Building a 2-day MVP with AI software engineering: Insights

The development of a minimum viable product would require months. Today, with the assistance of AI-driven tools, founders will be able to create working MVPs within a few days. Though this speed is exciting, it comes with new challenges and trade-offs. In our live program, MVP in 2 Days - Hype or Real Deal, we were asked a lot of insightful questions that we simply did not have time to respond to. This guide unites them all, complete answers and useful experience to navigate the rapid MVP development. The following are some of the recommendations in the form of Q&A based on actual interviews with product builders, founders, and engineers.

The agility of MVPs built in two days, which is made possible by such tools as Replit, Cursor, and VS Code with Copilot, has its own distinct advantages and disadvantages.

Advantages:

• Speed to market: Get ahead of the competition and find the early adopters, and test ideas on the fly
• Performance efficiency: Reduced initial investment; allocate resources to the essential features only
• Quick learning: Lightening-fast feedback mechanism and switch to the next pivot fast
• Investor attraction: Have shown execution ability and penetrated the market increasingly quicker

Cons:

• Technical debt: Rapid code can need to be rewritten to scale, escalating costs in the long term. To minimise this risk, make use of presets: ready-made structures, reusable components and proven combinations of structures that allow teams to start quick and not jeopardise future expansion
• Shallow validation: It is possible that the small amount of user testing will overlook serious bugs or market topics
• Scope mismanagement: Under- or over-scoping may be the reason to have unusable or delayed MVPs

State of the art AI and no-code enable non-developers to prototype and test ideas within a short period of time. Non-technical entrepreneurs can pay attention to:

• MVP created with AI/no-code. Nonetheless, there remains a need to bring on board a technical specialist in the future to eliminate security and scalability threats as well as infrastructure risks
• Focusing on user feedback instead of technical excellence
• Putting the requirements on paper and thinking things out
• Hiring technical assistance to go over things or scale up as required
• Developing a prototype early in order that engineers may subsequently develop an MPV much quicker since they are capable of comprehending the business logic

Knowing some fundamental technological terminology can assist when posing improved questions and making improved product choices.

Yes, applications such as Figma allow no-code prototyping to be done easily. It is ideal in making clickable and interactive prototypes to be used to at least do preliminary validation and feedback, and even to present to investors. In case, you require the users to actually use the core functionality (e.g. sign-up or transactions), combine Figma with no-code app builders to have a fully AI-driven MVP.

MVPs can be developed within hours on a variety of no-code and low-code development platforms and tested with actual users on user testing tools. This gives you the chance to prototype fast and your product meets the actual user needs.

AI decreases the cost of scaling, though it does not avoid the unscalable founder efforts. The guidance is elaborated by the phrase "do things that do not scale," which should be applied to the situation by implementing AI to do repetitive tasks, and the founders should work to build high-impact, manual validation and customer relationships. Such startups, which combine AI in product development efficiency with immersive learning, will de-risk their scaling and have more robust foundations.

Both Lovable and Cursor are good solutions to development and deployment of MVPs to test B2B clients. Lovable is particularly accessible to non-technical founders, whereas Cursor is more flexible to those who would like to work with code (including support of AI). Both platforms strive to reduce the established barriers of launching and iterating on an MVP, with the help of AI MVP tools and no-code MVP environments.

It is a matter of your technical background. We recommend two paths.

When you are a non-technical entrepreneur:

• Design: Figma, Marvel
• Build: Lovable, Bubble
• Deploy: Webflow, Lovable
• Test: Maze, UserTesting, Hotjar
• Manage: Trello, Productboard, FigJam

This is the path of focusing on no-code tools that are easy to use, have built-in integrations, and easy to deploy. It enables MVP to be launched quickly and validated quickly without having to code.

In case you are a technical founder or a coder who feels comfortable with AI-assisted code:

• Design: Figma, Marvel
• Build: Windsurf, Cursor, Claude Code, Copilot Agent
• Deploy: Windsurf, Cursor
• Test: Maze, Hotjar, custom event tracking
• Manage: Jira, GitHub Projects, Linear

This combination is fast and flexible, which incorporates AI-based tools to startups and lets you customise and access developer workflows. In the case of both paths, a combination of AI, existing skeletons, and transparent version control boundaries can make the difference between speed and scaling MVP effectively.

