AI in 2025: A Flood of Tools

Every week, there’s a new “must-try” AI tool promising to streamline design, write your code, automate workflows, or even build an entire website from scratch. In the middle of this tooling tsunami, it’s easy to lose track of what’s out there, let alone know what’s actually worth your time. 

And that’s the real challenge: among all these AI development tools, some are real game-changers, but others are just noise. 

This article will break down the current AI tool landscape, so you can get some clarity, tell the difference between what’s useful and what isn’t, and build a reliable and efficient AI stack. From UI generation to backend scaffolding and internal tools, we’ll share where AI actually adds value (and where it really doesn’t), based on real-world use cases and what we’ve learned using them ourselves.

Why the AI Tool Landscape Feels So Overwhelming Right Now

If you’ve been feeling overwhelmed by the amount of AI tools out there, you’re not alone. Whether you work in tech or another industry, the sheer volume of new tools can feel impossible to keep up with. Constant launches, overlapping features, and vague positioning make it hard to figure out what is relevant for your specific needs. 

Add to that the speed at which AI capabilities are advancing, and the result is a landscape that’s changing faster than most teams can realistically evaluate or adapt. Just when you’ve wrapped your head around one tool, five new ones launch with slightly different features and even bolder claims. What felt like innovation at first now feels like saturation. 

Part of the problem is that new tools are being released constantly, often with only minor differences between them, but all pitched as the next big breakthrough. And as these tools evolve, so do their features, often bleeding across categories: design tools start adding dev features, dev tools start generating content, and suddenly it’s unclear where any of them actually fit.

Finally, there’s the issue of longevity. Many AI startups pop up quickly to fade just as fast. Committing to an AI tool can feel risky when you’re not sure it will exist six months from now.  So, let’s save you the headache, and have a look at the tools that are currently available to you, their use cases, and their efficiency. 

AI Tool Breakdown: What’s Actually Out There

Not all AI tools are built the same, and especially not to solve the same problem. While the flood of new products can make everything feel interchangeable, there are actually a few distinct categories in the current AI landscape. Here’s a breakdown of the AI tools for development we’re seeing most often, and what they are designed to do. 

Design, Web, and UI Generation

Framer AI, an AI-powered website builder and design tool, helps users generate responsive web pages using text prompts. It offers templates, color schemes and layouts suggestions, and is able to fill in content. This tool allows designers and non-designers to quickly turn ideas into live, interactive websites with minimal manual design work. This is especially useful for MVPs and marketing sites, but is often limited when it comes to customization and nuance. 

Galileo takes a similar approach, but focuses more on UI mockups and layouts. It’s able to convert text and image prompts into editable interface designs for web and mobile. It’s a tool aimed at design ideation, allowing product teams to get early-stage visuals without starting from a blank canvas. It’s fast, but still needs human direction to turn a rough draft into something that’s truly on-brand.

Relume AI Sitemap Builder generates sitemaps, responsive wireframes, and initial copy from prompts. You can then export wireframes to Figma for design or to Webflow for development.

Figma AI adds automation to everyday design work: layout suggestions, design cleanup, variant generation, and more. It’s a great tool to accelerate ideation and prototyping. 

Similarly to Framer AI, Weblow’s AI Site Builder generates tailored website themes based on basic project inputs. Users can then customize layouts, colors, fonts, and components using a curated design system. Paired with the Webflow AI Assistant, it helps refine and launch a fully functional site. 

Webflow CMS AI Generator is a tool within Webflow’s services that generates placeholder copy for you so it is more aligned with your project than the typical “Lorem Ipsum” text. It helps get a more realistic idea of what your site will look like. 

Visual Creation and Assets

Midjourney is an AI-powered image and concept generator, widely used for creating graphics, illustrations, and web design inspiration. It generates high-quality, stylized images via prompt-based input. 

Freepik AI is a suite of AI-powered tools for image, video, and graphic creation and editing. It offers quick, ready-to-use visuals (icons, illustrations, photos, etc.) that are great for lightweight marketing needs. 

Adobe Photoshop’s AI features include Generative Expand, Generative Fill, AI-powered Remove Tool, and text-to-image capabilities.They help speed up creative workflows and are a serious upgrade to a designer’s toolkit.

Sora (OpenAI) creates short, realistic video clips based on natural language prompts. While still early-stage and not widely available, it's opening new possibilities for creative teams looking to prototype video without cameras or crews.

Code and Backend Tools

Supabase AI Assistant, as its name suggests, is an AI-powered assistant within the Supabase dashboard. It automates repetitive tasks and offers code suggestions, natural-language-to-SQL conversion, and in-editor support. It’s a lightweight developer copilot that integrates with an already popular backend stack, and can be helpful for fast prototyping or when working with unfamiliar queries.

GitHub Copilot is one of the most used AI code tools out there. Working as an assistant, it provides developers with real-time code suggestions, helps with debugging, and accelerates development by auto-completing code in various languages and frameworks.

