Most development applications require an llm to parse logs for processes - if this can be centrally managed by the .kno toolkit, we will see a markable improvement in log recognition for repeated errors, and could also see reduced api costs.
For this to work, the logs should be embedded as a live stream, and mapped to the codebase embeddings, before being provided via a log-viewer tool.
Even better optimizations can be achieved by pre-processing the logs with NLP before embedding, identifying and tagging metadata such as [Error] [Progress] [TimeStamp] for each log
Most development applications require an llm to parse logs for processes - if this can be centrally managed by the .kno toolkit, we will see a markable improvement in log recognition for repeated errors, and could also see reduced api costs.
For this to work, the logs should be embedded as a live stream, and mapped to the codebase embeddings, before being provided via a log-viewer tool.
Even better optimizations can be achieved by pre-processing the logs with NLP before embedding, identifying and tagging metadata such as [Error] [Progress] [TimeStamp] for each log