fabceolin

4 ms·

1. 1.▲
   by fabceolin 8mo ago

   Clean context for each iteration will make the LLM give your better results. Using LLM loop you will full the context faster degrading the LLM responses. Tea supports create a workflow from dot file https://fabceolin.github.io&#x
2. 2.▲
   by fabceolin 8mo ago

   Clean context for each iteration will make the LLM give your better results. Using LLM loop you will full the context faster degrading the LLM responses.
3. 3.▲
   by fabceolin 8mo ago

   Yes, I wrote an article about this: Truth Resolution Agent: A Multi-Source Judicial Framework for Sports Disputes (Senna 1989 Case Study) using llm as a judge and prolog neurosymbolic as a judge https://fabceolin.github.io/t
4. 4.▲
   by fabceolin 8mo ago

   The project started to be a Cyclic State Graph orchestrator, statically defined via YAML, leveraging Neurosymbolic validation (Prolog) to ensure deterministic transitions in edge environments. Langraph also it is, but python and the thread
5. 5.▲
   by fabceolin 8mo ago

   We have checkpoints implemented to save the state in the middle of graph navigation and we can restart from there. It's useful to implement interviews process like https://fabceolin.github.io/the_edge_agent/article
6. 6.▲
   by fabceolin 8mo ago

   Thanks
7. 7.▲
   Show HN: Build agents via YAML with Prolog validation and 110 built-in tools(fabceolin.github.io)

   11 points by fabceolin 8mo ago|11 comments