X · by edwinfmesa

How Jev and Laya help support agents prepare context

Both models tried on an agent’s context step.

Edwin Mesa

@edwinfmesa

Explorando qué se puede hacer con Jev y Laya, que (los de moda), me pregunté si podrían ayudar a un agente de soporte a preparar el contexto mientras el usuario escribe su mensaje. El contexto es clave para un LLM, así que armé una startup ficticia con 7 documentos. 🧵

Sep 24, 2026 · 1 likesOpen on X

edwinfmesa explores how Jev and Laya help support agents prepare their context.

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tamara
@tamarajtran
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
XContext and memory
Pick
Instant compaction for Claude
Baris Terzioglu
@terzioglubrs
Agent memory is usually just an append-only Markdown file that grows forever, and most frameworks load the whole thing into context on every single run. That has been bothering me for a while, but never quite enough to fix it for our agents. Jev feels like it might be a low effort patch to this, by simply scoring each memory against the prompt first, then load only the relevant bits for that specific request.
XContext and memory
Agent memory is usually one growing file
たく｜ガチのCopilot達人
@taku_ai_case
これはめちゃくちゃ勉強になりました。 Jevに回答させるのではなく、CodexやClaude Codeへ渡す記憶候補を選ばせる。文章を書けないAIの特性を、ここまで実用的な仕組みに落とし込む発想がすごいです。
XContext and memory
Let Jev pick the memories, not the answer

How Jev and Laya help support agents prepare context

More like this

Instant compaction for Claude

Agent memory is usually one growing file

Let Jev pick the memories, not the answer