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AI Agents Listing is a curated discovery directory for AI agents, MCP servers, and agent skills. Rather than treating these as unrelated catalogs, it cross-links the three layers so visitors can see which skills extend an agent, which MCP servers expose its tools, and how products relate to one another. The site offers searchable listings, categories, alternatives, comparisons, engagement-based rankings, and an MCP endpoint that agents can query directly. It is aimed at developers, makers, technical buyers, and researchers mapping the fast-moving agent ecosystem. The September 2026 launch announcement and official site establish human-reviewed submissions, free maker listings, public pricing for paid placement, and a clear separation between featured placement and ranking signals. It is useful as a meta-tool for discovering and evaluating agent infrastructure, especially as skills and MCP servers become distribution surfaces alongside standalone applications.
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.
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>
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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.
Career & productivityDefine your habits and get a beautiful interactive HTML habit tracker with daily check-off, streak counting, weekly heatmap visualization, and progress stats. Saves state in browser localStorage so it persists between sessions. No apps to install.
Career & productivityPaste a meeting transcript or rough notes and get a structured summary with decisions made, action items with owners and deadlines, open questions, and a one-paragraph executive summary. Saves hours of post-meeting cleanup.
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Prompt lists are useful, but the real leverage comes from repeatable AI workflows with inputs, checks, and reusable outputs.
Jev is a new kind of AI model from TypeSafe that returns typed decisions instead of chat. Here is where constrained models fit, and where the vendor claims still need testing.
Claude’s persistent Projects point to the next AI product shift: delegated work that continues after you leave. The real feature is not autonomy, but evidence, review, and approval…
