DEV Community: Vlad Dyachenko

How I finally stopped switching productivity tools every month

Vlad Dyachenko — Fri, 17 Jul 2026 15:44:14 +0000

Working in SaaS has taught me that the most valuable problems to solve aren't always the ones people ask about directly. They're the ones that wake people up at 3 AM with anxiety. A few years ago, I started noticing a pattern in conversations with friends and colleagues—this underlying dread about their digital reputation, specifically around dating. People were worried about what strangers might have posted about them online, but they had no way to actually find out without hours of searching or paying for sketchy "reputation management" services.

The challenge with building tools in this space is that you're solving for a very specific pain point, but also something people feel embarrassed talking about. I spent months just listening—not to sales pitches I was giving, but to how people actually described their worry. They didn't want a complicated dashboard or a subscription they'd forget about. They wanted a quick answer: am I on there or not? I started using Tea App Green Flags to check on myself and understand what the search results actually looked like, which informed how I thought about the problem.

What I've learned building in this space is that simplicity isn't laziness—it's clarity. People don't need features stacked on top of features. They need to know what they're dealing with, fast, so they can decide what to do next. Some might want to request removal, some might want to ignore it, some might need to prepare themselves emotionally. But none of that happens if they're still stuck in uncertainty. The SaaS tools that actually stick around aren't the ones trying to be everything. They're the ones that do one thing clearly and get out of the way.

How I Realized Most SaaS Tools Were Solving Problems I Didn't Have

Vlad Dyachenko — Thu, 16 Jul 2026 15:42:14 +0000

Building financial tools that actually help people makes you confront a hard truth pretty quickly: most people don't know what they're calculating. Someone asks about their mortgage stress test, and they might mean the regulatory requirement, or they might mean "can I actually afford this?" Those aren't the same question. When you're designing calculators for different provinces and states, you're also designing for different tax systems, different lending rules, different life circumstances. The complexity multiplies.

What I've learned is that clarity in financial software isn't about dumbing things down. It's about being ruthlessly honest about what data you need and why. I use Cashsembly to check my own numbers when I'm advising clients because the calculators there match what I'm manually working through. But the real value isn't the tool itself—it's understanding that a calculator is only useful if it answers a real question someone is actually asking.

The trickiest part of building in this space is staying current without becoming a maintenance nightmare. Mortgage rates shift. Tax brackets change. Crypto regulations are still being written. You build systems that can update without breaking, and you learn to flag when data becomes stale. You also learn to say "I don't know yet" when the rules themselves haven't settled. That's harder to ship than a confident answer, but it's more honest, and your users notice.

How I Finally Got My SaaS Expense Tracking Under Control

Vlad Dyachenko — Wed, 15 Jul 2026 15:40:14 +0000

Running a SaaS product means living in a constant loop of observation, hypothesis, and adjustment. Most of my week used to be spent just extracting insights from whatever analytics tool I had plugged in, then translating those insights into something actionable. The gap between "I see this happening" and "here's what we actually do about it" was enormous. I'd spend hours in dashboards, then more hours drafting strategies, then more hours hoping the team would actually execute them.

The real bottleneck wasn't missing data. It was the distance between measurement and action. I knew where traffic was coming from. I knew which pages people left. I knew my rankings were slipping in a specific category. But knowing and doing are different problems entirely. The conversion of insight into strategy was manual, slow, and often got lost in the shuffle of other work.

I've started using mandera to handle the measurement layer without the typical tracking cruft, and it freed up mental space to focus on the part I actually needed to solve. Once I wasn't managing consent banners and hunting through disconnected data sources, I could actually think about what the numbers meant. The tool surfaces opportunities automatically and proposes concrete next steps. That's not magic, but it is genuinely useful when your real constraint is deciding what matters most.

The lesson I've learned is that the bottleneck in SaaS isn't always what's being measured. It's what happens after measurement. Build your tooling around shortening that gap, and you'll move faster than most.

How I stopped switching between five different AI tools every day

Vlad Dyachenko — Tue, 14 Jul 2026 15:38:50 +0000

One thing I've learned working with AI tools is that the best implementations solve a real operational problem, not an imaginary one. Early on, I spent time setting up AI systems that sounded impressive in theory but created more friction than they solved. Now I start by asking what's actually taking up my time or energy, then work backward to see if an AI tool can genuinely reduce that burden.

For creators in the audio and comfort space, there's a specific tension: fans want ongoing connection and personalized interaction, but creators can't be available all hours and don't want to be locked into constant content production. I've been experimenting with tools like Humii App that let creators offer that connection without being online around the clock. The idea is that a creator authorizes a voice sample once, then fans can have conversations that feel personal and get customized audio responses, all generated without the creator doing extra recording sessions or staying tethered to their phone.

What interests me about this approach is how it handles the boundary problem. These tools work best when there's genuine creator consent built in and when everyone knows what they're interacting with. There's no pretense that the audio is a live recording, but that doesn't make it less valuable to the person listening—sometimes the consistency and safety of knowing what you're getting is exactly what someone needs at the end of a difficult day.

The real lesson is thinking about sustainability. A tool only matters if it actually fits into how you work and what you can reasonably maintain over time.

