stephenpontes

It's interesting to see Anthropic branching out into different areas now. First artifacts and now taking market share from companies like devin.

AI enables quick shipping, but the traditional moat of development no longer applies.

Didn't quite nail the labeling of each piece correctly for a small form factor PC build:

https://flipbook.page/n/12267bbfdeb043c3aa477337950b2b71

- M2 is labeled as GPU

- GPU is labeled as M.2 and RAM?

- RAM is labeled as GPU

- Random plant inside the case?

- This is also not a typical layout for a SFF PC

Great demo, interesting transitions and UI, but the model / generated information is definitely not correct.

I'd seen this bubble up through various feeds over the past week and didn't think to do any due diligence on the claims myself. It was particularly rampant on LinkedIn as evidence of turning the corner on validating LLMs and layoffs - "Look what you can accomplish with two people!"

I've had this discussion with a few colleagues now around how it feels like the same snake-oily SEO "experts" that popped up years ago have switched the grift to "AI". It's scarier now since LLMs feel like even _more_ of a black box than SEO to a lot of people.

Agree. I feel like most of the people sounding the alarm have been in the software-focused job hunting market for 6+ months.

Those who downplay it are either business owners themselves or have been employed for 2+ years.

I think a lot of software engineers who _haven't_ looked for jobs in the past few years don't quite realize what the current market feels like.

I’ve seen this play out at multiple startups. The people holding things together often don’t fit neatly into the KPIs or eng chart levels. They’re mentoring juniors, redesigning workflows, updating documentation, and bridging gaps between departments. Because their scope isn’t bundled into a single “initiative,” reviews don’t always capture their true impact.

I’ve felt this personally working on the design system used across the entire engineering org. Three years after I left, that system is still the foundation the team builds on. At the time, the cross-team coordination and invisible maintenance work pulled me away from more visible deliverables, so it was harder to show impact in a review cycle. But the endurance of that system is its own validation—it shows how much hidden glue work pays off when invested properly.

The takeaway for me is that the best orgs figure out how to see this kind of work before it fades into the background. If you can spot and reward it early, you not only retain the people doing it, you build resilience into the team itself.

I remember first hearing about protein folding with the Folding @Home project (https://foldingathome.org) back when I had a spare media server and energy was cheap (free) in my college dorm. I'm not knowledgable on this, but have we come a long way in terms of making protein folding simpler on today's hardware, or is this only applicable to certain types of problems?

It seems like the Folding @Home project is still around!

I grew up loving magic. I watched David Copperfield on a grainy old tv, and vividly remember rewatching taped performances of "The World's Greatest Magic" (https://en.wikipedia.org/wiki/The_World%27s_Greatest_Magic) trying to figure out how the big illusions were done. I was part of a magic club and loved peeking behind the curtain. It fascinated me how as you learned those building blocks of simple sleight of hand, you could compound and build on those components to pull off more and more impressive tricks. A double lift, palming, french drop, etc...all pulled together to a cohesive "trick".

I feel like a lot of what entertained me about magic also pulled me towards web development. Sites and interactions online seem like magic until you realize they also break down into simple problems, simple components that build upon one another to deliver the trick. That interest in figuring out how things work just never went away I guess!

I had almost this exact interview experience recently with a popular AI startup. The exercise was to build a search UI over a static array of dictionary terms. It was a frontend role so I wired it up with filter and startsWith and spent more time polishing the UI and UX.

The final interview question was: “Okay, how do you make this more performant?” My answer was two-tiered:

- Short term: debounce input, cache results.

- Long term: use Algolia / Elastic, or collaborate with a backend engineer to index the data properly.

I got rejected anyway (even with a referral). Which drove home OP's point: I wasn't being judged on problem solving, but auditioning for the "senior eng" title.

With candidate interview tools and coding aids increasingly hard to detect in interviews, this gap between interview performance and delivering in the role is only going to widen. Curious how many of these "AI-assisted hires" will start hitting walls once they're outside of the interview sandbox.

This immediately reminded me of the teamLab Planets [1] experience in Tokyo, Japan.

Specifically "Flowers and People, Cannot Be Controlled but Live Together" [2] - the entire soundtrack for the experience was incredible, and this ambient garden took me right back. Thank you for sharing!

[1] https://www.youtube.com/watch?v=F7nODEETR4s

[2] https://music.apple.com/jp/artist/hideaki-takahashi/30588056...

DIY Perks also attempted to make artificial sunlight at home, also focusing on things like Rayleigh scattering! This was a great watch:

https://www.youtube.com/watch?v=6bqBsHSwPgw

EDIT: After reading the article, I see the OP calls out DIY Perks specifically - the OPs design is much more compact :)

It's compact. The total size is 19cm x 19cm x 9cm. This is quite compact for a 5cm focal length and an effective lighting area of 18cm x 18cm. Reflective designs like the DIYPerks video or commercial products like CoeLux do not achieve this form factor.