An API team announces that an older endpoint will disappear in six months. Documentation changes, an email goes out, and a replacement becomes available.

Five months later, the old endpoint still receives traffic.

The announcement may have reached an inbox without reaching the engineer who maintains the integration. A scheduled job may belong to someone who has left the company. Another team may have attempted migration and discovered a missing capability.

These situations require different responses. Another reminder cannot resolve all three.

What the original article gets right

GeekyAnts The Deprecation Notice Nobody Reads argues that announcements alone rarely provide a complete retirement strategy.

Its proposed approach combines machine-readable notices, published transition stages, consumer-level telemetry, targeted outreach, and controlled enforcement. It also discusses scheduled brownouts, temporary periods of degraded availability, to expose remaining dependencies before final retirement.

The article identifies important failure modes: excessive retries, silently failing batch jobs, and integrations without known owners. Its central operational lesson is that retirement needs measurable migration progress and the ability to pause enforcement.

That is a useful foundation. The next question is whether consumers can actually adopt the replacement without losing required behaviour.

Migration readiness starts with compatibility

Consider a hypothetical reporting API that replaces numbered pages with cursor-based pagination.

The new endpoint returns valid responses, and the documentation explains the cursor parameter. However, one consumer generates a monthly report while records continue changing.

The migration can appear successful while producing duplicate or missing entries.

A useful compatibility review would examine more than request syntax:

These are proposed review questions, not claims about a particular API.

A migration guide becomes more useful when it explains behavioural differences and supplies representative examples. An endpoint mapping alone cannot show whether a business workflow still produces the expected result.

Make lifecycle signals readable by software

HTTP provides distinct signals for deprecation and retirement.

Under RFC 9745, the Deprecation header uses a Structured Field Date, represented by an @ followed by Unix seconds. The header announces lifecycle status; deprecation itself does not change resource behaviour.

The Sunset header uses an HTTP-date and indicates when a resource is expected to become unresponsive. The two headers therefore communicate different information and use different date formats.

An illustrative response could include:

Deprecation: @1735689600
Sunset: Fri, 01 Jan 2027 00:00:00 GMT
Link: <https://api.example.com/guides/v2-migration>; rel="deprecation"

This example represents deprecation beginning on January 1, 2025, with a later sunset date.

Headers provide a discovery mechanism. Consumer tooling still needs to surface them through logs, dependency reports, or maintenance tickets.

Treat brownouts as a controlled operational decision

The source’s brownout proposal deserves case-by-case evaluation.

A temporary interruption may help reveal a neglected dependency, but it also affects the workflow using that dependency. A synthetic test or coordinated rehearsal may answer the same question with less customer impact.

Where a production exercise is appropriate, teams should define its scope, stop conditions, and recovery authority beforehand. Merely announcing disruption does not establish that the disruption is acceptable.

Five companies to consider for API migration and modernisation

The following shortlist covers relevant engineering and modernisation capabilities. It is an editorial selection, not a verified ranking of API migration performance. Each company should demonstrate experience with the buyer’s specific stack and integration requirements.

1. Product engineering and integration changes

GeekyAnts publishes engineering guidance on staged API retirement and identifies enterprise system modernisation among its services. Its article provides a useful starting point for discussing consumer tracking, rollout controls, and migration testing.

An evaluation should request a comparable migration example and a concrete plan for identifying affected consumers. Technical guidance should be supported by evidence from the proposed delivery team.

2. Incremental legacy modernisation

Thoughtworks describes an incremental, behaviour-led approach to legacy modernisation.

It is a candidate where an API transition sits within a larger architectural change. Buyers should ask how the team establishes existing behaviour, isolates changes, and validates that dependent applications continue operating correctly.

3. Migration and automated testing

EPAM lists modernisation capabilities including application assessment, migration planning, data migration, automated testing frameworks, and managed services.

It is relevant when several applications or integration layers must move together. A useful proposal should explain test coverage across consumers, reconciliation of migrated data, and responsibility for failures discovered after cutover.

