Returning to the Rails World: What’s New and Exciting in Rails 8 and Ruby 3.3+

It’s 2025, and coming back to Ruby on Rails feels like stepping into a familiar city only to find new skyscrapers, electric trams, and an upgraded skyline.
The framework that once defined web development simplicity has reinvented itself once again.

If you’ve been away for a couple of years, you might remember Rails 6 or early Rails 7 as elegant but slightly “classic.”
Fast-forward to today: Rails 8 and Ruby 3.4 together form one of the most modern, high-performance, and full-stack ecosystems in web development.

Let’s explore what changed from Ruby’s evolution to Rails’ latest superpowers.


The Ruby Renaissance: From 3.2 to 3.4

Over the last two years, Ruby has evolved faster than ever.
Performance, concurrency, and developer tooling have all received major love while the language remains as expressive and joyful as ever.

Ruby 3.2 (2023): The Foundation of Modern Ruby

  • YJIT officially production-ready: Introduced a new JIT compiler written in Rust, delivering 20–40% faster execution on Rails apps.
  • Prism Parser (preview): The groundwork for a brand-new parser that improves IDEs, linters, and static analysis.
  • Regexp improvements: More efficient and less memory-hungry pattern matching.
  • Data class proposal: Early syntax experiments to make small, immutable data structures easier to define.

Ruby 3.3 (2024): Performance, Async IO, and Stability

  • YJIT 3.3 update: Added inlining and better method dispatch caching big wins for hot code paths.
  • Fiber Scheduler 2.0: Improved async I/O great for background processing and concurrent network calls.
  • Prism Parser shipped: Officially integrated, paving the way for better tooling and static analysis.
  • Better memory compaction: Long-running apps now leak less and GC pauses are shorter.

Ruby 3.4 (2025): The Next Leap

  • Prism as the default parser making editors and LSPs much more accurate.
  • Official WebAssembly build: You can now compile and run Ruby in browsers or serverless environments.
  • Async and Fibers 3.0: Now tightly integrated into standard libraries like Net::HTTP and OpenURI.
  • YJIT 3.4: Huge startup time and memory improvements for large Rails codebases.
  • Smarter garbage collector: Dynamic tuning for better throughput under load.

Example: Native Async Fetching in Ruby 3.4

require "async"
require "net/http"

Async do
  ["https://rubyonrails.org", "https://ruby-lang.org"].each do |url|
    Async do
      res = Net::HTTP.get(URI(url))
      puts "#{url} → #{res.bytesize} bytes"
    end
  end
end

That’s fully concurrent, purely in Ruby no threads, no extra gems.
Ruby has quietly become fast, efficient, and concurrent while keeping its famously clean syntax.


The Rails Revolution: From 7 to 8

While Ruby evolved under the hood, Rails reinvented the developer experience.
Rails 7 introduced the “no-JavaScript-framework” movement with Hotwire.
Rails 8 now expands that vision making real-time, async, and scalable apps easier than ever.

Rails 7 (2022–2024): The Hotwire Era

Rails 7 changed the front-end game:

  • Hotwire (Turbo + Stimulus): Replaced complex SPAs with instant-loading server-rendered apps.
  • Import maps: Let you skip Webpack entirely.
  • Encrypted attributes: encrypts :email became a one-line reality.
  • ActionText and ActionMailbox: Brought full-stack communication features into Rails core.
  • Zeitwerk loader improvements: Faster boot and reloading in dev mode.

Example: Rails 7 Hotwire Simplicity

# app/controllers/messages_controller.rb
def create
  @message = Message.create!(message_params)
  turbo_stream.append "messages", partial: "messages/message", locals: { message: @message }
end

That’s a live-updating chat stream with no React, no WebSocket boilerplate.


Rails 8 (2025): Real-Time, Async, and Database-Native

Rails 8 takes everything Rails 7 started and levels it up for the next decade.

Turbo 8 and Turbo Streams 2.0

Hotwire gets more powerful:

  • Streaming updates from background jobs
  • Improved Turbo Frames for nested components
  • Async rendering for faster page loads
class CommentsController < ApplicationController
  def create
    @comment = Comment.create!(comment_params)
    turbo_stream.prepend "comments", partial: "comments/comment", locals: { comment: @comment }
  end
end

Now you can push that stream from Active Job or Solid Queue, enabling real-time updates across users.

