• A trio of journalists tried three big vibe coding apps to see how they stack up.
• We each attempted to build an app on Cursor, Lovable, and Base44.
• With the same prompt on each system, we wanted to see how far we could get.

We three writers have been handed a gift with seemingly infinite potential. A sparkling promise, from vibe coding startups, that we can build anything without understanding a word of code.

Gone are the days, these companies say, when coding novices needed to rely on their techie friends to troubleshoot mistakes.

Over a dozen firms have rolled out tools offering the ability to build apps in seconds. All you need is a good idea and the platforms' free coding credits.

With a growing wave of vibe-coding startups raising big money, questions are emerging: Are these tools meaningfully different? Is the market already crowded? And can this be a sustainable business?

We tried three of the most popular platforms — Cursor, Lovable, and Base44 — to find out what each platform really offers and where they fall short.

Our prompts

We started this experiment at different levels of proficiency.

Chong Ming had coded an app at a vibe-coding workshop. Cheryl, self-taught, had experimented with five vibe-coding platforms and made three web apps. Aditi was a true beginner.

We each set out to make an app. For Chong Ming, a writing companion in the shape of a cute creature. For Cheryl, a newsroom dashboard, a lite version of Asana to keep her team's work organized. And for Aditi, an app that acted like a newsroom photo coach, to deem whether a photo was good to publish.

First impressions

Chong Ming: When I asked Base44 to plan the app, it responded with a few questions to clarify my prompt, with cute emojis. The plan it generated wasn't as detailed as what I've seen from Cursor, but it was user-friendly.

Lovable's plan was even simpler, pared down to a few bullet points. It was just as easy to use as Base44. Within minutes, it generated a web app similar to Base44's.

Cursor's interface seemed built for serious builders looking to ship real products. Its planning questions were more advanced and thoughtful, asking if I wanted an MVP build plan, a clickable prototype, or a full product spec — the kind of distinctions a software engineer would make.

Cheryl: I've always been one for the rule of cool, and Cursor looked really cool, with its all-black dashboard. But there was some charm in logging onto Lovable, with its girlypop, pink-heavy interface, and Base44 offered some cheerful vibes on its tangerine-colored interface, too.

Base44 and Lovable felt more like signing into a website and conversing with a chatbot. With Cursor and its MacBook app, I felt like I was hacking into the mainframe, with all its complicated scrolling lines of code.

Aditi: Off the bat, Cursor looked intimidating, and the option to sync GitHub when logging in made me think it wasn't a platform for a non-technical user.

Meanwhile, Base44 and Lovable were friendly and reassuring, with their gentle prompts: "What will you build next?" and "Ready to build, Aditi?"

Learning curve

Chong Ming: Base44 and Lovable were easy to use. The app plans were written in plain language for everyday users, and the interface was beginner-friendly. It was clear where to click if I needed help or wanted to tweak something.

Cursor was a different story. There were things I had to decipher on my own, like "frontend built with Next.js, React, and TypeScript."

Cheryl: I have never felt more like a dinosaur than when I first tried using Cursor. It was embarrassing having to look up basic terms to know what I was dealing with.

On Base44 and Lovable, I consistently typed in plain English and made the app edits accordingly. I felt like a wizard, watching the app preview morph and shift into view.

Aditi: I'd never tried vibe coding, so I asked AI for help understanding AI. I asked ChatGPT to help me refine my initial prompt into something I could plonk into the vibe coding platforms.

With Lovable and Base44, the learning was intuitive, and it felt like I was talking to ChatGPT. With Cursor, I was completely lost and had no idea where to start.

Then it was time to build the apps

Chong Ming: Base44 built me a writing companion app with a cute egg. The layout felt bland, but it was a full-fledged, functional app created without using up all my credits.

Lovable's build was similar and didn't use all credits.

Both platforms could generate the app; the main variation was in aesthetics. I did appreciate that Base44 and Lovable let me edit the app directly in the interface.

