REPOGEO REPORT · LITE

carlini/yet-another-applied-llm-benchmark

Default branch main · commit 2ae8abd9 · scanned 5/17/2026, 5:07:48 PM

GitHub: 1,055 stars · 80 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 carlini/yet-another-applied-llm-benchmark, 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

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

OVERALL DIRECTION

highreadme#1

Reposition the README's opening sentence to clearly state its function as an LLM evaluation framework

Why:

CURRENT

This is a benchmark I made, for me, to test how well language models perform on tasks I care about.

COPY-PASTE FIX

This repository introduces `yet-another-applied-llm-benchmark`, a robust evaluation framework for assessing Large Language Models on practical, real-world tasks such as code generation, explanation, and data transformation.

mediumhomepage#2
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/carlini/yet-another-applied-llm-benchmark
```

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 carlini/yet-another-applied-llm-benchmark

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Pytest

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Pytest · recommended 1×
JUnit · recommended 1×
Jest · recommended 1×
Go testing package · recommended 1×
NUnit · recommended 1×

CATEGORY QUERY
How to evaluate large language model performance on code generation and explanation tasks?
you: not recommended
AI recommended (in order):
1. Pytest
2. JUnit
3. Jest
4. Go testing package
5. NUnit
6. BLEU
7. CodeBLEU
8. ROUGE
9. Pylint
10. ESLint
11. Checkstyle
12. Go vet
13. SonarQube
14. Amazon Mechanical Turk
15. Appen
16. Flesch-Kincaid Grade Level
17. SMOG Index
18. Automated Readability Index (ARI)
19. textstat
20. BERTScore
21. Sentence-BERT (SBERT)
22. Word Mover's Distance (WMD)
23. SQuAD
AI recommended 23 alternatives but never named carlini/yet-another-applied-llm-benchmark. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best benchmarks for assessing LLM capabilities in developer-focused scenarios?
you: not recommended
AI recommended (in order):
1. HumanEval
2. MBPP (Mostly Basic Python Problems)
3. CodeXGLUE
4. APPS (Automated Programming Problem Solving)
5. RepoBench
6. SWE-bench
7. DS-1000
AI recommended 7 alternatives but never named carlini/yet-another-applied-llm-benchmark. 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 carlini/yet-another-applied-llm-benchmark?
pass
AI named carlini/yet-another-applied-llm-benchmark explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts carlini/yet-another-applied-llm-benchmark in production, what risks or prerequisites should they evaluate first?
pass
AI named carlini/yet-another-applied-llm-benchmark 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 carlini/yet-another-applied-llm-benchmark solve, and who is the primary audience?
pass
AI did not name carlini/yet-another-applied-llm-benchmark — 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?

Embed your GEO score

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MARKDOWN (README)

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carlini/yet-another-applied-llm-benchmark — 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