Solid Queue and Solid Cache

Rails 8 introduces two built-in frameworks that change production infrastructure forever:

  • Solid Queue: Database-backed job queue think Sidekiq performance without Redis.
  • Solid Cache: Native caching framework that integrates with Active Record and scales horizontally.
# Example: background email job using Solid Queue
class UserMailerJob < ApplicationJob
  queue_as :mailers

  def perform(user_id)
    UserMailer.welcome_email(User.find(user_id)).deliver_now
  end
end

No Redis, no extra service everything just works out of the box.

Async Queries and Connection Pooling

Rails 8 adds native async database queries and automatic connection throttling for multi-threaded environments.
This pairs perfectly with Ruby’s improved Fiber Scheduler.

users = ActiveRecord::Base.async_query do
  User.where(active: true).to_a
end

Smarter Defaults, Stronger Security

  • Active Record Encryption expanded with deterministic modes
  • Improved CSP and SameSite protections
  • Rails generators now use more secure defaults for APIs and credentials

Developer Experience: Rails Feels Modern Again

The latest versions of Rails and Ruby have also focused heavily on DX (developer experience).

  • bin/rails console --sandbox rolls back all changes automatically.
  • New error pages with interactive debugging.
  • ESBuild 3 & Bun support for lightning-fast JS builds.
  • Improved test parallelization with async jobs and Capybara integration.
  • ViewComponent and Hotwire integration right from generators.

Rails in 2025 feels sleek, intelligent, and incredibly cohesive.


The Future of Rails and Ruby Together

With Ruby 3.4’s concurrency and Rails 8’s async, streaming, and caching power, Rails has evolved into a true full-stack powerhouse again capable of competing with modern Node, Elixir, or Go frameworks while staying true to its elegant roots.

It’s not nostalgia it’s progress built on the foundation of simplicity.

If you left the Rails world thinking it was old-fashioned, this is your invitation back.
You’ll find your favorite framework faster, safer, and more capable than ever before.


Posted by Ivan Turkovic
Rubyist, software engineer, and believer in beautiful code.

What You Should Learn to Master but Never Ship

Every engineer should build a few things from scratch search, auth, caching just to understand how much complexity lives beneath the surface. But the real skill isn’t rolling your own; it’s knowing when not to. In the age of AI, understanding how things work under the hood isn’t optional it’s how you keep control over what your tools are actually doing.

There’s a quiet rite of passage every engineer goes through. You build something that already exists. You write your own search algorithm. You design your own auth system. You roll your own logging framework because the existing one feels too heavy.

And for a while, it’s exhilarating. You’re learning, stretching, discovering how the pieces actually work.

But there’s a difference between learning and shipping.


The Temptation to Reinvent

Every generation of engineers rediscovers the same truth: we love building things from scratch. We tell ourselves our use case is different, our system is simpler, our constraints are unique.

But the moment your code touches production when it has to handle real users, scale, security, and compliance you realize how deep the rabbit hole goes.

Here’s a short list of what you probably shouldn’t reinvent if your goal is to ship something that lasts:

  • Search algorithms
  • Encryption
  • Authentication
  • Credit card handling
  • Billing
  • Caching systems
  • Logging frameworks
  • CSV, HTML, URL, JSON, XML parsing
  • Floating point math
  • Timezones
  • Localization and internationalization
  • Postal address handling

Each one looks simple on the surface. Each one hides decades of hard-won complexity underneath.


Learn It, Don’t Ship It

You should absolutely build these things once.

Do it for the same reason musicians practice scales or pilots train in simulators. You’ll understand the invisible edges where systems fail, what tradeoffs libraries make, how standards evolve.

Build your own encryption to see why key rotation matters.
Write your own caching layer to feel cache invalidation pain firsthand.
Parse CSVs manually to understand why “CSV” isn’t a real standard.

You’ll emerge humbled, smarter, and far less likely to call something “trivial” again.

But then don’t ship it.


The Cost of Cleverness

Production is where clever ideas go to die.

The real cost of rolling your own isn’t just the initial build. It’s the invisible tax that compounds over time: maintenance, updates, edge cases, security audits, integration testing.

That custom auth system? It’ll need to handle password resets, MFA, SSO, OAuth, token expiration, brute-force protection, and GDPR deletion requests.

Your homegrown billing service? Get ready for tax handling, currency conversion, refund flows, audit trails, and legal exposure.

Most of us underestimate this cost by an order of magnitude. And that gap between what you think you built and what reality demands is where projects go to die.


The Wisdom of Boring Software

Mature engineering isn’t about novelty it’s about leverage.

When you use battle-tested libraries, you’re not being lazy. You’re standing on top of millions of hours of debugging, testing, and iteration that others have already paid for.

