Alternatives
Products that do what Pythagora – auto generated integration tests for Node.js API servers does
Hi HN, I just released my first npm package so wanted to share it here. Pythagora is a Node.js module that creates automated integration tests for MERN/MEAN apps by recording server activity. To integrate Pythagora, you need to paste one line of code to your repository and run the Pythagora capture CLI command. Then, just play around with your app and from all API requests and database queries Pythagora will generate integration tests. When running tests, it doesn’t matter what database is your Node.js connected to or what is the state of that database. Actually, that database is never…
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Save 30% of dev time - generate tests without writing code
2023
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Colin here, creator of Nub. I’ve had the general shape of this in mind for years. Nub runs your code with stock `node`, augmented with a `--require` preload hook[0] that adds a transpiler (oxc-powered, packaged as a Node-API add-on), registers a module resolution hook[1], and injects polyfills as needed for APIs like `Worker`, `Temporal`, etc. All purely additive, your code ultimately runs using Node’s actual engine & stdlib implementations. [0] https://nodejs.org/api/cli.html#-require-module [1]…
Jun 2026 · github.com
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The goal of Carton is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from…
2023 · carton.run
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Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com
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Hi all! A little background: I've been working on natto.dev, a spatial environment for JavaScript. I'm really excited about new interfaces for code (leveraging metaphors we're good at, spatial reasoning, making state visible, design tools, etc). With all the buzz around PyScript, I discovered Pyodide and got it working inside natto. This Python version is a stripped down version of https://natto.dev (eg interactive outputs, multiplayer) so please check that out if this interests you. I'm excited to share this spatial environment for Python. Imagine Jupyter cells arranged on a 2D…
2022 · python.natto.dev
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2024 · pykidos.github.io
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Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…
2024 · github.com
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`mandala` is a framework I wrote to automate tracking ML experiments for my research. It differs from other experiment tracking tools by making persistence, query and versioning logic a generic part of the programming language itself, as opposed to an external logging tool you must learn and adapt to. The goal is to be able to write expressive computational code without thinking about persistence (like in an interactive session), and still have the full benefits of a versioned, queriable storage afterwards. Surprisingly, it turns out that this vision can pretty much be achieved with two…
2024 · github.com
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Instantly generate openapi (OAS) 3.0.0 REST + GraphQL APIs
2019
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Hi HN, Recently, I’ve noticed there’s a decently high barrier to entry in developing competitive, full-stack SaaS applications. Beside the standard, boring features that take months to implement, you typically have to know several languages and frameworks, and be familiar with fancy frontend styling classes. I’m working hard right now to solve this problem by building PySaaS- The 100% pure Python SaaS starter kit. PySaaS is a boilerplate Python codebase that takes care of the fundamental components standard to all SaaS applications. The codebase uses the Pynecone web framework to compile…
2023 · pysaas.io
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Hello! I’m So Tokumaru ( https://tokumaru.work ), high school student from Japan working on ways to leverage large language models (LLMs) for K-12 programming education. Recently, I built a project called "TutoriaLLM," a self-hosted application designed to support educators and students through a multi-modal LLM that offers guided tutorials. TutoriaLLM integrates voice (voice-to-voice) and text-based AI interactions into Blockly, a block editor widely known for its use in Scratch. It enables server-side code execution and allows students to test their code directly in Minecraft…
2024 · github.com
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Hi, this is Mish and Sebastian. We are working on Step CI - a fully automated API testing platform for developers. Step CI works programming-language independent and for different API paradigms (REST, GraphQL, XML). Our CLI and test runner are available on GitHub (https://github.com/stepci) under the MPLv2 license. Since our last launch, Step CI is now able to generate automated tests for your API based on your OpenAPI (Swagger) spec. This saves you a lot of time as you never have to write and maintain your tests again! We would like to invite you to try our tool and give us…
2022 · stepci.com
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Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to…
2023 · github.com
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I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com
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2023 · codecept.io
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I built this because Cursor, Claude Code and other agentic AI tools kept giving me tests that looked fine but failed when I ran them. Or worse - I'd ask the agent to run them and it would start looping: fix tests, those fail, then it starts "fixing" my code so tests pass, or just deletes assertions so they "pass". Out of that frustration I built KeelTest - a VS Code extension that generates pytest tests and executes them, got hooked and decided to push this project forward... When tests fail, it tries to figure out why: - Generation error: Attemps to fix it automatically, then tries again -…
Jan 2026 · keelcode.dev
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