标签：#ssi

The YouTube-to-prestige-horror pipeline is looking very strong.

A complete ML pipeline: engine, backprop, binary format, and a live browser demo. Zero dependencies. Under 200 KB total. If you have built machine-learning projects before, you have probably done it by importing PyTorch, TensorFlow, or scikit-learn and calling .fit() . Those are excellent libraries. This article is about what happens when you deliberately do not use them — when you build every piece of the pipeline yourself, in a language that compiles to WebAssembly, and the result runs live in the browser with no server, no Python, and no cloud bill. Here is the live demo: move four sliders, watch the predicted Iris species update in real time. The model is running entirely inside your browser tab, loaded from a 1.1 KB binary file, powered by ~100 KB of WebAssembly compiled from pure Rust. This is the story of how I built it and why the engineering choices made it work. Why Rust? Why WebAssembly? Why zero dependencies? Three constraints drove every design decision. WASM requires no_std or a carefully limited std . The wasm32-unknown-unknown target has no operating system, no file system, and no libc. A crate that links against rand , ndarray , or any library that makes OS calls will not compile to it without significant plumbing. An engine built from nothing but the Rust standard library compiles cleanly to every target, including WASM. A zero-dependency std -only crate is uniquely auditable. There are no transitive dependency trees to vet, no supply-chain risks, no version conflicts. Every line of code that runs in the user's browser lives in this repository. The deployment story becomes the technical story. A 100 KB WASM blob that runs locally in the browser is not just a cost optimisation — it is a privacy guarantee (user inputs never leave the machine) and a latency guarantee (inference is microseconds, not a round trip to a cloud API). That story is only possible because the engine has no external dependencies that would bloat the binary. The architecture: ei

Disclosure: I work on Passlens, a browser-first passport and ID photo maker. This post is about the product decisions behind that workflow, not a neutral review of every tool in the space. A passport photo looks simple until you try to make one that an upload form will actually accept. It is a headshot, yes, but it is also a small chain of constraints: physical size, pixel size, background, head position, print scale, and whatever the destination country's portal decides to reject that week. That is why generic photo editors feel slightly wrong for this job. They can crop. They can resize. They can export. The hard part is not any one of those actions. The hard part is keeping all of them tied to the document rule the user picked. The unit problem For developers, document photos are awkward because two units matter at the same time. A user may need a 2x2 inch passport photo. A visa portal may ask for 600x600 pixels. A print sheet may need 35x45 mm photos at 300 DPI. These are not the same request, but people often treat them as if they are. If the app only thinks in pixels, the print can come out the wrong physical size. If it only thinks in millimetres or inches, the digital upload can be rejected for the wrong pixel dimensions. A good workflow has to keep both ideas alive: the document size and the export target. That is the main reason Passlens keeps presets and print layouts as first-class pieces of the workflow instead of treating them as labels on a crop box. The crop is not the output Another small trap: the crop the user sees is not always the final output. For a digital upload, the crop usually becomes one image file. For printing, the same crop may become several photos arranged on 4x6, A4, or Letter paper with spacing, margins, and optional cut marks. If that print sheet is scaled by the browser or printer dialog, the whole thing is wrong. So the editor needs to separate three things: the face and shoulder crop the finished document-photo size the print s

Pullfrog is an open-source AI-powered GitHub bot by Colin McDonnell, designed for automation in GitHub Actions. It supports a model-agnostic approach, allowing integration with various LLM providers. Key features include orchestration for pull request reviews, issue triage, and CI remediation, all managed within GitHub's environment. The tool operates with a bring-your-own-key model for access. By Daniel Curtis

Authorities in the Netherlands have arrested the co-owners of two related Internet hosting companies for operating IT infrastructure used by Russia to carry out cyberattacks, influence operations and disinformation campaigns inside the European Union. The two men were the focus of a 2025 KrebsOnSecurity story about how their hosting companies had assumed control over the technical infrastructure of Stark Industries Solutions, an Internet service provider sanctioned last year by the EU as a frequent staging ground for cyber mischief from Russia's intelligence agencies.

Explore our update on GitHub’s accessibility strategy, and learn how you can join us in building a culture of accessibility. The post Building GitHub’s next chapter in accessibility appeared first on The GitHub Blog .

Learn about the experimental general-purpose accessibility agent that GitHub is piloting. The post Building a general-purpose accessibility agent—and what we learned in the process appeared first on The GitHub Blog .