AB's Reflections

Tag: vibe coding

  • GitHub’s SpecKit: The Structure Vibe Coding Was Missing

    GitHub’s SpecKit: The Structure Vibe Coding Was Missing

    When I first started experimenting with “vibe coding,” building apps with AI agents felt like a superpower. The ability to spin up prototypes in hours was exhilarating. But as I soon discovered, the initial thrill came with an illusion. It was like managing a team of developers with an attrition rate measured in minutes—every new prompt felt like onboarding a fresh hire with no idea what the last one had been working on.

    The productivity boost was real, but the progress was fragile. The core problem was context—a classic case of the law of leaky abstractions applied to AI. Models would forget why they made certain choices or break something they had just built. To cope, I invented makeshift practices: keeping detailed dev context files, enforcing strict version control with frequent commits, and even asking the model to generate “reset prompts” to re-establish continuity. Messy, ad hoc, but necessary.

    That’s why GitHub’s announcement of SpecKit immediately caught my attention. SpecKit is an open-source toolkit for what they call “spec-driven development.” Instead of treating prompts and chat logs as disposable artifacts, it elevates specifications to first-class citizens of the development lifecycle.

    In practice, this means:

    • Specs as Durable Artifacts: Specifications live in Git alongside your code—permanent, version-controlled, and not just throwaway notes.
    • Capturing Intent: They document the why—the constraints, purpose, and expected behavior—so both humans and AI stay aligned.
    • Ensuring Continuity: They serve as the source of truth, keeping projects coherent across sessions and contributors.

    For anyone who has tried scaling vibe coding beyond a demo, this feels like the missing bridge. It brings just enough structure to carry a proof-of-concept into maintainable software.

    And it fits into a larger story. Software engineering has always evolved in waves—structured programming, agile, test-driven development. Each wave added discipline to creativity, redefining roles to reflect new economic realities—a pattern we’re seeing again with agentic coding. Spec-driven development could be the next step:

    • Redefining the Developer’s Role: Less about writing boilerplate, more about designing robust specs that guide AI agents.
    • Harnessing Improvisation: Keeping the creative energy of vibe coding, but channeling it within a coherent framework.
    • Flexible Guardrails: Not rigid top-down rules, but guardrails that allow both creativity and scalability.

    Looking back, my dev context files and commit hygiene were crude precursors to this very idea. GitHub’s SpecKit makes clear that those instincts weren’t just survival hacks—they pointed to where the field is heading.

    The real question now isn’t whether AI can write code—we know it can. The question is: how do we design the frameworks that let humans and AI build together, reliably and at scale?

    Because as powerful as vibe coding feels, it’s only when we bring structure to the improvisation that the music really starts.

    👉 What do you think—will specs become the new lingua franca between humans and AI?

  • The Economic Reality and the Optimistic Future of Agentic Coding

    The Economic Reality and the Optimistic Future of Agentic Coding

    After a couple of months deep in the trenches of vibe coding with AI agents, I’ve learned this much: scaling from a fun, magical PoC to an enterprise-grade MVP is a completely different game.

    Why Scaling Remains Hard—And Costly

    Getting a prototype out the door? No problem.

    But taking it to something robust, secure, and maintainable? Here’s where today’s AI tools reveal their limits:

    • Maintenance becomes a slog. Once you start patching AI-generated code, hidden dependencies and context loss pile up. Keeping everything working as requirements change feels like chasing gremlins through a maze.
    • Context loss multiplies with scale. As your codebase grows, so do the risks of agents forgetting crucial design choices or breaking things when asked to “improve” features.

    And then there’s the other elephant in the room: costs.

    • The cost scaling isn’t marginal—not like the old days of cloud or Web 2.0. Powerful models chew through tokens and API credits at a rate that surprises even seasoned devs.
    • That $20/month Cursor plan with unlimited auto mode? For hobby projects, it’s a steal. For real business needs, I can see why some queries rack up millions of tokens and would quickly outgrow even the $200 ultra plan.
    • This is why we’re seeing big tech layoffs and restructuring: AI-driven productivity gains aren’t evenly distributed, and the cost curve for the biggest players keeps climbing.

