This dynamic studio is intended to provide meta-recursive self-generation of synthetic data/information and provide self-learning/self-improving
It is being designed to improve human-LLM communication, evaluation, education, compression To reduce overall computation power costs that LLMs are currently providing that impact the environment, by using this the goal is to reduce computational cost by improving communication.
Using multiple compression algorithms with content aware dynamic compression and providing Large Language Models (LLMs) with:
- A symbolic output that provides the methods of compression to decompress
- Allows for unique encryption algorithms if each side knows the cipher
- Combination of Errors, Log Files, Warnings, Network Requests, Test coverage/output and provides this with lower tokenization for LLMs
This is a studio application in which is being designed to improve LLM/Agent communication, improve personal education, allow for evaluation of LLM/Agent prompts through (WIP) semantic analysis and provide traceability
Generation of synthetic data (multiple file extensions)
- Repetitive Text
- Structured Data
- Binary Data
- JSON Objects
- XML Documents
- Log Files
- Source Code (WIP) Markdown Content CSV Data Random Data
Compression Challenges Edge cases (WIP) Performance Tests Stress Tests Realistic Scenarios
generation of synthetic media (video, image, audio)
LLM/Agent, Prompts, Workflows
