DEV Community: Vlad Dyachenko

How Harth Saved Me Hours Organizing Client Files and Design Versions

Vlad Dyachenko — Thu, 13 Aug 2026 05:10:03 +0000

I've been spending way too much time bouncing between Revit and spreadsheets trying to coordinate with my team on projects, and it's honestly exhausting. That's where Harth came in. It's basically a workspace that sits on top of what I'm already doing instead of forcing me to learn some completely new system. I can pull files straight from Revit or ArchiCAD, work with them in the browser, and push everything back without any weird conversion issues.

The thing that actually matters is that I don't have to change my workflow. My CAD files stay in sync with everything else, and I'm not exporting and re-importing constantly. For architects working in teams, especially if you're dealing with multiple software platforms, this cuts down a lot of the friction that eats up your day. It just handles the file management piece that nobody enjoys anyway.

If you're tired of manual file juggling and want something that actually integrates with the tools you're already using, it's worth trying. I've noticed real time savings just in the past few weeks.

Check it out: Harth

How I Finally Stopped Switching Between Ten Different Tools

Vlad Dyachenko — Tue, 11 Aug 2026 20:10:49 +0000

I've spent the last few years building and refining SaaS products, and one pattern I keep noticing is how easy it is to overcomplicate what users actually need. We tend to load features into our platforms thinking more options equals more value, when really people just want clarity about themselves and straightforward guidance on what to do next.

The friction point I see most often is that gap between data and action. A platform can collect information, run analysis, generate reports—but if the user can't connect that output to something concrete they can do, the tool becomes noise. I started thinking about this seriously when working on a project that needed to deliver personalized insights at scale. The insight itself wasn't the hard part; the challenge was making recommendations that felt real and achievable to each person. When I use something like Clinic Evo, I notice how it sidesteps that common trap by focusing recommendations on everyday changes rather than trying to suggest everything possible. That clarity of purpose matters.

What I've learned is that the best SaaS work happens when you ruthlessly prioritize what actually moves the needle for your user. Don't just track things—help people understand what the tracking means for them specifically. Don't just generate recommendations—make them actionable within their real life and preferences. The technical sophistication matters less than whether someone can actually use what you've built to make a decision or take a step forward.

Check it out: Clinic Evo

How I Finally Stopped Letting Email Interrupt My Deep Work

Vlad Dyachenko — Mon, 20 Jul 2026 15:50:14 +0000

I get about 120 emails a day, and for years I treated inbox management like a chore that happened between actual work. I'd batch process emails at the end of the day, answer the urgent ones first, and usually end up with 200+ unread messages by Friday. The system wasn't broken exactly, but it was bleeding mental energy I could use elsewhere.

The real shift came when I stopped thinking about email triage as something I should handle manually and started thinking about what information I actually needed from my inbox. I don't need to read every newsletter. I don't need to be reminded about purchase confirmations. I need to know when someone's waiting on me, when I'm waiting on someone else, and when something truly requires my attention. Those are maybe 15 to 20 emails per day out of the 120 that arrive.

I use NeatMail to automate the sorting, and it handles the repetitive work—auto-labeling, archiving resolved threads, drafting replies so I'm not starting from scratch. What matters is that it's one less mental loop running in the background. I spend maybe five minutes on email now instead of thirty. The tools change, but the principle stays the same: identify what you actually need to think about versus what just needs to be processed, then make the processing automatic.

That freed-up time is where real productivity happens. Not in managing your inbox faster, but in having a clearer inbox so you can actually focus on what matters. The goal is always to get decision-making fatigue out of email and back into your actual work.

How I finally stopped paying for five different SaaS tools I didn't need

Vlad Dyachenko — Sun, 19 Jul 2026 15:48:14 +0000

I've been building SaaS tools long enough to know that the worst problem to have is something nobody actually needs. The best problems are the ones where you find yourself solving it over and over, manually, until you finally get frustrated enough to build something real.

That's how most of my workflows evolved. I'd be researching a YouTube creator's entire body of work, or trying to analyze patterns across a bunch of educational videos, and I'd end up spending hours downloading transcripts one at a time. Manually. Copying. Pasting. Waiting. The friction was real, but more importantly, it was avoidable. So when I discovered I could use YtBulkTranscript to pull all the transcripts from a channel at once and get them as a ZIP, it removed a bottleneck I didn't even realize was slowing down my analysis work.

This is the core insight I keep coming back to in SaaS: good products don't feel like products at all. They're just solutions that get out of your way. The ones that survive are usually solving a problem that affects enough people that you can't ignore it, but specific enough that the solution feels tailored rather than generic. There's a difference between a tool that does everything and a tool that does one thing well enough that you use it without thinking.

In my experience, the validation doesn't come from a survey or a landing page. It comes from whether you actually reach for the tool when you have the problem. That's when you know you've built something that matters.

How I Finally Stopped Comparing My Sound Setup to Everyone Else's

Vlad Dyachenko — Sat, 18 Jul 2026 15:46:14 +0000

I've been making ASMR content for about three years now, and one thing I've learned is that the technical side matters way more than most creators realize. People come to ASMR for the sensation, and if your audio doesn't deliver the spatial immersion they're expecting, they'll just click away. The problem is that achieving that ear-to-ear binaural effect—where it feels like someone's whispering directly into your ears—traditionally requires expensive equipment and serious recording skills.

For a while I was trying to fake it with panning and reverb, but it never quite worked. Then I started using ASMRify to process my voice recordings, which handles the spatial positioning automatically. It's made a real difference in production time since I can batch-process multiple clips at once instead of manually tweaking each one. The key insight I had was understanding that ASMR listeners are incredibly sensitive to authenticity, so whatever processing you do has to feel natural, not processed. Robotic-sounding effects will kill the entire experience.

What's changed my approach fundamentally is realizing that good ASMR audio is about intention. Before I hit any processing, I'm thinking about the scenario—am I close to the microphone? Am I moving around? What distance is implied by my voice? When you nail that conceptually, the technical tools just enhance what's already there. The worst thing you can do is try to fix weak source material with fancy effects. Start with genuinely good voice work, and then the rest follows.

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.