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I Developed AI Memory Booster: Self-Hosted AI with Long Term Memory

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I Developed AI Memory Booster: Self-Hosted AI with Long Term Memory

I recently developed and open-sourced a project called AI Memory Booster, which combines Ollama (for running local LLaMA models) with ChromaDB to give AI systems persistent memory across sessions. The project is fully self-hosted and privacy-first — everything runs locally via a Node.js API, a simple React UI, and Docker support for easy deployment. Key Features: 1. Ollama-powered inference (LLaMA 3.2 and other models). 2. Persistent memory via ChromaDB (store and recall data across sessions). 3. Works on CPU or GPU, tested on local laptops and free-tier cloud VMs. 4. API-first approach with /learn and /recall endpoints. 5. Ready-to-use React web interface + install.sh script for fast setup. Use Cases: 1. Build a local AI chatbot with memory. 2. Power a self-hosted assistant that remembers conversations or tasks. 3. Add a memory layer to Ollama agents or automation workflows. 4. Integrate into existing Node.js applications. The source code is now available on Github: https://github.com/aotol/ai-memory-booster I’d love feedback from the community — especially ideas on improving long-term memory handling or other integrations you’d find useful!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, llama · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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