razoorka
Joined 1 year ago
Karma
49
Submissions
47
Member since
Oct 4
Recent submissions
Comment · 3 months ago
Five US AI labs held every advantage and spent it on distraction, fear pricing and internal purges, while China closed most of the gap under chip sanctions.
Comment · 5 months ago
Spotify told investors that 99% of engineers use AI weekly. Their own engineering blog tells a different story about what that means.
Comment · 1 year ago
This isn’t about innovation — it’s about metrics.
Sora 2 is the perfect example: a dopamine machine disguised as progress. It doesn’t solve real problems; it just creates endless engagement loops to inflate “usage” numbers for investors.
The identical logic drives AI erotica — maximize attention, claim it’s “user engagement,” and call it growth. When the core product stops evolving, they start monetizing loneliness.
It’s not about what people need, it’s about what still moves the charts.
Comment · 1 year ago
Exactly! software is the only engineering discipline where failure has no liability and degradation has no visibility.
Bridges collapse once. Software collapses silently, one abstraction at a time. The incentives are inverted: short-term growth gets rewarded, long-term stability gets deprecated.
When there’s no physical consequence for failure, “good enough” becomes the design philosophy.
Comment · 1 year ago
Completely agree, software’s “materials” haven’t improved, only the scaffolding around them.
We’ve industrialized the process without industrializing the discipline. The result is mass-produced code built on shaky abstractions, fast to assemble, and faster to decay.
Linux and curl weren’t built on sprints or OKRs. They were built on ownership, long time horizons, and the idea that stability is innovation when everyone else is optimizing for speed.
Comment · 1 year ago
That’s spot on - and it’s amplified by how the market rewards velocity signaling over craftsmanship.
Teams are optimized for output volume, not outcome quality. Hiring pipelines favor those who can “ship fast,” while the systems they ship into grow exponentially more complex. The result: shallow competence at scale.
AI just poured fuel on it - it lets everyone look 30% more productive while compounding the same underlying brittleness.
Comment · 1 year ago
Exactly! abstraction is the new bureaucracy.
Each layer promises efficiency but adds hidden coordination cost. Ten years ago, a web app meant a framework and a database. Now it’s React → Electron → Chromium → Docker → Kubernetes → managed DB → API gateway - six layers deep to print “Hello, world.”
Every abstraction hides just enough detail to make debugging impossible. We’ve traded control for convenience, and now no one owns the full stack - just their slice of the slowdown.
I write about engineering leadership, system design, and the uncomfortable truths behind tech’s productivity myths. Background in Node.js, React, TypeScript, and AI-driven architectures.
From Ukraine, currently working remotely with US-based companies. Obsessed with data, performance, and reducing noise in engineering management.
Interests:
Systems that improve engineering velocity without adding bureaucracy
AI in production (not demos)
Hiring accuracy, 90-day trials, and retention frameworks
The intersection of engineering culture, quality, and economics