Alex Newman
37f836b719
Documentation: - v5-reddit-post.md: v5.0-specific post focusing on hybrid search breakthrough - v5-reddit-FINAL-DRAFT.md: General claude-mem post with timeline examples - v5-reddit-post-story.md: Architecture evolution narrative - v5-reddit-post-draft.md: Early draft with search examples - v5-linkedin-post.md: Professional LinkedIn announcement - reddit-posts.md: Research and reference materials These are working drafts for community announcements. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
114 lines
5.2 KiB
Markdown
114 lines
5.2 KiB
Markdown
# Your Claude forgets everything after /clear. Mine doesn't.
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You know the cycle.
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/init to learn your codebase. Claude reads everything, understands your architecture, builds context.
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You work for a while. Context window fills up. Eventually you hit /clear.
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Everything's gone.
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Next session: Claude reads CLAUDE.md again. Does the research again. Re-learns your codebase again.
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**Tokens cost money. Research takes time. Claude forgets.**
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This cycle is killing productivity.
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## I built persistent memory that survives /clear
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Not summaries. Not compressed conversations. [Actual persistent memory](https://github.com/thedotmack/claude-mem)—capture everything Claude does, process it with AI, make it instantly recallable across sessions.
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Early on I tried vector stores, MCPs, memory tools. ChromaDB for vector search. But documents were massive—great for semantic matching, terrible for context efficiency.
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That led to the hybrid approach.
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## How it works
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SQLite database with semantic chunking. ChromaDB for vector search when you need it—incredibly fast, incredibly relevant. FTS5 keyword search as fallback.
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The magic? This loads automatically at every session start. No /init. No research phase.
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Here's what I see when I start a new session on my "claude-mem-performance" project:
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```
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📝 [claude-mem-performance] recent context
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────────────────────────────────────────────────────────────
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Legend: 🎯 session-request | 🔴 bugfix | 🟣 feature | 🔄 refactor | ✅ change | 🔵 discovery | 🧠 decision
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💡 Progressive Disclosure: This index shows WHAT exists (titles) and retrieval COST (token counts).
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→ Use MCP search tools to fetch full observation details on-demand (Layer 2)
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→ Prefer searching observations over re-reading code for past decisions and learnings
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→ Critical types (🔴 bugfix, 🧠 decision) often worth fetching immediately
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Nov 3, 2025
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🎯 #S651 Read headless-test.md and use plan mode to prepare for writing a test (Nov 3, 1:27 PM) [claude-mem://session-summary/651]
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🎯 #S650 Read headless-test.md and use plan mode to prepare for writing a test (Nov 3, 1:27 PM) [claude-mem://session-summary/650]
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test_automation.ts
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#3280 1:31 PM ✅ Updated test automation prompts for Kanban board project (~125t)
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🎯 #S652 Read headless-test.md and use plan mode to prepare for writing the test (Nov 3, 1:32 PM) [claude-mem://session-summary/652]
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General
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#3281 1:33 PM 🔵 Examined test automation script (~70t)
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test_automation.ts
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#3282 1:34 PM 🟣 Implemented full verbose output mode for tool execution visibility (~145t)
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#3283 1:35 PM ✅ Enhanced plan generation streaming with partial message support (~109t)
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🎯 #S653 Read headless-test.md and use plan mode to prepare for writing the test (Nov 3, 1:35 PM)
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Completed: Modified the generatePlan function in test_automation.ts to support `includePartialMessages: true` and integrate the streamMessage handler for unified streaming output. This improves the real-time feedback mechanism during plan generation.
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Next Steps: 1. Read and analyze headless-test.md to understand test requirements. 2. Use plan mode to generate a test implementation strategy. 3. Write the actual test based on the plan.
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```
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**What you're seeing:**
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- Session summaries (🎯) - what you were working on
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- What Claude learned - observations with type indicators (bugfix, feature, change, discovery)
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- Token costs - so you know what's expensive to recall
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- Chronological flow - recent work, newest first
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- Loaded in <200ms at session start
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Timeline order: your past sessions, Claude's work, what was learned, what's next.
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And when you need something from weeks ago? Natural language search + instant timeline replay gets you there in <200ms.
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## The breakthrough: temporal context
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Most memories are duplicate knowledge. Your architecture doesn't fundamentally change every session.
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But some memories are **changes**. Bugfixes. Refactors. Decisions.
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Without timestamps, without knowing what's "newest," your information is stale. And stale information means Claude has to research—the token-heavy work I'm trying to eliminate.
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## The paradox
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Claude-mem's startup context got so good that Claude rarely uses the search tools anymore.
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The last 50 observations at session start is usually enough. /clear doesn't reset anything—next session starts exactly where you left off.
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But when you need to recall something specific from weeks ago, the context timeline instantly gets Claude back in the game for that exact task.
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**No /init. No research phase. No re-learning.**
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Just: start session, Claude knows your codebase, you work.
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Development becomes pleasant instead of repetitive. Token-efficient instead of wasteful. Focused instead of constantly re-explaining.
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---
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**claude-mem v5.0** just shipped: https://github.com/thedotmack/claude-mem
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Python optional but recommended for semantic search. Falls back to keyword search if you don't have it.
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**Install in Claude Code:**
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```
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/plugin marketplace add thedotmack/claude-mem
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/plugin install claude-mem
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```
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Anyone else tired of both paying and WAITING for Claude to re-learn their codebase after every /clear? |