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The 5 software development trends that actually matter in 2026 (and what they mean for your startup)

Muhammad Arslan Aslam — Tue, 24 Mar 2026 04:34:43 +0000

84% of developers now use AI tools. Only 29% trust them.

That's not a typo, and it's not cherry-picked. The Stack Overflow 2025 Developer Survey collected 49,000+ responses across 177 countries and landed on the same conclusion the DORA 2025 report reached across nearly 5,000 technology professionals: AI adoption is near-universal, and confidence in AI output is falling. Positive sentiment toward AI tools dropped from over 70% to 60% in a single year. The developers building your software are using tools they increasingly don't believe in.

I keep thinking about a project we took over earlier this year. The codebase had been built in about six weeks, partly with AI assistance, and it worked. Demos ran fine. Investors saw a functional product. Then real users showed up and everything started breaking in ways that were hard to trace, because the code looked right on the surface. Duplicated logic in every module, inconsistent error handling, auth that failed under load. The founder wasn't careless. They'd just moved fast with tools that rewarded speed over structure.

That project cost more to fix than it would have cost to build correctly. And the pattern is getting more common, not less.

This article covers the five trends I think founders actually need to understand this year, grounded in what the research says and what I'm seeing across client work at SociiLabs. Not predictions. Not hype. What's already happening.

AI agents went from "interesting demo" to "actual workflow"

The biggest shift this year isn't that AI writes more code. It's that AI does more work.

In 2024, AI tools were fancy autocomplete. You'd type a function name and Copilot would guess the rest. Useful, but limited. In 2026, we're dealing with AI agents that can take a spec, break it into tasks, write the code, run the tests, and open a pull request. Gartner named AI-native development platforms their top strategic technology trend for 2026 and predicted that 60% of new code will be AI-generated by year's end.

That number feels high to me, but directionally correct. At SociiLabs, we're already generating maybe 30-40% of our boilerplate through AI workflows. Config files, test scaffolding, standard CRUD operations, auth flows we've built dozens of times. The stuff that makes you want to quit programming and become a carpenter.

The research here is actually contradictory. A randomized controlled trial by METR, studying 16 experienced open-source developers across 246 real-world tasks, found AI tools actually increased task completion time by 19% for expert developers working in familiar codebases. The developers themselves thought they were 24% faster. They weren't.

Meanwhile, Microsoft Research ran field experiments across 4,867 developers and found a 26% increase in completed tasks, with less experienced developers benefiting the most.

What does this mean for founders? AI agents are real and they're changing how software gets built. But they're not magic. They make good teams faster and they make bad processes worse. If your engineering setup is already messy, AI will generate mess at machine speed.

When we rebuilt PlayBombhole's scoring engine, a real-time system handling four game modes with sub-300ms latency to TV screens, AI agents handled the repetitive parts. The WebSocket boilerplate. The CRUD for location management. The test scaffolding. But the polymorphic scoring architecture? The decision to build interchangeable rule sets so new game types could be added without touching existing code? That was human judgment. AI doesn't know your product roadmap.

ThoughtWorks captured this well in their Technology Radar Vol. 33. Their CTO noted that "vibe coding has practically disappeared" in favor of what they're calling context engineering: structured approaches to giving AI agents the right information to work with. The skill isn't prompting anymore. It's knowing what context matters.

The quality crisis is here, and most teams aren't ready for it

This is the trend I care about most, because it's the one most likely to burn founders who aren't paying attention.

We're generating more code than ever. The quality of that code is getting worse. AI is actually quite good at certain types of code. The problem is volume without oversight.

The DORA 2025 report, the gold standard for measuring software delivery performance, studied nearly 5,000 technology professionals and found something that should make every founder uncomfortable: AI adoption correlates positively with delivery speed and with higher instability. More change failures. More rework. Longer resolution cycles. Their central question for 2026 is blunt: "We may be faster, but are we any better?"

The code-level data backs this up. A GitClear analysis of 153 million changed lines of code found code duplication increased 4x with AI usage. CodeRabbit found that pull requests containing AI-generated code had 1.7x more issues than human-written code. Research accepted at ICSE 2026 found that 29.1% of Python code generated by AI contains potential security weaknesses.

I see this in client work constantly. A founder comes to us with an MVP that was "80% built by AI." The code runs. It passes basic tests. And underneath, it's a tangle of duplicated logic, inconsistent error handling, and security gaps that would take weeks to untangle. We took over Helm's platform after exactly this kind of situation: passwords stored in plain text, authentication failing intermittently, every fix breaking something else. The previous build had been done fast. It had not been done well.

Gartner's prediction here is alarming: by 2028, prompt-to-app approaches adopted by citizen developers will increase software defects by 2,500%. That's not a typo.

The 77% of developers who told Stack Overflow that "vibe coding" is not part of their professional work have drawn a firm line. Given the data, that's a reasonable place to stand.

What does this mean for you? If you're building a product that needs to scale, handles user data, or processes payments, you cannot ship AI-generated code without serious human review. The code review step isn't overhead. It's the product.

At SociiLabs, every line of AI-generated code goes through our custom PR review pipeline before a human ever looks at it. The pipeline checks for security issues, suggests test cases, flags performance problems. Then a senior developer reviews what's left. This isn't slow. It's how you avoid spending $40K on a rewrite six months later.

If you're building something and feeling uneasy about code quality, that's worth a conversation. We audit codebases regularly and the pattern is almost always the same: speed without structure.

Platform engineering isn't optional anymore

I'll be honest: "platform engineering" sounds like something only big companies need to worry about. It's not.

The DORA 2025 report found that roughly 90% of enterprises now have internal developer platforms. That's ahead of Gartner's prediction of 80% by 2026. The reason is straightforward: AI tools work dramatically better when they operate inside a well-structured environment. High-quality platforms amplify AI's benefits. Low-quality platforms make AI useless or actively harmful.

For startups, this translates to something simpler: your development infrastructure matters more now than it did two years ago. The way your code gets deployed, tested, and monitored is a product quality concern, not just an ops one.

Stack Overflow's survey found Docker adoption surged to 71%, up 17 percentage points in a single year. That's not a trend. That's near-universal adoption. Developers report losing 6+ hours per week to tool fragmentation. High-maturity platform setups report 40-50% reduction in cognitive load for developers.

