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Show HN: Lossless-memory – a personal AI memory that never summarizes

2026-09-21 | original
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Why This Matters

This project challenges the prevailing wisdom in AI memory design, which typically relies on summarization or semantic embedding for long-term recall—both of which discard exact wording and timing. By preserving full, timestamped logs and enabling time-first search, it addresses a real pain point for users who want their AI to recall precisely what was said, not a lossy paraphrase, which matters for trust, accuracy, and continuity in daily human-AI relationships.

Key Takeaways
Worth a Look

Samsung T7 Portable SSD — If you're building a system that keeps every line of conversation forever without summarizing, you'll want reliable fast storage to hold all that raw log data. A portable SSD gives you the space and speed to store and index growing personal AI archives without lag.

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Lossless long-term memory for a personal AI — never summarize, keep every line, and put a timestamp on everything.

Most long-term memory systems for AI do one of two things: they summarize conversations into compact notes, or they embed them and retrieve "similar" chunks. Both lose the thing that matters most to a person who talks to the same AI every day: what was actually said, and when.

This project takes the opposite position.

Keep every line. Raw conversation logs are stored in full. Nothing is summarized, ever. Summaries are a map; the log is the territory.

Raw conversation logs are stored in full. Nothing is summarized, ever. Summaries are a map; the log is the territory. Timestamp everything. Every record — utterance, action, document chunk — carries a timestamp, and every index is built on top of that time axis. We call this the Temporal Backbone.

Every record — utterance, action, document chunk — carries a timestamp, and every index is built on top of that time axis. We call this the Temporal Backbone. Search by time first, words second. "Yesterday evening, about the budget" is a valid query. The time phrase narrows the range; the words rank within it. Results come back in chronological order, unsummarized, with their timestamps.

"Yesterday evening, about the budget" is a valid query. The time phrase narrows the range; the words rank within it. Results come back in chronological order, unsummarized, with their timestamps. Inject "where we are" every turn. A small index called LLL tells the model which topic the conversation is in right now, so identity and context survive context-window compaction and session boundaries.

The design lineage goes back to December 2025 — the first ancestor of this system (a memory-inheritance tool for an earlier AI) ran that month, and a predecessor system carried the same ideas in daily use from January 2026. This implementation has been running every day since July 2026 for a single user, as the memory of one AI assistant, with raw logs reaching back to June 2026. It is small, boring, and it works. The failures along the way are documented too — see docs/lessons.md .

What this is / what it is not

It is:

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Explore topics: lossless memory temporal backbone ai memory system long term memory conversation logs
Related:
The Case for Saving Every AI Chat

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