Debugging endless code, racing against deadlines, and juggling clunky tools—sounds like just another day in the life of a developer. And then there’s your boss, who expects you to stay on top of new AI trends because the shinier, the better, right?

There had to be a better way to hit those impossible deadlines and still deliver great work. So, I got to work testing AI tools that automate the mundane, speed up workflows, and make life a whole lot easier.

In this article, I’ll share 23 of the best AI tools for developers in 2025. From setting up large language models locally to exploring no-code platforms and AI coding assistants, these tools will save you time, simplify your workflows, and bring your ideas to life.

Let's go!

1. Llama 3.2: Run AI models locally

Relying on cloud services for AI tasks can be frustrating due to usage costs, internet dependency, and privacy concerns. I needed a solution that offered robust AI capabilities without these limitations.

With Ollama, I run large language models like Llama 3.2 on my machine to access advanced AI features without relying on cloud services or incurring ongoing costs.

Here’s how I use Llama 3.2:

• Generate code: I use Llama 3.2 to write and debug code without connecting to the cloud.
• Create content: Llama 3.2 helps me generate content across various formats while maintaining complete privacy and control.
• Produce embeddings: I generate word and sentence embeddings for SEO tasks like keyword mapping, all locally.
• Integrate with tools: With Ollama’s local API, I can integrate Llama 3.2 into custom tools and workflows without relying on third-party services.

2. Open WebUI: User-friendly interface for local AI models

It’s challenging to run AI models locally if you’re uncomfortable with the command line or dealing with code. I wanted a solution that made this process more accessible for those who prefer graphical interfaces over text-based commands.

Open WebUI is an open-source interface that builds upon Ollama. I use it to run open-source AI models like Llama 3 on my machine. Additionally, I can perform tasks I would typically do in ChatGPT, but locally without complex setups.

Here’s how I use Open WebUI:

• Write App Script code: I prompted Open WebUI to generate code that produces embeddings from OpenAI and inserts them into Google Sheets.
• Copy-paste code into Google Sheets: I generated the code, pasted it into Google Sheets, and ran it as a function, allowing for seamless integration into my workflow.
• Accessible for non-coders: This tool makes advanced AI capabilities available to those who prefer using graphical interfaces instead of working with the command line.

3. LM Studio: A seamless interface for local AI models

With LM Studio, I can easily download and run models like LLaMA 3.2 and interact with them through an intuitive interface. There’s no need for complex setups or coding skills—just download the model, and you’re ready to go. 

Here’s how I use LM Studio:

• Generate content: LM Studio generates AI content for blogs, social posts, and more without needing cloud-based services.
• Produce embeddings for clustering and classification: Use embeddings to group similar texts or classify content based on semantic similarity, improving content organization and analysis.

4. gpt4all: Accessible local AI models

With GPT4All, I can perform tasks similar to ChatGPT without relying on cloud services or worrying about data privacy. The setup was smooth: install the application, download my model, and I’m ready. No advanced technical skills are required.

Here’s how I use GPT4All:

• Perform AI tasks offline: From generating content to running embeddings, GPT4All offers the same functionality I’d get with ChatGPT but with local control.
• Document analysis: Using the RAG (Retrieval-Augmented Generation) feature, I upload local documents and quickly query them for insights.

5. Msty: Combine local and cloud AI models

While local AI models offer control and privacy, sometimes I need the advanced capabilities of cloud services like ChatGPT. Msty combines open-source models running on my machine with cloud-based models for more flexibility and performance.

With Msty, I pick the best model for each task—handling privacy-sensitive jobs locally and switching to cloud models for more complex tasks. This hybrid approach optimizes my workflow, blending the power of cloud services with the control and efficiency of local models.

Here’s how I use Msty:

• Run hybrid AI workflows: I use local models for privacy-sensitive tasks and cloud models for more computationally demanding tasks.

• Easily switch between environments: Msty lets me switch between environments, offering a dynamic AI approach without added technical complexity.

6. Colab: Streamline coding with AI models

When I need to run AI models or write code without setting up complex environments, I turn to Colab. I use this Google tool to run Python code in the cloud, making it perfect for tasks like Natural Language Processing (NLP) or data analysis. It’s easy to use and even integrates with AI models like Gemini. As Britney Muller demonstrated, Colab simplifies the entire process.