The best engineers I know are boring. They use Postgres, Redis, S3. They trust Stripe for billing, Auth0 for authentication, Cloudflare for caching. They’d rather spend their creative energy on business logic and user experience the parts that actually differentiate a product.

Boring software wins because it doesn’t collapse under its own cleverness.


Why This Matters Even More in the AI Era

Today, a new kind of abstraction has arrived: AI.
We don’t just import libraries anymore we import intelligence.

When you integrate AI into your workflow, you’re effectively outsourcing judgment, reasoning, and data handling to a black box that feels magical but is still software under the hood.

If you’ve never built or understood the underlying systems search, parsing, data handling, caching, numerical precision you’ll have no intuition for what the AI is actually doing. You’ll treat it as oracle instead of a tool.

Knowing how these fundamentals work grounds you. It helps you spot when the model hallucinates, when latency hides in API chains, when an embedding lookup behaves like a fuzzy search instead of real understanding.

The engineers who will thrive in the AI era aren’t the ones who blindly prompt. They’re the ones who know what’s happening behind the prompt.

Because AI systems don’t erase complexity they just bury it deeper.

And if you don’t know what lives underneath, you can’t debug, govern, or trust it.


When It’s Worth Reinventing

There are exceptions. Sometimes the act of rebuilding is the product itself.

Search at Google. Encryption at Signal. Auth at Okta.

If your business is the infrastructure, then yes go deep. Reinvent with intention. But if it’s not, your job is to assemble reliable systems, not to recreate them.

Learn enough to understand the tradeoffs, but don’t mistake knowledge for necessity.


The Real Lesson

Here’s the paradox: you can’t truly respect how hard these problems are until you’ve built them yourself.

So do it once. In a sandbox, on weekends, or as a thought exercise. Feel the pain, appreciate the elegance of the libraries you once dismissed, and move on.

That humility will make you a better engineer and a more trusted builder in the AI age than any clever homegrown library ever could.


Final thought:
Master everything. Ship selectively.

That’s the difference between engineering as craft and engineering as production.
And it’s the difference between using AI and actually understanding it.

?? → BI → ML → AI → ??

AI’s past and the future

Where acronyms in business come from, what they sold, who won, and what might come after “AI”

Acronyms are the currency of business storytelling. They compress complex technology into a neat package a salesperson can pitch in a single slide: CRM, ERP, BI, ML, AI. Each one marked a shift in what companies sold to their customers and how value was captured. I want to walk through that history briefly, honestly, with business examples and what “winning” looked like in each era and then make a practical, evidence-based prediction for what comes after AI. I’ll finish with concrete signs companies and entrepreneurs should watch if they want to be on the winning side next.


The pre-acronym age: data collectors and automation (before CRM/ERP took over)

Before the catchy three-letter packages, businesses bought automation and niche systems: financial ledgers, bespoke reporting scripts, and the earliest mainframe systems. The selling point was efficiency: replace paper, reduce human error, scale payroll or accounting.

Winners: large system integrators and early software firms that could deliver reliability and scale. Value to the customer was operational: fewer mistakes, faster month-end closes, predictable processes.

This era set the expectation that software replaces tedious human work an expectation every later acronym exploited and monetized.


CRM / ERP the era of process standardization and cross-company suites

Acronyms like ERP and CRM told customers what problem a vendor solved: enterprise resource planning for the core business, customer relationship management for sales and marketing. The message was simple: centralize and standardize.

Business sales example: SAP and Oracle sold ERP as a bet on process control; Siebel (then Oracle) sold CRM as the way to professionalize sales organizations. Projects were expensive, multi-year, and became investments in repeatability and governance. The commercial model was license + services. Success looked like longer, stickier contracts and high services revenue.

Winners: vendors who could sell a vision of stability and then deliver implementation expertise.


BI (Business Intelligence) data becomes a product

BI formalized the idea that data itself is valuable: dashboards, reports, and the ability to make decisions from consolidated datasets. The term was popularized in the late 1980s and 1990s as companies realized that aggregated data and fact-based dashboards could change executive decision making. BI vendors promised that data could be turned into actionable insight.

Business sales example: BusinessObjects, Cognos, MicroStrategy sold a reliable narrative centralize data, produce dashboards, enable managers to make informed choices. Customers were large enterprises whose decisions had big dollar consequences: pricing, inventory, and marketing allocation.

Success metric: adoption by management, ROI from better decisions, and a move to subscription models as vendors evolved. BI also laid the foundation for data warehouses and ETL pipelines the plumbing later eras would rely on.


