ammar_x

Is there some sort of a leaderboard for this test? Like if you'd give each of Opus 4.8 and GPT 5.5 a score out of 100, what would the scores be?

Absolutely! We need new and better benchmarks like this.

I have a question: why not use the maximum available reasoning on each LLM? For example, I see that Opus 4.7 at `max` reasoning but Sonnet 4.6 at `high`. Wouldn't it be a fairer comparison if all were at max?

I usually do this for complex features:

- Opus 4.7 writes the code - I make GPT-5.5 in Codex to review it (given context) - I provide the review back to Opus and ask it to verify the review findings - Make Opus plan the fixes then execute them - Ask GPT-5.5 to review the fixes and check if they solve the problems

Cool, but body font size is too small for comfortable reading!

I've used Brave Search and found it better than Google's in some cases

This looks great for quick audio operations without the need to use heavy apps.

One question: I tried the "Fade In" effect; is there a way to control its timing (i.e. the part of the clip where the effect is applied) ?

My "trick" was to divide things into batches (which can be big with LLMs with larger context sizes) and classify the items in each batch, then take the resulting categories from each batch and feed them into an LLM to group semantically similar categories into groups with a representative category for each group. The representative category can be chosen from the group or created by the LLM. This is an over-simplification of the process but that's the gist of it.

Language support is not mentioned in the repo. But from the paper, it offers extensive multilingual support (nearly 100 languages) which is good, but I need to test it to see how it compares to Gemini and Mistral OCR.

Claude Skills seem to be the option that offers highest flexibility to add more capabilities at most simplicity. Better than MCP in my opinion. Hope it becomes a standard and get adopted by OpenAI and the rest of labs.

Good question! I selected the edition with the smallest Goodreads ID¹ that has the publication date and cover photo available. If all editions don't have publication date nor cover photo, then we get the one with the smallest ID.

And you're right, in a few cases, this resulted in getting less widely read editions for some books.

1: Assuming smaller ID means earlier addition to Goodreads' database.