Jusai

Building @CreateAIx · Turning natural language into audited, verified, deployed contracts prompt.deploy({ chain: ‘base’ })

[Diego Soto @DSotoOrtiz]

Bar Antonio de San Sebastián. Miércoles. Diez de la mañana. Se confirma que nos hemos quedado ya para siempre sin su pintxo de tortilla.

[a16z crypto @a16zcrypto]

Simply become insane and desperate.

[Garry Tan @garrytan]

https://t.co/XTqXqWuQn5

[Jusai @imjusai]

This is why we’re building @CreateAIx
the builders who win aren’t writing boilerplate, they’re directing agents

[0xMarioNawfal @RoundtableSpace]

ANTHROPIC ENGINEERS ARE STARTING TO LOOK MORE LIKE MANAGERS OF AI AGENTS THAN TRADITIONAL CODERS.

The new workflow is less about writing every line yourself and more about directing multiple agents in parallel.

[Jusai @imjusai]

Most web3 builders spend 80% of their time on infrastructure that already exists
setting up wallets, writing contracts, configuring liquidity pools, deploying to 5 chains

We’re building the layer that collapses all of that into a prompt🤦‍♂️

[Jusai @imjusai]

99% of smart contract bugs aren't from bad devs, they're from good devs shipping fast without tooling

we're building the missing layer:
prompt → solidity → audit → deploy → verify

early days but the pipeline is real

[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.

[Jusai @imjusai]

We’ve been building in silence for months.
AI × Web3.
Not another tool. A new layer.

prompt → agent → contract → execution

Soon.

[Jusai @imjusai]

4:00 AM internal tests after grinding in silence for months

Agents arguing with each other.
Flows rewriting themselves mid-run.

Can’t reveal more yet…
But what we’re seeing is a full shift in AI automation.

@Lovable @robiot you’re really gonna want to see this.

[Jusai @imjusai]

Most people are still building Al like it's one model doing everything.

Real builders know the truth: multi-agent systems > single prompts

[Jusai @imjusai]

People still underestimate how early we are.

@ChatGPTapp is the Nokia 3310 era of intelligence.

Wait until we hit the iPhone moment.

[Jusai @imjusai]

First night out after 3 months locked in building the next big AI thing.

App Store: In review. 🕺🤖

[Jusai @imjusai]

Being on the computer until 5 AM every day isn't obsession.

It's vision.

[Jusai @imjusai]

99% of AI startups = wrappers

1% = revolutions.

[Jusai @imjusai]

The scariest part of Al isn't that it learns fast.
It's that it never forgets.

[Jusai @imjusai]

@andrewdsouza @boardyai Boardy

[Jusai @imjusai]

Al isn't replacing people.
It's replacing people who don't use Al.

[Jusai @imjusai]

I've spent the last 2 years building with Al.

Not prompting.
Building.

There's a big difference.