ML (Machine Learning) predictions replace static dashboards

Machine learning shifted the promise from describing the past to predicting the future. ML isn’t a single product but a set of techniques that let systems learn patterns recommendations, fraud detection, demand forecasting. Its commercialization accelerated as larger datasets and compute made models useful in production. (Timeline of ML milestones is long from perceptrons to ImageNet and modern deep learning.)

Business sales example: Netflix used ML for recommendations (watch time → retention); Amazon used ML for recommendations and dynamic pricing; banks used ML for fraud detection. The product pitch became “we will increase revenue (or reduce losses) by X% using model-driven predictions.”

Success metric: measurable impact on key business metrics (conversion, churn, fraud rate) and repeatable MLops pipelines. Winning companies built both models and the integration into products and workflows the second part mattered as much as the model.


AI (Artificial Intelligence) foundation models, agents, and ubiquity

“AI” is a broader, more emotionally charged badge than ML. It promises not just predictions, but agency: systems that write, design, plan, and interact. The recent leap in capability comes from large foundation models and multimodal systems, and the market’s attention has become concentrated on a smaller set of platform players. OpenAI is the obvious poster child widely integrated and publicly visible and it’s now part of a small club of companies shaping how enterprises adopt AI. Others Anthropic, Google/DeepMind, Microsoft (as a partner and investor), Nvidia (as the infra champion) are also core to who wins in the AI era. Recent reporting and market movement underscore how concentrated and influential these players are.

Business sales example: AI is sold as both a strategic platform and as task automation. Microsoft + OpenAI integrations sell enterprise productivity gains; Anthropic partners with platforms and enterprise vendors to bring chat/agent capabilities into products; Nvidia sells the hardware that makes large models economically viable. Sales morph into partnerships (platform + integration) and usage-based monetization (API calls, seats for AI assistants, compute consumption).

Success metric: ecosystem adoption and sticky integrations. The winners aren’t just model makers they are the platforms that make models reliably usable within enterprise apps, the cloud vendors that provide infra, and the companies that embed AI into workflows to measurably lower costs or multiply revenue.


What’s next? Predicting the post-AI acronym

Acronyms rise from what businesses need to sell next. Right now, AI sells capability; tomorrow, the market will demand something different: not raw capability but safe, contextual, composable, and human-centric value. Based on where the money, engineering effort, and regulatory focus are going, here are a few candidate acronyms and my pick.

Candidate futures (short list)

  • CAI: Contextual AI
    Focus: models that understand user context (company data, regulations, customer history) and deliver context-aware outputs with provenance. Selling point: trust and relevance. Businesses pay for AI that “knows the company” and can operate under constraints.
  • A^2I / AI²: Augmented & Autonomous Intelligence
    Focus: agents that both augment humans and act autonomously on behalf of businesses (book meetings, negotiate, execute trades). Selling point: time reclaimed and tasks delegated with measurable outcomes.
  • DAI: Distributed AI
    Focus: moving models to the edge, on-device privacy, and federated learning. Selling point: privacy, latency, and regulatory compliance. Monetization: device + orchestration + certification.
  • HXI: Human-Centered Experience Intelligence (or HCI reimagined)
    Focus: design + AI that measurably improves human outcomes (productivity, wellbeing). Selling point: human adoption and long-term retention; less hype, more stickiness.
  • XAI: Explainable AI (commercialized)
    Focus: regulations and auditability breed a market for explainable models as first-class products. Selling point: compliance, audit trails, and legally defensible automation.

My prediction (the one I’d bet money on)

CAI: Contextual AI.
Why? The immediate commercial friction after capability is trust and integration. Companies will not pay forever for raw creativity if outputs can’t be traced to corporate data, policies, and goals. The era of foundation models created broad capabilities; the next era will productize those capabilities into contextualized, policy-aware services that integrate directly into enterprise systems (CRMs, ERPs, legal, finance) and produce auditable actions. In short: AI + enterprise context = the next product category.

Concrete signs for CAI already exist: enterprises demanding model fine-tuning on private corpora, partnerships between model-makers and enterprise software vendors, and regulatory attention pushing for explainability and provenance. Those are the ingredients for a context-first commercial product.

(If you prefer the agent narrative, A^2I where agents actually do things reliably and accountably is a close second. But agents without context are liability; agents with context are product.)


What winning looks like in CAI

If CAI becomes the next category, how do businesses win?

