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AI Tools Are Becoming Feedback Loops

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Glide is an AI tool that enables easy creation and deployment of custom apps without the need for coding. By simply adding a column to a table, users can harness AI capabilities to automate tasks like generating emails, product descriptions, and summaries effortlessly. Glide handles complex AI processes behind the scenes, removing the burden of managing models or APIs. It seamlessly converts JSON into code in various languages, making it versatile for different development needs. Glide works with Google Sheets, Excel, or Airtable to build apps and websites swiftly. Its user-friendly approach and streamlined automation set it apart, offering a convenient solution for app development. Start building your first app with Glide for free today!

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Subterranean is a no-code platform for building AI-native full-stack apps with specialist agents handling core parts of the product lifecycle. It presents itself as a technical co-founder style system that brings together data, functions, user interface generation, and deployment in one place, reducing the friction of stitching together multiple tools. This makes it appealing for founders, makers, and product teams who want to create complete applications with AI assistance while keeping control over the end result. Instead of limiting users to a chatbot or a narrow code generator, Subterranean focuses on full application assembly for practical business and product use cases. It fits the growing category of agent-assisted app creation tools aimed at rapid shipping, iterative product development, and AI-first software workflows.

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tree0 is more than just a website builder; its an AI-powered solution that revolutionizes web design. With natural language prompts, you can craft stunning, mobile-friendly websites in minutes. No coding skills needed, just your creativity.

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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 & strategy

Audit 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 & productivity

Turn 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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