TL;DR
This article lists my top 5 AI tools I use daily to be a 10x AI developer in 2025. โ
These tools are mostly aimed at integrating missing AI pipelines, image generation, plug-and-play AI copilots, and AI web app builders. ๐ฅ
If you are in the development and AI field, I can tell you'll also be using at least a few in your daily life. ๐คซ
1. KitOps - AI pipeline integration
โน๏ธ Open-source DevOps tool for packaging and versioning AI/ML projects.
KitOps is an open-source DevOps standard packaging and versioning tool that does simple and secure packaging of AI/ML projects into reproducible artifact called a ModelKit.
ModelKits represent a complete bundle of a project's artifacts, including models, datasets, and code.
ModelKits can be stored in a container registry, providing a way to track and audit the history of changes. ModelKits are immutable and work with all existing tools, allowing you to use the same deployment pipelines and endpoints as in your development process.
๐ก NOTE: A ModelKit can contain only one model but can include multiple datasets or codebases.
๐ฎโ๐จ Problem with the Traditional Workflow
The traditional workflow involves moving a model from Jupyter Notebook to an ML tool, then manually deploying it to a working dev/production server like Kubernetes, which is difficult and slow.
๐ This is where KitOps shines!
KitOps is built to standardize the entire traditional process of packaging, deployment, and tracking of AI/ML models, making them universal and capable of running anywhere, similar to application code.
Introducing Kitfile
At the heart of any AI or ML project managed by KitOps is a Kitfile. It's a YAML-based manifest file that helps share the project artifacts. The Kitfile is stored with the ModelKit.
Think of it as the blueprint of your project, ensuring that all the components in your application are properly organized and accessible.
You can extract a Kitfile from a ModelKit in the current directory with a single command:
kit unpack [registry/repo:tag] --config -d .
There are 4 main section to a Kitfile,
package
: Stores the ModelKit metadate like author details, name, description.code
: Path to the Jupyter Notebook directory.model
: Path to the serialized Model.datasets
: Path to the datasets.
A Kitfile may or may not have all of the sections. The only mandatory compulsion is that it needs to have the manifestVersion
and at least one of code
, model
, docs
or datasets
.
Here's how a sample Kitfile looks which introduced a pair of datasets:
manifestVersion: v1.0.0
datasets:
- name: training data
path: ./data/train.csv
- description: validation data (tabular)
name: validation data
path: ./data/test.csv
โ ๏ธ NOTE: You must always use relative paths in a Kitfile. Absolute paths are not allowed.
This ModelKit and Kitfile workflow not only standardizes the steps but also minimizes the risk of mistakes that can occur in manual processes.
Standard KitOps Workflow
โน๏ธ These are the basic steps involved in the KitOps workflow.
- Installation ๐ ๏ธ
To work with KitOps, first, you need to install it locally, follow the installation guide available on their page here.
- Initialize Kitfile ๐บ๏ธ
To create a Kitfile, run the following command:
kit init .
This command creates a Kitfile in your current directory. It will define dependencies, models, datasets, and configurations.
- Pack and Push the ModelKit to registry ๐
Suppose, you completed fine-tuning of your model and the registry of your choice is GitLab(registry.gitlab.com
), the repository is called chat-assitant
and the name of the model is aichat
, then you can use the following command to pack and push it to the registry:
kit pack . -t registry.gitlab.com/chat-assistant/aichat:tuned
kit push registry.gitlab.com/chat-assistant/aichat:tuned
Now, as you push the changes, all the members in your team can be notified of the change in the model and can pull, run and inspect the model.
Now, once the model completes the training or the tuning, the team can then pull the updated model with the kit pull
command:
kit pull registry.gitlab.com/chat-assistant/aichat:tuned
Hugging Face Import Workflow ๐ต๏ธโโ๏ธ
After the recent release of KitOps v1.0.0, you can use kit import
command to import any HuggingFace model and convert it into a ModelKit that you can push to image registries such as DockerHub.
๐ When you run microsoft/phi-4
, the Kit CLI will:
Download the
microsoft/phi-4
huggingface repository locally.Generate the configuration required to package that repository into a ModelKit.
Package the repository into a locally-stored ModelKit
Once this is done, you can simply run the following command to push it to the registry and share it with the team. ๐ฎ
kit push microsoft/phi-4:latest docker.io/my-organization/phi-4:latest
As you can already imagine, using KitOps saves a significant amount of development time for a team. With everything stored in one place, it becomes incredibly convenient to query and work with.
