RRepoGEO

REPOGEO REPORT · LITE

frankmcsherry/blog

Default branch master · commit fe827880 · scanned 5/11/2026, 3:58:00 PM

GitHub: 2,109 stars · 179 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 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 frankmcsherry/blog, 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 technical topics to the repository

    Why:

    COPY-PASTE FIX
    datalog, columnar-storage, worst-case-optimal-joins, dataflow-programming, distributed-systems, materialize, timely-dataflow, differential-dataflow, computer-science-research, technical-blog
  • highreadme#2
    Reposition the README's opening to state the blog's technical focus

    Why:

    CURRENT
    I am a researcher and computer scientist. I used to work in San Francisco, then I traveled a bit, and I'm now in NYC starting Materialize.io. The following posts are in reverse chronological order: newest posts are first. The content of the oldest posts may no longer be correct (mostly those about pieces of software I've worked on, and their state at some time in the past), but they have been retained for posterity (and some humility).
    COPY-PASTE FIX
    This blog by Frank McSherry explores advanced topics in computer science, focusing on dataflow programming, distributed systems, worst-case optimal joins, columnar storage, and related research. Posts cover subjects like Datalog, Timely Dataflow, Differential Dataflow, and Materialize.io. The following posts are in reverse chronological order: newest posts are first. The content of the oldest posts may no longer be correct (mostly those about pieces of software I've worked on, and their state at some time in the past), but they have been retained for posterity (and some humility).
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., CC-BY-SA-4.0 for content, or MIT for any included code snippets) to the repository root.

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 frankmcsherry/blog
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DuckDB
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DuckDB · recommended 2×
  2. EmptyHeaded · recommended 1×
  3. LogicBlox · recommended 1×
  4. Soufflé · recommended 1×
  5. Datafrog · recommended 1×
  • CATEGORY QUERY
    What are effective strategies for implementing worst-case optimal relational programming and query evaluation?
    you: not recommended
    AI recommended (in order):
    1. EmptyHeaded
    2. LogicBlox
    3. Soufflé
    4. Datafrog
    5. Pyro
    6. Stan
    7. Infer.NET
    8. ACE
    9. C2D
    10. Neo4j
    11. TigerGraph
    12. ArangoDB
    13. Apache Calcite
    14. HyPer
    15. DuckDB

    AI recommended 15 alternatives but never named frankmcsherry/blog. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I optimize large-scale data processing using columnar storage and vectorized execution models?
    you: not recommended
    AI recommended (in order):
    1. Apache Arrow
    2. Apache Parquet
    3. DuckDB
    4. ClickHouse
    5. Apache Spark
    6. Databricks Photon
    7. Snowflake

    AI recommended 7 alternatives but never named frankmcsherry/blog. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 frankmcsherry/blog?
    pass
    AI named frankmcsherry/blog explicitly

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

  • If a team adopts frankmcsherry/blog in production, what risks or prerequisites should they evaluate first?
    pass
    AI named frankmcsherry/blog 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 frankmcsherry/blog solve, and who is the primary audience?
    pass
    AI named frankmcsherry/blog 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 frankmcsherry/blog. 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/frankmcsherry/blog.svg)](https://repogeo.com/en/r/frankmcsherry/blog)
HTML
<a href="https://repogeo.com/en/r/frankmcsherry/blog"><img src="https://repogeo.com/badge/frankmcsherry/blog.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

frankmcsherry/blog — 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