    What the Data Tells Us

    That research paper—Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity—had a surprising conclusion:

    Not only did experienced developers see no time savings on real-world coding tasks with AI, but costs increased as they spent more time reviewing, correcting, and adapting agent output.

    The lesson:

    AI shifts where the work happens—it doesn’t always reduce it. For now, scaling with agents is only as good as your processes for context, review, and cost control.

    Why I Remain Optimistic

    Despite the challenges, I’m genuinely excited for what’s coming next.

    • The platforms and models are evolving at warp speed. Many of the headaches I face today—context loss, doc gaps, cost blind spots—will get solved just as software engineering best practices eventually became codified in our tools and frameworks.
    • Agentic coding will find its place. It might not fully automate developer roles, but it will reshape teams: more focus on high-leverage decisions, design, and creative problem-solving, less on boilerplate and “busy work.”

    And if you care about the craft, the opportunity is real:

    • Devs who learn to manage, review, and direct agents will be in demand.
    • Organizations that figure out how to blend agentic workflows with human expertise and robust process will win big.

    Open Questions for the Future

    • Will AI agentic coding mean smaller, nimbler teams—or simply more ambitious projects for the same headcount?
    • How will the developer role evolve when so much code is “synthesized,” not hand-crafted?
    • What new best practices, cost controls, and team rituals will we invent as agentic coding matures?

    Final thought:

    The future won’t be a return to “pure code” or a total AI handoff. It’ll be a blend—one that rewards curiosity, resilience, and the willingness to keep learning.

    Where do you see your work—and your team—in this new landscape?

  • The Law of Leaky Abstractions & the Unexpected Slowdown

    The Law of Leaky Abstractions & the Unexpected Slowdown

    If the first rush of agentic/vibe coding feels like having a team of superhuman developers, the second phase is a reality check—one that every software builder and AI enthusiast needs to understand.

    Why “Vibe Coding” Alone Can’t Scale

    The further I got into building real-world prototypes with AI agents, the clearer it became: Joel Spolsky’s law of leaky abstractions is alive and well.

    You can’t just vibe code your way to a robust app—because underneath the magic, the cracks start to show fast. AI-generated coding is an abstraction, and like all abstractions, it leaks. When it leaks, you need to know what’s really happening underneath.

    My Experience: Hallucinations, Context Loss, and Broken Promises

    I lost count of the times an agent “forgot” what I was trying to do, changed underlying logic mid-stream, or hallucinated code that simply didn’t run. Sometimes it wrote beautiful test suites and then… broke the underlying logic with a “fix” I never asked for. It was like having a junior developer who could code at blazing speed—but with almost no institutional memory or sense for what mattered.

    The “context elephant” is real. As sessions get longer, agents lose track of goals and start generating output that’s more confusing than helpful. That’s why my own best practices quickly became non-negotiable:

    • Frequent commits and clear commit messages
    • Dev context files to anchor each session
    • Separate dev/QA/prod environments to avoid catastrophic rollbacks (especially with database changes)

    What the Research Shows: AI Can Actually Slow Down Experienced Devs

    Here’s the kicker—my frustration isn’t unique.

    A recent research paper, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, found that experienced developers actually worked slower with AI on real-world tasks. That’s right—AI tools didn’t just fail to deliver the expected productivity boost, they created friction.

    Why?

    • Only about 44% of AI-generated code was accepted
    • Developers lost time reviewing, debugging, and correcting “bad” generations
    • Context loss and reliability issues forced more manual intervention, not less

    This matches my experience exactly. For all the hype, these tools introduce new bottlenecks—especially if you’re expecting them to “just work” out of the box.

    Lessons from the Frontlines (and from Agent Week)

    I’m not alone. In the article What I Learned Trying Seven Coding Agents, Timothy B. Lee finds similar headaches:

    • Agents get stuck
    • Complex tasks routinely stump even the best models
    • Human-in-the-loop review isn’t going anywhere

    But the tools are still useful—they’re not a dead end. You just need to treat them like a constantly rotating team of interns, not fully autonomous engineers.