  1. Data integration champions vendors that make it trivial to connect enterprise data (ERP, CRM, contracts) to models with privacy and governance baked in. The sales pitch: “We connect, govern, and make AI outputs auditable.”
  2. Actionable interfaces not just a chat box, but agents that produce auditable actions inside workflows (e.g., “Create invoice,” “Propose contract clause,” “Adjust inventory reorder”). The pitch: “We reduce X hours/week for role Y.”
  3. Regulatory and risk products explainability, model cards, audit logs, and compliance workflows become table stakes. Vendors packaging those for regulated industries will command higher multiples.
  4. Infra + economics hardware and cloud vendors that optimize cost/performance for fine-tuned, context-rich models (Nvidia-like infra winners) will capture a slice. Recent market moves show infrastructure captures enormous value; watch the hardware and cloud players.

Practical advice for sellers and builders today

  • If you sell to enterprises: stop pitching “we use AI.” Start pitching what measurable outcome you deliver and how you keep it governed. Show integration architecture diagrams: where the data lives, what’s fine-tuned, and where the audit logs are.
  • If you build products: invest in connectors, provenance, and reversible actions. A product that lets customers roll back an AI decision will win trust and enterprise POs.
  • If you’re an investor or operator: look for companies that own context (industry datasets, domain rules, vertical workflows). Horizontal foundation models will be commoditized; contextual wrappers will be the economic moat.
  • If you’re an infra player: optimize for cost + compliance. The market will pay a premium for infra that matches enterprise security and cost constraints.

Example scenarios; how each era turned into commercial value

  • BI era: a retail chain buys a BI suite to consolidate POS data across stores. Result: optimized promotions, fewer stockouts, 3% margin improvement. The seller (BI vendor) expanded into recurring maintenance and cloud hosting.
  • ML era: an e-commerce platform adds recommendation models. Result: personalized homescreens boost AOV by 7%. The ML vendor sells models + integration and gets paid per API call and for model retraining.
  • AI era: an agency uses generative models to prototype marketing copy at scale. Result: faster iteration and lower creative costs; large platforms (OpenAI, Anthropic, Google) sell the models, cloud vendors sell the compute. OpenAI’s integrations made it a visible “winner” for developers and enterprises adopting chat/assistant features.
  • CAI era (predicted): the same retail chain buys a contextual assistant that reads contracts, vendor SLAs, and inventory rules, then suggests optimal promotions aligned with margin and regulatory rules. Result: promotions that respect contracts, better margins, and an auditable decision trail. Pricing: subscription + outcome share.

Acronyms are marketing. Value is behavioral change.

Acronyms succeed when they promise a specific, repeatable business result and when vendors can deliver measurable change in behavior. BI helped managers act on facts. ML helped products predict user intent. AI made interaction and creativity broadly available. The next profitable acronym my money is on CAI (Contextual AI) will sell trustworthy, context-aware automation that actually becomes part of the way companies operate.

If you’re building, selling, or investing: focus less on the label and more on the edges where value is realized integration, governance, measurable business outcomes. That’s where the next winners will be, and where your clients will write the checks.

The Vibe Code Tax

Momentum is the oxygen of startups. Lose it, and you suffocate. Getting it back is harder than creating it in the first place.

Here’s the paradox founders hit early:

  • Move too slowly searching for the “perfect” technical setup, and you’re dead before you start.
  • Move too fast with vibe-coded foundations, and you’re dead later in a louder, more painful way.

Both paths kill. They just work on different timelines.

Death by Hesitation

Friendster is a perfect example of death by hesitation. They had the idea years before Facebook. They had users. They had momentum.

But their tech couldn’t scale, and instead of fixing it fast, they stalled. Users defected. Momentum bled out. By the time they moved, Facebook and MySpace had already eaten their lunch.

That’s hesitation tax: waiting, tinkering, second-guessing while the world moves on.

Death by Vibe Coding

On the flip side, you get the vibe-coded death spiral.

Take Theranos. It wasn’t just fraud, it was vibe coding at scale. Demos that weren’t real. A prototype paraded as a product. By the time the truth surfaced, they’d burned through billions and a decade of time.

Or look at Quibi. They raced to market with duct-taped assumptions the whole business was a vibe-coded bet that people wanted “TV, but shorter.” $1.75 billion later, they discovered the foundation was wrong.

That’s the danger of mistaking motion for progress.

The Right Way to Use Vibe Coding

Airbnb is the counterexample. Their first site was duct tape. Payments were hacked together. Listings were scraped. It was vibe code but they treated it as a proof of concept, not a finished product.

