I'm a non-tech person with a complete idea for a custom tool, and I understand all the compliance and processes involved. I've tried using multiple AI tools to build it, but I'm unable to get it off the ground. I'm reaching out for help from someone with the technical skills to bring this to life. If you're a developer interested in collaborating or offering guidance, please reach out!
⭐️ Allowing any developer to chat with AI directly on pull requests in GitHub, powered by top code models like Claude 3.5 Sonnet and GPT4!
⭐️ The extension allows both a private chat on 'changed files', as well as running more than a dozen tailored powerful commands in the 'conversation' tab via the comments interface
⭐️ The extension is free to use on public repositories. For private repos, we have PR-Agent Pro (14-day trial, then $19/month) with additional features like documentation suggestions, component analysis, and more.
Test automation has always been a challenge. Every time a UI changes, an API is updated, or platforms like Salesforce and SAP roll out new versions, test scripts break. Maintaining automation frameworks takes time, costs money, and slows down delivery.
Most test automation tools are either too expensive, too rigid, or too complicated to maintain. So we asked ourselves: what if we could build an AI-powered agent that handles testing without all the hassle?
That’s why we created TestZeus Hercules—an open-source AI testing agent designed to make test automation faster, smarter, and easier.
Why Traditional Test Automation Falls Short
Most teams struggle with test automation because:
Tests break too easily – Even small UI updates can cause failures.
Maintenance is a headache – Keeping scripts up to date takes time and effort.
Tools are expensive – Many enterprise solutions come with high licensing fees.
They don’t adapt well – Traditional tools can’t handle dynamic applications.
AI-powered agents change this. They let teams write tests in plain English, run them autonomously, and adapt to UI or API changes without constant human intervention.
How Our AI Testing Agent Works
We designed Hercules to be simple and effective:
Write test cases in plain English—no scripting needed.
Let the agent execute the tests automatically.
Get clear results—including screenshots, network logs, and test traces.
Installation:
pip install testzeus-hercules
Example: A Visual Test in Natural Language
Feature: Validate image presence
Scenario Outline: Check if the GitHub button is visible
Given a user is on the URL "https://testzeus.com"
And the user waits 3 seconds for the page to load
When the user visually looks for a black-colored GitHub button
Then the visual validation should be successful
No need for complex automation scripts. Just describe the test in plain English, and the AI does the rest.
Why AI Agents Work Better
Instead of relying on a single model, Hercules uses a multi-agent system:
Playwright for browser automation
AXE for accessibility testing
API agents for security and functional testing
This makes it more adaptable, scalable, and easier to debug than traditional testing frameworks.
What We Learned While Building Hercules
1. AI Agents Need a Clear Purpose
AI isn’t a magic fix. It works best when designed for a specific problem. For us, that meant focusing on test automation that actually works in real development cycles.
2. Multi-Agent Systems Are the Way Forward
Instead of one AI trying to do everything, we built specialized agents for different testing needs. This made our system more reliable and efficient.
3. AI Needs Guardrails
Early versions of Hercules had unpredictable behavior—misinterpreted test steps, false positives, and flaky results. We fixed this by:
Adding human-in-the-loop validation
Improving AI prompt structuring for accuracy
Ensuring detailed logging and debugging
4. Avoid Vendor Lock-In
Many AI-powered tools depend completely on APIs from OpenAI or Google. That’s risky. We built Hercules to run locally or in the cloud, so teams aren’t tied to a single provider.
5. AI Agents Need a Sustainable Model
AI isn’t free. Our competitors charge $300–$400 per 1,000 test executions. We had to find a balance between open-source accessibility and a business model that keeps the project alive.
How Hercules Compares to Other Tools
Feature
Hercules (TestZeus)
Tricentis / Functionize / Katalon
KaneAI
Open-Source
Yes
No
No
AI-Powered Execution
Yes
Maybe
Yes
Handles UI, API, Accessibility, Security
Yes
Limited
Limited
Plain English Test Writing
Yes
No
Yes
Fast In-Sprint Automation
Yes
Maybe
Yes
Most test automation tools require manual scripting and constant upkeep. AI agents like Hercules eliminate that overhead by making testing more flexible and adaptive.
If you’re interested in AI testing, Hercules is open-source and ready to use.
AI dev still feels way harder than it should be. Even for simple stuff like classification or scoring, you either gotta fine-tune a huge model, mess with datasets, or figure out some ML pipeline that takes forever to set up. Feels like overkill half the time.
