Esther

SDE | Making games for fun
小红书/b站：鲨鱼恶魔哒

[Esther @SharkDemonYa]

I made an explorable poolrooms dreamcore game, using Three.js @threejs + Opus 5 & Fable 5 @ClaudeDevs

Every texture and sound is generated in code. Actually the whole game is just a single HTML file :)

Play it in your browser: https://t.co/IDARb5uQRO

[Esther @SharkDemonYa]

@kevin_t_ngo Very cute game demo! I didn’t know Claude could model characters so well, at least in my own game lol. Also, very good prompts to learn 🙌 thank you for sharing!!

[Esther @SharkDemonYa]

Agency is the new intelligence.

[Esther @SharkDemonYa]

#NewProfilePic

[Esther @SharkDemonYa]

Tested 3D modeling abilities in my game using Opus5…

Not bad, and I like it!

[Esther @SharkDemonYa]

literally me lol

[Zara Zhang @zarazhangrui]

The #1 thing I want from any model right now: speed

Intelligence is already good enough. But waiting 1-5 min per task is the worst possible window: too short for deep work, too long to just stare at the screen. So I end up scrolling X

Agents are making all of us more ADHD

[Esther @SharkDemonYa]

Me watching Fable 5 come up with a transition in my game I didn’t even ask for: 🤯🤯

[Esther @SharkDemonYa]

Made my dream game with Fable5 in 5 days.

Just Claude Code + Fable5. No other AI tools. No external skills.

The results were amazing 🥺

I deployed the game to Vercel. Anyone interested in trying it? I’d love to get some feedback on the user experience and gameplay…

[Esther @SharkDemonYa]

yay!

[Claude @claudeai]

Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits.

Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit.

Demand for Fable has been challenging to predict, which is why we rolled it out to subscription plans in stages, extending access several times as we secured additional capacity.

[Emil Kowalski @emilkowalski]

The people getting great UI results with AI aren’t using a secret model or a magic prompt. They know what a great interface looks like, and they know how to steer AI towards it.

Work that used to take a week takes a day, and the quality bar doesn’t drop, it goes up, because you spend your time on the things that matter.

At the end of the day, people that use AI best are the ones that were excellent before LLMs as well. The ones that got the fundamentals right, went that extra mile, and cared about the thing they were working on.

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[数字生命卡兹克 @Khazix0918]

https://t.co/J2ojwxpyBE