Explore Our AI-Powered Creations
Explore free, public projects that make different parts of an AI-enabled product tangible. Rather than treating every example as a chatbot, look at how each one gathers input, applies instructions, guides a user through a task, and produces output that can be reviewed or used in a wider workflow.
What each project demonstrates
These examples have different audiences and interfaces. The notes below separate what the existing project does from the agent-workflow principle it can help you evaluate.
💻 CodeSphereby Walkover Web Solutions
Online coding assessments for hiring and universities
A next-generation coding assessment platform for recruitment, academia, and skill benchmarking with secure proctoring and instant analytics.
Workflow connection
CodeSphere demonstrates a structured application workflow rather than an open-ended chat: an assessment is presented, work is completed in a defined environment, and the result becomes reviewable output. That same pattern matters for agents that collect required inputs, follow ordered steps, and pass a useful result to a recruiter, instructor, or operations team. Its proctoring and analytics features are specific to the assessment product; the GTWY connection is the broader workflow design of turning user activity into a consistent process.
How to evaluate it
Try the candidate-facing flow and note whether instructions, task boundaries, progress, and completion states are clear. Consider which steps require deterministic application logic and where an AI assistant could explain a task or summarize submitted material without making the final evaluation.
🤖 PromptCraftAI That Writes Prompts for You
Generate polished AI prompts in seconds
Create context-aware prompts for GPT workflows, content creation, and automation in just a few clicks—consistent, optimized, and ready to use.
Workflow connection
PromptCraft demonstrates how a short request can be expanded into a more structured prompt with context and usable instructions. This is directly relevant to agent configuration: a system prompt needs an explicit role, constraints, expected output, and enough context to guide repeatable behavior. The tool is also a practical example of one AI step preparing input for another model or workflow, instead of trying to complete an entire business process in a single response.
How to evaluate it
Start with the same goal phrased once broadly and once with audience, format, and constraints. Compare the generated prompts for specificity, missing assumptions, and whether they preserve your intent. Then test the result in the model you plan to use; a polished prompt still needs task-specific review.
🧬 ReplicaVerse
Create a digital twin that mirrors your voice
Design AI-powered digital replicas that reflect your personality, communication style, and preferences for creators, educators, and entrepreneurs.
Workflow connection
ReplicaVerse demonstrates persona configuration: shaping generated responses around a person’s stated voice, communication style, and preferences. In an agent workflow, comparable controls can govern tone, vocabulary, response structure, and the context remembered during an interaction. It also illustrates an important boundary—style guidance and supplied preferences can shape an experience, but users should still distinguish generated output from the person it is designed to resemble.
How to evaluate it
Use non-sensitive sample information and try requests that call for different tones or formats. Check which traits remain consistent, where the output becomes generic, and whether you can correct an unwanted behavior. Those observations translate into better system instructions, examples, memory rules, and disclosure for a custom agent.
Evaluate the workflow, not just one answer
Begin with a normal task, then repeat it with missing context, an ambiguous instruction, and a request outside the project's intended scope. Look for clear guidance, useful opportunities to revise input, consistent output structure, and an understandable completion state. When a project generates content, verify important details independently and avoid entering confidential or personal information into a public demo.
To translate an observation into your own GTWY project, identify the system instructions that define behavior, the knowledge an agent is allowed to use, any tools it may call, and the point where a person should review or take over. Explore custom AI agents for scoped workflows, the managed AI API for application integration, or templates for reusable starting structures.