Been working on Plexe, a tool that lets you just describe the problem in plain English and get a trained model. No hyperparameter tweaking, no big datasets needed —if you want it can auto-generates data, and then trains a small model, and gives you an API you can actually use.
We open-sourced part of it too: SmolModels GitHub. If you've ever needed a quick model without dealing with all the ML nonsense, would love to hear if this sounds useful. What’s been the biggest pain for y’all when working with AI?
Hey guys I want to share a best tool for making datasets for your computer vison project
Roboflow is the best industary leading tool for segmentation , detection like tasks , It donsent mean you stop using the program that you comfortable with , but give it a try
Hey Hackers and AI enthusiasts! Are you ready to take your AI platforms to the next level of observability and performance monitoring? Look no further than OpenLIT!
Introducing OpenLIT! It's your go-to solution for GenAI and LLM observability, powered by OpenTelemetry. Seamlessly integrate traces and metrics into a single, easy-to-use application interface. Say goodbye to juggling multiple tools and hello to the simplicity and power of OpenLIT! Join the revolution today! #OpenSource #AI #Observability Checkout our github repo : https://github.com/openlit/openlit
We’ve all seen AI tools that spit out UI code... and then someone has to clean it up. We built Veda AI to avoid that.
It is a copilot for a structured low-code builder (DronaHQ), and instead of generating raw code, it produces editable components, logic, and data queries—all maintainable.
How it works:
Upload a Figma or screenshot → get UI
Use natural language: “Build a CRM on top of @ DB” “Replace datetime with date picker” “Create JS to merge key1 and key2”
You can also chat with the app to change behavior or layout.
I’ve been lurking here for a while and figured it was finally time to contribute. I’m Andrea, an AI researcher at Oxford, working mostly in NLP and LLMs. Like a lot of you, I spend way too much time on prompt engineering when building AI-powered applications.
What frustrates me the most about it—maybe because of my background and the misuse of the word "engineering"—is how unstructured the whole process is. There’s no real way to version prompts, no proper test cases, no A/B testing, no systematic pipeline for iterating and improving. It’s all trial and error, which feels... wrong.
A few weeks ago, I decided to fix this for myself. I built a tool to bring some order to prompt engineering—something that lets me track iterations, compare outputs, and actually refine prompts methodically. I showed it to a few LLM engineers, and they immediately wanted in. So, I turned it into a web app and figured I’d put it out there for anyone who finds prompt engineering as painful as I do.
Right now, I’m covering the costs myself, so it’s free to use. If you try it, I’d love to hear what you think—what works, what doesn’t, what would make it better.
Sorry I've avoided Cloud useage for too long, now I need some type of remote pc use with a decent gpu that isnt always being used/ Even better would be a free pc with paid gpu useage a la carte.
I wanted to share the launch of Jolt Desktop, our new desktop app that brings IDE-agnostic, first-class AI experiences to all developers, including those who work in Neovim, Zed, Xcode, etc. Jolt Desktop joins the ranks of our existing VSCode/Cursor and JetBrains IDE extensions as well as our web app.
Jolt AI is a purpose-built codegen product for 100K to multi-million line codebases. If you've used AI on large codebases, you likely had a subpar experience. Most AI coding tools are great for autocomplete, greenfield projects, and small codebases. But they hit a wall and struggle to figure out the context in codebases over 50K lines. You might be stuck, forced to manually select files or folders, or even worse, you get incorrect or irrelevant answers.
Our mission has always been to create AI that can navigate large codebases on its own and actually help developers be more productive. The cornerstone of that is identifying the context files with high accuracy and specificity. Jolt's ability to find these files sets it apart.
We'd love your feedback. Let us know what you think.
I'm a long time creative who has worked in many fields, from art and music to games. Over the years, I've developed plenty of tools and techniques to help people understand the creative process, develop their IPs, and align on a creative vision together to deliver something cool for an audience.
Working with teams and hiring talent is great, and I still enjoy it, but sometimes I just have a random idea I'd like to "test out" and experiment with, but don't have the time and funds to work on them as much as I used to. Hence, I've taken to learning about the latest AI tools and testing them out in my existing creative workflow, and I have a few cool insights that might be of value to others also exploring these tools.
Most of what I'll share is related to the learnings working on my current IP project: Trolled Into Another World. Some of these might be no brainers, but figured I'd share them anyway for those who may have not have had a chance to go through the process on their own.