The moment they proved demand (“people really will rent air mattresses from strangers”), they rebuilt. They didn’t cling to the prototype. They moved fast, validated, then leveled up.

That’s the correct use: vibe code as validation, not as production.

The Hidden Tax

The vibe code tax is brutal because it’s invisible at first. It’s not just money.

  • Lost time → The 6–12 months you’ll spend duct-taping something that later has to be rebuilt from scratch.
  • Lost customers → Early adopters churn when they realize your product is held together with gum and string. Most won’t return.
  • Lost momentum → Investors don’t like hearing “we’re rebuilding.” Momentum is a story you only get to tell once.

And you don’t get to dodge this tax. You either pay it early (by finding a technical co-founder or paying real engineers), or you pay it later (through rebuilds, lost customers, and wasted months).

How to Stay Alive

  1. Be honest. Call your vibe-coded MVP a prototype. Never pitch it as “production-ready.”
  2. Set a timer. Airbnb didn’t stay in duct tape land for years. They validated and moved on. You should too.
  3. Budget for the rebuild. If you don’t have a co-founder, assume you’ll need to pay engineers once the prototype proves itself.
  4. Go small but real. One feature built right is more valuable than ten features that crumble.

Final Word

The startup graveyard is full of companies that either waited too long or shipped too fast without a foundation. Friendster hesitated. Theranos faked it. Quibi mistook hype for traction.

Airbnb survived because they paid the vibe code tax on their terms. They used duct tape to test, then rebuilt before the cracks became fatal.

That’s the playbook.

Because no matter what the vibe code tax always comes due.

Is AI Slowing Everyone Down?

Over the past year, we’ve all witnessed an AI gold rush. Companies of every size are racing to “adopt AI” before their competitors do, layering chatbots, content tools, and automation into their workflows. But here’s the uncomfortable question: is all of this actually making us more productive, or is AI quietly slowing us down?

A new term from Harvard Business Review “workslop” captures what many of us are starting to see. It refers to the flood of low-quality, AI-generated work products: memos, reports, slide decks, emails, even code snippets. The kind of content that looks polished at first glance, but ultimately adds little value. Instead of clarity, we’re drowning in noise.

The Illusion of Productivity

AI outputs are fast, but speed doesn’t always equal progress. Generative AI makes it effortless to produce content, but that ease has created a different problem: oversupply. We’re seeing more documents, more proposals, more meeting summaries but much of it lacks originality or critical thought.

When employees start using AI as a crutch instead of a tool, the result is extra layers of text that someone else has to review, fix, or ignore. What feels like efficiency often leads to more time spent filtering through workslop. The productivity gains AI promises on paper are, in practice, canceled out by the overhead of sorting the useful from the useless.

Numbers Don’t Lie

The MIT Media Lab recently published a sobering study on AI adoption. After surveying 350 employees, analyzing 300 public AI deployments, and interviewing 150 executives, the conclusion was blunt:

  • Fewer than 1 in 10 AI pilot projects generated meaningful revenue.
  • 95% of organizations reported zero return on their AI investments.

The financial markets noticed. AI stocks dipped after the report landed, signaling that investors are beginning to question whether this hype cycle can sustain itself without real business impact.

Why This Happens

The root cause isn’t AI itself it’s how organizations are deploying it. Instead of rethinking workflows and aligning AI with core business goals, many companies are plugging AI in like a patch. “We need to use AI somewhere, anywhere.” The result is shallow implementations that create surface-level outputs without driving real outcomes.

It’s the same mistake businesses made during earlier tech booms. Tools get adopted because of fear of missing out, not because of a well-defined strategy. And when adoption is guided by FOMO, the outcome is predictable: lots of activity, little progress.

Where AI Can Deliver

Despite the noise, I don’t think AI is doomed to be a corporate distraction. The key is focus. AI shines when it’s applied to specific, high-leverage problems:

  • Automating repetitive, low-value tasks (think: data entry, scheduling, or document classification).
  • Enhancing decision-making with real-time insights from complex data.
  • Accelerating specialized workflows in domains like coding, design, or customer support if humans remain in the loop.

The companies that will win with AI aren’t the ones pumping out endless AI-generated documents. They’re the ones rethinking their processes from the ground up and asking: Where can AI free humans to do what they do best?

The Human Factor

We have to remember: AI isn’t a replacement for judgment, creativity, or strategy. It’s a tool one that can amplify our abilities if used thoughtfully. But when used carelessly, it becomes a distraction that actually slows us down.

