RRepoGEO

REPOGEO REPORT · LITE

alxndrTL/mamba.py

Default branch main · commit 5c4136c2 · scanned 5/28/2026, 5:07:00 PM

GitHub: 1,463 stars · 130 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface alxndrTL/mamba.py, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    mamba, state-space-models, ssm, pytorch, mlx, deep-learning, neural-networks, transformers, jamba, vision-mamba
  • highreadme#2
    Reposition the README's opening sentence to clarify its role as a library

    Why:

    CURRENT
    A straightfoward implementation of Mamba in PyTorch with a simple parallel scan implementation, offering an major speedup over a sequential implementation, as the parallel scan allows the parallelization over the time dimension.
    COPY-PASTE FIX
    mamba.py is a straightforward and efficient Python library providing a pure PyTorch and MLX implementation of the Mamba architecture, designed for researchers and developers to easily build and experiment with modern state-space models.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/alxndrTL/mamba.py

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface alxndrTL/mamba.py
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HazyResearch/state-spaces
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. HazyResearch/state-spaces · recommended 1×
  2. LSSL (Linear State-Space Layers) · recommended 1×
  3. PyTorch-SSM · recommended 1×
  4. Hand-rolled PyTorch implementation · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Looking for a straightforward and performant state-space model implementation in pure PyTorch.
    you: not recommended
    AI recommended (in order):
    1. S4 (Structured State Space Sequence Models) (HazyResearch/state-spaces)
    2. LSSL (Linear State-Space Layers)
    3. PyTorch-SSM
    4. Hand-rolled PyTorch implementation

    AI recommended 4 alternatives but never named alxndrTL/mamba.py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient Python libraries for implementing modern state-space architectures with parallelization?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. mamba_ssm (HazyResearch/State-Spaces)
    3. transformers
    4. JAX
    5. equinox
    6. flax
    7. jaxs4
    8. TensorFlow
    9. Keras
    10. ssm-jax

    AI recommended 10 alternatives but never named alxndrTL/mamba.py. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of alxndrTL/mamba.py?
    pass
    AI did not name alxndrTL/mamba.py — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts alxndrTL/mamba.py in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alxndrTL/mamba.py explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo alxndrTL/mamba.py solve, and who is the primary audience?
    pass
    AI named alxndrTL/mamba.py explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of alxndrTL/mamba.py. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/alxndrTL/mamba.py.svg)](https://repogeo.com/en/r/alxndrTL/mamba.py)
HTML
<a href="https://repogeo.com/en/r/alxndrTL/mamba.py"><img src="https://repogeo.com/badge/alxndrTL/mamba.py.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

alxndrTL/mamba.py — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite