GrowSrc

Our revenue operations consultancy specializes in helping B2B SaaS and service businesses achieve rapid growth with customized solutions.

We use AI-enhanced cold outreach and strategic RevOps initiatives to optimize sales velocity.

Founder

Bushido

Lead Software Engineer

DoltHub

First hire at a startup building Dolt and DoltHub: Git and GitHub for data.

Software Engineer

Make Apps

Make Apps and its sibling brand Make Bots built cross-platform webapps, chatbots, and APIs using the most exciting new technologies available.

We also provided advice and assistance on webapps, chatbots, startups, and tech in general.

Co-Founder

Mooch

We developed a web/mobile learning management system for students, then pivoted to a teaching assistant chatbot.

Co-Founder/Chief Technical Officer

Software engineer turned B2B rainmaker ☔ | Helping software development companies get more leads, make more sales, and get paid more for them

High-revving business strategist and founder of a revenue acceleration agency for B2B SaaS and beyond.

If you're a founder, CEO, or marketing director looking to boost revenue by $200,000+ annually, reach out via DM for tailored sales velocity optimization solutions.

The cost of building software is collapsing to zero.

That sounds like great news until you realize what it actually means: you can now build the wrong thing at scale.

When a feature cost six months and $500K, that cost was a filter. Bad ideas died because they were too expensive to build. Organizations were forced to think carefully about what they actually wanted.

Remove the cost filter and the incentive to specify well evaporates.

The bottleneck didn't disappear. It shifted from production to specification.

AWS built Kiro, a dev tool whose entire purpose is forcing you to write specs before code. Amazon, a company that profits when you ship faster, decided the most valuable intervention was to slow you down.

Most teams are still optimizing for production speed. The ones pulling ahead are optimizing for specification quality.

Matt Jesuele

Founder, Ansatz | Your agentic dev team. Production code shipped weekly.

Your compiler is the cheapest senior reviewer you have. Stop firing it.

Reviewed a PR series recently. 40+ `any` casts. `(ctx.prisma as any)` repeated 20 times. Seven runtime bugs that TypeScript would have caught at compile time.

The worst one: users configured campaign targeting in the UI. The data saved to the database. When the campaign actually fired, those fields were silently dropped at the API integration layer. It had no fields for them. Users were setting up targeting that had zero effect on anything.

I caught all of this in code review. But code review is expensive and late — the code is already written, already in a PR, already taking someone's time to review. The type system would have caught it for free, before anyone had to look.

Every `any` cast is a decision to turn off your cheapest quality gate. Sometimes the developer knows the contract and is just skipping the types. Often they don't. They're just being sloppy and `any` makes the red squiggles go away. Either way, the type system stops catching contract violations between layers.

When an agent writes the code, it's worse. The contract needs to be encoded in a form the toolchain can enforce. Docs might cover it, but an agent following docs is probabilistic. An agent following the type system is deterministic.

Every `any` cast switches off the primary feedback loop that catches these violations automatically — and the agent reaches for `any` first because it's the path of least resistance.

The fix: `noImplicitAny` in your tsconfig. `no-explicit-any` in your linter. Enforce both in a pre-commit hook so the code can't even land with type system escapes.

The agent gets the error as it's working and fixes it on the spot. You don't need to prompt better types. You configure the environment to reject bad ones.

Your team adopted AI tools and productivity went down. That's not a failure. That's the J-curve.

METR ran a controlled trial — experienced open-source devs on their own repos. 19% slower with AI. They estimated they were 24% faster.

The perception gap is the real problem. Teams that feel faster while actually being slower don't look for structural fixes. They just keep prompting.

But the other side of the curve is real. Klarna went from $344K to $821K revenue per employee in two years. Cursor runs at a staggering $16M. Both restructured how work gets done around AI.

The J-curve compresses when you treat AI adoption as a workflow problem — how agents get context, how quality gets enforced, how work gets specified before generation starts. It stretches indefinitely when you treat it as a tool problem.

AI tools introduced a new failure mode that looks nothing like procrastination.

You spend a full day building. Features ship. Code compiles. Tests pass. You go to bed feeling productive. Then you realize none of it moved the needle on the thing that actually matters.

The old procrastination was obvious. You scrolled Twitter, you reorganized your desk. The new procrastination is invisible because it IS work. Just not the work that matters.

Building is the escape hatch. The hard thing isn't production. It's deciding what to produce.

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