The real productivity gains won’t come from delegating everything to AI. They’ll come from combining human strengths with AI’s capacity, cutting through the noise, and resisting the temptation to let machines do our thinking for us.


Final thought: Right now, most companies are stuck in the “workslop” phase of AI adoption. They’re generating more content than ever but producing less clarity and value. The next phase will belong to organizations that stop chasing hype and start asking harder questions: What problem are we actually solving? Where does AI fit into that solution?

Until then, we should be honest with ourselves: AI isn’t always speeding us up. Sometimes, it’s slowing everyone down.


20+ Years as a CTO: Lessons I Learned the Hard Way

Being a CTO isn’t what it looks like from the outside. There are no capes, no magic formulas, and certainly no shortcuts. After more than two decades leading engineering teams, shipping products, and navigating the chaos of startups and scale-ups, I’ve realized that the real challenges and the real lessons aren’t technical. They’re human, strategic, and sometimes painfully simple.

Here are the lessons that stuck with me, the ones I wish someone had told me when I started.


Clarity beats speed every time

Early in my career, I thought speed meant writing more code, faster. I would push engineers to “ship now,” measure velocity in lines of code or story points, and celebrate sprint completions.

I was wrong.

The real speed comes from clarity. Knowing exactly what problem you’re solving, who it matters to, and why it matters that’s what lets a team move fast. I’ve seen brilliant engineers grind for weeks only to realize they built the wrong thing. Fewer pivots, fewer surprises, and focus make a team truly fast.


Engineers want to care, they just need context

One of the most frustrating things I’ve witnessed is engineers shrugging at product decisions. “They just don’t care,” I thought. Until I realized: they do care. They want to make an impact. But when they don’t have context, the customer pain, the market reality, the business constraints,they can’t make informed decisions.

Once I started sharing the “why,” not just the “what,” engagement skyrocketed. A well-informed team is a motivated team.


Vision is a tactical tool, not a slogan

I’ve been guilty of writing vision statements that sounded great on slides but did nothing in practice. The turning point came when I started treating vision as a tactical tool.

Vision guides decisions in real time: Should we invest in this feature? Should we rewrite this component? When the team knows the north star, debates become productive, not paralyzing.


Great engineers are problem solvers first

I’ve worked with engineers who could write elegant code in their sleep, but struggle when the problem itself is unclear. The best engineers are not just builders, they’re problem solvers.

My role as a CTO became ensuring the problem was well-understood, then stepping back. The magic happens when talent meets clarity.


Bad culture whispers, it doesn’t shout

I’ve learned to pay attention to the quiet. The subtle signs: meetings where no one speaks up, decisions made by guesswork, unspoken assumptions. These moments reveal more about culture than any HR survey ever could.

Great culture doesn’t need fanfare. Bad culture hides in silence and it spreads faster than you think.


Done is when the user wins

Early on, “done” meant shipped. A feature went live, the ticket closed, everyone celebrated. But shipping doesn’t equal solving.

Now, “done” only counts when the user’s problem is solved. I’ve had to unteach teams from thinking in terms of output and retrain them to think in terms of impact. It’s subtle, but transformative.


Teams don’t magically become product-driven

I used to blame teams for not thinking like product people. Then I realized the missing piece was me. Leadership must act like product thinking matters. Decisions, recognition, discussions, they all reinforce the mindset. Teams reflect the leadership’s priorities.


Product debt kills momentum faster than tech debt

I’ve chased the holy grail of perfect code only to watch teams get bogged down in building the wrong features. Clean architecture doesn’t save a product if no one wants it. Understanding the problem is far more important than obsessing over elegance.


Focus is a leadership decision

I once ran a team drowning in priorities. Tools, frameworks, and fancy prioritization systems didn’t help. The missing ingredient was leadership. Saying “no” to the wrong things, protecting focus, and consistently communicating what matters that’s what accelerates teams.


Requirements are not the problem

If engineers are stuck waiting for “better requirements,” don’t introduce another process. Lead differently. Engage with your team, clarify expectations, remove ambiguity, and give feedback in real time. Requirements are never the bottleneck leadership is.


The hard-earned truth

After twenty years, I’ve realized technology is the easy part. Leadership is where the real work and the real leverage lies.

Clarity, context, vision, problem-solving, culture, focus these aren’t buzzwords. They are the forces that determine whether a team thrives or stalls.

I’ve seen brilliant teams fail, and ordinary teams excel, all because of the way leadership showed up. And that’s the lesson I carry with me every day: if you want speed, impact, and results, start with the leadership that creates the conditions for them.