The Cursor build process wasn't as hands-off. Unlike Base44 and Lovable, which ran start to finish, Cursor required me to approve commands and grant permissions to override folders on my computer. As it generated code, I could pause and review it, something that would likely appeal to developers who want control.

Cheryl: The best things in life are free, and vibe coding credits are one of them. On Base44 and Lovable, both platforms make it clear to users that they're cooking with limited credits, and that's fair — compute is costly. The mileage on each platform, however, was slightly different.

Lovable gave me good bones for the project up front and created something that was, aesthetically and functionally, closest to what I wanted. But it burned through more free credits than my Base44 project did, and some things still weren't working in the web app. I was stuck waiting for new credits to drop before I could make tweaks.

Base44 gave me something very close to a complete dashboard, but it lacked some key functionality — the option to delete tasks, or to drag and drop unscheduled tasks into the calendar frame. But that was ironed out within minutes with two additional message prompts.

Cursor's steeper learning curve and multi-step process made it far harder for me to work things out. After 10 minutes of Googling, I gave in and typed into the Cursor chat: "I'm confused. What do I do now? Give me a guide."

I was told to go to Supabase and make some adjustments to the settings, then try to ship it via a local server. At that point, I was coming up on half an hour of getting frustrated with the process.

Aditi: The development process was smooth sailing with Lovable and Base44. With one initial prompt and two additional tweaks, both platforms gave me usable apps that I thought would be handy newsroom tools.

I first tried Base44 and felt childlike wonder when it produced a clean, minimalist interface that let me drag a photo in and judge its quality.

After the initial merriment wore off, I started testing the features. One thing I had not realized was how specific my prompts needed to be, expecting it to anticipate my needs. For example, both platforms initially did not allow me to crop the image or adjust the framing, and instead automatically chose the subject for me. An easy second prompt brought the apps closer to my initial vision — although I quickly learned how to ration my prompts lest I run out of my daily free credits.

Lovable's interface had a neat little photo-scanning animation that I thought added visual interest to the otherwise simple interface.

Now for Cursor. I had to download the app on the MacBook, while the others could run in the browser. When I finally downloaded it and fed it my prompt, it ran lines of obscure code, asked for permissions to things I didn't understand, and made me lose motivation to build anything.

I eventually gave up on trying to make it work, but the app kept prodding me with pop-ups for permissions all day until I force-quit it.

I'll stick to my beginner-friendly platforms until further notice.

How the platforms stack

Chong Ming: Lovable and Base44 delivered working apps and refinements fast, but the quality didn't match Cursor. Cursor broke down what it added and made changes in detail, even if some of the jargon flew over my head.

When I refined the app, Cursor didn't just tweak surface-level things. It suggested enhancements such as adding extra animation frames or making the pet move faster. When I said I didn't want a simple egg, it flagged that a drawn mascot or pixel pet would require new assets — a level of clarity the others didn't offer.

By comparison, Lovable and Base44 suggested things like adding entrance animations, which felt more gimmicky than meaningful.

If I were building something serious, I'd go with Cursor, even if it takes more time and effort to get up to speed.

Cheryl: On both Lovable and Base44, I managed to build workable newsroom calendars and get them from first prompt to publishing within 10 minutes. Base44 gave me a complete, fully functional project I could immediately use and share with my team — and within the free credit range, too. The next day, I used my new set of credits on Lovable to make final tweaks, resulting in a publishable dashboard with all the functions I wanted.

On Cursor, however, I just couldn't figure out what I was doing wrong and why the code wasn't running as I intended. I never got my dashboard off the ground there. Cursor: It's not you, it's me.

If you have a nontechnical background, a clear vision for the app you want to build, but limited time to pick up a little more coding, a one-stop shop like Lovable and Base44 would be more your speed. If you do have more coding know-how, Cursor will give you access and oversight over the coding process within its free credit limit.

Aditi: As a colors-obsessed, minimalism-loving, non-technical person who just wants to build a simple app, here's my leaderboard: Lovable, Base44, Cursor.

