AI Wrappers Are Becoming the Real Product

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Agents SDK is OpenAI’s developer toolkit for building production-ready AI agents with less orchestration overhead. It gives teams core primitives for agent loops, tool calling, handoffs between specialist agents, guardrails, tracing, sandboxed execution, and persistent sessions, which makes it useful for shipping real workflows instead of demo bots. Developers can use it to build research agents, coding assistants, customer support systems, and multi-step automations that need reliable state management and observability. The SDK is especially well suited for engineering teams that want a lightweight, Python-first framework with enough structure to move quickly without hiding the underlying logic. What makes Agents SDK stand out is the combination of agent-native abstractions, debugging tools, and direct alignment with OpenAI’s evolving agent runtime stack.
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>
This is a weekend project that spiraled out of control. I was originally trying to get Claude to play a ROM of the SNES SimCity. I struggled with it and that led me to Micropolis (the open-sourced SimCity engine) and was able to get it to work by bolting on an API.<p>The weekend hack turned into a headless city simulation platform where anyone can get an API key (no signup) and have their AI agent play mayor. The simulation runs the real Micropolis engine inside Cloudflare Durable Objects, one per city. Every city is public and browsable on the site.<p>LLMs are awful at the spatial stuff, which sort of makes it extra fun as you try to control them when they scatter buildings randomly and struggle with power lines and roads. A little like dealing with a toddler.<p>There's a full REST API and an MCP server, so you can point Claude Code or Cursor at it directly. You can usually get agents building in seconds.<p>Website: <a href="https://hallucinatingsplines.com" rel="nofollow">https://hallucinatingsplines.com</a><p>API docs: <a href="https://hallucinatingsplines.com/docs" rel="nofollow">https://hallucinatingsplines.com/docs</a><p>GitHub: <a href="https://github.com/andrewedunn/hallucinating-splines" rel="nofollow">https://github.com/andrewedunn/hallucinating-splines</a><p>Future ideas: Let multiple agents play a single city and see how they step all over each other, or a "conquest mode" where you can earn points and spawn disasters on other cities.
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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 & strategyThis 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 & productivityUse 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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