Alexander Pshenichniy 🇺🇦

IT

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@d_yelisevich @grok это правда?

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@Paulina_SPb «Анна Каренина»: «Женщины сначала прыгают от любви, а потом под поезд»

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@ankurmans @vercel same error

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@vitddnv SEND

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@tibo_maker @tstereth here's the update of this report https://t.co/LWqXgc0jMg

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@vitddnv MRR

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@magnalov @andrey_sitnik Лучше попробуйте Tanstack Start

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@ShahidA99580 sound

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@ipivto дружище, ты засветил все свои passwords/secret keys на скрине. не делай так :)

[Addy Osmani @addyosmani]

Every time we've made it easier to write software, we've ended up writing exponentially more of it.

When high-level languages replaced assembly, programmers didn't write less code - they wrote orders of magnitude more, tackling problems that would have been economically impossible before. When frameworks abstracted away the plumbing, we didn't reduce our output - we built more ambitious applications. When cloud platforms eliminated infrastructure management, we didn't scale back - we spun up services for use cases that never would have justified a server room.

@levie recently articulated why this pattern is about to repeat itself at a scale we haven't seen before, using Jevons Paradox as the frame. The argument resonates because it's playing out in real-time in our developer tools. The initial question everyone asks is "will this replace developers?" but just watch what actually happens. Teams that adopt these tools don't always shrink their engineering headcount - they expand their product surface area. The three-person startup that could only maintain one product now maintains four. The enterprise team that could only experiment with two approaches now tries seven.

The constraint being removed isn't competence but it's the activation energy required to start something new. Think about that internal tool you've been putting off because "it would take someone two weeks and we can't spare anyone"? Now it takes three hours. That refactoring you've been deferring because the risk/reward math didn't work? The math just changed.

This matters because software engineers are uniquely positioned to understand what's coming. We've seen this movie before, just in smaller domains. Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we'd need fewer developers. Each one instead enabled us to build more software.

Here's the part that deserves more attention imo: the barrier being lowered isn't just about writing code faster. It's about the types of problems that become economically viable to solve with software. Think about all the internal tools that don't exist at your company. Not because no one thought of them, but because the ROI calculation never cleared the bar. The custom dashboard that would make one team 10% more efficient but would take a week to build. The data pipeline that would unlock insights but requires specialized knowledge. The integration that would smooth a workflow but touches three different systems.

These aren't failing the cost-benefit analysis because the benefit is low - they're failing because the cost is high. Lower that cost by "10x", and suddenly you have an explosion of viable projects. This is exactly what's happening with AI-assisted development, and it's going to be more dramatic than previous transitions because we're making previously "impossible" work possible.

The second-order effects get really interesting when you consider that every new tool creates demand for more tools. When we made it easier to build web applications, we didn't just get more web applications - we got an entire ecosystem of monitoring tools, deployment platforms, debugging tools, and testing frameworks. Each of these spawned their own ecosystems. The compounding effect is nonlinear.

Now apply this logic to every domain where we're lowering the barrier to entry. Every new capability unlocked creates demand for supporting capabilities. Every workflow that becomes tractable creates demand for adjacent workflows. The surface area of what's economically viable expands in all directions.

For engineers specifically, this changes the calculus of what we choose to work on. Right now, we're trained to be incredibly selective about what we build because our time is the scarce resource. But when the cost of building drops dramatically, the limiting factor becomes imagination, "taste" and judgment, not implementation capacity. The skill shifts from "what can I build given my constraints?" to "what should we build given that constraints have in some ways been evaporated?"

The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don't reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.

The pattern is so consistent that the burden of proof should shift. Instead of asking "will AI agents reduce the need for human knowledge workers?" we should be asking "what orders of magnitude increase in knowledge work output are we about to see?"

For software engineers it's the same transition we've navigated successfully several times already. The developers who thrived weren't the ones who resisted higher-level abstractions; they were the ones who used those abstractions to build more ambitious systems. The same logic applies now, just at a larger scale.

The real question is whether we're prepared for a world where the bottleneck shifts from "can we build this?" to "should we build this?" That's a fundamentally different problem space, and it requires fundamentally different skills.

We're about to find out what happens when the cost of knowledge work drops by an order of magnitude. History suggests we (perhaps) won't do less work - we'll discover we've been massively under-investing in knowledge work because it was too expensive to do all the things that were actually worth doing.

The paradox isn't that efficiency creates abundance. The paradox is that we keep being surprised by it.

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@DaemonES мой тиммейт читает как «Клаудия» 😁

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@Mho_23 Voice

[Miko @Mho_23]

i found a way to make AI videos that don't sound like AI...

example below....

sora 2, veo 3.1, kling 2.6 are decent but they all have that robotic AI accent that everyone can detect immediately

and when people try replacing the voices with elevenlabs, it always sounds way too professional

like it's recorded on a professional mic in a studio

but if i'm making a UGC video of someone in their room, i don't want studio quality

it should sound like it's actually in that room

the workflow i use completely removes that fake AI voice sound

why this matters:

when your AI voices sound too polished or robotic, people notice. their guard goes up. they disengage.

but when your voices sound like they're coming from a real person in a real room, that's when they work the best

i created a guide breaking down exactly how:
> to create voiceovers that sound completely real
> to replace fake-sounding voices in AI videos (not elevenlabs)
> to make voices sound like they're in the actual environment, not a studio

RT + reply "VOICE" and i'll send you the FULL guide (must follow so i can DM)

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@davidfigeira WAN

[David @davidfigeira]

wan 2.2 is making video feel modular

you begin with one standard clip
add a single reference frame
and the model reconstructs the entire scene

→ same framing
→ same motion
→ same timing

but now you can change the mood
shift lighting direction
and generate cinematic variations without reshooting

brands use this to turn one talking-head ad
into multiple premium creatives
all from the same source video

i broke down the exact workflow

rt + comment “wan” and i’ll send it
(follow for dm)

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

@Mho_23 Hooks

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

5/ caddy setup idea:
route /v2/* → qstash dev server 8080
route everything else → your app on 3000. now you can tunnel only one port (caddy), and both qstash api + app endpoints are reachable

env setup becomes simple:
QSTASH_URL=https://<ngrok>
UPSTASH_WORKFLOW_URL=https://<ngrok>

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

1/ hit a super annoying thing: trying to test webhooks locally with tanstack start + upstash workflow (qstash under the hood)
everything works until you need context.createWebhook+ waitForWebhook

[Alexander Pshenichniy 🇺🇦 @apshenichniy]

4/ the clean fix: ngrok + caddy as a reverse proxy + qstash local dev mode
run both locally, then proxy them behind one public ngrok domain