# Everlabs – full content index

> Web and mobile software development agency. Agents do the work in an agentic AI workflow; Everlabs answers for delivery and quality. Logistics, SaaS, and e-commerce for startups and enterprise. Ruby on Rails, React, iOS, Android – full-cycle from ideation to support.

Everlabs is a software development agency. We design and build full-cycle products: ideation, UX/UI, web development, mobile development, and ongoing support – all delivered through an agentic AI workflow where agents do the work and Everlabs answers for delivery and quality. Agents are in the setup from day one: a working proof of concept on real client data within days, AI running through the whole pipeline from PRD and discovery to tested, verified releases – with human engineers accountable for everything that ships. The discipline behind it is context engineering: feeding agents the right specs, codebase knowledge, and acceptance criteria so their output ships reliably. Our specialty stack is Ruby on Rails on the backend with React and native mobile on the frontend, and we have shipped logistics platforms, SaaS products, and e-commerce systems used by millions. Everlabs has operated since 2013 and is rated 5.0/5 on Clutch across 10 verified client reviews, with a willingness-to-refer score of 5.0.

This document inlines the full body of every public article and case study, in addition to listing the URLs. For the link-only short version see `https://everlabs.com/llms.txt`. Markdown mirrors of every page are available by appending `.md` to any URL on this site.

## FAQ

- **How fast can Everlabs deliver a proof of concept?** A working proof of concept on your real data, built and demonstrated in days – you see results before you commit to a build.
- **What is an agentic development workflow?** Agents do the work; Everlabs answers for it. AI runs through the whole delivery pipeline – PRD and discovery, specs, coding, QA, release verification – while the responsibility for delivery and quality never leaves our engineers.
- **What is context engineering?** Feeding agents the right specs, codebase knowledge, and acceptance criteria so their output ships reliably.
- **What does Everlabs build, and for which industries?** Custom web and mobile applications – Ruby on Rails on the backend, React and native iOS/Android on the frontend – mostly for logistics, SaaS, and e-commerce.
- **Who is responsible for quality when AI agents write code?** Everlabs engineers. Every release is tested and verified before it ships.
- **What do Everlabs projects cost?** Engagements start at around $5,000; most projects fall in the $50,000–200,000 range at $25–49/hr, per the public Clutch profile.
- **How long has Everlabs been operating?** Since 2013, with offices in Cherkasy, Ukraine and Dover, Delaware, US.

## Main pages

- [Home](https://everlabs.com/index.md): Services overview, featured portfolio, technology stack, testimonials, and contact.
- [Blog](https://everlabs.com/blog.md): Long-form articles on web and mobile development.
- [Careers](https://everlabs.com/careers.md): Open positions and team culture.
- [Terms](https://everlabs.com/terms.md): Terms of service and privacy policy.

## Case studies


### Gold Avenue

URL: https://everlabs.com/projects/gold-avenue
Markdown: https://everlabs.com/projects/gold-avenue.md

Buy, sell & store precious metals

Buy, sell & store precious metals

**Technologies:** RoR, HTML5, CSS, JavaScript, React, Next.js, TypeScript, Node.js

**Services:** Iconography, Information architecture, UI/UX, Web development, API & Integrations


### Trainual

URL: https://everlabs.com/projects/trainual
Markdown: https://everlabs.com/projects/trainual.md

Onboarding staff worldwide via app

Onboarding staff worldwide via app

**Technologies:** KOTLIN, SWIFT, HTML5, CSS, RoR, JavaScript, React

**Services:** Web development, Mobile development, API & Integrations


### ChangeSync

URL: https://everlabs.com/projects/changesync
Markdown: https://everlabs.com/projects/changesync.md

Enterprise Change Management Software

Enterprise Change Management Software

**Technologies:** React, RoR

**Services:** Research, Information architecture, UI/UX, Web development, API & Integrations, Support, Consulting


### AssetSpace

URL: https://everlabs.com/projects/assetspace
Markdown: https://everlabs.com/projects/assetspace.md

Bringing Clarity to Fixed Asset Management

Bringing Clarity to Fixed Asset Management

**Technologies:** KOTLIN, React, SWIFT, RoR

**Services:** Research, Brand design, Imagery, Iconography, UI/UX, Web development, Mobile development, API & Integrations, Support


### Visit Cherkasy

URL: https://everlabs.com/projects/visit-cherkasy
Markdown: https://everlabs.com/projects/visit-cherkasy.md

Social travel-promotion project

Travel software, created in collaboration with local state institution. The mobile app is designed and crafted for free as part of a social travel-promotion initiative.