Here’s how I use Colab:

• Run Python code in the cloud: I can run natural language processing models and data analysis scripts in Colab without worrying about local setups.
• Debug code with AI assistance: When errors arise, I paste them into the tool, and the AI offers debugging support.
• Process large datasets: For keyword research and topic modeling, I use Colab to process CSV files of keywords with AI SEO tools like BERT topic modeling.
• Integrate with AI models: Colab supports integrations with AI models like Gemini, giving me the power to run advanced machine learning tasks seamlessly in the cloud.

7. Gemini + Colab integration: AI code execution

One of the most efficient ways I’ve improved my workflows is by combining Gemini with Colab. I can tap into Gemini’s language model directly within the Colab environment, making coding and AI tasks faster and more intuitive.

Here’s how I use Gemini with Colab:

• Run Python code with advanced AI assistance: I use Gemini’s AI capabilities to execute complex data processing and natural language tasks in Colab.

• Keyword clustering: Gemini helps me organize large sets of SEO keywords into clusters based on intent or topics.

• Content generation: I use the integration to generate content ideas and automate parts of the content creation process using Python scripts in Colab.

8. Programming Helper: AI coding assistance

Programming Helper offers AI support for writing and debugging code, making it essential for website optimization, integrating APIs, or creating automation. It covers multiple programming languages and provides solutions where ChatGPT may not suffice.

Here’s how I use Programming Helper:

• Code generation: I provide a plain language description of what I need, and Programming Helper generates code in various languages. This helps with data analysis scripts, SEO automation, or web scraping.

• Learning support: When I face unfamiliar programming challenges, the tool offers code examples and explanations, making new concepts easier to grasp.

• Debugging: Programming Helper identifies issues and suggests fixes, streamlining the debugging process and improving code functionality.

9. Llama Index: Build RAG systems

Finding the most relevant information quickly becomes a challenge when handling vast amounts of content. Traditional search methods often fail to deliver context-rich responses, especially for SEO tasks that require precise accuracy.

Llama Index creates RAG systems by indexing large document collections, allowing AI to find the most relevant text based on a query. While it doesn’t support creating an index from sitemaps, I developed custom code to add this feature, making it more effective for content retrieval.

Here’s how I use Llama Index:

• Generate accurate content: Llama Index retrieves precise information to automate complex content tasks.

• Optimize content strategy: I use Llama Index to dig into large datasets and uncover critical insights that improve my content strategies.

10. LangChain: Build AI agents

LangChain is my go-to framework for building AI agents that can perform tasks based on real-time data. It enables me to integrate language models into workflows and automate processes beyond basic tasks. 

Here’s how I use LangChain:

• Connect to real-time data sources: I connect LangChain to tools like Google Analytics and Search Console to retrieve and analyze real-time data.

• Automate technical SEO tasks: I can write code for LangChain to automate actions like keyword tracking, meta tag analysis, and crawling for SEO issues.

• Generate content based on live data: I create agents that generate content based on current trends or data pulled from my connected sources.

• Build custom AI workflows: I use LangChain to design workflows that integrate AI models with any application, enabling more versatile and complex automation.

11. LangFuse: Manage and observe AI prompts

When working with multiple AI models and complex workflows, it’s crucial to track how prompts perform and understand the inner workings of each interaction. Without visibility into prompt usage, I risk inefficiencies and missed opportunities for optimization.

LangFuse solves this by providing a comprehensive view of AI prompts, showing how they perform, when they’re used, and where adjustments are needed.

Here’s how I use LangFuse:

• Track prompt performance: I monitor how each prompt performs across different workflows to identify which generates the best outcomes.

• Optimize prompt management: I tweak prompts to increase efficiency and improve overall workflow performance by observing usage patterns.

• Manage complex workflows: LangFuse helps me oversee AI interactions in workflows involving multiple models, ensuring that everything runs smoothly and effectively.

• Improve AI output quality: With real-time insights into how prompts are used, I can make data-led adjustments that improve the quality of AI content.

12. Regexer: AI regex generation 

Writing regular expressions (regex) can be challenging, especially if you’re not familiar with syntax—or, like me, you’d rather not write regex at all. Regexer solves this by generating regex patterns from natural language descriptions.

Here’s how I use Regexer:

• Generate regex patterns: Instead of coding complex regex manually, I describe the task, and Regexer creates the pattern, saving time when handling large datasets or complex URLs.