The market's flooded with options, so take your pick while companies are being generous with credits

The apps we tried are just a sampling of the vibe coding offerings out there. Other companies, like Emergent and Replit, also offer one-stop-shop platforms that take ideas from conception to shipping fast.

The barrier to entry is low, particularly with free credits on entry-level plans.

So if there was ever a time to try vibe-coding, it's now.

• Cursor left out one key detail about its new coding model: it started from Kimi K2.5.
• Composer 2 is cheaper, more capable — and built on a Chinese open-source model, Cursor's executives said.
• An X user spotted code suggesting Kimi under the hood, sparking disclosure.

Cursor just acknowledged that its latest coding model has Chinese roots — a detail it left out the first time around.

In a series of posts on X over the weekend, Cursor executives said Composer 2 was initially built on top of Kimi K2.5, an open-source model developed by Chinese startup Moonshot AI.

"We've evaluated a lot of base models on perplexity-based evals and Kimi k2.5 proved to be the strongest!" said Cursor's cofounder Aman Sanger on X on Saturday.

"It was a miss to not mention the Kimi base in our blog from the start," he added.

The disclosure appears to have been sparked by an X user named Fynn, who posted on Friday that Composer 2 was "just Kimi 2.5" with additional reinforcement learning.

To support the claim, the user pointed to code snippets that appeared to reference Kimi as the underlying system.

'At least rename the model ID," the user wrote.

In response to the user's X post, Cursor's vice president of developer education, Lee Robinson, acknowledged that Composer 2 was built on Kimi K2.5 as an open-source base.

"We will do full pretraining in the future," Robinson said.

"Only ~1/4 of the compute spent on the final model came from the base, the rest is from our training," he added.

Robinson also said the company is complying with the model's licensing terms through its inference provider.

The Chinese startup posted on X on Saturday that Cursor is using Kimi K2.5 under an authorized commercial partnership.

"Seeing our model integrated effectively through Cursor's continued pretraining & high-compute RL training is the open model ecosystem we love to support," the post read.

Cursor was last valued at $29.3 billion in November.

Cursor's new model is cheaper and better

Cursor said in a blog post on Thursday that Composer 2 is "frontier-level at coding" and priced at $0.50 per million input tokens and $2.50 per million output tokens, calling it "a new, optimal combination of intelligence and cost."

By comparison, Anthropic's Claude Opus 4.6 is priced at $5 per million input tokens and $25 per million output tokens, while Claude Sonnet 4.6 costs $3 and $15, respectively, according to the company's website.

That puts Composer 2 at roughly one-tenth the cost of Opus 4.6 and about one-sixth the cost of Sonnet 4.6 on both input and output tokens.

Users on X have added to the debate, with some praising the performance of Kimi after learning that Composer 2 was built on top of it.

"As someone who basically lives in opus 4.6, seeing an open-weight kimi 2.5 fine-tune actually beat it on coding benchmarks is wild," one X user wrote in response to Fynn's post.

"Well that's a sign for RL Chinese is in new game," another user wrote, referring to reinforcement learning.

Others were more critical of Cursor's handling of the disclosure, questioning why the company did not acknowledge Kimi upfront.

"Cursor is becoming a model routing layer, not an IDE. they pick the cheapest model that clears a quality bar per task, wrap it in their UX, and pocket the margin," one user who goes by aira wrote on X.

• Miro's CEO says the company is plowing cash into AI subscriptions to help employees level up.
• "Our L&D budget is unlimited tooling," Andrey Khusid said.
• AI adoption is accelerating, and with it come questions about the technology's ROI.

Plenty of companies are still debating whether costly AI subscriptions are worth it. Miro has gone the other way.

Andrey Khusid, cofounder of Miro, the maker of a popular online whiteboard platform, says the company gives employees essentially unlimited access to the latest AI tools as a way to speed up how quickly they learn and work.

That approach is possible, he said, because Miro has been profitable since 2016. The company has raised $476 million to date, and Khusid suggested it does not expect to need more capital.

Khusid framed the spending as a core part of more traditional workplace training. "Our L&D budget is unlimited tooling," he said.