It also makes it easy to trace which models are running, where they were pulled from, and the status of the running models. ๐
Check out their DEV profile to learn more about KitOps as they share a lot of articles on how to utilize it to it's full potential. ๐
2. Nebius AI Studio - AI models like image-generation
โน๏ธ The Nebius AI Studio is not just limited to image-generation, it's a suite of AI models. You can find all here.
There are tons of AI models available in Nebius to fine-tune your image generation.
And the best part is they are super affordable and already come with $1 credit to try out the models before purchasing.
Recently, my friend @arindam_1729 built a project that uses the Nebius models, like Flux Schnell and SDXL 1.0, to generate logos based on user prompts.
๐ก If you wish to give it a look, check out this repository.
To add text-to-image generation to your application, it is as simple as adding the following lines of code:
from openai import OpenAI
client = OpenAI()
client.images.generate(
model="stability-ai/sdxl",
prompt="An elephant in a desert",
response_format="b64_json",
extra_body={
"response_extension": "webp",
"width": 512,
"height": 512,
"num_inference_steps": 30,
"seed": -1,
"negative_prompt": "Giraffes, night sky"
}
)
You can also interact with Nebius Cloud through your command line. ๐งโ๐ป
To get started with Nebius, simply fire up the following curl
command:
curl -sSL https://storage.eu-north1.nebius.cloud/cli/install.sh | bash
Re-source your shell with exec -l $SHELL
, and to check if you have successfully installed it, try running nebius version
.
3. CopilotKit - Seamless AI Copilots, Ready to Go
โน๏ธ Virtual colleague into your product that fully understands your application & your users.
CopilotKit is not just another AI application with chatbot features, it can understand the real-time state of your application and make changes as needed.
I am a big user of this tool, as I have built a couple of projects using CopilotKit. You can find them all on my GitHub profile.
๐ค Why use CopilotKit as compared to other alternatives?
If you are not convinced why you should bother trying out yet another AI addition to your application, let me show you some reasons to consider using CopilotKit.
โ Super Easy to integrate
To add CopilotKit in any application, it is just as simple as wrapping your application in the <CopilotKit />
provider.
import "./globals.css";
import { ReactNode } from "react";
import { CopilotKit } from "@copilotkit/react-core";
export default function RootLayout({ children }: { children: ReactNode }) {
return (
<html lang="en">
<body>
{/* Use the public api key you got from Copilot Cloud */}
<CopilotKit publicApiKey="<your-copilot-cloud-public-api-key>">
{children}
</CopilotKit>
</body>
</html>
);
}
โ Comes with Built-in UI
โน๏ธ Copilot UI ships with a number of built-in UI patterns, choose whichever one you like.
All the UI components are pre-built for you to plug in and get started, with no need to design anything from scratch.
You can pick from CopilotChat
, CopilotPopup
, and CopilotSidebar
.
To add any UI component, itโs as simple as importing the component and placing it in the desired location:
import { CopilotPopup } from "@copilotkit/react-ui";
export function YourApp() {
return (
<>
<YourMainContent />
<CopilotPopup
instructions={"You are assisting the user as best as you can. Answer in the best way possible given the data you have."}
labels={{
title: "Popup Assistant",
initial: "Need any help?",
}}
/>
</>
);
}
The steps will be very similar for all the other available components. You can always refer to the link above to learn more.
If you want to design everything on your own, there is also an option for Headless UI, where you can design everything to suit your needs. ๐ฅ
โ CoAgents Support
If you want to use your custom agents, you can do so as well. With this, you can have a shared state between your agent and the application, an agentic generative UI, add a human-in-the-loop stage, and implement real-time frontend actions using the useCoagentStateRender()
and useCoAgent()
hooks.
There's an example travel application they have to demonstrate the use of custom agents, check out here.
Check out their DEV profile to learn more about CopilotKit as they share a lot of articles on how to utilize it to it's full potential. ๐
4. Latitude - Build, Test and Deploy LLM features
โน๏ธ Latitude helps you refine your prompts with data to deliver reliable AI products with confidence.
Latitude is a platform that helps developers and product teams build AI features with confidence. Track and evaluate your prompts, improve them using real data, and deploy new changes easily.
The best part about Latitude is that it gives you 40K prompt and evaluation runs every month for free. ๐คฏ
Itโs designed to be easy to use, so you can get started quickly and iterate on your prompts without needing to worry about the infrastructure or tooling.
