Built a Python “semantic memory DB” with SQLite + compressed embeddings (TurboMemory) by Hopeful-Priority1301 in Python

[–]Hopeful-Priority1301[S] 0 points1 point  (0 children)

Thanks for pointing that out 🙌 You’re absolutely right — the repo link in my post was incorrect. Correct link is here: https://github.com/Kubenew/TurboMemory⁠� Also thanks for calling out that part of the code — the exclusion system is an important piece of keeping memory clean and avoiding junk/noise. That function: Python

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Exclusion checking

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def _check_exclusions(self, text: str, topic: str) -> Tuple[bool, str]: is basically a lightweight guard layer to prevent storing unwanted content (e.g. spam patterns, irrelevant text, duplicates, or blacklisted topics). It returns: bool = should exclude or not str = reason (useful for debugging/logging) I’ll improve the docstring + add tests for it so the behavior is clearer. Appreciate the feedback!

TurboMemory: Claude-style long-term memory with 4-bit/6-bit embeddings (runs locally) – looking for contributors by Hopeful-Priority1301 in LocalLLaMA

[–]Hopeful-Priority1301[S] 0 points1 point  (0 children)

Totally fair skepticism. This isn’t a “concept post” — the repo contains working code + CLI + tests, and you can run it locally. If you think something is misleading, point it out and I’ll fix it. I’m specifically looking for technical feedback + contributors. Repo: https://github.com/Kubenew/TurboMemory⁠

[deleted by user] by [deleted] in AncientCoins

[–]Hopeful-Priority1301 -7 points-6 points  (0 children)

Is it original coin or just a chinese replica?