Why We Built UncannyDevs: The $50k MVP Problem, AI Code Security, and Building Things That Matter

Nasir's path into engineering wasn't traditional. He self-taught Python as his primary language, went through a coding bootcamp, and along the way built a genuine obsession with shipping things people actually use.

During his bootcamp, he built VaccineWA, a Covid-19 vaccine finder app for Washington State. It shipped. Real people used it to find appointments during one of the most stressful periods in recent memory. It won graduating-class honors, but not for being technically clever, but for being useful, under pressure, on a deadline that actually mattered. That experience set a template he has returned to ever since. Building something useful.

That path eventually led Nasir to gener8tor^{1gener8tor — global startup acceleratorOpens in new tab ↗}, a global startup accelerator, where he came on as a software engineer. Five months in, his teammate left for another opportunity. He was handed a half-built enterprise accelerator platform and told it was his now.

What followed was nearly two years as the sole engineer on that platform. Nasir taught himself what he didn't know, then built it, shipped it, and kept it running in production. No safety net. Just documentation, late nights, and an obsessive commitment to getting it right.

That was the day job. Nights and weekends went elsewhere: nonprofits that couldn't afford engineers, friends' startups that needed something real.

Each year, each new project, the same thing kept proving true: the best software comes from people who take ownership, not just of the code, but of the outcome. Building it.

Every project put him next to engineers who'd shipped, failed, and shipped again. A community of like-minded builders started to take shape. Different codebases, same obsession. They shared notes and hard-won lessons. The same principles kept showing up, no matter the project, the client, or the stack. The kind of studio Nasir had always wanted to work with, but never quite found, started to have a name.

UncannyDevs is where all of that lands.

We are an engineering studio. We build for clients and we ship our own products, and we hold both to the same standard.

We build software worth building:

• Product development — full-stack product building from idea to launch. We take ownership of the entire build: architecture, implementation, and delivery. Production-ready software and code you own outright.
• Engineering consulting — architecture reviews, technical strategy, and team augmentation from engineers who have designed and shipped production systems at scale.
• AI / LLM integration — RAG pipelines, autonomous agents, evaluation frameworks, and token-cost optimization that keeps spend sane. Built by engineers who use these tools daily. No demos, no prototypes that fall apart.
• Custom tooling — most teams are duct-taping multiple SaaS tools together that almost do what they need. We build the internal tools, automations, and integrations that fit your workflow exactly.

This is how we show up:

• Integrity, first. Our founding principle. We do right by clients — fair scope, straight answers, and we never take advantage of a team that's trusting us. That means fixed-price when we know what we're building, hourly when we need to figure it out together — you're never paying for us to wander. No surprises, no padded estimates, no change orders that appear out of nowhere.
• You get the builders. No hand-offs, no junior bait-and-switch. The people you talk to are the people writing the code, senior engineers who love this craft and sweat the details. You're never someone's first real project.
• AI accelerates. Engineers decide. AI is a workhorse, not a decision-maker. It speeds up execution; our engineers own the judgment. That's how we ship production-quality results faster, and keep pricing scoped and reasonable.
• Ship over scope. Specs don't ship. Software does. We optimize for working software in customer hands, not exhaustive specs. We ship fast, iterate with you, and never disappear into a black box.
• Long-term thinking. We build like we'll maintain it because more often than not, we do. We're not project-to-project engineers. We like to build long-term relationships with the teams we work with and remain available long after launch.

Building the kind of studio we would want to work with.

Those principles aren't abstract. We founded UncannyDevs on them, and to answer two problems we keep watching hurt good founders. This is the first. Through our founder's work at a startup accelerator, he hears the same story from founders constantly.

"We got quoted $50,000 for an MVP."

"We paid $80,000 and got a bug-ridden app."

"We couldn't afford to add more features because we'd already spent our budget on the initial build."

This is not a niche complaint. It is one of the primary ways early-stage startups die; not because their idea was bad, but because they burned their runway on a build they should have been able to afford.

In 2026, charging $50,000 for a standard MVP is indefensible.

We want to be precise about what that means, because it's not a broad swipe at everyone who codes for a living. Complex, enterprise-grade platforms built for regulated industries with real compliance requirements, multi-tenant architecture, and deep integrations: that work is expensive. As it should be. We know what that scope actually costs.

But a founder with a clear idea, defined scope, and a real budget getting quoted $50,000 for a product that a senior engineer with modern tooling could ship in three to five weeks? That's not a reflection of the work. That's overhead, account management layers, and junior developers being billed at senior rates.

Here's the economics of 2026 that most agencies aren't advertising: a skilled senior engineer using AI like Claude Code or Cursor, builds faster than a large team thats charging triple the cost. The cost basis for excellent software came down dramatically. The invoices at most dev shops didn't follow.

That price gap is a choice. UncannyDevs was built so we could compete and offer excellent software at a lower price.

We are normalizing a fixed-price for a defined scope. Transparent time-and-materials when discovery genuinely requires it. The person you talk to is the person writing your code. Senior execution at the rates the current landscape actually justifies.

The first problem costs founders money. The second can cost them everything. More and more founders are skipping the agency entirely and building with AI tools themselves. Claude, Cursor, ChatGPT, Replit: the barrier to getting a working prototype in front of real users has never been lower in the history of software.