Why AI won’t solve these problems

With all the excitement around AI today, it’s tempting to think that tools can fix everything. Need better requirements? There’s AI. Struggling with design decisions? AI can suggest options. Want faster development? AI can generate code.

Here’s the hard truth I’ve learned: none of these tools solve the real problems. AI can assist, accelerate, and automate but it cannot provide clarity, set vision, or foster a healthy culture. It doesn’t understand your users, your market, or your team’s dynamics. It can’t decide what’s important, or make trade-offs when priorities conflict. Those are human responsibilities, and they fall squarely on leadership.

I’ve seen teams put too much faith in AI as a silver bullet, only to discover that the fundamental challenges alignment, focus, problem definition, and decision-making still exist. AI is powerful, but it’s a force multiplier, not a replacement. Without strong leadership, even the most advanced AI cannot prevent teams from building the wrong thing beautifully, or from stagnating in a quiet, passive culture.

Ultimately, AI is a tool. Leadership is the strategy. And experience with decades of trial, error, and hard-won insight is what turns potential into real results.

Stop procrastinating! How to prevent it.

Still trying to stop procrastinating?

stop procrastinating

There are probably numerous days that you site behind the computer to do some research or get some work done and doing a short break to read some news or check social updates and as you done this you aren’t aware that time passes as you jump from one link to another while the time is passing by rapidly. You have probably tried many things, like avoiding to use those sites, setting time aside for short breaks or some other solution but every time you spend more time doing nothing than to spend that time into something productive.

My way of curbing procrastination time to minimum

You probably procrastinate as everyone but you don’t do it so efficiently, there are many ways to curb this behavior especially with avoiding reading the news updates all the time. We live in an era of information overload and we developed a habit of a need to be constantly updated with latest updates.

I am using few tools which are completely free of cost for basic usage you will need to prevent procrastination. Here is how I do it:

I have installed self control app on my computer and you can get it at this link. It is free of cost. Basically what it does is that you add a list of web sites that you want to block for a selected period of time.

You are probably wondering now which pages should I add to this blacklist. Well the obvious sites should be the social networks. Most of the links we click to other sites are coming from there to funny videos or interesting stories. There are ton of these sites, but major one should be Facebook, Twitter, Google+ etc. Though have in mind if you develop for social network logins you have to keep alert to remove them for that period otherwise you can do no related work to it for that day. Other useful list of sites that need to be blacklisted are on your history list in your favorite browser. Go through the history and check all the sites that shouldn’t be there during your work hours and add them to the list.

Next step is to add time inside self control for how long it should be blocked. First advice never do mistake and set it over 24 hours. Perfect time to set the limits is 10-12 hours. You would probably say I don’t work so many hours and I agree you mustn’t but from the time you set the time and all other chores you need to do during the day believe me that is the most optimal time especially if you are working for yourself or being in a startup environment. It is dynamic over the day so keep on tracking your time that way.

So next step is to activate the self control and you magically stop procrastinating. Wrong. Keep on reading.

Continue reading “Stop procrastinating! How to prevent it.”

Four Questions About Leadership

I hear four questions asked about leadership often. This article gives a short answer to each of these important questions.

Why Does Leadership Matter?

Parents universally hope that their children develop leadership qualities. They know that leaders are people who are effective in what they do, are respected by others, and typically rewarded for those skills in a variety of ways. It is in these formative years that, through our parents, we first see leadership as desirable and important.

As young people we look up to people around us that motivate and listen to us people that seem like real-life heroes. We consider these people leaders.

As we grow we begin to relate leaders to their jobs and ministers, teachers, police officers. And later Mayors, Presidents, and CEO’s…

As adults all of these thoughts and experiences define why we think leaders have desirable traits and play roles we admire (and why we desire these things for our children).

All of these experiences and thoughts help us define why leadership matters and it matters because leaders make a difference and can shape the future. It matters because leaders are valued and valuable. In everyone’s mind leadership, especially when it is good, matters. Continue reading “Four Questions About Leadership”

How to be success

You may be smart, you may be talented, you may be handsome, but you aren’t successful. After all, the guy that never was that gifted or never had great ideas and he has less brains than you, is now your supervisor!

How did that happen? Why do all of us with the knack to be great, never saw our dreams come true? And what is the common characteristic between successful people?

According to statistical researches, what divides successful people from non-successful is that the former had a particular goal in their life and worked hard to achieve it. On the contrary, people that had many goals and ideas, but spent more time talking about them than working on them, never managed to make their dreams come true. Continue reading “How to be success”