Vagmi Mudumbai

Shawn (swyx) Wang wrote last week: “RIP Vibe Coding. [Feb–Oct 2025]” I don’t think it’s dead. I think it’s evolving. > #VibeCoding was great for simple greenfield projects and proof-of-concepts. > #ViableCoding is the next step, making AI useful for real, heavy-duty codebases. In my talk at AI Eng World's Fair 2025, I shared posts from industry leaders exploring this shift: How to move from vibe to viable by • using AI across the entire SDLC (e.g. AI code review, testing, RCA) • tightening integrations across diverse, orthogonal tools (you’ll see more and more of these) • and leveraging a strong context engine to make it all work accurately (critical).

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I am not excited about visual workflow builders 1. Not simple enough for the average user. I believe there should be a simpler way to create, modify, and adapt no-code agents 2. Not scalable for complex use cases Wrote a little blog: https://lnkd.in/gDV8pECM

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You might need a new CTO soon. If your CTO recklessly uses AI everywhere, you'll either (A) burn your customers (B) get into maintenance & bug hell or (C) don't save a single dime but instead put most of your resources on QA. Be smart. Leverage AI, but don't do so recklessly. -- PS: We build software that actually scales and is being used.

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Nobody really talks about this part of building products. The real work doesn’t start with code. It starts with scoping. Behind the scenes of how we scope a product at Sqaleup Inc. We don’t ask, “What features do you want?” We ask, “What problem must this product solve for the business to survive?” Then we strip it down: - What happens if this feature doesn’t exist? - What breaks if we delay it? - What makes money vs what looks impressive? Most products fail at the scoping stage, not execution. Bad scope leads to: 📌 Endless revisions 📌 Bloated MVPs 📌 Burned budgets 📌 Missed launches Good scope does one thing: It protects momentum. If you get scoping right, development becomes boring. And boring is good. That’s the part nobody posts on on the internet. Sayonara✌🏽

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Starting today, our engineers stop writing code. For the next 30 days, we're moving our entire team — engineering, GTM, and design — from writing and reviewing code to providing architecture and scaffolding for agents to take features from spec to production. We're going to fail. Probably a lot. But we're doing it anyway. Here's why: - A few days ago at a True Ventures founder gathering, I watched teams demo what AI-first SDLC actually looks like now. Not in theory. In production. - 3 engineers doing 2,000 commits/month. - GTM teams of ONE producing thousands of pieces of content, measuring impact, and iterating — all using autonomous agents. This wasn't possible even 3 months ago. The advances in coding agents have crossed a threshold that changes what a small team can do. And if you're not experimenting with this right now, the gap is growing fast. We're going to share what works and what doesn't along the way. If you want to follow along, we'd love the company. #aifirst #ainative

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💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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