Alternatives
Products that do what I got tired of print(x.shape) so I built runtime type hints for Python does
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…
- 1GT
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
- 2IA
Hi HN! This is my pet project, written from scratch because there is so much to discover and learn in the process. The focus is on simplicity and incremental updates. Progress is slow because I do not have much spare time to work on this, but I would love to hear some feedback. Regards
2022 · github.com
- 3PT
Hi HN, I'm excited to share Python-Type-Challenges, a collection of hands-on, interactive challenges designed to help Python developers master type annotations. Whether you're new to type hints or looking to deepen your understanding, these exercises provide a fun and educational way to explore Python's type system. I'd love to get your feedback and contributions! https://github.com/laike9m/Python-Type-Challenges
2023 · python-type-challenges.zeabur.app
- 4AP
2015 · github.com
- 5DB
Hey HN, I’m Jimmy, co-founder of Dropbase (https://www.dropbase.io). We are an internal tools builder for Python developers. All you have to do is import any Python scripts/libraries, declare UI components, and layer app permissions so you can share them with others. We’re a middle ground between Airplane and Retool—simpler UI creation than Airplane, more code-centered than Retool. UI building is declarative and you can bind Python scripts/functions to UI components. You can write Python scripts/functions using our App Studio with support from a Python Language…
2023 · github.com
- 6GJ
Hey HN, I've been using GPT a lot lately in some side projects around data generation and benchmarking. During the course of prompt tuning I ended up with a pretty complicated request: the value that I was looking for, an explanation, a criticism, etc. JSON was the most natural output format for this but results would often be broken, have wrong types, or contain missing fields. There's been some positive movement in this space, like with jsonformer (https://github.com/1rgs/jsonformer) the other day. But nothing that was plug and play with GPT. This library consolidates…
2023 · github.com
- 7AS
2021 · github.com
- 8AD
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
- 9LD
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
- 10
- 11PD
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
- 12PT
2017 · github.com
- 13IB
I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com
- 14TA
Hi community, we just released https://github.com/tonbo-io/typed-arrow. When working with arrow-rs, we noticed that schemas are declared at runtime. This often leads to runtime errors and makes development less safe. typed-arrow takes a different approach: - Schemas are declared at compile time with Rust’s type system. - This eliminates runtime schema errors. - And introduces no runtime overhead — everything is checked and generated by the compiler. If you’ve run into Arrow runtime schema issues, and your schema is stable (not defined or switched at runtime), this project…
2025 · github.com
- 15IB
I spent a long time working in the payments industry, specifically on a rather niche reporting/aggregation platform with spiky workloads that were not easily parallelized. To pump as much data through our pipeline as possible, we had to rely on complex locking schemes across half a dozen or so not-so-micro services - keeping a clear mental picture of how the services interacted for a given data source was a major headache. This problem always intrigued me, even after I no longer worked at the company, and lead to the development of Wool. If you've worked with frameworks like Ray or…
Mar 2026 · github.com
- 16PA
I’m sure many of you are familiar, but there’s a treacherous gap between finding (or building) a model that works in PyTorch, and getting that deployed into your application, especially in consumer-facing applications. I’ve been very interested in solving this problem with a great developer experience. Over time, I gradually realized that the highest-impact thing to have was a way to go from existing Python code to a self-contained native binary—in other words, a Python compiler. I was already pretty familiar with a successful attempt: when Apple introduced armv8 on the iPhone 5s, they…
2025 · blog.fxn.ai
- 17AL
Hey HN! I built a local Python prototyping tool that is finally the Python development environment I've always wanted. It has a Jupyter notebook for data crunching, a database of your choice (Python or MongoDB), and a Streamlit app for building a frontend visualization. You can edit the Streamlit backend via an embedded VSCode editor, or locally on your own IDE. The best part for me is that the database connectors within Jupyter and Streamlit are configured out-of-the-box, so you don't need to spend time thinking about how to tie all that together - you can just pick the database you want to…
2023 · github.com
- 18PA
Hello all, Very excited to share this project with you all! Panoptisch scans your Python file or module to find it's imports (aka dependencies) and recursively does so for all dependencies and sub-dependencies. It then generates a dependency tree in JSON for you to parse and enforce import policies. Supply chain attacks are no joke, and this is one way to transparently analyze your dependencies to see if any malicious imports are taking place. For example, your yaml parser, nor it's sub-dependencies should import socket, or sys. Panoptisch is in early stages, with known limitations (for…
2022 · github.com
- 19IE
Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com
- 20BP
What? Another flow based programming library for Python? Yes. All the FBP libraries out there for Python need to be run as a self contained application. They are not components that could be integrated into your existing data workflows. Barfi, on the other hand can be integrated. At the moment it has a Streamlit component that you can use in your Streamlit apps. Currently, I am working on a Jupyter notebook widget.
2022 · github.com
- 21
Real Goroutines for Python 3.13t+ free-threaded. Contribute to robertsdotpm/runloom development by creating an account on GitHub.
Jul 2026 · github.com
- 22VU
Visions is a python library for working with user defined data type systems. Out of the box, it provides type inference and automated data cleaning of sequence data with backend specific implementations for pandas, spark, python, and numpy. We often use it as a first pass cleaning step when working with tabular data and to simplify the backend logic of both pandas-profiling[1] and our tabular data compression library compressio[2]. Because data types are user defined, we can build user customizable libraries based around types without adding code complexity. In the case of compressio that…
2022 · github.com
- 23FS
I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library. The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware.…
2023 · ashvardanian.com
- 24AE
2016 · github.com
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