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4,157 ideas

I built a Cargo-like build tool for C/C++

I love C and C++, but setting up projects can sometimes be a pain.Every time I wanted to start something new I'd spend the first hour writing CMakeLists.txt, figuring out find_package, copying boilerplate from my last project, and googling why my library isn't linking. By the time the project was actually set up I'd lost all momentum.So, I built Craft - a lightweight build and workflow tool for C and C++. Instead of writing CMake, your project configuration goes in a simple craft.toml: [project] name = "my_app" version = "0.1.0" language = "c" c_standard = 99 [build] type = "executable" Run craft build and Craft generates the CMakeLists.txt automatically and builds your project. Want to add dependencies? That's just a simple command: craft add --git https://github.com/raysan5/raylib --links raylib craft add --path ../my_library craft add sfml Craft will clone the dependency, regenerate the CMake, and rebuild your project for you.Other Craft features: craft init - adopt an existing C/C++ project into Craft or initialize an empty directory. craft template - save any project structure as a template to be initialized later. craft gen - generate header and source files with starter boilerplate code. craft upgrade - keeps itself up to date.CMakeLists.extra.cmake for anything that Craft does not yet handle.Cross platform - macOS, Linux, Windows.It is still early (I just got it to v1.0.0) but I am excited to be able to share it and keep improving it.Would love feedback. Please also feel free to make pull requests if you want to help with development!

Hacker News5mo agoToolAI

55FL score
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Relvy (YC F24) – On-call runbooks, automated

Hey HN! We are Bharath, and Simranjit from Relvy AI (https://www.relvy.ai). Relvy automates on-call runbooks for software engineering teams. It is an AI agent equipped with tools that can analyze telemetry data and code at scale, helping teams debug and resolve production issues in minutes. Here’s a video: [[[https://www.youtube.com/watch?v=BXr4_XlWXc0]]]A lot of teams are using AI in some form to reduce their on-call burden. You may be pasting logs into Cursor, or using Claude Code with Datadog’s MCP server to help debug. What we’ve seen is that autonomous root cause analysis is a hard problem for AI. This shows up in benchmarks - Claude Opus 4.6 is currently at 36% accuracy on the OpenRCA dataset, in contrast to coding tasks.There are three main reasons for this: (1) Telemetry data volume can drown the model in noise; (2) Data interpretation / reasoning is enterprise context dependent; (3) On-call is a time-constrained, high-stakes problem, with little room for AI to explore during investigation time. Errors that send the user down the wrong path are not easily forgiven.At Relvy, we are tackling these problems by building specialized tools for telemetry data analysis. Our tools can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context.Anchoring the agent around runbooks leads to less agentic exploration and more deterministic steps that reflect the most useful steps that an experienced engineer would take. That results in faster analysis, and less cognitive load on engineers to review and understand what the AI did.How it works: Relvy is installed on a local machine via docker-compose (or via helm charts, or sign up on our cloud), connect your stack (observability and code), create your first runbook and have Relvy investigate a recent alert.Each investigation is presented as a notebook in our web UI, with data visualizat

Hacker News5mo agoToolAI

67FL score
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