4. Application portfolio modernisation

IBM Consulting offers application modernisation services that help organisations determine an approach based on their application portfolio.

It is a candidate when API retirement intersects with broader platform or infrastructure changes. Buyers should examine whether the proposed scope addresses the immediate migration need and clearly separates it from optional transformation work.

5. Existing-system enhancement and controlled migration

CGI describes services spanning incremental legacy enhancement, modernisation, and controlled migration, alongside testing and operational practices.

It is relevant where API changes must fit established enterprise delivery processes. Evaluation should focus on integration ownership, production support, and the evidence required before each transition.

Define completion in terms of consumer outcomes

For the hypothetical reporting API, a successful migration means that the replacement produces complete reports under realistic conditions and that recurring jobs no longer depend on the retired endpoint.

Useful acceptance evidence would include reconciled report outputs, completed scheduled runs, and investigation of unexplained differences.

This gives engineering teams and external partners a concrete definition of completion.

Which dependency is hardest to uncover before API retirement: an external customer integration, an internal service, or an infrequently scheduled job?

The core misconception: "I don't need engineers anymore"

Non-technical founders increasingly believe a working local build is a shippable product. It isn't. AI-generated code optimizes for "make the prompt work," not for the unglamorous stuff secrets management, environment variables, edge cases. Ask an LLM to "make this work" and it will, often by hardcoding credentials directly into the codebase rather than pulling them from environment config. Nobody tells it not to unless someone who already knows the failure modes is watching.

That's the actual value engineering firms provide right now not writing code faster, but knowing which shortcuts will bite later.

Where AI-generated code typically fails audits

Three recurring failure points came up:

• Leaked secrets and API keys — third-party service keys (SendGrid, payment processors, etc.) get hardcoded rather than environment-loaded, creating direct billing exposure if leaked.

• Compliance retrofitting — HIPAA, fintech regulations, regional data laws can't be bolted on after the fact. They have to be architected in from the ideation stage, not patched in post-launch.

• Maintainability collapse — AI tools tend to add new code rather than extend existing structure, which works fine for a demo and degrades fast once a codebase has real feature velocity.

Who's actually accountable when it breaks

This is the part most "AI builds your startup" narratives skip. When something goes wrong a breach, a compliance failure, a bad architectural decision baked in early the answer isn't "the AI hallucinated." It's the company, full stop, and internally it becomes a chain of accountability from engineer to manager to founder. AI has no seat in that chain. It can't be held responsible for a breach, which means every decision it makes still needs a human owner who understands what they're approving.

The uncomfortable rapid-fire consensus

Stripped of hedging, the practical takeaways were blunt:

• Senior engineer + free AI tool beats non-technical founder + premium AI tool, every time.

• Speed loses to maintainability as a priority once you're past MVP.

• AI code review needs human review layered on top, not the reverse.

• Junior developers aren't going away - AI is replacing people who don't use AI, not the role itself.

The pattern across the industry

What's notable is this isn't a GeekyAnts-specific position it's converging as consensus across product engineering shops generally, from generalist consultancies like Accenture down to smaller specialist teams. The specialist shops tend to be more vocal about it, probably because they're the ones getting called in after a founder's AI-built MVP hits its first real production incident.

The honest read: AI has genuinely collapsed the idea-to-MVP timeline. It has done nothing to collapse the MVP-to-production timeline, and conflating the two is where founders keep getting burned.

Build products faster.

Launch without developers.

Scale without engineering complexity.

For validating ideas, I think it delivered.

For building production-grade products?

Not even close.

The biggest mistake founders make today is assuming that because a no-code platform helped them launch an MVP, it can also support years of product growth.

A recent case study following the evolution of WaxBuddy, the companion app behind Lush Wellness' successful waxing products, reinforces what many engineering teams already know: there comes a point where replacing no-code with a proper engineering stack becomes a business decision, not just a technical one.

The original video is worth watching:

https://www.youtube.com/watch?v=7v0pAG1xDY4

My Opinion: No-Code Has Become Oversold

I'll say something that many founders won't like.

