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.
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VivaTechnics commented on LLMs generate 'fluent nonsense' when reasoning outside their training zone venturebeat.com/ai/llms-g... · Posted by u/cintusshied
VivaTechnics commented on Advice for Tech Non-Profits mitchellh.com/writing/adv... · Posted by u/ksec
100% agreed. Awesome!
Here is our 501(c)(3) tech non-profit. All corporate profits are directed to children. Clear and transparent.
VivaTechnics commented on MCP Tools with Dependent Types vlaaad.github.io/mcp-tool... · Posted by u/vlaaad
Good work!
`type<T>` (generic types) can mimic dependent types for this?
>These are all excellent names
Wholeheartedly agree. what about something like Sage? B? You/U? Manifest?
- Maybe, design your language first, then name it.
- 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.
Something short, simple, fundamental, low-level and deeply techie:
Xor, XORY
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These are all excellent names: C, C++, Rust, Ada, Julia, Shell, Bash, etc.
VivaTechnics commented on Index 1.6B Keys with Automata and Rust (2015) burntsushi.net/transducer... · Posted by u/djoldman
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.