• Filter data: I use Regexer to filter data in tools like Screaming Frog or Google Analytics, helping me focus on specific content or areas of a website.

• Create custom redirects: Regexer generates patterns for setting up redirects on large sites, especially when cleaning up outdated URL structures.

• Extract data from logs: Regexer extracts key insights from server logs by generating patterns to match specific details, improving log analysis for site optimization.

13. Literal AI: LLM monitoring and evaluation for product teams

AI development often requires robust monitoring and evaluation to ensure reliable outputs, which can be challenging when deploying LLM applications at scale. 

Literal AI addresses this by offering an end-to-end platform for observability, evaluation, and prompt management tailored for developers and product teams.

Here’s how I use Literal AI:

• Prompt testing and debugging: Literal AI’s prompt playground lets me create, test, and refine prompts in real-time. With in-context debugging and session visualization, I can easily adjust prompts to improve accuracy and output quality.

• Performance monitoring: I track essential metrics like latency and token usage and setup alerts to notify me if I exceed performance thresholds.

• Comprehensive evaluation: Literal AI supports offline and online evaluations, A/B testing, and RAG workflows. This variety of options helps me assess model accuracy and efficiency under different conditions.

• LLM observability: With multimodal logging, Literal AI captures LLM behavior across text, image, and audio inputs, giving me insights that inform adjustments and improve my models for better performance.

14. ConsoleX: ChatGPT with more control for advanced workflows

When I need more control over prompts and outputs for complex tasks, ConsoleX offers a level of precision that standard versions of ChatGPT don’t. It fine-tunes interactions and customizes outputs for various applications.

Here’s how I use ConsoleX:

• Customize prompts for coding and data analysis: ConsoleX gives me more control to do complex coding tasks, debug issues, and run data analysis workflows more accurately.

• Multi-step workflow automation: I can create multi-step workflows where ConsoleX follows a sequence of commands to perform tasks like code generation, data extraction, and reporting.

• Custom output formatting: ConsoleX lets me tailor the format of its responses, which is useful when I need structured data or reports that fit into my existing processes.

• Advanced technical troubleshooting: I use ConsoleX to provide more nuanced and detailed responses for complex technical challenges, offering targeted solutions and actionable insights.

15. ChatArena.ai: Compare output from different LLMs

ChatArena.ai lets me compare outputs from multiple large language models (LLMs) like ChatGPT, Claude, and Llama in one interface. This feature is valuable when evaluating how different models handle the same tasks or prompts.

Here’s how I use ChatArena.ai:

• Compare LLM outputs: I test multiple LLMs on the same prompt, to assess their strengths and weaknesses, particularly for projects that require precise language understanding or creative problem-solving.

• Evaluate code generation: Different models often produce varying results when generating or debugging code. ChatArena.ai  compares how each LLM handles the same coding query so I can choose the most accurate output.

• Refine prompt engineering: Since models interpret prompts differently, I use ChatArena.ai to refine my prompts, ensuring the best outcome.

16. Octoparse: Combine a scraper with generative AI

Scraping data from websites can be time-consuming, especially with large datasets. Octoparse simplifies this process with a no-code platform that automates web scraping, so I can collect data quickly without coding skills.

Here’s how I use Octoparse in my workflow:

• Automated web scraping: I can extract data from multiple websites, such as competitor analysis, product listings, or keyword data, in a fraction of the time it would take manually. 

• Data structuring and export: After scraping the data, I organize it into structured formats like Excel or CSV for further analysis.

• Use cases with external AI tools: I often export the scraped data and feed it into tools like ChatGPT for tasks like summarizing or generating content ideas.

• Lead generation and link building: Octoparse scrapes directories and forums for potential leads or link-building opportunities, making outreach more efficient.

17. Replicate: Run any AI model as an API

When I need to integrate AI models into my workflows quickly and without hassle, Replicate is my go-to. I can run any AI model as an API and integrate different models into my existing systems without technical overhead. 

Here’s how I use Replicate:

• Run AI models as APIs: Deploy AI models instantly as APIs, eliminating time-consuming setup.

• Content generation: Integrate content-generation models into workflows for faster, scalable AI content creation.

• Model versioning: Test different model versions and revert to previous versions if needed.

• Collaborate with teams: Share and access models across teams, enabling easier collaboration on AI projects.