Not every MVP constructed using AI MVP tools is similar. Others graduate well due to right decisions made at the beginning. Others hit a ceiling. As an illustration, they are unable to manage the heavy traffic, their infrastructure is not secure and flexible. The reason it is important to revise your MVP in advance of scaling is then. Ask yourself what was assumed or omitted in the initial development, what was hard-coded or not taken care of, etc. The following is what frequently must occur:

• Refactor and review code: The MVP code might require cleaning or rewriting
• Upgrade infrastructure: Early versions may not be scalable. Check traffic restrictions, hosting, database configuration, and cloud configuration
• Introduce security: Introduce authentication, separation of databases and middleware where none exist
• Use technical skills: AI applications facilitate, but it still needs talented engineers
• Thought of architecture: Shift to firm, modular systems
• Get market confirmation: A single buyer is not sufficient and hence demonstrate steady demand before expanding the staff or the expenditure

The same is essential when a minimum viable product that has been rushed through passes to an all-encompassing-scale solution via AI in product development.

Yes, small, agile teams can be assisted with such tools as Windsurf and Cursor not only in the initial stage but also in the distributed teams with the appropriate configuration. These are already capable of collaborating, managing tasks, and sharing project memory, so they can be helpful not only when performing initial validation. In long term work, the size of the team has little bearing other than how the team is organized around the tools:

• Clear project planning
• Proper access control and workflows
• Strong documentation and communication habits
• Stable speed-up of development, even on large projects up to 30%
• Automation of routine engineering tasks
• AI-driven refactoring of legacy code

So it can be said the size of the team does not matter, but how well the team uses the tools.

Backend: Yes, such tools as Windsurf and Cursor are capable of creating front and back end, APIs, and logic. Complexity: It is possible to create quite sophisticated applications, up to 60-70 percent of a full product, though only with a high-quality practitioner who can use the tools in the right direction, since they do not supplant engineering knowledge but only enhance it. Having experience, you can create project structure, styling, security presets, linters, and prompt rules that can allow AI to do the majority of the work with greater reliability. Otherwise, it might appear to be functional but difficult to scale or maintain. Error repair: Inbuilt linters, frequent commit, smaller prompts, and locally test are all good practices that help in fixing errors. This combination of automation and control is important to startups that are interested in developing a product quickly or rapidly iterating.

When you create an MVP in a short time with the help of such tools as Windsurf and Cursor, it is so easy to forget about the security, and it may be dangerous. You may leave API keys in your code, forget to authenticate or leave your database unintentionally. In order to be safe:

• Do not use actual user data, use test data
• Install a safeguard between your application and your data
• Do not post personal links without having the right access
• Request the AI to adhere to the best practices in creating code in terms of security
• Engage a technical individual to be aware of what to inspect and where to search

Speed is not all that goes in creation of MVP today. It is all about being clear, organized and learning quickly. According to the panel discussion, this is what the experts suggest:

Best practices:

• Validate before you build: Talk to real users. Do not test your whole product with your MVP but only one core idea or one feature
• Keep it lean and mean: Do not go wide. Begin with a barebones product (the feature that addresses a real pain point)
• Apply AI intelligently: Do not blindly give AI important design choices. Breadth prepare prompts, divide tasks and tools such as TaskMaster to control flow
• Plan effectively: Even fast builds have a PRD or spec. It provides guidance and eliminates redundancy
• Designing for change: MVPs can be abandoned. Concentrate on education, not excellence
• Security issues early: Provide guardrails, e.g. middle layers, sandbox data, and do not expose credentials
• Test, test, test: Linters, preview, and AI can also be used to find bugs quickly by even a solo developer
• Know when to restart: It is sometimes quicker and safer to write a new application than to maintain a haste MVP
• Introduce a person with sound technical proficiency preferably at the earliest stage to prevent bigger problems in the future

The practices described above can assist any team, including none technical founders, in transforming a mere prototype into a validated startup MVP in a much shorter period of time.

• Fast with cross-platform frameworks and no-code/low-code builders
• Use startups (Windsurf, Cursor, Claude Code, Copilot) to generate and debug code with the help of AI
• Secure codebase and information through encryption, access controls and secrets managers
• Train team on security and incident response plan
• Only licensed and trusted AI tools should be used so that all complies, is reliable and supported

These measures assist in enhancing the speed of delivery without compromising your MVP of B2B SaaS or consumer applications.

You can lean on the vibe coding, with the help of such tools as Windsurf and Cursor, which is complemented with AI agents in an unexpectedly broad scope of operations. The following are things that you can safely do with vibe coding:

• Build full-stack MVPs with working frontends and backends
• Prototyping and refactoring, rapidly develop features and flows
• Automating boilerplate, CRUD operations, basic auth, and dashboards
• Integrating third-party APIs
• Spinning up internal tools or custom dashboards
• Quickly test and refactor AI outputs, rather than just accepting them as pure and trustworthy

Big-scale infrastructure or multi-service orchestration Build a habit of reviewing and refactoring AI outputs, not just accepting them as pure and trustworthy.