**Technologies:** KOTLIN, SWIFT, RoR

**Services:** UI/UX, Mobile development, API & Integrations, Support


### Fuel delivery

URL: https://everlabs.com/projects/fuel-delivery
Markdown: https://everlabs.com/projects/fuel-delivery.md

Mobile app for curbside fuel delivery

Custom iOS and Android mobile apps for the company that delivers fuel direct to parked cars on behalf of car-sharing companies and private clients

**Technologies:** KOTLIN, SWIFT, RoR, React, HTML5, CSS, JavaScript

**Services:** Print design, Information architecture, UI/UX, Research, Web development, Mobile development, API & Integrations


## Articles


### From a Loom walkthrough to user stories: an open Agent Skill that turns screen recordings into specs

URL: https://everlabs.com/blog/video-to-spec
Markdown: https://everlabs.com/blog/video-to-spec.md
Author: Oleg Pasko
Published: 2026-08-07
Reading time: 6 min

Recording feedback as a Loom is easy; turning it into work is not. video-to-spec is the open Agent Skill I built to close the gap – user stories with the screenshots attached.

Most of my product feedback – and by now a lot of our projects' feedback – starts as a Loom. I open the thing I want to improve, hit record, and think out loud while browsing it: ideas, small annoyances, half-formed feature requests, the occasional "wait, no, forget that", "this is weird". It is the most honest snapshot of what I want, and nobody can build from it.

So someone watches it and writes the tasks. What comes out is their reconstruction of my vision, and the part carrying most of the meaning – what was on screen when I said it – does not survive the trip into a ticket.

Writing the requirement down properly is itself the problem. You censor the brainstorm out of it, because a strict spec is a courtesy – you respect whoever has to work with that file after you. (Not that we do not respect AI – I would rather stay on its good side, and we do say thank you.)

That respect is what restricts you. No jumping between ideas, no going back on what you said two minutes ago, no half-formed thoughts on the page. A recording has none of those rules, and cleaning it up afterwards is mechanical work – the part AI is actually good at.

## What I built

video-to-spec turns a screen recording into specs: a folder of user stories, each carrying the exact screenshots I was looking at when I spoke. It reads anything ffmpeg reads: a downloaded Loom, a QuickTime capture, `.mp4`, `.mov`, `.webm`. No new service, no account, no Loom integration: you point it at a file on your disk and it writes markdown next to your code. MIT-licensed, and the first skill in our [public-skills repo on GitHub](https://github.com/everlabs/public-skills).

![How video-to-spec works: recording to frames and transcript, to timeline, to task extraction, to spec folder](/images/blog/video-to-spec/scheme.svg)

## How it works

ffmpeg pulls one frame per second out of the video. A perceptual hash throws away the near-identical ones – ImageMagick if you have it, Python's imagehash as the fallback – so a cursor drifting across a static page collapses into one frame, while modals, scrolls, navigation and form input survive as separate screens.

For the words, the cheapest path is the one where you already have them: if the recording comes with an `.srt` or `.vtt` beside it, the skill skips transcription. Loom transcribes on every plan, though [downloading the caption file](https://support.atlassian.com/loom/docs/loom-video-transcription-and-closed-captions) is a Business or Enterprise feature.

Otherwise it transcribes. If mlx-whisper or openai-whisper is installed on your machine, the skill prefers those – free, and your audio never leaves the laptop. If neither is there, it can call OpenAI's API at their [published Whisper price](https://developers.openai.com/api/docs/pricing) of $0.006 per minute as of August 2026; there it is told to estimate the cost, say plainly that your audio is being uploaded, and wait for a yes. It never installs anything on your behalf.

Every spoken segment is then matched to the screen that was visible when it was said. The skill reads that timeline looking for four kinds of signal:

- **Direct tasks** – "we need to make sure...", "let's fix...", "this should...".
- **Reversals** – "wait, actually", "no, scratch that". The later statement wins, and the earlier intent is rewritten or dropped.
- **Dissatisfaction** – "this is weird", "doesn't make sense", "would be better if". These become a task only when the change can be named; otherwise it is logged as an open question.
- **Indirect requirements** – something visibly wrong in the frame that I am describing but never actually asking anyone to fix.

The frame, not the words alone, is the source of truth for what I was looking at. That is the reason to extract tasks from a video rather than its transcript.