When we built Navia's AI marketing platform, the architecture decisions we made on day one, containerized deployment, proper CI/CD, automated testing, infrastructure-as-code, weren't because the founder asked for them. They were because we knew the AI features they wanted to build (content generation, brand voice training, multi-platform publishing) would only work reliably inside a disciplined deployment pipeline. The result: 95+ Lighthouse score, 150-300ms API responses, zero technical debt at launch. Four months from kickoff to production.

Gartner says organizations without platform teams will lag in deployment frequency by 80%. I'd put it more plainly: if your deployment process involves someone SSHing into a server and running commands manually, AI-assisted development will make your problems worse, not better.

Supply chain security went from "we should probably worry about this" to actual crisis

This one's less sexy than AI agents, but it might matter more to your business.

Open-source malware detections jumped 73% in 2025, according to ReversingLabs' supply chain security report. The Verizon 2025 Data Breach Investigations Report found third-party involvement in breaches doubled to 30% of all confirmed breaches. Software dependencies, build pipelines, and container images now account for 75% of supply chain attack entry points.

And here's the part that connects directly to the AI quality problem: nearly one-third of AI-generated Python code contains security weaknesses. Only 24% of organizations conduct comprehensive security evaluations of AI-generated code. So we have AI generating more code, that code having more security issues, and most teams not checking for those issues. This is how breaches happen.

The regulatory environment is catching up fast. The EU's Digital Operational Resilience Act took effect in January 2025 for financial services. The U.S. Department of Defense launched its SWFT Initiative for secure software procurement. JPMorgan Chase's CISO published an open letter calling for supply chain risk to be treated as systemic rather than a niche application security concern.

For founders, the practical takeaway is this: know what's in your software. If your team can't tell you exactly which open-source packages are in your production build, which versions they're running, and whether any have known vulnerabilities, you have a problem. This isn't paranoia. Black Duck's OSSRA 2025 report found the average codebase contains 581 vulnerabilities.

When we set up Helm's infrastructure after the emergency migration, one of the first things we implemented was automated dependency scanning. Not because it was cool. Because the previous build had packages with known CVEs sitting in production, and nobody knew they were there. That's the kind of thing that ends up in a breach notification email.

The developer workforce is being reorganized around AI

Software engineering job postings have dropped 49% from their peak. CS program enrollment has dropped 20% at universities. The global developer population is still growing, but the growth rate has decelerated from 21% to 10%, according to SlashData's Developer Nation survey.

These numbers don't mean software is dying. They mean the economics of building software are changing.

The Forrester 2026 predictions report projects that time to fill developer positions will double. The developers companies need now look different than they did three years ago. Gartner's "tiny teams" prediction envisions small groups of senior developers paired with AI producing the output that previously required much larger teams.

I'm seeing this play out in real time. Two years ago, a project like Navia's AI marketing platform would have required a team of 6-8 developers working for 8-10 months. We built it with a smaller team in 4 months. Not by working harder. By having AI handle the parts that used to require warm bodies writing boilerplate, while our senior engineers focused on architecture, security, and the AI integration layer that is the actual product.

The tension shows up most at the junior level. GitHub reports 80% of new developers use Copilot within their first week. Which sounds great until you realize the traditional path of learning by writing bad code, debugging it, and understanding why it was bad is being shortcut. The Atlassian State of DevEx 2025 survey captured the paradox: developers report saving 10+ hours weekly from AI, but losing 10+ hours weekly to organizational inefficiencies. Net productivity gain: approximately zero.

The developer who succeeds in 2026 is the one who can evaluate AI output critically, architect systems that hold up under real traffic, and decide which parts of a product actually need human judgment. For founders hiring developers or choosing an agency: stop asking "do you use AI?" Everyone uses AI. Start asking "how do you catch the mistakes AI makes?" That question tells you everything.

So what does this actually mean for your startup?

Here's the short version of all five trends:

AI is generating more code than ever, but the code needs more oversight than ever. The tools are powerful. The risks are proportional. The teams and processes you build around these tools matter more than which tools you pick.

If I had to give a founder one piece of advice for 2026, it would be this: invest in quality infrastructure before you invest in speed. A clean deployment pipeline, automated security scanning, proper code review, structured testing. Boring stuff. The stuff that means your AI-assisted development actually produces a product you can trust.

We learned this the hard way at SociiLabs. When we first started integrating AI into our workflow, our output went up and our bug rate went up with it. It took us months to build the review processes, the custom PR agents, the testing pipelines that turned raw AI output into production-grade code. Now we're faster and more reliable. But the "and" part took real work.

Where SociiLabs fits in all of this

You've read 2,000+ words of my opinions backed by other people's research. Fair to explain why I wrote them.

SociiLabs builds software for startups. We use every AI tool I mentioned in this article. We also built the review systems, the PR agents, and the security pipelines that keep those tools from quietly wrecking production code. That combination, AI speed with human quality control, is what we sell. I'm not going to pretend otherwise.

But I also wrote this because I spent months reading these reports and none of the coverage I found was written for the person who actually needs to make decisions based on them. The founder with a $50K budget and a 4-month runway. The non-technical CEO trying to figure out if their dev team is doing things right. The operator who keeps hearing "AI changes everything" and wants to know what specifically it changes for their product.

If that's you and you want to talk specifics, here's our calendar. If you just needed someone to read the DORA report and the Stack Overflow survey so you didn't have to, happy to be that person. Either way, the trends above aren't slowing down, and the gap between teams who understand them and teams who don't is getting expensive fast.

The $40K Code Review Tax: Why Manual Reviews Are Bleeding Your Engineering Budget

Muhammad Arslan Aslam — Fri, 05 Dec 2025 03:50:00 +0000

Your senior engineers spend 8-12 hours per week reviewing code. At $150K salary, that's $9,600/year per engineer just on reviews.

For a team of 5 seniors? That's nearly $48,000 annually. And here's the kicker: most of that time isn't catching the bugs that matter.

The Real Cost Nobody Talks About

I run a development agency. We build software for startups and SMBs. Last quarter, I tracked how much time our senior engineers actually spent on code reviews.

The average: 9.5 hours per week. At their billing rate, that's over $10,000 per engineer per year just reviewing code.

Here's what bothered me: 70% of the review comments were about formatting, naming conventions, and style inconsistencies. Things a linter could catch. Things that don't matter.

Only about 20% of comments caught actual logic errors or potential bugs. The stuff that actually breaks in production.