    Best Practices: How to Keep AI Agents Under Control

    So how do you avoid the worst pitfalls?

    The answer is surprisingly old-school:

    • Human supervision for every critical change
    • Sandboxing and least privilege for agent actions
    • Version control and regular context refreshers

    Again, Lee’s article Keeping AI agents under control doesn’t seem very hard nails it:

    Classic engineering controls—proven in decades of team-based software—work just as well for AI. “Doomer” fears are overblown, but so is the hype about autonomy.

    Conclusion: The Hidden Cost of Abstraction

    Vibe coding with agents is like riding a rocket with no seatbelt—exhilarating, but you’ll need to learn to steer, brake, and fix things mid-flight.

    If you ignore the leaky abstractions, you’ll pay the price in lost time, broken prototypes, and hidden tech debt.

    But with the right mix of skepticism and software discipline, you can harness the magic and avoid the mess.

    In my next post, I’ll zoom out to the economics—where cost, scaling, and the future of developer work come into play.

    To be continued…

  • The Thrill and the Illusion of AI Agentic Coding

    The Thrill and the Illusion of AI Agentic Coding

    A few months ago, I stumbled into what felt like a superpower: building fully functional enterprise prototypes using nothing but vibe coding and AI agent tools like Cursor and Claude. The pace was intoxicating—I could spin up a PoC in days instead of weeks, crank out documentation and test suites, and automate all the boring stuff I used to dread.

    But here’s the secret I discovered: working with these AI agents isn’t like managing a team of brilliant, reliable developers. It’s more like leading a software team with a sky-high attrition rate and non-existent knowledge transfer practices. Imagine onboarding a fresh dev every couple of hours, only to have them forget what happened yesterday and misinterpret your requirements—over and over again. That’s vibe coding with agents.

    The Early Magic

    When it works, it really works. I’ve built multiple PoCs this way—each one a small experiment, delivered at a speed I never thought possible. The agents are fantastic for “greenfield” tasks: setting up skeleton apps, generating sample datasets, and creating exhaustive test suites with a few prompts. They can even whip up pages of API docs and help document internal workflows with impressive speed.

    It’s not just me. Thomas Ptacek’s piece “My AI Skeptic Friends Are All Nuts” hits the nail on the head: AI is raising the floor for software development. The boring, repetitive coding work—the scaffolding, the CRUD operations, the endless boilerplate—gets handled in minutes, letting me focus on the interesting edge cases or higher-level product thinking. As they put it, “AI is a game-changer for the drudge work,” and I’ve found this to be 100% true.

    The Fragility Behind the Hype

    But here’s where the illusion comes in. Even with this boost, the experience is a long way from plug-and-play engineering. These AI coding agents don’t retain context well; they can hallucinate requirements, generate code that fails silently, or simply ignore crucial business logic because the conversation moved too fast. The “high-attrition, low-knowledge-transfer team” analogy isn’t just a joke—it’s my daily reality. I’m often forced to stop and rebuild context from scratch, re-explain core concepts, and review every change with a skeptical eye.

    Version control quickly became my lifeline. Frequent commits, detailed commit messages, and an obsessive approach to saving state are my insurance policy against the chaos that sometimes erupts. The magic is real, but it’s brittle: a PoC can go from “looks good” to “completely broken” in a couple of prompts if you’re not careful.

    Superpowers—With Limits

    If you’re a founder, product manager, or even an experienced developer, these tools can absolutely supercharge your output. But don’t believe the hype about “no-code” or “auto-code” replacing foundational knowledge. If you don’t understand software basics—version control, debugging, the structure of a modern web app—you’ll quickly hit walls that feel like magic turning to madness.

    Still, I’m optimistic. The productivity gains are real, and the thrill of seeing a new prototype come to life in a weekend is hard to beat. But the more I use these tools, the more I appreciate the fundamentals that have always mattered in software—and why, in the next post, I’ll talk about the unavoidable reality check that comes when abstractions leak and AI doesn’t quite deliver on its promise.

    To be continued…