u/HectorSmith687

Four different frontier models were given the same prompt to generate three self-contained HTML5 canvas scenes with real-time physics simulations.

The results say a lot about where AI models are today.

Prompts:

• A train derailing off a broken bridge into the water
• Two cars jumping off ramps and colliding mid-air over a canyon
• A monster truck crushing a row of parked cars

Results:
Fable 5: Produced the best overall physics and scene logic, but at a cost of $3.12 (62k+ tokens).
GPT-5.5: A strong runner-up with impressive results for $1.14 (37k+ tokens).
Opus 4.8: Delivered solid, usable code for $0.56 (22k+ tokens).
GLM 5.2: Had the weakest physics results, but cost cheapest $0.08 (36k+ tokens).

The benchmark highlights a tradeoff that a lot of us deal with: better results often come with a higher API bill. Fable 5 produced the strongest output but paying several times more than something like Opus 4.8 isn't always worth it, especially for large-scale workloads.

That's also why more teams are paying attention to the quality of the data they send into these models
Firecrawl have become useful for that same reason bc instead of passing raw webpages directly into a model, teams can clean and structure the content first, reducing garbage before it reaches the model.

At the end of the day, it comes down to the tradeoff: do you need the best possible output every time, or is a cheaper model with a better workflow the more practical choice?

This AI idea from Andrej Karpathy called the "LLM Wiki" is very very intresting IMO.

Everyone use AI mainly as a coding assistant or a faster way to search for information. The idea here is a little different: use AI to build a second brain instead of using it just to write code.

The setup connects an AI tool to your local notes, and the agent constantly reads and indexes your entire knowledge base on your own machine, instead of starting from scratch every time you open a chat. The more data you feed it, the smarter your local repository becomes.

The quality of the notes matters more than the model, so always check if your files are duplicated or full of junk.

That’s why building a clean background pipeline is one of the most critical part of the process. The people running these setups are automating these works manually or by relying on firecrawl to pull articles from the web and turn them into cleaner markdown files that are easier to store and search later.

The AI isn't really the interesting part here but having years of your notes, projects, research, and ideas stored in one place where you can find and use them later.