And here's the part that kept me up at night: even with all those hours spent reviewing, bugs still shipped. Not because anyone was lazy or incompetent. Because by the time you're reviewing the 4th PR of the day, you're scanning, not thinking.

Breaking Down the $40K Tax

Here's what manual code reviews actually cost your team:

Direct time costs:

Junior engineer reviews: 4-6 hours/week
Mid-level engineer reviews: 6-8 hours/week
Senior engineer reviews: 8-12 hours/week

For a typical 10-person engineering team (3 juniors, 4 mids, 3 seniors):

Juniors: 3 × 5 hours × 52 weeks × $100K salary = $15,000/year
Mids: 4 × 7 hours × 52 weeks × $130K salary = $37,440/year
Seniors: 3 × 10 hours × 52 weeks × $150K salary = $28,800/year

Total: $81,240 per year on code reviews alone.

But wait—it gets worse.

The Opportunity Cost You're Not Measuring

Every hour your senior engineer spends checking if someone used const instead of let is an hour they're not architecting your next major feature or mentoring your mid-level engineers.

Do the math for a typical team with 3 senior engineers spending 30 hours per week combined on reviews:

That's 1,560 hours per year. Nearly a full-time senior engineer's worth of capacity. You could hire another senior for less than the opportunity cost of manual reviews.

Quality Issues That Slip Through Tired Reviewers

We did an analysis. Pulled 500+ PRs from our own repos and a few open source projects we contribute to. Categorized every review comment.

The breakdown:

73% were about formatting, naming, style issues
18% caught actual logic errors or potential bugs
9% were bikeshedding about architecture decisions that should've happened before the PR

The problem is obvious: human reviewers are pattern-matching machines. We're great at spotting "this doesn't look right." We're terrible at sustained deep analysis—thinking through every execution path, considering edge cases, spotting race conditions.

Why? Cognitive fatigue. By your 4th PR of the day, you're scanning, not analyzing.

The Review Fatigue Problem

We started tracking review quality at SociiLabs six months ago. Simple metric: how thorough were reviews at different times of day?

Morning reviews (before 11 AM): detailed feedback, clarifying questions, alternative approaches suggested. Average review time: 25 minutes.

Evening reviews (after 4 PM): "LGTM" on 400-line PRs. Average review time: 3 minutes.

Same reviewers. Same types of PRs. Completely different quality.

One of our clients is a fully remote company. Their PRs sit for 18 hours on average waiting for review because reviewers in different timezones are always catching them at the end of their day. When the review finally comes, it's surface-level.

Their developers spend 2-3 hours per day context-switching back to old PRs. That's 12-15 hours per week per developer just reloading context.

The Distributed Team Multiplier

Async code reviews kill developer flow. Here's what happens:

Developer submits PR
Moves to new task
Gets review feedback 6-8 hours later (different timezone)
Stops current task
Reloads mental context for old PR
Makes changes
Repeat

That context switching adds up. For remote teams, it's often 2-3 hours per day per developer.

What Could You Build Instead?

Let's get specific. If you freed up 50% of senior engineering review time, what does that actually unlock?

For a 3-person senior team: 15 hours/week freed up = 780 hours/year. That's a complete checkout flow optimization, or a mobile app MVP, or 3-4 major feature releases.

For a 5-person team: 30 hours/week = 1,560 hours/year. That's an entire customer analytics platform. Or the enterprise features that unlock your next $500K in ARR.

Your senior engineers didn't join your startup to check indentation. They joined to solve hard problems and build something that matters.

The Sales Pitch

Look, I'm not pretending this is purely educational. We built an AI-powered code review agent at SociiLabs because we had this exact problem.

We tried everything: GitHub Actions, linters, review checklists, rotating review responsibilities. Nothing fixed the core issue. Humans are good at pattern matching but bad at sustained deep analysis. And code review needs both.

So we built an agent that handles both:

Catches style and formatting instantly
Actually analyzes logic, edge cases, potential bugs
Runs 24/7, no timezone issues, no fatigue
Frees up your senior engineers to do senior engineer work

We're launching it as open source in a few weeks. Not because we're altruistic—because we think more teams will adopt it if it's open source, and we can build a business around support and hosted versions.

What's Next

This is the first post in a series about code review costs and how we're fixing it:

Coming next: The psychological cost of review culture (why your juniors are scared to ship)
How AI code review actually works (and what it still gets wrong)
Case study: One startup cut review time by 60%

If you're tired of burning engineering budget on code review overhead:

Track your costs – Time track for one week. The numbers will shock you.
Star our GitHub repo – We'll notify you at launch: [link coming soon]
Book a call – Want to audit your code review process? Drop me a message via the link on my profile.

The $40K code review tax is optional. Most teams just don't know they're paying it.

Web App or Mobile App? Choosing Your MVP Platform

Muhammad Arslan Aslam — Tue, 02 Dec 2025 00:50:19 +0000

Last Tuesday, a founder asked me: "Should I build a mobile app or a web app first?"

I asked her three questions:

Where are your users when they need your product most?
Can they accomplish the core task on a laptop?
Do you have 16 weeks or 8 weeks to validate your idea?

She went quiet. Then: "I haven't thought about any of that."

Most founders haven't. They just know they need an app—capital A, as if "app" only means one thing.

Let's fix that, because this decision will either accelerate your startup or drain six months before you realize you chose wrong.

The Unsexy Truth About Platform Choice

Here's what nobody tells you: The "right" platform isn't about what's trendy or what your competitor built. It's about where your specific users need to solve their specific problem.

A meditation app that expects users to sit at their desktop three times a day? Dead on arrival.

A B2B analytics dashboard that sales teams only check from their phones? Also dead.

The platform isn't a preference. It's a constraint dictated by user behavior.

When Mobile Makes Sense (And When It Doesn't)

Mobile apps are seductive. They feel "real" in a way web apps don't. You can hold them. They have an icon. Your mom can find them in the App Store.

But mobile is expensive and slow. Here's when it's worth it:

Your product requires mobility

Not "would be nice to have on mobile." Requires it.

Examples that require mobile:

Ride-sharing (obviously)
Fitness tracking (people don't run with laptops)
Food delivery (customers order from anywhere)
Field service management (technicians work on-site)
Location-based social apps (the whole point is you're out in the world)

Examples that don't require mobile (despite what founders think):

Most B2B SaaS (decision-makers work at desks)
Project management tools (detailed work needs screen space)
CRM systems (sales teams can wait until they're at a computer)
Analytics dashboards (complex data needs real estate)

You need native device features

The camera. GPS. Push notifications. Biometric authentication. Offline functionality that actually works.