## What comes out

A folder in your repo, named after the recording and the day it was made: an index with the task list and open questions, one numbered file per task, and the frames they reference.

Each task file carries a short imperative title, a type, the source timestamps, a user story, the motivation, the context with screenshots inline, and what to do. What and why, not how.

Everything is drafted first, then the skill asks its questions in one batch: ambiguities, contradictions, places it over-reached. You answer once and the affected specs are updated. I supervise at a named gate, I do not author.

The folder is delegable. To sub-agents, to an AI coding agent, or to a human team – it is markdown with screenshots, and a person reads it as easily as a model.

## A real run

The last one I ran was 4 minutes 16 seconds long: a walkthrough of a playground project of mine, a small macOS menu-bar app that records my meetings. It came back with nine task specs. All nine are implemented, and one of them by not building it: the spec had captured a non-goal.

Part of that recording is me demoing [Wispr Flow](https://wisprflow.ai/r?OLEG44) (referral link), a voice-to-text tool I really like, and pointing at its dropdown because I wanted the same submenu in mine. The spec came back carrying my own words – "Past notes. I also would like to introduce a new past notes. We already have the account and we can pull it from the hub." – and the exact frame I was pointing at.

![A generated task spec: Add a Past notes list, with user story, motivation quoting the recording, and the matched frame](/images/blog/video-to-spec/example-spec.svg)

The transcript actually heard "fast notes". It came back in the batch of clarifying questions, I confirmed "Past notes", and the spec was fixed before any code was written.

## One SKILL.md, any agent

video-to-spec is an **Agent Skill** – a folder with a `SKILL.md` in it, following the standard documented at [agentskills.io](https://agentskills.io). The format was originally developed by Anthropic, released as an open standard, and is now read by a growing number of agent products.

So it works with Claude Code, Codex, Cursor, OpenCode, Gemini CLI and dozens of other skills-compatible agents – not because anyone shipped an integration, but because they all read the same file. Progressive disclosure keeps it cheap: your agent loads only the name and description at startup, about a hundred tokens, and reads the rest when a task matches.

One caveat, since I would want to know. I have run it in Claude Code; elsewhere it should behave the same – it is spec-conformant and calls nothing product-specific – but I have not tested each harness.

## Install

```
git clone https://github.com/everlabs/public-skills.git
cp -R public-skills/video-to-spec ~/.claude/skills/
```

That is the Claude Code path; for Codex, Cursor, OpenCode and Gemini CLI, copy it into `~/.agents/skills/` instead – the interoperable path they all read. It needs ffmpeg and an image hasher, and no pip packages on the default path. Then name your file and say "turn this walkthrough into tasks".

The point was never the transcription. The point is that the spec is the artifact – not the ticket, not somebody's notes from the call – and a spec with the screenshot attached is delegable, so the code gets written against the actual screen, not someone's memory of a walkthrough. Ambiguity is cheapest to kill early, and mine starts in a recording.

It is open source at [github.com/everlabs/public-skills](https://github.com/everlabs/public-skills). Try it on your next walkthrough and tell me where it gets you wrong.



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### Is Ruby on Rails still worth using in 2020?

URL: https://everlabs.com/blog/is-ruby-on-rails-still-worth-using-in-2020
Markdown: https://everlabs.com/blog/is-ruby-on-rails-still-worth-using-in-2020.md
Author: Oleg Pasko
Published: 2020-07-24


Ruby was realized in 1996, and now it's pretty mature language. Is it still actual nowadays?

If you doubt about choosing technology for your startup, you probably heard about Ruby on Rails (ROR) framework. Everyone related to startups was talking about it all the past decade. I consistently was fronting with it reading non-technical books like _Hatching Twitter: A True Story of Money, Power, Friendship, and Betrayal_ (as you likely know Twitter originally was built with Ruby on Rails) or _Deep Work: Rules for Focused Success in a Distracted World_ (there are some parts dedicated to the efficiency of David Heinemeier Hansson, creator of Ruby on Rails). Also, I remember an IT-conference from 5 years ago where the speaker said, 'Everyone is going crazy about Ruby on Rails here in Silicon Valley.' 

  

So, 5 years passed from that conference, 10 years from I started learning it, and almost 15 years since Ruby on Rails was invented. I'll not dig into the history of the framework but focus on actual usage.

  

The main reason to use ROR nowadays is its ability to build web applications and APIs really fast. Also, it allows you to change and pivot your project in a pretty fast and cost-effective way. I've never met a project which was never changed during the development stage. We live in dynamic and active times. The only option for a 'static' way is development for space projects, some medicine software for hardware, etc., something that requires a predictive approach and waterfall methodology.

