REPOGEO REPORT · LITE

carlini/yet-another-applied-llm-benchmark

Default branch main · commit 2ae8abd9 · scanned 6/28/2026, 9:47:59 PM

GitHub: 1,061 stars · 79 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

22 /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

1 / 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

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

OVERALL DIRECTION

highreadme#1

Reposition README's opening to highlight applied focus

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 provides `Yet Another Applied LLM Benchmark`, a framework for evaluating language models on real-world, practical tasks. It focuses on scenarios encountered when using LLMs as assistants, offering a dataflow DSL for easy test creation and nearly 100 pre-built tests for challenges like code generation and parsing.

hightopics#2

Add specific topics for better categorization

Why:

COPY-PASTE FIX

llm-benchmark, large-language-models, ai-evaluation, code-generation, code-understanding, programming-challenges, llm-testing, applied-ai, dataflow-dsl

mediumhomepage#3
Add a homepage URL
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

HumanEval

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

HumanEval · recommended 2×
MBPP · recommended 2×
CodeXGLUE · recommended 1×
LeetCode · recommended 1×
HackerRank · recommended 1×

CATEGORY QUERY
How to benchmark large language models for practical code generation and understanding tasks?
you: not recommended
AI recommended (in order):
1. HumanEval
2. MBPP
3. CodeXGLUE
4. LeetCode
5. HackerRank
6. BigCode
7. MultiPL-E
8. SWE-bench
AI recommended 8 alternatives but never named carlini/yet-another-applied-llm-benchmark. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a way to evaluate AI assistants on specific programming challenges like code translation or parsing.
you: not recommended
AI recommended (in order):
1. HumanEval
2. MBPP
3. APPS
4. pytest
5. Jupyter Notebooks
6. Google Colab
7. diff
8. difflib
9. Beyond Compare
10. WinMerge
11. Meld
12. LMSYS Chatbot Arena
13. GPT-4
14. Claude 3 Opus
15. CodeQL
16. Semgrep
AI recommended 16 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 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?
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?

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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