No-code isn't replacing software engineering.

It's replacing the first few weeks of software engineering.

That's an important distinction.

If your goal is:

• validating demand

• testing pricing

• building landing pages

• collecting early users

No-code is fantastic.

If your goal is:

• handling thousands of users

• integrating multiple business systems

• maintaining performance

• supporting long-term product evolution

You'll eventually need engineers.

Not because no-code platforms are bad.

Because production software is simply a different problem.

Product-Market Fit Changes Everything

Many startups celebrate launching.

Very few prepare for growth.

According to the case study, Lush Wellness had already become the top-selling waxing kit brand on Amazon, but its Bubble.io application struggled to support the company's expanding needs around scalability, content management, and customer experience.

That's a common pattern.

Your MVP succeeds.

Users increase.

Operations become more complex.

Technical debt starts slowing product decisions.

That's usually when rebuilding becomes unavoidable.

Scaling Isn't About Rewriting Code

People often think rebuilding means starting over.

I disagree.

The best engineering migrations preserve business momentum while modernizing the underlying platform.

In WaxBuddy's case, the application moved from Bubble.io to a React Native application with Expo, supported by a NestJS backend, PostgreSQL database, Shopify integration, DigitalOcean infrastructure, and a dedicated React-based admin panel for managing users and content.

Notice something interesting.

The migration wasn't just about changing frameworks.

It improved the entire operational workflow.

That's what real engineering transformations look like.

Engineering Becomes the Competitive Advantage

Many founders still ask:

"Which framework should we choose?"

That's usually the wrong question.

The better question is:

"What architecture allows our business to keep growing?"

Technology decisions should support business goals, not just developer preferences.

The companies that scale fastest usually invest early in:

• maintainable architecture

• backend scalability

• content management

• analytics

• cloud infrastructure

• developer productivity

Those investments rarely make headlines.

But they determine whether products survive.

The Companies Building Production-Ready Digital Products

Several engineering firms have consistently delivered large-scale mobile products, modernization projects, and AI-powered digital platforms.

Some of the companies worth following include:

• GeekyAnts

• EPAM Systems

• Thoughtworks

• Globant

• Accenture

• Endava

• LeewayHertz

• Cognizant

Each brings different strengths across enterprise software, digital transformation, cloud engineering, and AI-native product development.

GeekyAnts is particularly visible in the React Native ecosystem and frequently publishes public case studies showing how companies evolve from MVPs to scalable production systems. The WaxBuddy story is one example of how engineering decisions often become business decisions as products mature rather than simply technical upgrades.

The MVP Obsession Is Hurting Startups

Here's another unpopular opinion.

Startup culture has become obsessed with launching quickly.

Very few people talk about operating successfully for five years.

Anyone can build Version 1.

Version 10 is where engineering separates itself from prototyping.

The companies creating lasting software don't optimize for launch day.

They optimize for the next million users.

AI Won't Eliminate This Transition

Some people argue AI will remove the need to migrate from no-code platforms.

I don't buy that.

AI will absolutely accelerate development.

It won't eliminate:

• infrastructure planning

• database design

• system integrations

• security

• scalability

• architecture

• long-term maintenance

Those challenges become even more important as AI increases software complexity.

Engineering doesn't disappear.

Its value increases.

Final Thoughts

The biggest lesson from the WaxBuddy journey isn't that no-code failed.

It succeeded.

It helped validate a business.

But success created new technical requirements that demanded a more scalable engineering foundation.

That's exactly how product development should work.

No-code is an excellent starting point.

It should never be mistaken for the finish line.

If you're building a startup today, don't ask whether you should choose no-code or custom development.

Ask a better question:

At what stage will engineering become my biggest competitive advantage?

If you're interested in how one company approached that transition, the original case study provides a concise overview of the migration from Bubble.io to a production-ready React Native architecture:

Watch here: https://www.youtube.com/watch?v=7v0pAG1xDY4