Claude AI, from Anthropic, is a conversational AI known for its long-context processing, lower hallucination rate, and measured, thoughtful responses. While it is highly used for its coding and automation capabilities, it is designed as a general-purpose AI assistant with a broad range of applications across business, content creation, data analysis, customer support, and more. It’s not as IDE-integrated as GitHub Copilot, but its strength lies in clarity, structure, and logic.

Cursor AI is a coding IDE (Integrated Development Environment) that helps developers speed up programming workflows. It is able to generate code based on natural language queries and offers in-editor explanations and refactors.

Vercel v0 is a frontend code generation tool that allows developers and product teams to generate full React components, pages, and app scaffolding from natural language prompts. You tell it what you want, and it generates clean, editable code that works inside your Next.js app, not just a visual mockup.

Automation and AI Agents

n8n is a powerful open-source workflow automation platform. It integrates advanced AI tools for building intelligent workflows, such as chatbots, personalized assistants, document summarization, and data extraction. It’s part of a growing category of tools that use AI not for visuals, but for workflow automation and internal tool building.

GPT agent frameworks (like Auto-GPT, LangChain, or even custom internal setups) are also gaining traction. They’re more experimental, but offer the ability to perform tasks such as content generation, code writing, customer support, and workflow automation. They can be embedded into web apps to automate customer interaction, generate or edit website content, handle support queries, or even write and debug code.

These tools cover a wide range of areas, from design and content to infrastructure and automation. They each tackle different issues; the key is to understand when and how to use them. 

Use Cases That Actually Work

With so many tools making big promises, it’s easy to assume that they’re all meant to revolutionize your workflow. But in reality, most of them are useful for one or two specific tasks. Here are a few instances where we’ve seen AI actually add value.

Prototyping & Design Ideation

In the early stages of product development, tools like Framer AI, Galileo, Webflow AI Site Builder, Relume AI Sitemap Builder, and Figma AI can be genuinely helpful. They save time by allowing you to quickly generate layout ideas, sketch landing pages, or explore UI directions without having to start from scratch. These tools are especially useful for non-designers who need to visualize ideas before getting a design team involved. 

However, keep in mind that while they are quick, they are not the final solution. You’ll still need experienced designers to bring polish, cohesion, and brand consistency to the work, especially if you're building something meant to scale or stand out.

AI for Developers

GitHub Copilot, Supabase AI Assistant and Cursor AI help developers speed up routine tasks, like completing boilerplate, suggesting queries, and reducing friction in the early stages of writing code. They’re best used by experienced developers who can review, tweak, and integrate what the tool suggests. On their own, they don’t replace coding knowledge, but they do free up mental bandwidth and help teams move faster.

Vercel v0 adds another level of speed by allowing developers to build quickly production-ready pages that fit naturally into a Next.js + Tailwind workflow. It doesn’t just show you how it might look; it gives you real, editable code. 

Claude AI has also become a valuable tool for developers, particularly when working on high-context or multi-step tasks. It’s useful across the software development lifecycle, from planning and writing code to debugging, reviewing, testing, and documenting. 

Internal Automation & Ops

AI automation tools like n8n or GPT agents are great for internal use cases: automating repetitive tasks, generating reports, transforming structured data, or routing information between systems. These tools work well when you already know the logic you want, and just need help executing it. They’re especially helpful for lean teams that want to extend what they can do without expanding headcount.

Visual Creation

AI tools like Midjourney, Photoshop AI, Freepik AI, and other generative platforms can dramatically accelerate visual work for early stages. Whether you’re building a moodboard, exploring visual directions, or creating quick mockups for internal presentations, these tools help teams move from ideas to images in minutes. 

That said, these tools tend to replicate what’s trending or statistically “right,” not what’s creatively distinct. Without a strong point of view or human creative direction, the output can start to feel visually generic or disconnected from the brand. 

We explore this issue in depth in AI Killed the Brand Star,where we break down how overreliance on AI design tools is eroding creative differentiation. 

Writing, Research, and Summarization

When it comes to processing long and complex documents, structuring ideas effectively, or summarizing cluttered information into clear outputs, tools like Claude are quite helpful. Its thoughtful tone and longer context window make it a popular option for managing internal knowledge, generating drafts, and use cases that require a focus on reliability rather than speed.

Content & Drafting

For things like idea generation, first-draft copy, or internal documentation, AI tools (GPT, Claude, etc.) can save hours. They work well at creating drafts and turning scattered ideas into coherent text. This being said, their value decreases quickly when the content must reflect brand values, connect emotionally, or be strategically unique. For marketing, product messaging, and any public-facing content, the human element remains essential.

In each of these cases, the key takeaway is that AI doesn’t replace the work. It just speeds up the early stages by giving you a draft, a sketch, or a starting point. The value doesn’t come  from skipping the process, but from getting started faster. 

When AI Tools Help and When They Get in the Way

If used efficiently, AI tools can be of tremendous help streamlining your workflow and automating repetitive tasks. They work very well in low-risk environments, like drafting content, exploring design directions, generating early product ideas, or automating internal tasks that don’t require perfect precision. When the cost of failure is low, AI can help you move quickly and iterate fast. 

