Ask deva to help you get started with OpenClaw!

Dawn C. Hayes — Sun, 08 Feb 2026 20:13:15 +0000

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Conversational Experiences

What I Built: deva a conversation assistant to help you get started with OpenClaw.

try deva powered by Algolia Agent Studio

What I Built

deva is a lightweight, consumer‑facing conversational assistant designed to help users explore and understand OpenClaw without digging through scattered documentation. Instead of searching across READMEs, GitHub issues, and config examples, users can simply ask deva questions and get contextual, retrieval‑augmented answers. The experience is intentionally simple: a clean landing page, a focused chat interface, and a friendly agent that guides users through setup, configuration, and onboarding concepts for OpenClaw.

Demo
Live site: https://askdeva.netlify.app/

deva runs entirely in the browser using a Vite + React frontend and an Algolia Agent Studio backend.

Motivation
I wanted to use this challenge as a chance to explore Algolia’s new Agent Studio in a hands‑on, practical way. My goals were simple but meaningful:

experiment with building agents using Algolia’s new tooling
create a playful demo that still reflects a real use case I care about: DevRel and builder‑focused assistance
challenge myself to build something end‑to‑end in a solo sprint
dig deeper into the OpenClaw ecosystem and its documentation
and, honestly, just have fun with it deva became the perfect blend of exploration, creativity, and technical curiosity.

How I Used Algolia Agent Studio

I created a custom agent in Algolia Agent Studio and connected it to an index containing OpenClaw documentation, GitHub issues, and onboarding references. The agent uses retrieval to surface relevant content and summarize it conversationally.

A few key pieces of the setup:

Indexed data: README sections, configuration examples, GitHub issues, and onboarding‑related content from the OpenClaw ecosystem.
Retrieval‑augmented responses: The agent pulls relevant documents and uses them to answer user questions with context.
Targeted prompting:
Instructing the agent to avoid raw JSON output
Encouraging natural‑language summaries
Guiding the tone toward helpful onboarding support
Frontend integration: The Algolia chat widget connects directly to the agent using environment variables injected at build time.

This combination creates a smooth, conversational way to explore OpenClaw without manually searching through multiple sources.

Why Fast Retrieval Matters

OpenClaw’s documentation and examples are spread across different places — READMEs, issues, config snippets, and community discussions. Fast retrieval ensures that:

the agent can surface relevant information immediately
users don’t wait for long LLM reasoning cycles
the conversation feels responsive and natural
the assistant can handle broad or vague questions by grounding answers in indexed content

Algolia’s speed keeps the experience fluid, which is essential for a consumer‑facing conversational tool.

I was impressed with how easy it was to scaffold and deploy an agent in my app with minimal fuss. This could be a game changer for those seeking to integrate robust, intelligent search into their agentic applications and assistants! I definitely intend to extend deva and use Algolia Agent Studio to quickly build more AI search features in future apps.

Demo

[deva website] Try deva for yourself and let me know how you get along!(https://askdeva.netlify.app/)

Build A README-To-Comic Converter With Google AI Studio And Imagen

Dawn C. Hayes — Thu, 17 Jul 2025 06:45:48 +0000

This post is my submission for DEV Education Track: Build Apps with Google AI Studio.

What I Built

ComicReadMe is a web app (TypeScript) that transforms README.md files into comic book panels. It offers an entertaining way for potential users and contributors to learn about a project from its README content. I like interactive fiction and imagined a connection between the visual appeal of comics and interactive commands of IF (next iteration, perhaps). I used Copilot to improve my initial idea and prompt, which was partially influenced by the Google AI Studio tutorial. The final prompt ended up like so:

Build Brief: Comic Book README Converter

Objective:
Create a web app, “ComicReadMe,” that transforms any GitHub README into a fully illustrated comic-style instruction manual.

Technology Stack:

Google Imagen API for generating all artwork.
Google Gemini 2.5 for narrative text, speech bubbles, captions, and panel descriptions.

Core Features

Auto-Detect Project Type
- Analyze the README’s structure, headings, and keywords to classify it as a CLI tool, web app, or game.
- Tailor overall art style and panel themes (minimalist for CLI, tech-noir for web apps, retro sci-fi for games).
Character Avatars for Functions
- Map each key function or component (e.g., install(), build(), deploy()) to a quirky avatar: wizard, engineer robot, rocket pilot, etc.
- Generate a consistent “cast” for each project so readers instantly recognize the characters across panels.
Dynamic Panel Generator
- Panel 1: Title + project logo, stylized as a comic cover.
- Panel 2: “What is this?” overview, narrated by a mascot character.
- Panel 3: Installation sequence, with install() wizard casting spells.
- Panel 4+: One panel per major feature or usage example.
- Final Panel: Dramatic “Coming Soon” or “Next Release” teaser—colored splash page with bold text.
UX & Output
- Drag-and-drop a README.md or paste its URL.
- Preview the generated comic pages.
- Export as PDF or interactive web comic (with panel-by-panel navigation).

Demo

Try it out!

My Experience

While the builder feature works with concise, specific prompts, I have used more structured, detailed prompts with other AI editor tools. I was pleased to find the approach works with the model in Google AI Studio. I suspect more detailed prompts can be helpful in reducing the number of tweaks to make to core logic after a project is scaffolded. It may be more efficient in the long run, but it is important to keep potentially higher costs in mind with this approach.

I made adjustments to address basic issues like CORS, though the model's autofix tool did seem to make corrections without too many interventions (there was a stubborn issue where the model erroneously insisted a button fix was implemented). A tip for future use is to direct the model to account for common issues in advance to avoid unnecessary back and forth adjustments. Always review generated code. A plus: reviewing the model's "thinking" process in real time is helpful and a nice feature to have without an up-charge.

All in all, the experience was pretty smooth and fast, particularly for a cloud-based editor. In continuing ComicReadMe development, I would like to improve UI/UX, include dynamic onboarding/automated functions that enable use and deployment from within the comic panels and include social/share features. Feel free to follow the project here or on GitHub.

Forem: Dawn C. Hayes

Ask deva to help you get started with OpenClaw!

What I Built: deva a conversation assistant to help you get started with OpenClaw.

Demo

Build A README-To-Comic Converter With Google AI Studio And Imagen

What I Built

Build Brief: Comic Book README Converter

Core Features

Demo

My Experience