https://preview.redd.it/fjqmwpsbre3h1.jpg?width=637&format=pjpg&auto=webp&s=72114d4e53d7a18e35b795472fffe78f215dcf98 But one clue that I missed originally is the russian text at the timestamp. If you click on the "calendly" link it opens a calendar like this: https://preview.redd.it/llb5ip7gre3h1.jpg?width=1100&format=pjpg&auto=webp&s=876613e41eb2389194675c557526858f0a7371de Pretty convincing but you see the url is not calendly. It's a url with calendly attached. When you try to book an appointment you are shown this: https://preview.redd.it/3vnr00zjre3h1.jpg?width=610&format=pjpg&auto=webp&s=896a00cbe40ff8f104c1d43c1eb13fb84af2d65a Here the form won't submit until you "sign in with google". At this point I stopped as I use calendly myself and know it wouldn't require a login. Unfortunately others may not be aware. The main trigger for me is, why are there so many steps to scheduling an appointment? Just as a check I emailed "emma" and told her I could make a Thursday 2:30 time but the link was broken. Of course, instead of scheduling it for me she says the link works and please register there. Be careful out there! submitted by /u/theideamakeragency [link] [留言]

This week's Java roundup for May 18th, 2026, features news highlighting: GA releases of WildFly 40, Micronaut 5.0, Maven Embedded GlassFish Plugin 8.0 and Apache Fory 1.0; the May 2026 edition of Open Liberty; point releases of Gatherers4j, Apache and Kafka; and the seventh milestone release of Spring AI 2.0. By Michael Redlich

Here's an example of how to build an Ubuntu VM using OrbStack on macOS and then connect to the VM through Tailscale SSH using an auth key stored in Apple Keychain. For example, I can create a VM on my Mac mini at home that hosts a git repo or even a Forgejo server. A colleague in my Tailnet can then connect to this VM to clone, push or pull source code changes from a coffeeshop or airliner while not exposing the rest of my Mac mini.

Hey i built Aiki a lightweight tool that let's you chat with Wikipedia locally. https://i.redd.it/67mzfsrc6f3h1.gif what it does: Downloads and chunks wikipedia articles (u can choose those articles by their name or articles and also the option of downloading the similar topics) Uses a custom TF-IDF + cosine similarity retriever (built from scratch) Supports query expansion using Wikipedia links/redirects Optional answer generation with llm Very minimal dependencies and runs completely locally. Repo: https://github.com/yacine204/Aiki Would really appreciate your feedback. submitted by /u/Just_Jaguar3701 [link] [留言]

the AI in your texts that gets stuff done Discussion | Link

The Trust Identity Protocol is a free, open, post-quantum, patented standard for verified human identity and AI content provenance on the public internet. Built by The AI Lab Intelligence Unobscured, Inc.

A team of AI agents that runs your stores across channels Discussion | Link

Nathan Witkin, a research writer at NYU Stern’s Tech and Society Lab, writes damningly about the famous METR AI time horizons graph in the Substack publication Transformer: It is impossible to draw meaningful conclusions from METR’s Long Tasks benchmark — in particular once one realizes that its numerous flaws are probably compounding in unpredictable ways. The appropriate response to a study of this kind is not to assume it can be saved via back-of-the-envelope adjustments, or to comfort oneself that other anecdotal evidence implies that it is probably correct anyway. It is to cut one’s losses and move on in search of higher-quality information. … The METR graph cannot be saved. For all its sleekness and complexity, it contains far too many compounding errors to excuse. Among them is generalizing to the entire species data collected from a small group of the authors’ peers. Coming up with ever more dramatic ways to make this mistake has become a kind of sport among AI researchers. If the field has a central pathology, it is to aggressively overindex on a mix of anecdotal data from power-users, alongside a long list of benchmarks even more compromised than METR’s. One hopes that as the field matures, its participants will learn to stop making these mistakes. The errors include: Some of the human baselines data is not actually measured or collected from any empirical source, rather, it is just guesstimated by the authors A key variable in the data is how long it takes humans to complete certain tasks, but — when METR did actually measure this — it paid its human benchmarkers hourly, meaning they were incentivized with cash to take longer The sample of human benchmarkers was biased toward METR employees’ friends, acquaintances, and former colleagues (who are likely unrepresentative and possibly biased) Humans familiar with a codebase and a specific coding task were 5-18x faster at completing it, but METR used data from humans who were much slower because they had to s

Just thought I'd share, I ran a DCGAN on a dual core RISC-V microcontroller, the CH32H417 generating 64x64 cat faces. This is a new RISC-V MCU, so no TFLite, no CMSIS NN and no external memory. It's a pure C inference engine, bit-identical to PyTorch reference outputs. The model is 12.6M parameters with int8 per channel quantization. Intermediate activations are stored in DTCM and layer weights stream from SD card using double buffering so the next layer loads while the current one computes. The total available SRAM is 512KB shared between both cores and the inference engine and time to generate one image is 26 seconds, it could be faster, but SD card access speed is the bottleneck rather than computation. The z vector is seeded from 200 bytes of quantum random data (ANU QRNG vacuum fluctuation source), transformed via Box-Muller into the latent vector. which is not strictly necessary for image quality but it was a fun constraint for the art installation side of the project. The generated cat is classified as "motivated" or "demotivated" based on a single quantum bit, which selects from a phrase bank with four fragment slots combining into one of 131,072 possible spoken verdicts output through the onboard DAC... As far as I can tell nobody else is running GAN inference on these low cost RISC-V microcontrollers, cause ARM has the CMSIS NN ecosystem for this kind of thing but RISC-V MCUs especially in the CH32 space have nothing, so the entire inference engine is written from scratch. Paper: TinyGAN: Generative Image Synthesis on a RISC-V Microcontroller with Quantum Entropy Sampling submitted by /u/Separate-Choice [link] [留言]