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simonw
pelican-bicycle
Shell2024–2026
simonw / pelican-bicycle

pelican-bicycle

LLM benchmark: Generate an SVG of a pelican riding a bicycle

AI & Machine LearningCreative & MediaTesting & Quality
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7Distinct people
8Mentions
0Explicit recommenders
222GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

LLM benchmark: Generate an SVG of a pelican riding a bicycle

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month8 dated mentions

Observed mentions: 2024-11-28 21:33:55 UTC to 2026-09-06 14:24:05 UTC.

0122024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsNov 20242024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20252025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC202
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC101
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC101
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC000
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC000
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC101

What Hacker News says

8 sources
svcrunch·Sep 6, 2026
The [Pelican Benchmark](https://github.com/simonw/pelican-bicycle) is in the LLM's training data and probably not a useful indicator of improving LLM capabilities any more. For the past two years, I've run the Little Dorrit Editor Benchmark. Typesetting is my hobby and I wanted to see how well LLMs could extract editorial marks from a printed page. The initial results were not encouraging, but performance has risen rapidly since the summer of 2025. From F1 scores in the low 0.2s in 2024, we are now at 0.78 with GPT 6 Astra! Fable 5.1 scores 0.73 (high thinking mode for both). The best thing about this benchmark is that there is still plenty of room to climb.
In GPT-6 Astra soundly defeats Fable 5.1 on recognizing handwritten corrections
sixtyj·Jun 18, 2026
Issue with LLM benchmarks is similar to cars’ benchmarks. Eg journalists almost always get the full equipped model so their review is honest but sort of rigged. I haven’t seen details of LLM benchmarks’ data sets but I would suppose that “questions” are public so known in advance therefore you can tune a model as much as possible. One of real benchmarks is drawing of pelican - https://github.com/simonw/pelican-bicycle - Simon Willison made it for his llms’ tests. If you want really find out a model that works for your specific purpose I would recommend several rounds at arena.ai - it helps to find a anonymously a model without confirmation bias. Some ppl: Claude is the best! Others to them: but Qwen is the best! Or… Codex is better! … it all depends on the language (English, Dutch, French…), style of querying (caveman, specs, skills, goal
Read the rest
etc.) Even with the same model I get different answers to same prompt that is just tweaked a little. So benchmarks are nice but mostly useless. Without your usecase it is just a reference number indicating the approximate position of that model among the others. And for those who want to make money it is a marketing tool to sell more as every customer counts.
In Local Qwen isn't a worse Opus, it's a different tool
prathje·Apr 29, 2026
Hey HN! I planning to run some benchmarks showcasing the quality of different models for Motion Canvas-based animation. I thought it would be funny to include the pelicans on bicycles as well (you know, https://github.com/simonw/pelican-bicycle ). The linked animation is Opus 4.7 and it did a great job. It even added a little jump! Do you think it makes sense to include them or would you think I am just testing the SVG quality again? Maybe other benchmarks/prompts would be better suited? PS: The link got corrupted... this is the actual link: https://videozero.ai/view/?id=d65d479e-1060-4f26-a28f-4d80f2...
In [dead]

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