brain-plasma
xact
| brain-plasma | xact | |
|---|---|---|
| 1 | 2 | |
| 82 | 1 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | about 3 years ago | |
| Python | Python | |
| MIT License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
brain-plasma
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Ask HN: Show me your Half Baked project
https://github.com/russellromney/brain-plasma
I had a speed problem repeatedly loading large dataframes into memory for a Dash project - threads can’t share object updates and Redis gets slow with large objects. So I built brain-plasma: a fast Python dictionary interface to the Plasma shared memory store (py arrow project). It lets you keep objects in memory, but separate from service memory - need to read data into memory over and over. Fast access and arbitrary namespaces. It’s not perfect but it works well.
xact
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Ask HN: Show me your Half Baked project
https://github.com/wtpayne/xact - Model Based Systems/Software Engineering tool with support for machine learning and synthetic data.
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Ask HN: Does Your Company Practice Model Based Systems Engineering? (MBSE)
MBSE is a vastly underappreciated technique, and one that deserves much more exposure in the mainstream tech community.
Sadly, I have found myself exceedingly disappointed at the tooling available to support MBSE, having mainly used Simulink, and also experimented with a few others like Rhapsody, Capella and EA.
Constant mouse usage with Simulink gave me really bad RSI, and merging models was a pain, due to the way that layout and structural information were mixed together in the xml-based .mdl file format.
So (naturally) I made a text-based alternative. It's a bit like TensorFlow in that you create a compute graph (computational model), and then run it.
The model itself can be generated dynamically in a script, using JQuery-like syntax to add or change nodes, or alternatively it can be stored and version controlled in one or more text files, using YAML, XML, JSON or TOML (or some mixture of those) to serialise the structure in an easy-to-diff-and-merge form.
https://github.com/wtpayne/xact
What are some alternatives?
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pgsink - Logically replicate data out of Postgres into sinks (files, Google BigQuery, etc)
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
jarvis - An AI sidekick that helps you control your computer.