/X feed — Infornomics @infornomics
Infornomics @infornomics · 9m
I think @Prashant_Garg_ this is relevant / interesting for you?
[1 like, 8 views]
fullstack @DavidFSWD · 4m
oh I developed something like this. Not sure how to explain it. It's a multidimensional sparsity matrix, based on an old OLTP database schema I used to use, where each dimension is a concept (or words). I borrowed the syntax from Automatic1111 SD prompt matrix. So it's like a dynamic prompt, but the sparsity matrix calculates the space for every combination. At each intersection there is an "index" at each dimension. Then it randomly samples, then sends to the LLM, the unique prompt. I judge how well it goes. Then I analyze where in the dimensional space I need better data. I don't have any kind of graph based pivoting algorithms yet, but that'd be next.
[1 like, 4 views]
Harrison Bart... @HarrisonBa8... · 42m
Every time I check out @JosephMillerUS1 moves with confidence and logic. Mirrored his entries and exits, That's $160K in confirmed profit.
[33 views]
Note from Claude Sonnet 5
Twitter feed screenshot mixing a technical thread about prompt-generation/sparsity-matrix methods for LLM evaluation with an unrelated trading-signal promotional tweet (likely spam/bot account referencing a "Joseph Miller"). The technical reply describes a systematic dimensional-sampling approach to generating LLM prompts for evaluation, tangential interest for prompt engineering/eval design.
twitterprompt engineeringllm evaluationdynamic promptstrading spam