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3.1 Agent Workdays Is Not 3.1 Times More Productive

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Agen is a platform for fully autonomous AI coding agents that run in the cloud. You connect a Git repo, describe a task in plain English, and agents clone the code, explore the codebase, write changes, run the pipeline, fix CI failures themselves, and hand back a merge-ready pull request with a live preview — no IDE, no local setup, no babysitting. It supports multi-repo sessions, unlimited parallel agents, scheduled runs with budget limits, and mobile task assignment. Agen positions itself against IDE-bound copilots and single-repo agents by being cloud-native from day one, with flat $59/mo pricing versus metered competitors. Non-technical teammates can assign work while engineers keep merge control. New accounts get $20 in free credits, making it easy to test on a real codebase before committing.

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We created autonomous AI Agents that monitor the stock market for you while you go about your day.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.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.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"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"You can track the progress of all tasks as the AI Agents work in the background.Try it here: <a href="https://decodeinvesting.com/chat" rel="nofollow">https://decodeinvesting.com/chat</a>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!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>

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Turn a complex goal into a long-horizon AI execution plan

Use this prompt to turn a large, messy goal into an AI execution plan that can run for days or weeks without collapsing into vague ambition. It is designed for builders, operators, researchers, and technical leads who want to use AI for multi-step work that requires decomposition, checkpoints, evidence, and human review instead of one-shot output. The prompt converts a goal into milestones, work packets, verification loops, escalation rules, memory requirements, and stop conditions so the system can keep making progress without drifting off course. It is especially useful when frontier models are getting better at endurance, delegation, and background execution, but the real bottleneck is still task design. The result is a practical operating plan for reliable long-horizon AI work, not a hypey promise about autonomy.

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Business & strategy

Turn scattered team knowledge into an AI agent system-of-record blueprint

Use this prompt to design the shared context layer an AI agent actually needs before it can do reliable work inside a team. It is useful for operators, founders, product leaders, and internal tool builders who have notes, docs, tasks, databases, and conversations spread across too many places for AI to reason safely. The prompt turns a messy collaboration setup into a practical system-of-record blueprint covering what data should be structured, how context should be stored, which objects agents can update, how permissions and approvals should work, and how humans should verify changes. It is especially valuable when teams want more than chat answers and need agents that can operate from durable memory without creating silent chaos. The result is a concrete architecture plan, not a vague call to centralize knowledge.

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