标签：#r

From a Revolutionary War flag to the Statue of Liberty...

"The reality is, when you're optimizing for production, you start looking at a price/performance," Guillermo Rauch tells TechCrunch.

Nordstjernen is a web browser, written from scratch in C, focused on supporting the HTML and CSS standards. It runs on Windows, Mac and Linux, with an Android port in progress. Nordstjernen is built in Norway. submitted by /u/AndreasWeb [link] [留言]

I made this because I thought it would be useful to be able to use a webview in the terminal. I also provide an SDK called ratatui_orzma. For example, I think it could be used to render rich UI components such as charts with a webview as part of a TUI application, port web tools to ratatui, or embed games built with Wasm inside the terminal.

The update is part of Apple's broader effort to make Siri feel more natural and personal, as it rebuilds the assistant around generative AI.

Hey everyone, My background is in high-performance systems architecture and low-level optimization, and recently, the memory bloat in modern AI editors has been driving me crazy (WE OBVIOUSLY CAN DO BETTER, WHY ARENT WE???) So, I decided to build something significantly leaner and minimal. I built Axiom which uses up to 3.7x less memory than Cursor and 33% less than VSCode. To hit this benchmark, I took VSCode OSS and stripped Electron out completely. Instead of relying on the bundled Chromium instance, I made the editor run inside LaVista ( https://github.com/IASoft-PVT-LTD/LaVista ). This allowed me to drop the footprint of three idle windows down to just 759 MB, compared to the 2,802 MB you'd see in Cursor. What I added on top: AxiomAI: A Bring Your Own Key (BYOK) setup with a local autocomplete and local router system. Token Management: Built-in tracking to monitor, analyze, and set hard limits on your API token usage so you never get a surprise bill. FlowViz: A native visualization engine that lets you render plots, flowcharts, and fully interactive 3D scenes directly in the editor. I am currently rolling out the beta and would love for some technical folks to try it out and try to break it. You can check it out and register for the beta here: https://iasoft.dev/software-engineering/products/axiom/ Would def love to hear your thoughts on the native webview approach or answer any questions about the LaVista implementation! submitted by /u/I-A-S- [link] [留言]

We just open-sourced Synapse , a governed control plane for software composition analysis, recon, evidence, and reporting. It is built for people who have to scan a dependency tree, prove what they found, and hand over a report that holds up. Site: https://synapse.kkloudtarus.net/ Code: https://github.com/KKloudTarus/synapse-ce (Apache-2.0) Why we built it The usual workflow is fragmented. One tool for the SBOM, another for vulnerabilities, a spreadsheet for licenses, a folder of screenshots for evidence, and a report you assemble by hand. Nothing is reproducible, and when a client asks "how do you know this is real," the answer lives in someone's memory. Adding an LLM that writes your findings only makes that worse. We wanted the opposite: fast, but provable. What it is Synapse runs the assessment lifecycle behind one control plane, in Go, clean architecture. A few ideas hold it together: Deterministic-first. Scanning, matching, license classification, and reporting are pure, reproducible Go. There is no model in the report path. Scope-gated execution. Every engagement carries a scope and an authorization window, enforced server-side before any tool runs. Tools run via argument arrays, never a shell string. Tamper-evident evidence. Every artifact is hash-chained and append-only. A broken chain blocks the report. Bounded automation. The optional AI layer only ever proposes. A distinct verifier or a human confirms. The agent can never confirm its own claim. What it does today: SBOM across 15+ ecosystems, multi-source vulnerability detection with risk-based prioritization (KEV, then EPSS, then CVSS), license compliance, reachability, and deterministic reports in CycloneDX, SPDX, SARIF, and OpenVEX. Try it git clone https://github.com/KKloudTarus/synapse-ce.git cd synapse-ce docker compose -f deploy/docker-compose.full.yml up --build # open http://localhost:5173 Or gate CI on real risk: ./bin/synapse-cli scan . --fail-on high . We are looking for contributors Synapse i

I built a small Redis clone in C: a RESP parser, a command table, an append-only file for persistence. Recently I started building the same thing again in Rust, and rebuilding a project I had already finished has taught me more Rust than any from-scratch tutorial. The reason is simple. The second time, the design is already solved. I know what the AOF has to guarantee, what the command table dispatches, what the parser must reject. So none of my attention goes to what to build. All of it goes to how Rust wants it built. That turns the domain into a constant and the language into the only variable. Every difference I hit is pure signal about Rust, not noise about key-value stores. The first difference shows up before any logic runs. In C, I built the substrate first: my own dynamic strings, my own hashmap, my own linked list. Hundreds of lines before a single command worked. In Rust, Vec , String , and HashMap are just there, so that whole layer disappears and I start at the actual command logic. A standard library quietly decides where your project even begins. The sharper difference is in dispatch. In C it is a switch with argument counts I check by hand: if ( argc != 3 ) return err ( "wrong arg count" ); switch ( cmd ) { case CMD_SET : return do_set ( argv [ 1 ], argv [ 2 ]); case CMD_GET : return do_get ( argv [ 1 ]); /* forget a case and it is a runtime bug */ } In Rust the same dispatch is an enum and a match, and the compiler will not build until every case is handled: match cmd { Command :: Set { key , val } => self .set ( key , val ), Command :: Get { key } => self .get ( key ), } Same dispatch. One version cannot ship the missing-case bug I actually shipped in C. If you already know a project cold, rebuild it in the language you are learning. You stop thinking about the problem and start feeling the language.

Everything old is new again.