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]

Table of Contents Intro The Restaurant The Menu Placing the Order The Plate Coming...

submitted by /u/David-Kunz [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

The argument sounds reasonable: fewer lines of code mean fewer bugs. Simpler to review, easier to...

Our first look at a complete version of Ferrari's upcoming luxury EV that was designed by LoveFrom.

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]