  

Actually, nothing changed for the past years. There are many new frameworks and trendy solutions, but nothing has to replace Ruby on Rails in this area. Maybe the Laravel framework, but it looks like a compromise between impressive Rails architecture and the number of available PHP developers in the market.

  

Let's check [GitHub's rating of languages](https://madnight.github.io/githut/#/pull_requests/2020/1) for 2020 and select TOP-10. Github is the most popular platform for code storage, sharing, and collaborating. It is an excellent way to understand the popularity of technologies.

  

- JavaScript 18.703% (-1.406%)
- Python 16.238% (-1.654%)
- Java 10.938% (+0.538%)
- Go 9.005% (+0.978%)
- C++ 7.423% (+0.040%)
- Ruby 6.812% (+0.342%)
- TypeScript 6.769% (+1.522%)
- PHP 5.127% (-0.458%)
- C# 3.835% (+0.141%)
- C 3.181% (-0.203%)

  

First of all, GitHub themself written with Ruby on Rails. But let's check it one-by-one. 

Is **JavaScript** (JS) a good one language? For sure. All the programming languages in this list are good; otherwise, they didn't exist and were not so popular. Along with the **TypeScript** , this is a language for the Frontend part of your web-application. Any interactive, rich client-side frameworks like ReactJS, Angular, VueJS are based on JavaScript. Because it's pretty popular and has a low entry-level (many frontend guys start with HTML/CSS and add JS after), there are many ways to adapt it for other cases. Cross-platform apps for iOS, Android, macOS, Windows (Ionic, Electron, React Native) made with JavaScript. Backend frameworks, like NodeJS, use JavaScript too. 

So, it's really wide-used, and many developers use it. It's faster than ruby but much slower in project development speed in comparison to ruby. Ruby on Rails is a high-level framework created to make project development fast. NodeJS is useful for creating fast projects.

I like to repeat: 'Make your project popular first and decide how to improve the speed after.' Twitter is a great example. Initially built with Ruby on Rails, it doesn't use RoR as a core technology anymore.

  

Do you need faster solutions, e.g., processing millions of events per hour? Don't use Ruby on Rails. Use **Go lang**! Do you feel your Ruby on Rails app gets bottlenecks in performance? Separate a microservice with Go. It will be much slower in development speed but resolve a specific issue for your project.

If you already know that you have to handle high-load data processing, take a look at Ruby on Rails as a core for SaaS functionality. It's possible to build SaaS part with user registration, billing, management of app with RoR (Ruby on Rails is a top-notch technology for SaaS), and create a high-load piece with Amazon Lambda + Amazon GateWay, or mentioned Go, or NodeJS.

  

**Python** is the best language for Machine Learning and Artificial intelligence solutions. There is also a popular framework Django based on Python, but it has a much smaller community and libraries for the Web. This is a similar case to JavaScript. Someone knows Python and started to use it for the Web.

  

**С++** and **C** are not for the Web. They are too low-level languages and used to create other programming languages (e.g., ruby written in C) or firmware.

  

**PHP...** no. It was the right choice 20 years ago and still the right choice for your WordPress website. There are still many PHP developers, but I heard hundreds of cases of devs migration from PHP to Ruby/JS/Go/Python, etc., but none switched to PHP. Just use it to support and maintain current solutions. Most websites on the Web still work with PHP. I mentioned Laravel, but it doesn't have such a good community and ready solutions. Just notice, Ruby (6.812%) is almost entirely represented with Ruby on Rails. Laravel is only by a small percentage of active PHP-developers (5.127%).

  

**Java** is excellent for your next financial project, like banking, money transfer service. It has many related libraries. You can decide to use Groovy on Grails with Java, influenced by Ruby on Rails architecture.

  

And the last option is C# with likely **.NET** framework. A good one for large enterprise projects. If you build a startup with up to a few millions of people, go with Rails. It's all about startups. If you have millions to build enterprise solutions, go with .NET.

  

As you see, each language is perfect for each specific case. 

Choose RoR if you'd like to build a startup, SaaS product, social platform, API for a logistic app, e-commerce solutions (Shopify is written in Ruby on Rails), a marketplace, CRM, or another custom-made solution for your business. 

  

Fast in development, perfect for Rapid application development, loved by developers and community, has many ready solutions and libraries for the Web (aka 'gems'). 

**We still in passion with Ruby on Rails.**



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