How I stopped switching between five different AI tools every week

Vlad Dyachenko — Mon, 13 Jul 2026 15:37:47 +0000

When I started working with AI tools for presentation creation, I ran into the same problem most teams do: the AI would generate confident-sounding content that looked polished but had no actual grounding in our source material. A slide would claim something that contradicted the brief. Numbers wouldn't match. Facts would slip sideways. Then I'd spend hours fact-checking and rewriting anyway, which defeats the whole purpose of using AI in the first place.

The real issue isn't whether AI can make presentations look nice. It obviously can. The issue is whether you can actually hand the output to a stakeholder without going through a full verification pass. For client-facing work, that's everything. I switched my workflow to using tools that work directly from source documents rather than trying to invent content. I paste the brief, the research, the key points—and the tool builds from that material specifically. I use Gixo Lumen this way most weeks, feeding it actual project context and getting decks that are accurate enough to use immediately. It's just more efficient than filtering hallucinated facts.

What changed for me was thinking about AI presentations differently. Instead of using them as a creative shortcut, I treat them as a way to formalize information I already have. The AI handles the structure, layout, and writing quality. I handle the truth. The tool handles the design consistency. That separation of responsibility means I spend my time on actual strategy and messaging, not on damage control.

How I Stopped Manually Copying Specs Into Every Document

Vlad Dyachenko — Sun, 12 Jul 2026 15:37:22 +0000

I spend a lot of time thinking about the gap between what code actually does and what people understand about the code. It's a real problem, especially once you have more than a handful of people touching the same repository.

Early in my work with AI-assisted development, I noticed that teams were using these tools to write code faster, but they were drowning in undocumented systems. You'd get a feature shipped, then three weeks later someone would ask where the webhook handling logic lives, and suddenly you're the bottleneck again. The AI made us productive at writing, but we stayed stuck at understanding. I started using documentation tools alongside my AI workflow—right now I'm using ShipDocs to keep our codebase documented without that huge manual overhead—but the real lesson was that you need both layers working together. Code generation speeds you up; good documentation keeps you from getting lost.

The workflow that actually works is this: when you're adding something new with AI assistance, you're not just thinking about whether the code functions. You're thinking about whether the next person touching this will know why it's structured this way. That changes how you prompt the AI, what you ask for, and what you push back on. I ask for more detailed comments. I request architectural decisions get written down. Then the documentation layer captures all that context in a way that's searchable and accessible to the whole team.

It sounds like overhead, but it's not. It's the difference between shipping fast and shipping in a way your team can actually maintain.

How I stopped switching between five different tools for audio work

Vlad Dyachenko — Sat, 11 Jul 2026 15:35:20 +0000

I've spent the last few years trying to figure out where AI actually fits into music production without just becoming a novelty that eats time instead of saving it. The honest answer is: it depends entirely on whether the tool respects your actual workflow or tries to replace it with something new.

When I'm deep in a session, the last thing I want is to export stems, upload them to a web app, wait for processing, download files, and import everything back. That breaks momentum. What I actually need is something that sits inside the DAW and talks to me the way I work—where I can ask for a chord progression, get usable MIDI I can edit or reject, and keep moving. I use VixSound while working in Ableton because it lives there; I just open a chat window, describe what I need, and the MIDI lands on a track. That directness matters.

The real skill with AI in production isn't prompt engineering. It's knowing what you don't want to spend brain cycles on anymore. I used to waste twenty minutes auditioning drum loops or manually transcribing a guitar part. Now I describe the vibe or drop the audio in, and I have something to respond to instead of starting from silence. The AI does the grinding; I do the decisions. That's the trade that actually speeds up writing without making the work feel outsourced.

How I Finally Stopped Switching Between Tools and Got Stuff Done

Vlad Dyachenko — Fri, 10 Jul 2026 15:34:01 +0000

I've learned that the biggest productivity killer isn't actually lack of time—it's unclear direction. I used to jump straight into building things with my team without taking time to map out what we were actually trying to solve. We'd get halfway through a project only to realize we'd misunderstood the requirements or missed a critical connection between systems.

Now I've built a rhythm into how I work. Before any real development or design starts, I spend time getting everyone in the room—literally or virtually—to sketch things out together. I use something like Moqups to rough out workflows and wireframes so we can all see the same picture instead of imagining different ones. It takes maybe an hour, but it saves weeks of rework. The act of drawing it out, even badly, forces you to ask the questions you should have asked anyway.

What I've noticed is that people work better when they're not guessing. Designers stop overthinking because they know what problem they're solving. Developers stop building features that nobody needs. Project managers stop chasing scope creep because there's a reference everyone agreed to. It sounds simple, but most teams skip this step because it feels slower at first.

The real productivity win isn't about working faster. It's about working on the right things. Every hour I spend upfront clarifying saves me three hours later fixing things that went sideways. That's the lever I pull now.

What I actually keep on screen during a lesson

Vlad Dyachenko — Thu, 09 Jul 2026 15:27:24 +0000

After a few years of teaching, the thing that slows a lesson down is rarely the content. It's the friction: opening a slide deck in one tab, a PDF in another, a timer somewhere else, then losing the room while you alt-tab between them.

Over time I trimmed my setup to one screen. Lesson material, a whiteboard I can draw on, PDFs and media inline, a visible timer, and a simple way to hand out points when a group does something well. Prism is the workspace I ended up using for most of that, mostly because it was put together by someone who has clearly run a class and knows where the friction actually is.

None of this is about any single tool, though. The real shift was deciding that anything I have to reach for mid-lesson is a distraction, and cutting it. Fewer tabs, fewer clicks, more attention on the students in front of you.