Info (not recent) available here: https://awz.us/docs

    let mut msg = r#"
    We feel super sad.
    Rust in Peace.

    Steel dreams compile to dust,
    Silent threads unwind.
    Memory fades,
    Borrowed time returned.
    "#;

    println!("{}\n{}", mood, msg);
}

This will only compound wasted time on Claude.ai, which exploits that time to train its own models.

Why time wasted? Claude’s accuracy for shell, Bash, regex, Perl, text manipulation/scripting/processing, and system-level code is effectively negligible (~5%). Such code is scarce in public repositories. For swarms or agents to function, accuracy must exceed 96%. At 5%, it is unusable.

We do also use Claude.ai and we believe it is useful, but strictly for trivial, typing-level tasks. Anything beyond that, at this current point, is a liability.

It is trivially learnable, absurdly flexible, and unmatched in ecosystem leverage. No simpler language delivers comparable reach.

Python may not be so suitable for systems, real-time, or performance-critical work—that’s Rust, C, and C++.

Nevertheless, every serious engineer must know Python, just as they must know shell/bash scripting. Non-negotiable.

Here is our 501(c)(3) tech non-profit. All corporate profits are directed to children. Clear and transparent.

https://aid.aideo.us/

- Single-letter names are mostly taken (e.g., B: https://en.wikipedia.org/wiki/B_(programming_language)

- Focus on one key feature your language does better than others. Low-level languages are trending; high-level application languages are crowded. For example, if you could make assembly-style code user-friendly, that could be a strong niche.

Xor, XORY

------

These are all excellent names: C, C++, Rust, Ada, Julia, Shell, Bash, etc.

- The client queries for "alice123". - The Query Engine checks the FST Index for an exact or prefix match. - The FST Index returns a pointer to the location in Data Storage. - Data Storage retrieves and returns the full document to the Query Engine.

Also, what are the best strategies to rigorously validate inputs while minimizing latency?

Is this the best for Rust: https://github.com/modelcontextprotocol/rust-sdk

Technically, we could say?

(1) Single-loop: fixes actions within fixed rules, like Reinforcement Learning.

(2) Double-loop: questions and adapts the rules, somewhat like Meta-Reinforcement Learning.

Especially for Rust: https://rust-for-linux.com/coccinelle-for-rust

Thanks for sharing!

Compared to TigerGraph, Neo4j, JanusGraph, Dgraph, and ArangoDB, I’d love to see a benchmark-ish comparison of GenosDB in terms of performance, latency, scalability, modularity, and flexibility.