The results are often impressive. We've seen prototypes that would have taken a small team months to produce, built in days by a non-technical founder with good instincts and the right AI assistant.

But here's what we want every founder who has built with AI tools to understand before they go live:

"It works" and "it's secure" are not the same thing.

AI code generation tools are extraordinary at producing functional code. They are significantly less reliable at producing secure code. The gap between those two things is where startups get hurt. Sometimes badly.

This isn't anecdote and we're not talking s**t about people. The data from 2025 and early 2026 is consistent and sobering:

• 45% fail security tests. Veracode's 2025 GenAI Code Security Report tested more than 100 large language models across four programming languages. 45% of AI-generated code failed security tests; AI-written code is also 2.74x more likely to introduce cross-site scripting specifically.^{2Veracode — 2025 GenAI Code Security ReportOpens in new tab ↗} Apiiro's independent research across Fortune 50 enterprises corroborated the broader pattern: critical severity vulnerabilities appeared 2.5x more often in AI-generated code.^{3Apiiro — 4x Velocity, 10x Vulnerabilities (2025)Opens in new tab ↗}

• ~50% contain at least one exploitable flaw. Multiple independent security studies published in early 2025 found that approximately half of all AI-generated code contains at least one security vulnerability. That's up from around 32% in 2023. The tools are getting better at writing code. The security gap is widening, not closing.

• 86% failure rate on cross-site scripting. AI tools fail to prevent XSS vulnerabilities in 86% of tested scenarios. Log injection vulnerabilities appear in 88% of AI-generated cases. These aren't exotic attack vectors; they're the bread and butter of web application exploits.

• 10,000+ new security findings per month. By June 2025, AI-generated code was introducing over 10,000 new security vulnerabilities per month across studied repositories, a 10x increase from December 2024. The volume compounds as adoption grows.

• 10 million+ exposed secrets. Over 10 million API keys, credentials, and tokens were exposed in public repositories in 2025. A significant share traced back to AI-generated code that hardcoded secrets directly in files. Hardcoded credentials are one of the most elementary security failures in existence, and the models keep generating them.

• 58% of developers trust without testing. More than half of developers report trusting AI-generated code outputs without independent security testing. The confidence AI tools project in their output does not correlate with the security of that output.

For every 10,000 lines of AI-generated code in a production system, you're carrying nearly three times the security debt you'd have with human-written code. In a startup context where you're moving fast, working in public repositories, and often handling real user data before security gets the attention it deserves, these numbers have real consequences.

A close friend of Nasir is an AI-startup founder. They built their startup using AI-generated code. They moved fast, shipped early, got traction. The product worked. Users loved it.

Then they got hacked.

An attacker got into their system. Data was nearly ransomed. There was a terrifying window, hours that felt much longer, where they genuinely didn't know how bad it was going to be.

They were extraordinarily lucky. They hadn't yet collected PII from their users. The attacker had almost nothing to actually threaten to 'reveal'. The exposure was real but the damage was contained. They rebuilt some systems, patched the vulnerabilities, kept going.

Most founders in that situation don't get a second chance.

A data breach exposing real user information: emails, payment details, personal records. It doesn't just create regulatory exposure under GDPR or CCPA. It can end the relationship with your users permanently. Startups get one reputation. A security incident at the wrong moment, before you've established enough goodwill to survive it, can be the thing that ends a company that otherwise had every chance.

Their story had a lucky ending. We don't want other founders to need the same luck.

None of that is a reason to stop building with AI. We're not trying to scare you off the thing that's accelerating your launch. Heck, we love when clients come with software pre-built and designs polished. It makes our work easier.

But before you go live: before you start collecting real user data, before you open registrations, before you launch the payment flow. Get a senior engineer to review the code.^{4With a NDA protecting you of course}

Not because you did something wrong. Not because AI tools are bad. Because this is how good software gets built, has always been built and should be built.

You'll make mistakes. It's part of the industry. Senior engineers who believe they've got it all figured out are some of the most dangerous people in any codebase. You get familiar with your own patterns, your own blind spots become invisible, and you stop seeing the thing that's been sitting in front of you for weeks.

Every serious engineering team does code reviews. Not as a gatekeeping exercise. As a craft practice. A second pair of eyes, preferably one that didn't write the original code, catches things with a regularity that should humble every experienced developer.

AI-generated code needs this more, not less. The model has no stake in the security of what it produces. It doesn't know your threat model. It doesn't know what data you're collecting, who your users are, or what an attacker targeting your specific product would look for. A senior engineer who reviews code with those questions in mind provides something the model cannot.

That's the kind of partner we aim to be. Here's who we want to do it for:

Founders who've been quoted something for a MVP that made their stomach drop and want to understand if there's a better option.

Startups that built something with AI and just want senior eyes on it before they ship to real users.

Teams that want the builder in the room, not an account manager who forwards requests to a team they'll never meet.

Engineering leaders who need a trusted technical partner for a specific problem, not an agency relationship.

Organizations building software for social impact who need engineering that takes the work as seriously as they do.

If any of that sounds like you: Let's build something worth building.