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The Next AI Moat Is the Invocation Layer
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AI Gateway by Arnab758
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AI Gateway by Arnab758 is an open-source reverse proxy for reducing LLM API costs through semantic caching. It sits between an application and providers such as OpenAI or Groq, detects semantically similar repeat questions, and returns cached responses instead of sending every request to the model provider. The README frames the value for AI app builders with recurring support, education or knowledge-base questions, claiming potential 40–70% API bill reductions with no application-code changes beyond routing through the proxy. It is notable as a lightweight developer tool discovered through a fresh Show HN LLM launch and verified via the official GitHub repository and live-demo links. It should not be confused with existing vendor products like Vercel AI Gateway.
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'll get notifications (email + app) when significant events matching your criteria are detected. For short-term projects, you'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/daily/weekly/monthly/quarterly/yearly), others one-time.<p>Example prompts you can try: Long-term monitoring: - "Monitor Apple stock and notify me of any important events and red flags" - "Monitor Apple, Google, Microsoft, and Meta stock. Notify me if any of them start trending toward being undervalued"<p>Short-term analysis: - "Create a project to analyze the last 30 earnings calls for Tesla, spot trends, and how the business has evolved over time"<p>You can track the progress of all tasks as the AI Agents work in the background.<p>Try it here: <a href="https://decodeinvesting.com/chat" rel="nofollow">https://decodeinvesting.com/chat</a><p>This is still an early version - we're actively improving it based on feedback. Would love to hear what you think and what features you'd want to see next!<p>Previously shared our AI-powered Stock Market Research Analyst: <a href="https://news.ycombinator.com/item?id=41156478">https://news.ycombinator.com/item?id=41156478</a>
Clarm
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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.
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Related prompts
Business & strategy
Turn a repetitive business workflow into an AI agent deployment plan
Describe any recurring workflow — support triage, lead qualification, research ops, QA, reporting, or back-office reviews — and get a concrete AI agent deployment plan. The output maps the workflow into agent responsibilities, human approval points, tool access, permission scopes, failure modes, observability needs, and rollout phases. It is designed for teams that want to move from vague agent ideas to something production-ready without skipping governance.
Business & strategyAudit whether an AI agent feature is ready for real-world governance
This prompt helps teams evaluate whether an AI agent feature is actually ready for real-world deployment instead of just looking impressive in a demo. It is designed for product managers, founders, operators, and technical leads who need to assess permissions, observability, spend controls, approval checkpoints, failure handling, and auditability before putting agentic workflows in front of customers or employees. The output turns a vague concept or existing workflow into a governance readiness audit with specific risks, missing controls, and prioritized improvements. That makes it useful when a team is moving from prototype to production, preparing for enterprise buyers, or trying to avoid expensive trust failures. It focuses on the operational layer that determines whether an agent can be governed responsibly, not just whether the underlying model is smart enough.
Career & productivityTurn human-written documentation into an AI-agent-ready action spec
Use this prompt to convert messy human-oriented documentation into a structured action spec that an AI agent, automation system, or internal tool could follow more reliably. It is useful when teams have SOPs, onboarding docs, API notes, support playbooks, or internal process guides that are understandable to humans but too ambiguous for consistent machine execution. The output rewrites the material into clear steps, decision rules, required inputs, expected outputs, edge cases, and escalation paths, while preserving uncertainty instead of pretending the original documentation was complete. This makes it valuable for operations teams, product builders, AI workflow designers, and companies trying to make their institutional knowledge more machine-readable without rewriting everything from scratch. It focuses on practical clarity, not abstract theory about documentation quality.
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