Alternatives
Products that do what DSAfeed does
Data Structures & Algorithms Made Simple
- 1DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
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A Python-based educational playground for creating, exploring, and visualizing digital signal processing (DSP) algorithms using NumPy, Matplotlib and Jupyter Notebook.
2025 · github.com
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- 8AA
2021 · arc.tripl.ai
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2020 · github.com
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2014 · npmjs.org
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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
- 17BD
Hi everyone, I'd like to share my project, bridge-ds - a lightweight Python framework that simplifies how ML practitioners manage and interact with datasets. Why bridge-ds? It abstracts the repetitive parts of dataset handling in real-world ML workflows, but remains lean enough as to not force opinionated workflow or unnecessary dependencies. bridge-ds uses two complementary approaches: - Macro-level: Treat your entire dataset like a DataFrame—filter, sort, and modify with familiar, intuitive operations. - Micro-level: Efficiently handle individual samples with lazy loading, caching, remote…
2024 · github.com
- 18DA
Hi. :) I’m Andrey, the creator of dstack. I started this project while I was working at JetBrains where I helped the PyCharm team to improve support for Jupyter notebooks. As I was in close contact with many ML devs (who used PyCharm) I was able to see their struggle with running ML workflows. Unlike traditional dev workflows, ML workflows are difficult to run on a local machine (due to the lack of memory, more CPUs/GPUs, etc). This is why people often have to use remote machines (e.g. via SSH), or adopt one of the end-to-end MLOps platforms. Using remote machines is not difficult but…
2022 · github.com
- 19UD
I've been working a fair bit with DSPy lately, and I did some work in combining the benefits of vector search and LLMs (via a DSPy pipeline) to disambiguate records with a high degree of accuracy to help enrich a dataset. The blog post shows how this approach scales well, is very cost-effective and super concise - all it takes is < 100 lines of DSPy code and it all runs async. The code to reproduce is in this repo if anyone's interested (all tools are 100% free and open source, and the methodology will work with open weight LLMs too).…
2025 · blog.kuzudb.com
- 20DA
2022 · docs.dissect.tools
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I made a DSA practice tool for Ruby. It’s test-driven, runs locally, and is easy to extend with new exercises. Would love feedback on the interface and which problems to add next. The objective is to cement ability to implement core algorithms through repetition. This test suite is not like LeetCode. In fact, it’s more of a prerequisite to LeetCode. The test cases are not exhaustive in the spirit of checking for runtime performance, scalability, etc. Implementations are generalized, so that through practice using this tool, the user can begin avoiding having to think about the algorithmic…
2025 · github.com
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Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
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2016 · datafire.io
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I'm developing a storage system for versioning data at the subfile level, especially well suited for SSDs due to its log-structured COW nature. It implements a novel versioning algorithm called sliding snapshot, a diff-algorithm which makes use of our stable record-identifiers and optionally hashes, another diff algorithm for importing similar XML-documents as a versioned resource as well as novel XPath axis to navigate not only in space, but also in time. Recently, I've implemented a higher level, asynchronous REST-API with Kotlin (Coroutines) and Vert.x in a seperate module. The system is…
2018
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