If your core value proposition depends on these, mobile is non-negotiable.

But be honest with yourself. Do you need the camera, or do you just think it would be cool to have? Because "cool" costs weeks in development time.

Your users are mobile-first (actually)

Not "they use their phones a lot." Everyone uses their phones a lot.

I mean: they're solving this specific problem primarily from their phones, and forcing them to a desktop is friction you can't afford.

Consumer apps targeting Gen Z? Mobile-first makes sense.

Enterprise software for finance teams? They're on desktops with three monitors.

You're competing in an app store

Sometimes the distribution channel IS the strategy. If your users expect to find solutions in the App Store or Google Play, you need to be there.

But remember: App Store discovery is brutal. Getting featured is lottery-odds. Organic downloads without marketing are basically zero.

A web app can be discovered through Google, shared via link, and accessed instantly with no install friction. Don't underestimate that.

The Web App Case (Stronger Than You Think)

Web apps get dismissed as "less serious" than mobile apps. This is nonsense.

Slack built a $27B company on a web app. Notion became a household name with web-first. Linear, Figma, Airtable—all web-first, mobile later.

Here's when web wins:

Your MVP needs to ship in weeks, not months

Building a mobile app means:

Developing for iOS (Swift/SwiftUI)
Developing for Android (Kotlin/Jetpack Compose)
Or building cross-platform (React Native/Flutter) and fighting framework limitations
Submitting to app stores and waiting for approval
Dealing with versioning, updates, and review cycles

Building a web app means:

One codebase
Deploy whenever you want
Update instantly for all users
No approval process
Iterate based on feedback the same day

For MVPs, this velocity is everything. You're trying to learn if your idea works, not create the perfect product.

We recently worked with a founder on a contractor management platform. They initially wanted mobile apps. We asked why. "Because our competitors have apps."

We talked them into web-first. We built and launched in 8 weeks. They got early users, learned what features actually mattered, and are now building a mobile companion app for the one feature that needs it: time tracking on job sites.

If they'd gone mobile-first? 16 weeks, and half the learning because updating the app requires app store approval.

Complex interfaces or data-heavy workflows

Phones have 6 inches of screen space. Some tasks need more.

Financial dashboards, spreadsheet-heavy tools, design software, code editors, complex configuration interfaces—these fight mobile constraints instead of embracing them.

A responsive web app gives you desktop power when users need it and mobile access when they don't.

You're targeting businesses, not consumers

B2B users work differently than B2C users.

They're at desks. They have workflows. They need integrations with other tools. They want to open fifteen tabs and toggle between them. They're doing focused work that requires real estate and a real keyboard.

Yes, they'll eventually want mobile access for certain tasks. But if you make them download an app before they can even try your product, you've added friction to an already-complex B2B sales process.

A web app with a clean, bookmarkable URL beats "download our app" every time in enterprise sales.

Budget and timeline matter

Let's talk reality.

A native iOS app from a competent team: several months for an MVP

A native Android app: another several months

Both platforms: significant time investment (or you go cross-platform and deal with framework compromises)

A responsive web app that works everywhere: 6-10 weeks typically

Those numbers aren't arbitrary. Mobile development is just more complex because you're building for multiple platforms or dealing with cross-platform framework overhead.

If you're bootstrapping or pre-seed, that timeline difference is the difference between shipping and not shipping.

The Cross-Platform Middle Ground (Proceed With Caution)

"Why not React Native or Flutter? Then I get both platforms for the price of one!"

In theory, yes. In practice, it's complicated.

Cross-platform frameworks have gotten good. React Native powers Facebook, Instagram, Shopify. Flutter powers Google Ads, Alibaba, BMW. These aren't toys.

But they come with trade-offs:

You're fighting framework limitations

Native iOS and Android features take time to get implemented in React Native/Flutter. Sometimes they never do. You're always waiting for the framework to catch up.

Need the latest iOS feature Apple just announced? Native developers get it immediately. Cross-platform developers wait months for library support.

The 80/20 rule applies

80% of your app works great cross-platform. The other 20%—the weird edge cases, the platform-specific UX expectations, the native integrations—costs 80% of your development time.

Experienced mobile developers can navigate this. But if you're hiring an agency or junior developers, expect headaches.

Performance isn't quite native

For most apps, this doesn't matter. But if you're building something graphics-intensive or performance-critical, you'll notice the difference.

When cross-platform makes sense:

You need mobile (not web) but can't afford two native apps. Your app doesn't rely heavily on cutting-edge platform features. You have experienced React Native or Flutter developers. Your app is utility-focused, not trying to feel "premium" or compete with highly-polished native apps.

For MVPs, I'm skeptical. You're adding framework complexity when you're trying to learn fast. But if you're confident mobile is the right call and you need both platforms, it's a reasonable compromise.

The Decision Framework

Stop guessing. Use this:

Start with your core user behavior

Where are users when they need to solve the problem you're solving?

At their desk working? → Web On the go constantly? → Mobile Could be either? → Web first, mobile later.

Identify your must-have features

Make a list. Be ruthless. Not "nice to have"—must have for v1.

Any of these mobile-only?

Camera/photo capture
GPS/location tracking
Push notifications for time-sensitive actions
Offline functionality
Biometric authentication

If yes → Mobile app If no → Web app is probably faster and cheaper

Consider your distribution strategy

How will users discover you?

App Store/Google Play discovery → Mobile SEO and content marketing → Web Direct sales and demos → Web (easier to demo, no install friction) Viral sharing/invites → Web (links beat "download this app").

Run the economics

Time to MVP:

Web: 6-10 weeks
Mobile (one platform): 10-14 weeks
Mobile (both platforms): 14-20 weeks

Maintenance and updates:

Web: Deploy anytime, instant updates
Mobile: App store approval, versioned updates, user update friction

Be honest about your timeline. Ambitious founders always underestimate how long mobile takes.

Accept that you might need both eventually

This isn't web OR mobile forever. It's web or mobile FIRST.

Most successful products end up with both. The question is sequencing.

Slack started web. Then mobile. Instagram started mobile. Then web. Uber started mobile. (They had to.)

The pattern: start where your users are, nail the core experience, then expand to other platforms once you've proven the concept.

