RRepoGEO

REPOGEO REPORT · LITE

inference-labs-inc/zkml-blueprints

Default branch main · commit 39b07019 · scanned 5/18/2026, 3:23:17 PM

GitHub: 1,537 stars · 24 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 inference-labs-inc/zkml-blueprints, 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
  • highreadme#1
    Reposition README opening to clarify role as ZKML circuit design reference

    Why:

    CURRENT
    zkml-blueprints** is a collection of mathematical formulations and circuit designs supporting zero-knowledge (ZK) proofs for machine learning (ML) applications, focusing on efficiently implementing provable computations in ZK circuits. This repository provides formal descriptions, constraints, and structured blueprints for designing circuits that preserve privacy while ensuring verifiable correctness.
    COPY-PASTE FIX
    zkml-blueprints is a rigorous, community-driven collection of mathematical formulations and circuit designs, serving as a foundational reference for *building* efficient and verifiable zero-knowledge (ZK) proofs in machine learning (ML) applications. Unlike ZKML frameworks or libraries, this repository focuses on providing design patterns, constraints, and structured blueprints for implementing provable computations in ZK circuits, enabling developers and researchers to understand and construct their own ZKML solutions.
  • hightopics#2
    Add specific topics to improve categorization for ZKML circuit design

    Why:

    COPY-PASTE FIX
    zero-knowledge, zkml, machine-learning, cryptography, circuit-design, zk-snarks, privacy-preserving-ml, blueprints, reference, design-patterns
  • mediumreadme#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License(s) here, e.g., a custom license, or a combination of licenses]. See the [LICENSE](LICENSE) file for details.

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 inference-labs-inc/zkml-blueprints
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ConsenSys/gnark
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ConsenSys/gnark · recommended 1×
  2. zcash/halo2 · recommended 1×
  3. zkonduit/ezkl · recommended 1×
  4. iden3/circom · recommended 1×
  5. AleoHQ/leo · recommended 1×
  • CATEGORY QUERY
    How to design efficient zero-knowledge circuits for verifiable machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Gnark (ConsenSys/gnark)
    2. Halo2 (zcash/halo2)
    3. EZKL (zkonduit/ezkl)
    4. Circom (iden3/circom)
    5. Leo (AleoHQ/leo)
    6. Cairo (starkware-libs/cairo)

    AI recommended 6 alternatives but never named inference-labs-inc/zkml-blueprints. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking rigorous blueprints for implementing privacy-preserving computations in zero-knowledge machine learning applications.
    you: not recommended
    AI recommended (in order):
    1. OpenMined's PySyft & PyGrid
    2. Microsoft SEAL
    3. FHE.org
    4. Zama's Concrete-ML
    5. Google's TF Encrypted
    6. IBM's HElib

    AI recommended 6 alternatives but never named inference-labs-inc/zkml-blueprints. 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 inference-labs-inc/zkml-blueprints?
    pass
    AI did not name inference-labs-inc/zkml-blueprints — 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 inference-labs-inc/zkml-blueprints in production, what risks or prerequisites should they evaluate first?
    pass
    AI named inference-labs-inc/zkml-blueprints 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 inference-labs-inc/zkml-blueprints solve, and who is the primary audience?
    pass
    AI named inference-labs-inc/zkml-blueprints explicitly

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

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inference-labs-inc/zkml-blueprints — 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