Yes, there are tremendous changes in expectations. The MVP in most cases is deliberately small, i.e., geared towards the rapid validation of one fundamental idea. It is not likely to be robust or scalable. Speed, feedback, learning are more important than polish or technical depth. Nevertheless, when using a team that already has ready templates, re-usable infrastructure and rapid alignment, you can certainly have a production-ready MVP in 2 days, particularly in familiar areas. As an example, in our video, we demonstrated a complete prod-ready MVP that was created within less than 40 minutes. The solution is therefore related to the team configuration and preparation. It does not necessarily have to be coarse provided the groundwork is prepared. This is the way numerous startups can have a rapid MVP launch and early traction.

When dealing with non-technical, creative investors, there must be clarity, structure, and alignment in such a position. You are not selling a product but you are establishing faith in your process and vision. And here are how to bring down the stress levels in addition to enhancing teamwork:

• Be specific about your product: Use mockups or illustrations to explain your idea
• Explain your solution to the problem
• Describe your strengths (e.g., user insight, design, vision)
• Be transparent about where you need technical support

Communicate with structure:

• Prepare a simple deck with goals, roadmap, and early results
• Ask how they would support early-stage creative teams
• Mention the tools, advisors, or collaborators you are using

The next step is validation. As fast as you can, get your MVP before real users. Don't wait to polish. Do not rush up to fundraising or scaling. To begin with, present your MVP to the real users, receive feedback, and repeat quickly. Such an attitude will assist you to concentrate on learning what is and what is not working, then you will invest in growth. It can be approached in the following way:

• Start small with a pilot group or early adopters
• Working feedback: active, use interviews, surveys, usage monitoring
• Observing real use, not what people say, and foundational
• Construe the core message on actual user usage
• Refinement: you need to fix the fundamental hair on fire before you scale it out

You cannot just have a good idea and get investors to commit to it without demonstrating traction, clarity and learning on the fly. Investors would like to be sure that your startup will address a very real problem, and run at a fast pace. These are some of the most important tactics to aid you to receive that yes:

• Confirm with actual users and demonstrate traction
• Highlight one clear value
• Speak with clarity and confidence
• Show momentum and learn
• Use your MVP in the pitch
• Be honest and trusting
• Make a clear request

It is at this point where a properly developed minimum viable product can rapidly grow to be a strong fundraising tool, particularly when created with the assistance of modern AI-based MVP development tools.

• Writing and debugging code: with AI, the developers write functions, correct bugs, and refactor faster
• Automating the process, like data cleaning, formatting
• Generating content and emails, like marketing text, reports, and outreach drafts
• Analyzing data to draw insights, summarize trends, create visualizations, or identify anomalies

These applications are now at the core of AI in product development, especially in the deal of rapid prototyping or in the early MVP phase.

Yes we train our teams on the best use of AI tools, because this is critical to developing and integrating it. This is our general approach: Structured training: we use in-house workshops that are dedicated to real-life examples, and we have also organized hackathons to use various tools in practice. Ongoing education and best practice: When tools are brought into production projects we pilot them on internal projects. This allows us to test performance and usability on low risk environment. In our team, we exchange knowledge freely, as well as in the form of working examples, edge cases, and restrictions. Shared and encouraging culture: we allow room to explore. Each team has team members who have done a deeper discovery of certain tools and can lead other individuals. Together with real testing, practical events, open dissemination of knowledge, and team-work workshops, we will make sure our teams will be able to apply startup AI MVP tools not only efficiently, but effectively.

Lovable can create a stripped down version of LinkedIn (profiles, simple feed, job board) without writing any code, but not a complete, scalable social network. In the case of complex apps, use AI tools to help with MVP along with developer support.

When you can do both B2C revenue without compromising your B2B vision and not overworking your team, it can be a savvy means to achieve an early momentum and market understanding. All you need to do is to ensure that you maintain your long-term B2B goals and resource investment in mind. It is at this point that a lean MVP of B2B SaaS or a 2-day MVP of fast B2C testing should be used as a strategic tool of dual market testing.

Nowadays, you do not have to take weeks to roll-out an MVP. Two days would be sufficient to create something functional with the right tools and having a clear goal. We discussed the usage of AI, no-code, and low-code platforms by founders and teams, such as the one we have, to test ideas faster, receive real feedback, and start making progress without overbuilding. And now you can take the same number of days to go between idea and launch.