Don't try to be everywhere at once. You'll ship slower and learn less.

What About Progressive Web Apps?

Someone always asks about PWAs—Progressive Web Apps that work like native apps but run in the browser.

The promise: Best of both worlds. Web development speed with app-like features.

The reality: Promising but limited.

PWAs can do:

Work offline
Install to home screen
Send push notifications (on Android and some browsers)
Access camera and location
Feel app-like with proper UX

PWAs can't do:

List in the App Store (Apple doesn't allow it, really)
Access certain native features
Match native performance for complex apps
Get the "legitimacy" signal of being a "real app"

For certain use cases—especially consumer web apps that want app-like features without the overhead—PWAs are great.

But if your strategy depends on App Store distribution or advanced native features, PWAs won't cut it.

Real Talk: What We Actually Recommend

At SociiLabs, most of our MVP projects start with web apps. Not because we don't build mobile (we do), but because web gives founders the fastest path to learning.

Here's our usual recommendation flow:

Phase 1: Web MVP (6-10 weeks)

Build the core functionality as a responsive web app. Launch fast. Get real users. Learn what actually matters versus what you thought would matter.

Most features you think are critical? They're not. You learn this by shipping and watching what users actually do.

Phase 2: Iterate on web (1-3 months)

Fix what's broken. Add what's missing. Find product-market fit. Build out the features that drive retention and revenue.

This is so much easier on web. No app store approvals. No version fragmentation. Just ship.

Phase 3: Mobile companion (if needed) (8-12 weeks)

Once you know what works, build a mobile app that focuses on the specific use cases where mobile actually adds value.

Not "our whole product but on mobile." A focused mobile experience for the workflows that benefit from mobility.

Example: We built a project management tool for construction companies. Web app first. They used it for planning and reporting. Then we built a mobile app specifically for job site updates and photo documentation. The mobile app does three things really well. The web app does everything else.

That's the pattern. Prove it works, then expand strategically.

Not Sure Which Way to Go?

We build both web apps and mobile apps at SociiLabs, so we don't have a dog in this fight. What we care about is helping you make the right call for your specific situation.

If you're stuck on this decision, we're happy to talk it through. No sales pitch, no commitment—just a conversation about your users, your timeline, and what actually makes sense.

We use AI-assisted development to move faster than traditional agencies, which means we can typically deliver MVPs in 6-10 weeks instead of the usual 4-6 months. But speed doesn't matter if we're building the wrong thing on the wrong platform.

So before we talk about timelines or scope, we'll ask the annoying questions: Who's your actual user? Where are they when they need this? What are you really trying to learn with this MVP?

Because here's the thing: most founders who come to us wanting mobile apps actually need web apps first. And the ones who need mobile usually need a more focused version than what they're imagining.

Our job isn't to build what you ask for. It's to help you figure out what you actually need, then build that really well.

Want to talk about your project? Book a time here. We'll give you our honest take.

The Bottom Line

Web or mobile isn't about technology preferences. It's about user behavior, business constraints, and strategic sequencing.

Ask yourself:

Where are my users when they need this product?
What features are actually essential for v1?
How fast do I need to learn if this works?
What can I realistically build and maintain?

Answer those honestly, and the platform choice becomes obvious.

And if it's not obvious? That probably means web-first is the safer bet. You can always build mobile later. You can't get back the months you spent building the wrong thing.

What's your take? Have you launched web-first or mobile-first? What did you learn? Drop a comment—we'd love to hear what worked (or didn't) for your project.

What is the 40-20-40 Rule in Software Engineering?

Muhammad Arslan Aslam — Fri, 28 Nov 2025 03:39:01 +0000

Here's a question I get from founders at least once a week: "Why is my development team so slow?"

Usually, they're not slow. They're just drowning.

Drowning in feature requests from customers who threaten to churn. Drowning in technical debt that's piling up. Drowning in half-baked ideas that might be brilliant or might be distractions. And somehow, they're supposed to ship the next big feature that'll unlock the next funding round.

Welcome to startup engineering. Where everything is urgent, nothing is planned, and you wonder why you're rebuilding things every six months.

There's a better way. Actually, there are two better ways, and they're both called the 40-20-40 rule. Same name, different approaches, but they solve the same problem: the chaos of building software without a framework.

Let me explain both, because depending on where your startup is, you need one or the other. Or both.

The Two 40-20-40 Rules: Process vs. Product

The Process Rule breaks down how you should spend time on any given project:

40% on planning and requirements
20% on actual development
40% on testing and iteration

The Product Rule breaks down how your team should allocate time across your roadmap:

40% on innovation (new products, markets, capabilities)
20% on research and technical backlog (maintenance, exploration, paying down debt)
40% on loyalty (existing customers, improvements, bug fixes)

Different frameworks. Same insight: most startups are doing this backwards.

You're probably spending 80% of your time coding new features, 15% firefighting bugs, and 5% pretending you'll plan things properly next sprint.

I know because we used to do the same thing. Then we watched projects implode, customers churn, and technical debt compound until the entire codebase felt like a house of cards.

Let's break down both rules and figure out which one your startup actually needs right now.

The Process Rule: Why Your Last Feature Took Three Times Longer Than Expected

Last month, a founder called us in a panic. Their development team had spent six weeks building a new dashboard. It was finally "done." They showed it to three customers. All three customers were confused.

"This isn't what we asked for."

The founder was furious. "I gave them detailed specs!"

I asked to see the specs. It was a two-paragraph Slack message and a hand-drawn wireframe.

That's not specs. That's a vague idea. And building software from vague ideas is why your features take forever and still come out wrong.

The 40% Planning Phase: Where Startups Usually Fail

Here's what actually needs to happen before anyone writes code:

Requirements gathering - Not "I want a dashboard." What data? For whom? What decisions will they make with it? What happens if the data is wrong?

User research - Talk to actual customers. Not "we think users want this." Go ask them. Five conversations will save you five weeks.

Technical planning - Architecture decisions. Database schema. API design. The stuff that's expensive to change later.

Scope definition - The hardest part. What's in v1? What's in v2? What's never? Founders hate this because every feature feels critical. Do it anyway.

Does this take 40% of project time? On a four-week feature, that's almost two weeks before code gets written.

I can hear the objections already: "We're a startup! We need to move fast! We can't spend two weeks planning!"

Fine. Don't. Then spend six weeks building the wrong thing, another three weeks rebuilding it, and two more weeks after that fixing bugs. That's eleven weeks instead of four.

