AI Moat: Memory, Not Just Models

Street AI Memory is a cross-provider memory layer for LLM applications that reduces prompt bloat as conversations grow. It sits between an app and model providers such as OpenAI, Anthropic, Gemini, DeepSeek, Together, or Groq, stores conversation signals into stacks, decays stale data, and retrieves only relevant context for each turn. The project reports 55–80% input-token reductions in a 16-turn benchmark, with average savings around 68%. It is useful for developers building chatbots, agents, RAG apps, and long-running assistants that need continuity without repeatedly sending the full transcript. The fresh Show HN launch and official GitHub README verify an installable Python package, provider adapters, local embedding model setup, and alpha-stage API notes.

We created autonomous AI Agents that monitor the stock market for you while you go about your day.<p>How it works: Tell our AI Assistant what you want to monitor, and it creates a project for our team of autonomous AI Agents. You&#x27;ll get notifications (email + app) when significant events matching your criteria are detected. For short-term projects, you&#x27;ll be notified when your analysis is ready.<p>Behind the scenes: When you give the AI Assistant a request to monitor an entity (like a stock or group of stocks), an AI Project Manager plans the project and breaks the project down into manageable tasks. These tasks run asynchronously - some recurring (hourly&#x2F;daily&#x2F;weekly&#x2F;monthly&#x2F;quarterly&#x2F;yearly), others one-time.<p>Example prompts you can try: Long-term monitoring: - &quot;Monitor Apple stock and notify me of any important events and red flags&quot; - &quot;Monitor Apple, Google, Microsoft, and Meta stock. Notify me if any of them start trending toward being undervalued&quot;<p>Short-term analysis: - &quot;Create a project to analyze the last 30 earnings calls for Tesla, spot trends, and how the business has evolved over time&quot;<p>You can track the progress of all tasks as the AI Agents work in the background.<p>Try it here: <a href="https:&#x2F;&#x2F;decodeinvesting.com&#x2F;chat" rel="nofollow">https:&#x2F;&#x2F;decodeinvesting.com&#x2F;chat</a><p>This is still an early version - we&#x27;re actively improving it based on feedback. Would love to hear what you think and what features you&#x27;d want to see next!<p>Previously shared our AI-powered Stock Market Research Analyst: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=41156478">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=41156478</a>

Clarm is an AI inbound conversion platform that captures visitor questions across websites, Discord, Slack, and GitHub, then qualifies buyer intent and routes revenue opportunities automatically. Instead of treating inbound as a support-only problem, it aims to convert conversations from both humans and AI agents into faster responses, better qualification, and clearer pipeline generation. The product highlights instant response times, support deflection, and the ability to identify high-intent buyers without adding headcount, making it especially useful for technical B2B companies with active communities and documentation-heavy products. Clarm also positions itself as relevant for machine visitors doing product research, which is increasingly important in an agentic web. For teams balancing support, community engagement, and demand capture, it acts as a 24/7 AI layer for inbound revenue operations.