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);
}
Similar to Kiva Systems which was Amazon's best acquisition, Waymo is simply Google's best acquisition. (We live in San Francisco and it feels much safer around these Waymo cars than average "drivers".)
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.
LLMs operate on numbers; LLMs are trained on massive numerical vectors. Therefore, every request is simply a numerical transformation, approximating learned patterns; without proper trainings, their output could be completely irrational.
- 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.
Impressive! This approach can be applied to designing a NoSQL database. The flow could probably look something like this? Right?
- 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.
What you’ve described is the foundation of Lucene and as such the foundation of Elastic Search.
FSTs are “expensive” to re-optimize and so it’s typically done “without writes”. So the database would need some workaround for that low write throughput.
To save you the time thinking about it: The only extra parts you’re missing are what Lucene calls segments and merge operations. Those decisions obviously have some tradeoffs (in Lucene’s case the tradeoff is CRUD).
There are easily another 100 ways to be creative in these tradeoffs depending on your specific need. However, I wouldn’t be surprised if the super majority of databases’ indexing implementations are roughly similar.