We've run the numbers on our own projects. Features with proper planning phases take, on average, 60% less time than features where we jumped straight into code. Not because planning magically makes coding faster—because it eliminates the rewrites.

One of our clients came to us with a half-built product. They'd been working on it for eight months. Still wasn't done. We looked at the code and realized the problem: they'd changed their mind about core features four times during development. Each time, they just bolted new code onto the old code. The result was an unmaintainable mess.

We spent two weeks just planning a rebuild. Talking to their customers. Understanding the actual requirements. Designing a proper architecture. Then we built it in six weeks. Total time: eight weeks to replace eight months of flailing.

That's the power of planning. It feels slow. It's actually the fastest way forward.

The 20% Development Phase: Where the Magic Happens (If You Planned Right)

This is the part everyone romanticizes. Founders in hoodies shipping code at 2am. The hustle. The grind.

Here's the truth: if you're grinding, you planned wrong.

When you've done the planning properly, development is execution. You know what you're building. You know how it should work. You're solving implementation problems, not "wait, what are we actually making" problems.

This doesn't mean coding is easy. You're still solving hard technical challenges. But you're not also simultaneously figuring out requirements, debating product direction, and second-guessing architectural decisions.

This is where modern tooling actually earns its keep. We've invested heavily in eliminating the repetitive parts—intelligent autocomplete that understands context, AI that helps think through architectural trade-offs, starter templates that give us the boring infrastructure out of the box.

A feature that used to take two weeks now takes four days. Not because we're cutting corners—because we've automated the parts that don't require creative thinking.

But even without AI tools, proper planning makes development faster. You write code once instead of three times.

The 40% Testing Phase: Where Startups Usually Quit

"We'll test it in production."

Every time a founder says this, a developer somewhere quits their job.

Look, I get it. You're racing against a funding deadline. You're bleeding cash. You need to ship. Testing feels like a luxury.

It's not. It's the thing that separates "barely works" from "actually works."

Here's what the testing phase actually includes:

Integration testing - Making sure all your components work together, not just individually.

User acceptance testing - Putting it in front of actual users before you fully launch. You'll be shocked how many assumptions you got wrong.

Performance testing - Does it work with real data? What happens when you get a traffic spike?

Security review - Finding vulnerabilities before hackers do.

Bug fixing and iteration - Users find issues, you fix them, repeat until it's stable.

Documentation - So your team doesn't forget how anything works three months from now.

Does this take 40% of project time? Yes. And it's worth every hour.

We had a client who pushed to launch early. "We'll fix bugs after launch." We pushed back. We finished testing. We launched when it was ready.

Post-launch support tickets: 12 in the first month.

Another client who insisted on launching immediately without proper testing. Post-launch support tickets: 147 in the first month. Their team spent six weeks doing nothing but firefighting. No new features. No improvements. Just fixing things that should've been caught before launch.

Which scenario costs more? Which one builds customer trust?

The 40% testing phase isn't overhead. It's how you ship software that doesn't destroy your reputation.

The Product Rule: How to Stop Building Random Features

Now let's talk about the other 40-20-40 rule. This one's about resource allocation across your entire product roadmap.

Most early-stage startups operate like this:

A customer threatens to churn → emergency feature
Competitor ships something new → panic, build it too
Founder has an idea → drop everything, build it
Technical debt piling up → ignore it until systems start failing

There's no strategy. Just reaction. And you wonder why your product feels scattered and your team is burned out.

The Product Rule gives you a framework:

40% Innovation: Building the Future

This is new products, new features, new markets. The stuff that grows your business.

For early-stage startups, this might be 100% of what you think you should focus on. New features equal growth, right?

Wrong. If this is all you do, you'll end up with a bloated product that does a hundred things poorly instead of ten things well.

But you do need to invest here. This is how you stay competitive. How you unlock new customer segments. How you differentiate from competitors.

What innovation looks like:

Building that major feature that opens a new use case
Expanding into a new market segment
Adding capabilities that create a moat around your product
Experimenting with new technologies that could be game-changers

One of our clients was stuck at 50 customers for a year. Their product worked, but it only served one narrow niche. We helped them build out features for a second use case—same core product, different application. Six months later, they were at 200 customers.

That's innovation. Not building random features—building strategic features that unlock growth. Stuck at a growth plateau? Let's figure out what's actually blocking you.

20% Research and Technical Backlog: Paying Your Debts

This is the stuff founders hate because it doesn't directly generate revenue.

Refactoring messy code. Upgrading dependencies. Researching new approaches. Addressing technical debt. Improving developer tooling. Exploring technologies that might be useful later.

Most startups allocate 0% here until something breaks catastrophically.

Then they spend three months doing nothing but fixing technical debt. Then they go back to ignoring it. The cycle repeats.

Here's the thing about technical debt: it compounds. That hacky solution you shipped at 2am to meet a deadline? It's fine... until you need to build something related. Then it slows you down. Then it breaks. Then you need to rebuild it properly, except now it's intertwined with ten other systems.

We took over a project from another agency last year. The codebase was two years old. It was so full of technical debt that adding a simple feature took two weeks. Not because the feature was complex—because touching anything meant dealing with layers of poorly written code.

We spent a month refactoring. The founder was nervous. "We're paying you to not ship features?"

After refactoring, feature development sped up by 300%. A feature that would've taken two weeks now took three days. That one month of investment paid for itself ten times over.

This 20% isn't wasted time. It's an investment in your future velocity.

40% Loyalty: Keeping the Customers You Have

This is improvements for existing customers. Bug fixes. Small features they've been asking for. Polish. Making your product better at what it already does.

Early-stage founders often ignore this. "We need to acquire customers, not make existing ones slightly happier."

Except customer acquisition is expensive. Churn is expensive. And you know what drives churn? Promising a great product, delivering a buggy one, ignoring customer feedback, and then wondering why they leave.

What loyalty investment looks like:

Fixing the bugs that annoy power users
Improving the onboarding experience so new customers don't get confused
Adding small quality-of-life features that existing customers keep requesting
Optimizing performance so your product feels fast and responsive
Better documentation and support resources

We have a client who was growing fast but churning 15% of customers every quarter. We dug into why. The product worked, but it was rough around the edges. Confusing UI. Slow load times. Missing features that seemed obvious.

They spent one quarter focused almost entirely on loyalty. Bug fixes. Performance improvements. UI polish. Small features that existing customers wanted.