๐ก Long story short, it is a prompt-engineering platform that helps you build or iterate on your prompts before shipping it to the production so the output from the LLM is always what you would expect.
There's two-way of getting started with Latitude:
- Latitude Cloud: A fully managed solution that allows you to get started quickly without worrying about infrastructure.
- Latitude Self-Hosted: An open-source version that you can deploy and manage on your own infrastructure for complete control and customization.
You can refer to the quick introduction to Latitude and how to use it from one of the co-founders themselves: ๐
Check out their DEV profile to learn more about Latitude. ๐
5. Taipy - Python Data and BI Webapp Builder
โน๏ธ From simple pilots to production-ready web applications in no time.
With Taipy you can turn your Data and AI algorithms into production-ready web applications.
๐ค Who is Taipy for?
Taipy is designed for data scientists and machine learning engineers to build data & web applications.
Key Features of Taipy:
- โ Seamlessly embed ML models and data science workflows
- โ Easy to deploy anywhere in your custom domain
- โ For a VS Code user, Taipy has an extension that unlocks a graphical editor
- โ Taipy enhances performance with caching control of graphical events
To get started with Taipy, first, make sure that you have Taipy installed:
pip install taipy
Creating a simple sample dynamic web application in Taipy can be easily done with a few lines of Python code.
Create a Python file and paste the following lines of code:
from taipy.gui import Gui
import taipy.gui.builder as tgb
from math import cos, exp
value = 10
def compute_data(decay:int)->list:
return [cos(i/6) * exp(-i*decay/600) for i in range(100)]
def slider_moved(state):
state.data = compute_data(state.value)
with tgb.Page() as page:
tgb.text(value="# Taipy Getting Started", mode="md")
tgb.text(value="Value: {value}")
tgb.slider(value="{value}", on_change=slider_moved)
tgb.chart(data="{data}")
data = compute_data(value)
if __name__ == "__main__":
Gui(page=page).run(title="Dynamic chart")
Just by adding this code, we have a graph and a slider component which helps adjust the data parameter.
There are many others like bar chart visualization. To learn more, visit Taipy.
๐ก Taipy simplifies the process of turning complex Python algorithms into user-friendly web applications with just a few lines of Python, without requiring knowledge of additional languages.
Check out their DEV profile to learn more about Taipy. ๐
If you think of any other handy AI tools that I haven't covered in this article, do share them in the comments section below. ๐๐ป
So, that is it for this article. Thank you so much for reading! ๐๐ซก
Top comments (28)
Great list!
Thanks for Featuring My Project Brother!
Thank you. Cool project you've got. ๐
Arindam, which project is yours?
This one is Mine:
Arindam200 / logo-ai
AI Powered Logo Generator | Powered by Nebius AI
LogoAI - AI-Powered Logo Generator
LogoAI is a modern web application that leverages artificial intelligence to generate unique, professional logos. Built with Next.js, TypeScript, and powered by Nebius AI, it offers a seamless experience for creating custom logos for businesses and personal brands.
Features
Tech Stack
Oh, this is really cool!
Thanks Nathan, Means a lot!
In our team at EngioTech, we use Nebius studio a lot, especially the SDXL model, to generate images in batches as it costs much less than other models.
Great to hear, Lara. Thanks for sharing. Indeed, the SDXL model is much cheaper.
We were building our own chat infrastructure and almost got it deployed to production but it had so many issues. Then I came across CopilotKit and it not only worked out of the gate, but the prebuilt hooks and components gave us a lot of flexibility.
CopilotKit is awesome! The prebuilt hooks and components are super handy!
Great stuff!
Thanks, Nathan, for checking it out and also building such a great AI tool, CopilotKit. ๐ฅ
Nice, Check my recent project, what you think, any feedback ?
dev.to/shaijut/pro-in-flow-boost-y...
Thanks for checking it out @shaijut. That's an awesome project you're building. ๐ฅ
I'll try out your project and leave some feedback here.
Great going sathi. ๐ฅ
Thank you, Aayush!
Fantastic collection of developer tools ๐ฅ๐ฅ.
Thanks for sharing...
Thank you, @ddebajyati, for checking out! ๐
Awesome list, all tools are very helpful๐
Thank you, buddy! ๐
Feel free to share the AI tools you use in your daily workflow here. ๐
Engaging.
Thank you, Bryan!