Rather than asking employees to learn on their own time or pay out of pocket, he said, Miro wants that experimentation to happen inside the company, as a shared effort. He later added that there should still be a clear business case for buying any tool.

Miro's strategy is part of a wider shift in tech, where AI adoption is moving from optional to expected. A new study from engineering intelligence platform Jellyfish, based on data from more than 700 companies, found that 64% now produce a majority of their code with AI assistance. Tech giants like Google are pushing employees to use AI tools more aggressively, and Microsoft has begun tying AI usage to performance evaluations. As a result, AI fluency is quickly becoming a core workplace skill rather than a nice-to-have.

Still, Khusid says many executives ask the wrong question about AI ROI. Rather than judging the tools on individual productivity gains or subscription costs, he said Miro is trying to focus on whether the company is moving faster overall.

The company tracks projects through what he described as a "discover, define, deliver" process and measures how long it takes to move from one stage to the next. The goal is to compress that timeline as much as possible.

"The most important metric from my perspective is velocity of innovation," Khusid said. "If you don't innovate fast enough, you're out of the game."

Khusid said he doesn't think the way companies use AI today is necessarily the end state. He said it will take at least until the end of this year, or even next year, to see what a workplace shaped by these tools really looks like. At that point, Miro will take a harder look at which tools are worth the price tag.

For now, he said, Miro is already seeing time savings across engineering, product, and design. That's not always the case, though. Better tools speed code generation, he said, but code reviews can still bog down projects.

"Humans have to read it," Khusid said. At least for now.

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A few Fridays ago, I was feeling smug. I'd just sent another Tech Memo edition telling subscribers to stop worrying about AI eating tech jobs because Anthropic, the leading AI company pushing this narrative, is hiring so many engineers.

So clever! Until I got an email from a reader, Kiran Maya Sheikh. She has a computer science degree from the University of California, Irvine. It's a great school, and she graduated with an impressive GPA. And yet, she's struggling to land that all-important first full-time software engineering job.

"It's bad advice to 'not worry,'" she wrote. "AI is causing disruption in this job market. Employers are prioritizing hiring experienced workers, but not new graduates."

This week, I interviewed Kiran for Tech Memo. It was an eye-opening view into the realities of the new AI economy. Here are the highlights from our chat, edited for clarity and length.

Alistair: What did you think you were signing up for when you first chose computer science as a degree?

Kiran: After getting into UC Irvine in 2020, I took my first coding class and I really enjoyed it. The prospects at this time were that people were going into this major to get great jobs and it was very rewarding and I ended up liking the work.

What did you believe a career in computer science would give you financially, socially, and emotionally?

The dream at the time was definitely everyone was saying, "Let's go work for Google and the FAANG companies and get a six-figure salary." My motivation was just getting a stable job, getting enough money to take care of my family — what everyone wants. I expected that computer science would put me in a position to grow as a software engineer, first and foremost, and then maybe take me to more of the strategic side, the management side. The main thing that I did figure out was that I wanted financial stability and maybe financial independence as well.

Fast forward to late 2022, when ChatGPT launched. Did you see that as a tool at the time or a threat?

I was a hater at the beginning. Then, friends of mine started using ChatGPT and they're like, "Oh, you can just use it like Google. You can just text it and it'll give you the answer." And honestly, my first thought was like, "That's a bit lazy. You can get more learning out of doing the work yourself." But the more time went on, the more that people were using it, and they started using it for class. Suddenly, I was ahead in class. I was doing the assignments well and understanding more.

Was there a moment when you thought generative AI might reduce the need for junior engineers, or do you even believe that?

We all know the current job market. It's not too hot and a lot of companies are citing AI as part of the reason for layoffs — but maybe that they were going to cut those jobs anyway. At the time though, while I was in school and using ChatGPT, I honestly didn't think it would get this far. I expected AI would be integrated into software engineers' work and companies would start integrating it, but I didn't realize there would be potential for it to take over jobs that I was looking for.

