Vitalii Lakusta

I caught up with a friend who works at a mid-size Swedish tech company. Over the last 4 months, their shipping velocity has almost doubled – not because they hired more engineers, adopted some new agile framework, or worked late nights. It came down to a single change in how they build products: they started using Lovable to prototype features instead of writing traditional spec docs. Before Lovable, it usually went like this: PMs drafted long PRDs, trying to anticipate every detail. Multiple stakeholders reviewed these documents, leaving comments and raising concerns. The document grew with each iteration. Alignment meetings were frequent but often resulting in ambiguity. Engineers often began implementation while details were still debated. Inevitably, confusion emerged about trade-offs, timelines got pushed, and features shipped incomplete or scaled back. Now, PMs build interactive prototypes directly in Lovable. These aren’t wireframes or rough mockups – they’re fully clickable, end-to-end experiences that feel like the real product. Engineers don’t have to guess what the flow should be. Designers don’t have to explain interactions. Everyone sees the same thing, from day one. The end result is fewer meetings, fewer misunderstandings, fewer rewrites. What used to take weeks of coordination now happens in a single day. This is what has provided the most value for enterprises using Lovable so far. Over time, the increase of clarity and velocity saves the companies millions of $ in wasted effort.

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was at OpenAI hq yesterday and got to talk with their head of product about the new agentkit 🤯 a lot of people think this kills zapier (i even thought so at first). it doesn’t. agentkit is for devs, not no-coders. it’s about putting ai inside products, not automating workflows. this makes it easier for teams to build agents that live inside their own apps, not connect tools together. so yeah, this isn’t about replacing zapier. it’s about replacing ui. #ai #openai #chatgpt #agents #startups #developers

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What I learned watching thousands of people use our tools: We see it in forum posts. In support tickets. In cloneables people share. In live projects clients send us to review. And a few patterns come up over and over: 1.) Most “bugs” are actually unclear instructions. The product works but the way we explained it didn’t. Better docs or clearer UI can be the difference between “this doesn’t work” and “that was easy.” 2.) People learn in very different ways. One person calls our docs “the best they’ve ever used.” Another says the exact same docs are “the worst.” The same instructions can be crystal clear to one person and confusing to the next. That’s why we need written guides, videos, forum answers, and examples. 3.) Support really matters. It’s often the difference between a new subscription and a lost user. We invest our time and resources to get users through their use case implementation, even if they are not a paid user of the product. That first successful implementation can create a lifelong customer. 4.) We can’t make everyone happy. We used to try. But people think differently, work differently, and want different things. Now we focus on making our happiest customers even happier. Because the better we serve them, the stronger the community gets. Watching people use your product is humbling. It forces you to see where the real friction is and where the real value comes from. If you're not learning from your users, you're just guessing.

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In startups, there's no concept of being on call. At Alma, Everyone is on call. Always. At 2 AM when our document processing pipeline went down, it wasn't the "designated on-call engineer" who fixed it. It was whoever saw the Slack notification first. But here's what's changing fast - AI is reshaping what "on-call" actually means. Traditional on-call: Get paged → SSH into server → Debug logs → Apply hotfix → Write postmortem AI-enhanced on-call: Get intelligent alert → AI suggests root cause → Review proposed fix → Approve deployment → AI writes incident summary A few startups are already building this. The engineer's role is shifting from "human debugger" to "decision maker" btw, if you're an immigrant who's exploring work visa options like O-1, H-1B, EB-1A etc. - get in touch with us at Alma.

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It’s clear that almost everything about software development has fundamentally changed for good with AI agents. Hard to imagine another area of knowledge work that has had its underlying process get transformed so substantially in such a short period of time. If you went back even 2 years and told the average developer what software engineering would look like right now, it would not be comprehensible. We’ve already seen 3-4 major step functions changing workflows just in that time period alone. The ultimate outcome is clearly that software engineering will become heavily dependent on how good you are at prompting, how good you are at reviewing code, and the interplay between the two. Once the workflow becomes about kicking off agents to do work in the background, your success is dependent on the work that gets kicked off and how you review it. Importantly, both require a deep understanding of how to code, systems, and what you want to build; the greater the underlying skill of the engineer, the greater the leverage with AI agents. And while we’re seeing this workflow get defined first in software, a similar pattern will eventually emerge across all areas of knowledge work. We’re only in the earliest stages of seeing what this will look like. Crazy times.

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I never answer team member Slack DMs.   Instead, I ask them to repost it in a public channel.   I can practically hear the confusion through the screen. Did the CEO just...redirect me? Yes, I did, and for a very specific reason.   Questions, decisions, updates, discussions...everything happens on open channels where others can see it. This is an unwritten rule at Smile.io that throws new hires for a loop every single time.   When you work in an office, you overhear conversations and learn without even trying. Someone chatting over coffee, a teammate sharing a win, a question shouted across the desk. You pick up context just by existing in the same space.   But when you’re working remotely, all that knowledge gets trapped in DMs and 1:1 calls.   The sales team learns something important about customer behavior, but product never hears about it. Engineering discovers a workaround, but support is still manually fixing the same issue.   You only hear what you’re explicitly told, and that’s dangerous for team alignment, context, and growth.   That’s why, at Smile, we live by one core value: If it doesn’t have to be private, say it in public.   As a result, we’ve seen:   • Faster onboarding: New hires don’t need hours of training sessions; they ramp faster by observing real conversations as they happen. • Shared context: Teams better understand each other’s workflows, roadblocks, and the ripple effects of their decisions. • Better questions: When you know 50 people might see your question, you think before you type. • Searchable knowledge: Everything becomes documentation. That debugging session from six months ago? It’s right there in the thread, open to all.   When information lives in DMs, you're building a company where only half the team knows what's happening.

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