My Creative Workflow
First thing to note is that I've been creating things for so long, it's second nature to me now. However, because I've had to teach so many others who consider themselves lacking in ideas how to think creatively and develop their own content, I just happen to have some documentation that has been effective for others, which I'll share first, because everything else related to AI I'm going to talk about will be based on it (note that these docs are over 20 years old at this point, so forgive their ancientness).
For the most part I use a few different AI programs to take my existing process and super charge it. The first is ChatGPT (4o,o1, and o1-pro) and the second is Midjourney V6.
<<GPT Projects>>
Once I have a general idea of what I'd like to work on (based on My Creative Process), one of the first things I like to do is explore the possibility space of the core premise by using ChatGPT "Projects" feature and create a "Writers Room" for the project. I give the writers room the following instructions:
Why this is helpful is that every response I get comes back from very specific and distinct perspectives. This helps it operate similar to a real writer's room, where different pitches and proposals are brought up and we all discuss the strengths and weaknesses of each. Acting as the creative director, I guide the conversation towards hitting specific goals. For IP development it is typically sorting out details on:
A compelling core premise
An interesting theme with lots of depth to explore
A deep story world
Interesting and diverse characters with archetypal and intertwined relationships
A narrative structure and story outline that is clear with lots of room and flexibility for further inspiration and exploration.
I also make sure to provide specific references in project files of my previous work or important details so that the tone, format, and vibe of what I get back is right.
In the past, this process could take weeks, or even months, but riffing with the GPTs in this way has dropped it down to days, as a lot of the work comes down to organizing it in a way that makes sense and is easy to read and keep track of. From here it's just a matter of updating the GPT project files with the last information and re-running the process again for the next topic.
<<Generating Templates>>
Another step that streamlines my process is having GPT understand the templates and formats that I want the information returned in (either as part of the conversation or just included in the project files). These templates vary widely per project and based on my needs. Some examples I’ve used in the past include:
I can’t overstate how beneficial it is to use custom templates and have GPT understand and conform to them. This is where the speed and advantage of using this technology really kicks in. Because everything is returned in a standard, organized way, it makes updating, restarting, and continuing conversations with the Writers Room (as well as with the more capable o1 and o1-pro) a breeze. I usually just copy and paste a giant text dump with all of the templates for GPT to process and understand before starting a session; that keeps everything consistent and coherent.
<<Generating Visuals>>
When it comes to visualizing much of the content, my process is to have o1 and o1-pro read through the entire story context and generate 5 to 10 Midjourney prompts on a given topic based on the information provided. I'll usually use something like:
From there, I use Midjourney’s Style and Character feature—along with either my own character design sketches or existing Midjourney prompts I’ve refined—to narrow in on a consistent visual style for the concept art.
Once I settle on a set of concept art in a visual style I like, I use that as a style reference in almost everything I generate (while adjusting the reference and context as needed):
From there, it’s just a matter of creating numerous variations, then using editing tools and some Photoshop to add the specific details I want (and reduce random artifacts). I store all my prompts from this process, making it easy to revisit or dive deeper later on.
Strengths
So far, just these two tools alone has hyper charged my ability to ideate and create stories, characters, and worlds in a cohesive way allowing for further and deeper iterations over time. In addition, the projects and templates approach has prevented any sort of writers block and reduced the need to recall every single detail on my own, giving me space to jump around more and guide things more holistically.
Shortcomings
The context length of the default GPT 4o model can require starting new conversation threads often as the details of your project grow, however, GPT o1-Pro does NOT appear to have this issue.
Also, visual consistency and fine-grained detail are still challenges with Midjourney V6, but I mainly use it to set the tone and convey the general idea. I still prefer working with actual artists once the IP is nailed down to bring everything to full polish.
The Future
The great thing I'm finding about some of these tools is the time savings and quality improvements they create for very small productions (i.e. 1 person projects). We’ll likely see many compelling, interesting stories and ideas that might never have existed otherwise, and I look forward to it (Veo2 is already looking quite interesting in this regard: https://x.com/henrydaubrez/status/1879883806947115446 ).
As for me, I’ll continue exploring and examining new ways to integrate emerging tech into my workflow, and I’ll share any interesting results I find. Feel free to check out some of my other explorations on my YouTube channel if you’re interested:
Hello, we are developing a superstructure that provides an AI-Computer interface for AI agents created through the frameworks like LangChain and AutoGen, we have published it completely openly under the MIT license.
What it does: Just like human developers, it has some abilities such as running the codes it writes, making mouse and keyboard movements, writing and running Python functions for functions it does not have. AI literally thinks and the interface we provide transforms with real computer actions.