I don't think I was very attentive to the job market situation at the time, and I wasn't really thinking that far ahead. More of my worries at the time were just getting that first entry-level position. And I just thought it would be simple: I just get my degree and I would find a company that's hiring. Looking back, it was my mistake to not really research the current job market and maybe what some people were predicting about AI.

I didn't see it coming either. Few people did. Anyway, describe the moment when you realized the job market had changed?

I was already graduating, so this was after June 2025. I was getting into the reality of having to find my first job, and that's when I definitely started noticing something was wrong. A lot of my classmates, I haven't really heard of them getting any opportunities. Everyone's submitting so many resumes and there's a race to use AI to enhance resumes and send them out as fast as you can. And it seemed a lot more intense than I was prepared for.

A lot of my classmates and even students I know who are still in school are not even landing internships right now. It's not looking great. It's a very tough battle right now. So many people are quitting or getting fired or pivoting and there's new grads. Everyone is bracing, and it's a bloodbath right now.

Do you feel like you're competing against AI or laid-off senior engineers or both, or something else?

My fight is definitely with AI and all the competition with entry-level graduates — especially because AI is known to take over more junior roles. So it's important that we stay more relevant and offer something that AI can't. Scrolling through LinkedIn and on my job portals, I see more offers for mid-level positions, but I don't see as many for entry-level roles. So it's like I'm fighting AI and all these other graduates for roles that don't exist yet.

This job search so far, what has it done to your confidence?

I try to be optimistic. I am lucky to have a better situation than some other people do. I'm living at home with family, so I don't have to worry as much about expenses. Still, if I weren't doing anything about my situation, I would feel pretty bummed. I'd feel kind of trapped.

But I've been trying to work on building my network, finding people I know and learning from other people, just finding communities to be involved with. That's really helped my confidence because I find professionals that are trying to help — they are aware of the job market and they know how hard it is to get that first job. The one saving grace in this tough situation is definitely the community I've found and the people I know who are helping me through it.

Did you ever question your decision to study computer science?

Yes, I did question it. But I remember that I do like computer science and I did like what I learned. I really enjoyed my classes and programming. And instead of turning to a new discipline, I think I prefer to just specialize and find out new information and stay ahead of the news. And like I said, offer something that AI can't.

Do you feel like you were trained for a version of the tech industry that no longer exists?

I am a little salty, about this, if that's the right word. During my time at school, a lot of what the degree was about was learning the basics of software engineering. You learn programming languages and you learn how to set up your development and deployment. But right now there are so many more tools and I think that's the constant thing with the software engineering and the tech industry. There's always new technology and there's a lot of learning you have to keep up with.

But with AI in particular, I felt like I graduated a bit too early. Because now AI will probably be more integrated into learning. I had so many professors that were more welcoming towards AI. I remember a really cool professor who shared a website that would let you make your own LLM. And it's really useful stuff, but it wasn't part of the curriculum. It will be now, but I won't be there to see that change.

What I'm doing to help with that, and make the amends, is volunteering and doing more work on the side that involves newer technologies to just stay fresh and relevant and use all these new AI tools and see how I can leverage it.

If a high school senior asked you today whether they should major in computer science, what would you tell them?

It depends on what interests them about computer science. If it's absolutely something they're interested, they love learning about the technology and they want to code, I would still say go for it, but I would recommend how to position yourself for after college.

You need to start much earlier now, networking and knowing how to speak with people and how to apply, how to write a resume. And those all are also much more important now at the start of college, especially getting internships, if at all possible.

So, I would definitely recommend studying computer science, but being realistic about the opportunities available and keeping up with the news and the job market.

What would you say to potential employers out there?

The focus should still be in hiring entry-level talent if possible. I know it's tough with the current market and the economy and what's going on in the world right now. But entry-level talent is still important because you need to build this generation of professionals so that the future will have people to rely on. AI is still uncertain right now. People are still figuring out how it is impactful and it doesn't help to just force it upon your company.

Sign up for BI's Tech Memo newsletter here. Reach out to me via email at abarr@businessinsider.com.