• Specialized applications: Implement AI models for image recognition, data analysis, and other specialized tasks with minimal effort.

18. OpenAI’s API for speech-to-text (Whisper)

Transcribing audio or video content can be challenging, especially when dealing with poor audio quality or multiple speakers. I use OpenAI’s Whisper to generate accurate transcripts from complex audio or video files.

Here’s how I use Whisper:

• Transcribe webinars and meetings: I convert spoken content from webinars and meetings into detailed transcripts for future analysis.

• Turn video content into text: Whisper makes it easy to transcribe video content and repurpose into blog posts, articles, or other content formats.

• Handle complex audio: Whisper’s accuracy with poor-quality audio or multiple speakers ensures I never miss important details, making it a reliable tool for all my transcription needs.

19. Page Type Detection with GPT-4V

Manually categorizing web pages can be time-consuming when dealing with large volumes. I use GPT-4V (GPT-4 with vision) to analyze web pages visually, making it easier to detect and categorize different page types automatically.

Here’s how I use GPT-4V:

• Automated page type detection: Upload screenshots and have the AI classify each page as a product page, blog post, home page, etc.

• Content organization: GPT-4V organizes content by identifying page types based on visual input.

20. Galileo: From text/image to user interface (UI) design

Galileo transforms text descriptions or images into user interface (UI) designs, making it easier to generate high-quality mobile and desktop mockups. Whether describing an app idea or uploading an image, Galileo quickly produces design mockups I can export to platforms like Figma for further refinement.

Here’s how I use Galileo:

• Rapid UI prototyping: I describe a design idea or upload an image, and Galileo generates a UI prototype for apps or websites.

• Export designs: I export mockups to Figma for further refinement, streamlining my design workflow.

• Customize designs: Galileo adjusts UI elements, offering flexibility to customize designs for usability and aesthetics.

21. Bubble: No-code app builder for AI integration

Bubble is a no-code platform I use to build functional web apps quickly. I describe the type of app I want, and Bubble generates the core structure. The drag-and-drop interface lets me customize everything from design to AI integrations.

Here’s how I use Bubble:

• Build AI-powered apps: I create AI apps that automate tasks like content creation and SEO workflows.

• Drag-and-drop customization: I use the platform to design and build apps without technical skills.

• Integrate AI tools: Bubble supports integrating AI models, making it easy to add AI functionality to custom-built apps.

22. Streamlit: Turn AI models into web apps with ease

When I need to turn an AI model into a functional web app, I rely on Streamlit to create fully operational applications without worrying about complex infrastructure or front-end development.

Here’s how I use Streamlit:

• Create interactive SEO tools: I can transform an AI model into an interactive SEO tool that clients or my teams can use.

• Share data insights: Streamlit makes it easy to share data insights in an interactive and accessible web app format.

• Simplify app development: I upload my code, and Streamlit handles the entire web app infrastructure, removing the need for complex development.

23. Chainlit: Build conversational AI apps

Creating conversational AI applications can be daunting, but Chainlit simplifies the process. Whether I need to build a chatbot or automate internal workflows, Chainlit connects large language models to user-friendly interfaces.

Here’s how I use Chainlit:

• Build chatbots: I develop chatbots that can respond to client inquiries or provide real-time insights.

• Query data from Google Analytics: I use Chainlit to interact with AI models to extract and analyze data from platforms like Google Analytics.

24. There’s an AI for That: Find AI tools for any task

When I need a specific AI tool, I use There’s an AI for That. It aggregates AI tools, making it easier to discover new solutions based on my unique requirements.

Here’s how I use There’s an AI for That:

• Discovering new tools: I search across categories to find AI tools tailored to specific tasks.

• Comparing AI solutions: The platform helps me compare features and choose the best tools for my workflow.

• Staying up-to-date with new releases: It regularly updates its catalog, informing me about the latest AI tools.

Other articles in the Mike King Takeover Series:

• 13 Best AI Automation Tools to Increase Efficiency and Productivity
• 21 Top AI Tools for Content Creation in 2025
• 24 Ways I'm Using AI Tools for SEO

Now it’s your turn to start exploring options that resonate with your needs. The right AI tools create a multiplying effect that transforms your entire workflow. Whether it’s simplifying repetitive tasks or tackling ambitious projects, there’s a solution here to help you deliver better, faster, and smarter results.