Churn dropped to 4%. Revenue grew faster than before because they weren't just acquiring customers—they were keeping them.

Retention is cheaper than acquisition. Don't neglect the customers you already have.

Which Rule Does Your Startup Need?

Here's how to figure it out:

You need the Process Rule (40% planning, 20% dev, 40% testing) if:

Your features consistently take longer than estimated
You keep rebuilding things because requirements changed
Bugs keep slipping into production
Your team spends more time fixing than building
Customer feedback is consistently "this isn't what we needed"

You need the Product Rule (40% innovation, 20% backlog, 40% loyalty) if:

Your roadmap is chaos—just reacting to whoever screams loudest
You're shipping tons of features but growth has plateaued
Technical debt is slowing everything down
Churn is high despite new customer acquisition
Your team is burned out from context-switching

Honestly? Most startups need both.

The Process Rule tells you how to execute individual projects efficiently. The Product Rule tells you which projects to prioritize.

How We Actually Apply This at SociiLabs

Full transparency: we don't follow these rules religiously. They're frameworks, not laws.

A simple landing page? That's more like 20-40-40 on the process side. Less planning needed.

A complex B2B product? Might be 50-15-35. Even more planning because the stakes are higher.

For product allocation, it varies by stage. A pre-product-market-fit startup might be 70-10-20. You need to innovate fast to find what works. A post-PMF company growing aggressively might be 30-20-50. You've found what works; now make it bulletproof.

The point isn't to hit exact percentages. The point is to be intentional about where time goes.

When a client comes to us wanting to "move fast," our first question isn't "how fast can we code." It's "what happens if we build the wrong thing fast?"

When they want to skip testing, we show them the math: two weeks of testing now or two months of firefighting later.

When they want to build every feature every customer asks for, we ask: "Which of these actually moves the needle on your core metrics?"

What This Actually Means for Your Startup

If you're a founder reading this, you're probably thinking: "This sounds great in theory, but we're fighting for survival. We don't have time for frameworks."

Here's the thing: you don't have time NOT to have a framework.

Every hour spent building the wrong feature is an hour you can't get back. Every bug that ships to production is customer trust you have to rebuild. Every quarter spent reacting instead of planning is a quarter your competitors are pulling ahead.

The 40-20-40 rules aren't about moving slower. They're about moving deliberately.

We've worked with dozens of startups. The ones that succeed aren't the ones that move fastest. They're the ones that move efficiently. They plan properly. They test thoroughly. They balance innovation with maintenance. They keep customers happy while still pushing forward.

Here's what we recommend for early-stage startups:

Start with the Process Rule on your next feature. Actually plan it. Write proper requirements. Talk to customers. Design the architecture. Then build it. Then test it properly. Compare the timeline and outcome to your last five features. You'll see the difference.

Then look at your product roadmap. Be honest about where your time is going. Are you spending 90% on new features and ignoring everything else? Try shifting to 50-20-30 for one quarter. See what happens to velocity and customer satisfaction.

You don't need to adopt these frameworks perfectly. You just need to adopt them at all.

The Part Where I Try to Sell You Something

Look, I'm writing this partly to explain a useful concept, and partly because this is how we work at SociiLabs and we think it's worth talking about.

We're an AI-assisted development agency that builds software for startups and SMBs who need things done fast without sacrificing quality. We use the 40-20-40 process framework on every project because it works. We help clients think through the product framework because most founders are too deep in the weeds to see the forest.

We're 20-40% cheaper than most agencies—not because we're cheap, but because we're efficient. Proper planning means fewer rewrites. AI-assisted development means faster implementation. Thorough testing means fewer post-launch fires.

Projects that normally take six months take us three to four. Not by cutting corners. By not wasting time on the parts that don't matter and investing heavily in the parts that do.

We're not right for every project. If you just need someone to crank out code, we're probably overkill. If you need someone to help you figure out what to build, how to build it efficiently, and how to make sure it actually works—that's where we shine.

Want to talk about your project? Book a consultation. We'll tell you honestly whether these frameworks make sense for where you are. Sometimes they don't. Sometimes you need to just ship something and see if it works. But if you're tired of feeling like your development process is chaos, we should talk.

Because here's the thing: most startup failures aren't because the idea was bad or the team wasn't talented. They're because nobody took the time to plan properly, test thoroughly, or think strategically about where to invest resources.

The 40-20-40 rules won't solve every problem. But they'll solve the most common ones. And in a startup, solving the common problems means you actually have time to tackle the interesting ones.

How We Use AI to Speed Up Development (Without Sacrificing Quality)

Muhammad Arslan Aslam — Mon, 17 Nov 2025 23:29:41 +0000

Look, I'm tired of reading blog posts that are basically "We use ChatGPT and it's amazing!"

This isn't that.

Last month, a client came to us with a broken MVP, two weeks until launch, and almost no budget left. Their previous developers had spent months building something in Replit that barely worked. We took one look and thought, "This is impossible."

Two weeks later, they launched on time.

I'm not writing this to brag. I'm writing it because six months ago, we would've told that client we couldn't help them. The timeline was genuinely impossible using traditional development methods. But something changed in how we work, and I think it's worth explaining—because it's less about the tools and more about finally having time to think.

What Actually Happened

Here's the honest version: we didn't set out to become "AI-first developers" or whatever the current buzzword is. We just started using some new coding tools because they seemed useful. Windsurf for autocomplete. Claude when we got stuck on architecture problems. Nothing revolutionary.

Then we noticed something weird.

Our junior developers were writing code at the level of mid-level developers. Our senior developers were shipping features that would normally take a week in two days. Not all the time—but often enough that we started paying attention.

We weren't working longer hours. We weren't cutting corners. We were just... less exhausted.

The Boring Truth About Our "AI Stack"

Everyone wants to know about tools. Fine. But the tools matter less than you think.

We use Windsurf as our main coding assistant—not GitHub Copilot, which we tried and found limited. Windsurf understands context better. When you're three files deep into a feature and it suggests the exact function you need, complete with error handling that matches your patterns? That's not magic. That's just good autocomplete trained on enough code to be genuinely useful.

For architectural decisions—the big "should we build it this way or that way" questions—we use Claude for Code. Not because it's smarter than our developers, but because sometimes you need to rubber-duck with something that's read the documentation for every framework ever written.

