DSAfeed
Data Structures & Algorithms Made Simple
What it does
DSAfeed is a platform designed to simplify the learning. It aims to make complex concepts accessible to a wider audience, including students, aspiring software engineers, and anyone looking to strengthen their foundational computer science knowledge. By breaking down DSA into manageable parts, DSAfeed empowers users to build a solid understanding of these essential topics, which are crucial for problem-solving and efficient software development.
Does the same job
all alternatives →- DAdstack – an open-source tool to build data applications easily2020 · ▲134
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…

- OAOctopus – a directed acyclic graph for app development2023 · github.com · ▲84
Directed acyclic graphs are muched discussed in comp-sci, but octopus appears to be the first reusable, turnkey, ready-to-wear, off-the-shelf implementation of a DAG for application development, in any language, that I'm aware of. This is remarkable because DAGs hit a sweet spot in the middle of the three common programming paradigms (OO, event-driven, functional). Let's have a DAG as the top-level structure of our applications. Data-fetching and onChange handlers live in DAG nodes, next to the data they act on. The UI flows out from the DAG with fine-grained reactivity. Our app state is…

- APA Python-based educational DSP playground2025 · github.com · ▲6
A Python-based educational playground for creating, exploring, and visualizing digital signal processing (DSP) algorithms using NumPy, Matplotlib and Jupyter Notebook.

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