Fully relying on AI is, on the other hand, much riskier. Because they are built for speed, these tools often sacrifice depth. For instance, visual generators that look good but ignore UX principles, content writers that get the tone wrong, or code editors that inadvertently introduce subtle bugs and edge cases. You might get something that works in the short term, but that creates issues down the line.

We’ve seen this play out in real-world situations, where fast builds cannot scale. Lovable, Not Maintainable looks at the hidden risks of using no-code and AI tools as permanent infrastructure.

There are also trade-offs that many teams don’t fully consider until they’re deep into a tool’s ecosystem:

Flexibility vs. Lock-In

One of the biggest trade-offs when choosing AI tools is between short-term convenience and long-term flexibility.

Proprietary platforms often promise advanced features, smooth onboarding, and integrated support, which can all be hugely valuable early on. But the more you rely on closed systems, the harder (and more expensive) it becomes to switch later. Whether it’s proprietary APIs, unique file formats, or tightly coupled workflows, vendor lock-in can limit your ability to adapt as your needs evolve.

On the other hand, open-source and modular tools offer more flexibility. You can swap out components, integrate with different systems, and avoid being tied to a single provider. That freedom can save you from expensive rebuilds later on, but it usually requires more internal expertise and effort to start with and maintain.

Speed vs. Reliability

When using AI, finding the right balance between speed and output quality can be tricky. Whether that’s writing code for you, creating a prototype, or automating workflows, AI tools are great at helping teams move fast. But sometimes, “fast” can mean “fragile.”

A lot of quick AI solutions sacrifice depth and reliability for the sake of speed and efficiency. That might mean skipping rigorous testing, or accepting a “good enough” output to keep things moving. While that’s fine for some use cases, for anything high-stakes, it becomes a dangerous game. 

On the flip side, building for reliability means investing more time up front: thorough testing, better infrastructure, slower models, and higher costs. 

The key is knowing when to optimize for speed, and when not to. For internal tools, speed might win. But for anything public, critical, or deeply tied to user trust, reliability has to come first.

Our Own AI Product Stack (And What We've Learned So Far)

Like many product teams, we’ve spent the last years testing, adopting, and replacing a wide range of AI tools. Some have become part of our day-to-day. Others looked promising but didn’t work out for our needs. Here's where we’ve landed, and what we’ve learned along the way.

What We Use (And Why)

We’ve kept our stack focused on tools that actually improve speed or clarity without creating downstream mess. For design exploration and ideation, we’ve leaned on Figma AI and Relume AI Sitemap Builder to move quickly in early-stage prototyping. Midjourney and Freepik have been very handy to create unique visual assets for presentations and cases. For writing, research, and internal docs, Claude has become a core companion, especially when we need long-context summarization, structured thinking or to put conceptual schemas into visual overviews.

On the development side, we have favored Cursor AI, which is very good at taking into account the context of your code. It’s helped us accelerate common tasks, while Supabase AI Assistant has proven helpful for SQL-related work. Finally, Vercel v0 has been a great addition to our stack for our frontend development needs.

What Didn’t Work for Us

Among all the tools we’ve tested, some of them just didn’t work for us. Here’s why:

While interesting for beginners, in our opinion, Webflow's AI Site Builder didn't cut it for more professional websites. 

For visual asset creation, specifically videos, we tried Sora but couldn't get behind the current quality and feel of the videos it generates. They still look too generic and somewhat uncanny.

As far as AI for software development goes, we stopped using GitHub Copilot. Becoming increasingly slow and unreliable, it had started to negatively influence our workflow.

What We’ve Learned

The most important thing we’ve learned is that AI works best as a collaborator, not a crutch. The tools that have stuck are the ones that reduce friction without removing thinking. They help us move faster, but they still leave the hard calls to us.

We’ve also learned to be ruthless about evaluation. Just because a tool is new and looks fast and shiny doesn’t mean it makes sense for us to use it. We now ask three questions before adopting anything new:

• Does it help us move faster without creating long-term friction?
• Can we explain why we’re using it, not just that it feels impressive?
• Will it still make sense six months from now?

In a space this crowded, focus is an advantage. The fewer tools we use, the more we get out of the ones that actually work. 

Building The Best AI Product Stack For You: Use Fewer, Better Tools

As AI technology progresses quickly, it’s tempting to follow any new trend. Yet, simply having more tools doesn’t guarantee better outcomes. In fact, the teams that build the best products aren’t the ones using the most AI, they’re the ones using it well.

That means focusing on tools that actually fit your workflow, accelerate the right parts of your process, and give you control, not just speed and convenience. It means knowing when to automate, and when to pause and think. And above all, it means choosing tools that help you move faster without leaving a mess behind.

Building a product is hard. Building it with the wrong tools is harder. If you’re launching something new and want to move fast, without getting caught up in the noise, Miyagami is here to help. Contact us today, and let’s get you started.