Persistent AI Advisor with Cross-Platform Memory: Tracks Decision History for 3 Months
A Reddit user shared a project they built to stop repeating themselves to AI tools. The result is a persistent AI advisor agent that maintains decision history over three months across multiple interfaces.
Key Features
- Cross-platform memory: Works across Claude Code, Cursor, and a web interface with the same unified memory.
- Decision tracking: Remembers every product decision and the reasoning behind it.
- Contradiction detection: Catches when the user contradicts something they said earlier.
- Activity log: Keeps a log so users can see what the agent actually did.
Most Valuable Behavior
The user notes the most valuable aspect isn't automation but continuity. Month 3 conversations are noticeably sharper than month 1 because the agent builds a model of the project over time.
Try It
A live version is available at: https://agentid.live/share/studio/saas-dream-team/895c1947b8184fd2. It's free to set up something similar.
📖 Read the full source: r/openclaw
👀 See Also
Open source AI model stack for cost-effective Claude replacement
A Reddit user shares a working AI model stack using open source models like Llama 3.3 70b and DeepSeek R1 32b for local execution, reducing monthly AI costs from £60+ to under £3 by routing 90% of tasks to free models.
Claude Code's Plan-Skeptic Sub Agent Identifies Security Gaps in Generated Plans
A developer discovered Claude Code's plan-skeptic sub agent, which identifies gaps and issues in AI-generated development plans, particularly catching security concerns that weren't initially obvious. The agent works alongside the previously known security-sheriff sub agent to improve plan quality.
CLI-Anything-WEB: Open-source plugin that reverse-engineers any website into a Python CLI for Claude Code
CLI-Anything-WEB is an open-source Claude Code plugin that watches your browser traffic, reverse-engineers the protocol, and generates a full Python CLI with auth, tests, and --json support. 19 sample CLIs included for sites like Reddit, Booking, Airbnb, ChatGPT, and LinkedIn.
Fine-tuned Qwen3.5-2B with RAG-Engram architecture improves grounded answer accuracy from 50% to 93% at 8K context
A developer fine-tuned Qwen3.5-2B with a custom RAG-Engram architecture to address the 'lost in the middle' phenomenon, improving correct answers at 8K tokens from 50% to 93% on real-world queries. The system uses a two-level approach with static entity embeddings and dynamic chunk navigation.