Robin Penea

Software engineer / Teacher

[Simon Willison @simonw]

Big model release today - I wrote about Claude Opus 5.5, GPT-6 Sol, and GPT-6 Luna - plus comparison grids of pelicans by the different model families at different reasoning levels https://t.co/R5lSdOZyLj

[Robin Penea @voidrob_]

@valigo I buy albums in MP3 format from Amazon. No DRMs, metadata is clean, copy to self hosted drive and enjoy. Been doing it for 15y, works great.

[Robin Penea @voidrob_]

@wordmandotdev @badlogicgames If you do and I can test it somehow, let me know. I'm quite interested in being able to use pi inside JetBrains products

[Robin Penea @voidrob_]

@jetbrains @wordmandotdev Yep, would definitely use it!

[Robin Penea @voidrob_]

@badlogicgames Yep, good default!

[Jason Fried @jasonfried]

Bragging about how much software you’re shipping with AI is like holding down the shutter button and bragging about how many photos you took.

[Robin Penea @voidrob_]

I wrote about how I use LLMs to learn. From this perspective, we are living something truly magical: https://t.co/uFosPuTUJM

[Robin Penea @voidrob_]

TIL I learned that you cannot write SIMD in Kotlin. After rewriting this part in Java, ~x4 gain on a mandelbrot fractal generator 🤯! Read more https://t.co/p5O4OzmJGS

[Robin Penea @voidrob_]

I use Claude Code, OpenAI Codex, sometimes Gemini and Amp. Apart from the (net) superiority of the quality of the answer, I'm amazed of the *speed* of @AmpCode compared to the other agents.

Genuine question @thorstenball: how is Amp so so much faster?

[Robin Penea @voidrob_]

This post https://t.co/9bGRsitUcf by @crowprose struck a chord in me about the whole AI hype. Those who have been here long enough can feel this longing for a more "artisanal" approach, but the efficiency gains can't be denied. Moreover, the prose is just plain poetic!

[Robin Penea @voidrob_]

How AI Impacts Skill Formation ? Anthropic Safety Fellow Program published a paper researching about it: https://t.co/L1wUXOJoZh.

As are all interesting things, it's nuanced. The task, AI usage, dev background, and types of interactions with the AI have an impact.

[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.

[Thomas H. Ptacek @tqbf]

This is a perfect piece of technical writing. https://t.co/64vw45650w

[Robin Penea @voidrob_]

Dang, this is so cool: https://t.co/osnut4N9pm

[Robin Penea @voidrob_]

Incredible article https://t.co/oYlz6Ley32, it makes you think. Deeply. Thanks @tferriss with the always high quality 5-bullet Friday

[Mitchell Hashimoto @mitchellh]

I've shared the full transcript of every agentic coding session from implementing the unobtrusive Ghostty updates and provided commentary alongside about my thinking and process. Total cost: $15.98 over 16 sessions. "Vibing a Non-Trivial Ghostty Feature" https://t.co/kRUSWyMSyW

[Robin Penea @voidrob_]

@martinrue Switched from DDG to @KagiHQ since May 2024. Couldn't be more happy : search results quality are stellar, page load is fast, and a ton of cool features (bangs, image search, AI summary, etc)

[Tim Urban @waitbutwhy]

Whyyy did no math teacher of mine ever show me a visual like this

[Simon Willison @simonw]

If you try nothing else today, give the demo at https://t.co/kUyz0fxIfE a go - it lets you stream video and audio directly to Gemini 2.0 Flash and get audio back, so you can have a real-time audio conversation about what you can see with the model

Feels like science fiction!