Gipp 🦅(@gippp69):i’m f**king done with agents forgetting everything after one session... so i’m giving them a memory layer from a chinese team that actually persists across runs. tencent’s approach turns conversations, docs, and code into reusable memory instead of stuffing everything back into one giant prompt. [here’s how i’d structure it] 1. chat memory keeps decisions + preferences 2. skills preserve workflows that already worked 3. llm-wiki stores reusable knowledge 4. code-graph remembers how the codebase connects one correction should survive the next 50 runs, not disappear with one chat. that’s a way better scaling path than endlessly buying more context.

2026.09.20 14:58

i’m f**king done with agents forgetting everything after one session... so i’m giving them a memory layer from a chinese team that actually persists across runs. tencent’s approach turns conversations, docs, and code into reusable memory instead of stuffing everything back into one giant prompt. [here’s how i’d structure it] 1. chat memory keeps decisions + preferences 2. skills preserve workflows that already worked 3. llm-wiki stores reusable knowledge 4. code-graph remembers how the codebase connects one correction should survive the next 50 runs, not disappear with one chat. that’s a way better scaling path than endlessly buying more context.

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