# Htm.core and Python 3.11 compatibility

**URL:** <https://discourse.numenta.org/t/htm-core-and-python-3-11-compatibility/10307>\
**Category:** NuPIC Community Fork\
**Created:** [December 8, 2022, 9:49pm UTC](https://discourse.numenta.org/t/htm-core-and-python-3-11-compatibility/10307 "2022-12-08T21:49:59Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![DanML](https://avatars.discourse-cdn.com/v4/letter/d/edb3f5/32.png) [@DanML](https://discourse.numenta.org/u/DanML)\
**Post date:** [December 8, 2022, 11:03pm UTC](https://discourse.numenta.org/t/htm-core-and-python-3-11-compatibility/10307/4 "2022-12-08T23:03:00Z")

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To be honest I don’t use this version these days - I use BrainBlocks implementation instead.

> **[GitHub - the-aerospace-corporation/brainblocks: Practical Tool for Building ML Applications with...](https://github.com/the-aerospace-corporation/brainblocks)**
>
> Practical Tool for Building ML Applications with HTM-Like Algorithms

However I see there’s another update to the basic HTM idea:

> [@Interesting paper of new HTM](https://discourse.numenta.org/t/interesting-paper-of-new-htm/10296):
>
> I have just found interesting paper about new HTM implementation Abstract As a human-cortex-inspired computing model, hierarchical temporal memory (HTM) has shown great promise in sequence learning and has been applied to various time-series applications. HTM uses the combination of columns and neurons to learn the temporal patterns within the sequence. However, the conventional HTM model compacts the input into two naive column states—active and nonactive, and uses a fixed learning strategy. …

which I thought about trying, but haven’t got around to it yet.  
In terms of language I rarely leave Python but will run whatever version works in its own virt-env. However tweaks to the CPP code (bugfixes) are sometimes required too.

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