Warp handles our terminal work. I mention this only because watching our DevOps engineer describe what they want in plain English and get working bash commands back is still entertaining after six months.

We prototype interfaces in v0.dev and iterate designs with Figma's AI tools. This has genuinely changed client meetings—we can sketch ideas and see them rendered in minutes instead of waiting days for mockups.

That's it. That's the stack. No exotic tools. Nothing you can't start using tomorrow.

The Parts We Actually Built

The interesting stuff isn't the commercial tools—it's what we built on top of them.

Our PR review agent is a custom GitHub Actions pipeline that runs before any human looks at code. It checks for security issues, suggests test cases, flags performance problems, and yells at you if your function is 200 lines long with no comments.

Is it perfect? No. Does it catch the stupid mistakes we make at 4pm on Friday? Absolutely.

Our requirements analysis agent is trained on years of our project specs, proposals, and actual time logs. You feed it a project description, and it spits out functional specs, development plans, time estimates, and cost projections. The estimates are 90% accurate, which sounds impossible until you realize it's just pattern matching against hundreds of similar projects.

We also use Claude with Model Context Protocol integrations to plan development milestones. It pulls data from our repos and past projects to create roadmaps that actually account for how long things really take, not how long we wish they would take.

That Impossible Project I Mentioned

Back to the client with the broken Replit MVP.

When they first reached out, I almost said no. The scope was clear: complete frontend rewrite, authentication system, role-based access control, database integration, and a handful of core features. The timeline was not clear: two weeks. Their budget was even less clear: mostly spent.

We took the project anyway because... honestly? We were curious if our workflow could handle it.

Day 1-3: We pulled their code out of Replit and into our production boilerplate. This is normally a 2-3 week job—migrating someone else's messy code, understanding their logic, preserving what works, and rebuilding what doesn't. With Windsurf, our developers were moving entire components in hours instead of days. The AI wasn't writing the code for them—it was eliminating the mechanical parts so they could focus on the architectural decisions.

Week 2: Authentication, RBAC, and API integration. Our custom agents generated the auth flows, but our developers reviewed every line. The AI suggested middleware patterns, but we decided which ones fit. It wrote tests, but we determined what needed testing.

Here's what people get wrong about AI-assisted development: they think the AI does the work. It doesn't. It does the boring work. The work that makes you want to quit programming and become a woodworker.

Day 14: The client launched. On time. With a stable product.

Would we have pulled this off six months ago? Absolutely not. The timeline was genuinely impossible using traditional methods. We would've needed 6-8 weeks minimum, and that's if everything went perfectly (which it never does).

What This Actually Means for Clients

I'm going to be direct about the economics here because I'm tired of agencies being vague about pricing.

We're 20-40% cheaper than most development shops. Not because we're undercutting anyone or hiring cheaper developers. Because we're genuinely faster. Fewer billable hours for the same output. Simple math.

Projects that normally take 6 months take us 3-4. Not by cutting corners. Not by working 80-hour weeks. By eliminating the parts of development that are pure grinding: writing boilerplate, setting up standard configurations, hunting through documentation for syntax you've used a hundred times.

We catch bugs earlier. Our automated PR reviews find issues before they hit production. This means less time (and less of your money) spent fixing things after launch.

You get more features. When we finish ahead of schedule, we don't just bill you less and disappear. We ask what else you wanted to build. Clients regularly end up with features they thought were "phase 2" included in their initial budget.

The research backs this up, if you care about that sort of thing. McKinsey says AI-assisted developers are 35-50% more productive. GitHub's data shows tasks getting done twice as fast. Stanford and MIT found 56% time reductions. Our experience matches these numbers, sometimes exceeds them.

But honestly? The numbers matter less than this: we're building software the way it should've always been built—focusing human intelligence on problems that need human intelligence, and automating everything else.

The Unexpected Part

I expected AI to make my developers faster. I didn't expect it to make them happier.

One of our junior developers told me last month, "I used to spend half my day on Stack Overflow trying to figure out syntax. Now I spend half my day actually solving interesting problems."

Another developer—someone who's been coding for 15 years—said he's taking on projects he would've avoided before. Not because they're easier now, but because he has time to research and think instead of grinding through implementation details.

Nobody's burned out anymore. Or at least, not as burned out.

Before we started using these tools, our team would hesitate on complex features. Integrating with an unfamiliar API? That's a week of documentation and trial-and-error. Building something in a framework nobody's used before? Hope you like reading GitHub issues until 2am.

Now? They're confident. Not because AI does the work for them, but because they have backup. They can ask questions and get answers instantly instead of spending hours hunting through outdated documentation. They can prototype solutions in minutes and see if they work instead of committing to an approach and hoping for the best.

The result is a team that wants to tackle hard problems instead of avoiding them. And isn't that the point?

What We're Not Saying

AI isn't writing our code for us. It's not making architectural decisions. It's not talking to clients or understanding what they actually need versus what they say they need.

Our developers are still doing all of that. They're just not wasting time on the mechanical parts anymore.

Think of it like this: Before calculators, accountants spent hours doing arithmetic by hand. After calculators, they spent those hours on actual financial analysis. The calculator didn't replace the accountant—it made the accountant better at their job.

That's what's happening here. Our developers are still making every important decision. They're just not typing as much boilerplate.

Why I'm Writing This

I'm not writing this to convince you to hire us (though if you want to talk, the link's below).

I'm writing this because I've been building software for 13 years, and this is the first time I've felt like we're working with the computer instead of despite it.

For years, we've known what good software should look like. We've known how to architect it, how to test it, how to maintain it. The problem was always the same: there weren't enough hours in the day to do it right. So we cut corners. We skipped tests. We wrote "TODO: refactor this later" comments that never got addressed.

Now we have time. Time to think about architecture. Time to write proper tests. Time to refactor when something smells wrong. Time to actually do the work the way we always wanted to do it.

That's what changed. Not the tools—the time.

If you're building software the old way—manually grinding through every line of code, spending half your day on Stack Overflow, writing the same authentication logic for the tenth time—you're not being pure or principled. You're just being slow.

The tools exist. They work. They're not perfect, but they're good enough to change everything.

Want to talk about your project? Book a consultation

We'll tell you honestly if AI-assisted development makes sense for what you're building. Sometimes it doesn't. Sometimes the old way is fine. But if you're racing against a deadline, working with a tight budget, or trying to do something ambitious with limited resources—yeah, we should talk.