Alternatives
Products that do what PyPie does
Python DSL for type safe ML programs
- 1IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
2025 · runpyxl.com
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- 3IB
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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2017 · github.com
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2017 · pydantic-docs.helpmanual.io
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Hello and happy new year! We're excited to introduce the Pyper package for concurrency & parallelism in Python. Pyper is a flexible framework for concurrent / parallel data processing, following the functional paradigm. Source code can be found on [github](https://github.com/pyper-dev/pyper) Key features: Intuitive API: Easy to learn, easy to think about. Implements clean abstractions to seamlessly unify threaded, multiprocessed, and asynchronous work. Functional Paradigm: Python functions are the building blocks of data pipelines. Let's you write clean, reusable…
2025 · github.com
- 7PP
Hi HN! I built pyscn for Python developers in the vibe coding era. If you're using Cursor, Claude, or ChatGPT to ship Python code fast, you know the feeling: features work, tests pass, but the codebase feels... messy. Common vibe coding artifacts: • Code duplication (from copy-pasted snippets) • Dead code from quick iterations • Over-engineered solutions for simple problems • Inconsistent patterns across modules pyscn performs structural analysis: • APTED tree edit distance + LSH • Control-Flow Graph (CFG) analysis • Coupling Between Objects (CBO) • Cyclomatic Complexity Try it without…
Oct 2025 · github.com
- 8HP
I built a Rust-powered Wavelet Matrix library for Python. There were surprisingly few practical Wavelet Matrix implementations available for Python, so I implemented one with a focus on performance, usability, and typed APIs. It supports fast rank/select, top-k, quantile, range queries, and even dynamic updates. Feedback welcome!
Dec 2025 · pypi.org
- 9GT
Hi HN, In the past few years I've become more interested in machine learning. Since I'm sure the same is true for many here, I wanted to share this project I've been working on: glowstick uses type-directed metaprogramming to keep track of tensor shapes in Rust's type system and determine which operations are permitted or not at compile time. I find Rust has a lot of strengths when it comes to ML applications, but waiting until runtime to find shape related issues feels a bit strange since normally I don't run the code all that often while developing. Given Rust has fancy types available, I…
2025 · github.com
- 10TT
2020 · github.com
- 11OP
Hi HN! I built Oxyde because I was tired of duplicating my models. If you use FastAPI, you know the drill. You define Pydantic models for your API, then define separate ORM models for your database, then write converters between them. SQLModel tries to fix this but it's still SQLAlchemy underneath. Tortoise gives you a nice Django-style API but its own model system. Django ORM is great but welded to the framework. I wanted something simple: your Pydantic model IS your database model. One class, full validation on input and output, native type hints, zero duplication. The query API is…
Mar 2026 · github.com
- 12PP
I created this ORM to fill a gap in the Python ecosystem. Due to the nature of typing in Python there are no other Python ORMs that can provide correct type hints. Prisma Python manages to work around this by auto-generating python types. Aside from static type checking, providing type hints means that you will get autocomplete suggestions for you which for me is the killer feature for this ORM (see the GIF in the README for an example). It's also built on top of Prisma, a next-generation ORM for TypeScript which means that the core query building and connection handling has been battle…
2022 · github.com
- 13TS
We needed a side project to give agentic coding a try, and created tensorspy.com together with Junie and ChatGPT 5.2. Tensor Spy lets you quickly inspect the contents of numpy & pytorch tensors locally (your tensors are not uploaded to any servers). This is useful to validate your deep learning data pipelines, to check which layers in your diverging model are actually going haywire, and just because it's kind of cool & a lot more convenient for one-off inspections than loading things up in python. If you work with diffusion models, inspecting the latent space can be quite informative: you…
Mar 2026 · tensorspy.com
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2015 · github.com
- 15AD
Greetings! A 2.5 weekends project to teach myself newer Python features (>= 3.10). Conditions are written as Lambda expressions that annotate parameters and return types, and coexist with type annotations. Symbols to share values between conditions are also supported to a limited extend.
2022 · github.com
- 16PD
We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
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2025 · github.com
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Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
2023 · docs.giskard.ai
- 19IG
As a beginner learning to build ML models, I found it annoying to have to keep printing tensor shapes every other line, having to step through the debugger to check where did I mess up the shapes again. So I built Trickle, it takes the data that flows through your code, caches the types and display them inline (as if you have type annotations). The idea is: "Let types trickle from runtime into your IDE". You get types in Python without having the write them manually. It works by rewriting your Python AST at import time — after every variable assignment, it inserts a lightweight call that…
Mar 2026 · github.com
- 20PP
PyNeuraLogic is a framework that lets you use Python to create differentiable logic programs. The framework offers an elegant way to express and further extend GNNs, as well as go beyond graphs and tackle other complex (relational) scenarios. Feel free to check the repository and give us some feedback here or on Github. Thank you.
2022 · github.com
- 21FV
2025 · github.com
- 22PC
Hi HN!I built *pyproc* to let Go services call Python like a local function — *no CGO and no separate microservice*. It runs a pool of Python worker processes and talks over *Unix Domain Sockets* on the same host/pod, so you get low overhead, process isolation, and parallelism beyond the GIL. *Why this exists* * Keep your Go service, reuse Python/NumPy/pandas/PyTorch/scikit-learn. * Avoid network hops, service discovery, and ops burden of a separate Python service. *Quick try (\~5 minutes)* Go (app): ``` go get github.com/YuminosukeSato/pyproc@latest ```…
Sep 2025 · github.com
- 23ZA
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is. It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one…
2023 · github.com
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