voxleone

Circles are aristocracy in Flatland[0]. In advertising[1], they reign no less supreme.

[0] https://www.gutenberg.org/ebooks/45506 [1] https://inkbotdesign.com/circle-logos/

Quod abundat non nocet

Qt is heavily represented in training data. Qt has existed for decades and the model has likely seen Qt tutorials, screenshots, source code, discussions, etc. As a result, "Qt application" is a highly coherent concept in the latent space. "Qt app" is almost like a named distribution.

Regarding regulation: I'm deeply invested in computer vision systems and i fear that policymakers [who are not deeply familiar with the technical distinctions between AI systems] may write broad rules that cover "AI" generally. In that case, computer vision companies and industrial users could end up subject to requirements that were largely motivated by concerns about generative AI and LLMs.

Other Global South countries show the same trend. At roughly 1.6, Brazil is now in a similar range to countries such as the United States (slightly below replacement).

There is an implicit disbelief in human, liberated and civilized by technology, acting civilly by choice and just because it is the right thing to do.

Their offer for subscribers are nothing but beads and sequins. A genuine offer [the kind i could accept] should contemplate an ad, bot and algo-free experience.

I'm tired of sorting out what github link is worth clicking, considering the deluge of vibecoded [I dont like this verb] new repos, the pride of minor accomplishments, no matter how meaningless: 'Look Mom. I made it say hello!'.

I hate to say that but maybe some kind of vetting on those pages is in order.

Maps, a great way to present music. Congrats for the work, brought back fond memories.

I can only hope that you'll be right someday. As of now, an RTX 3090 struggles to run most of the good local models.

What about aluminum? I haven’t been able to find a welder willing to work on my custom bike frame, so I’m considering learning to do it myself after taking a welding course. The custom car builders on TV make it look easy, but aluminum seems like an incredibly difficult material to work with.

Weren't we supposed to be living in the post-scarcity era?

I agree with that and stand by these words. If people want to call it gatekeeping, so be it. Programming, software engineering if you will, is a serious discipline, and this craze needs to stop. Software building should be regulated and properly accredited as any serious activity.

Quaternion graph traversal and control system

https://github.com/VoxleOne/SpinStep

There's a customary way to post it here. "Show HN":

https://news.ycombinator.com/show

It’s almost suspiciously elegant: focus on transformations and their composition, and the structure takes care of itself.

We're heading to a future where (when) friction is a luxury. Anyways, I thank the organizers for the rare opportunity. Long live Blogosphere.app, long live blogs.

We went from NASA Jet Propulsion Laboratory’s Power of Ten rules to ‘have you tried restarting Microsoft Outlook?’

https://en.wikipedia.org/wiki/The_Power_of_10:_Rules_for_Dev...

This. My personal style have always been llm-like, including the generous use of em-dashes, and "not-only-this-that" style mannerisms. It' increasingly difficult to retain reputation.

That’s a very good objection, and it’s pointing at a real pressure point in our framework.

Short answer: it’s close, but incomplete. It’s not that time organizes a log of reality; rather, reality is the accumulation of committed transitions. What you’re calling a ‘log’ it’s the ontological structure itself.

I gather you're basically saying: what we see as a transition ≠ what’s actually happening at the fundamental level. This is a legitimate and deep problem.

You’re right that observed transitions may not compose cleanly. In the Functional Universe, composition is a property of fundamental transitions. What we observe are often coarse-grained projections of many underlying transitions, which can obscure compositional structure.

Neural cellular automata are interesting because they shift learning from “predict tokens” to “model state evolution.” That feels much closer to a transition-based view of systems, where structure emerges from repeated local updates (transitions) rather than being encoded explicitly.

I'm working on a theoretical/computational framework, the Functional Universe, intended for modeling physical reality as functional state evolution. i would say it could be used to replicate your CA process. Won't link it here to signal my good faith discussing this issue - it's on my GH.

> An LLM can't even learn mid-conversation.

There’s an implicit assumption that scaling text models alone gets us to human-like intelligence, but that seems unlikely without grounding in multiple sensory domains and a unified world model.

What’s interesting is that if we do go down that route successfully, we may get systems with something like internal experience or agency. At that point, the ethical frame changes quite a bit.

If the comment missed the mark, I’d genuinely like to know where. I was trying to connect the “active archive” idea to history-dependent bias in collapse. Dismissing without engaging doesn’t really move the discussion forward.

This is a very interesting direction. The idea that dark matter could be a record of past decoherence events that then feeds back into future structure formation reminds me of a more general principle: history-dependent dynamics. In your “active archive” picture, the gravitational field is encoding where previous quantum selections occurred, and then biasing where future ones are likely to happen. That effectively introduces a kind of “inertia in configuration space,” where past realizations constrain future possibilities without fully determining them.

What I find particularly compelling is that this connects to a broader class of ideas where gravity (or dark matter) behaves like an information/entropy-related phenomenon rather than a particle sector, similar in spirit to approaches like emergent gravity.

>Where does engineering go?

Up the abstraction ladder; we conceive axioms and constraints; we define actors and objects; we direct rules, flows, sequences and say when and how each one of them lives and dies.

May you live interesting times (some say this is a curse)

There are great mid-sized towns along the the route, especially in Parana and Sao Paulo. I'd have liked to read about any impressions.

I've been coding since I was about 15 and still love it. These days I mostly build tailored applications for small and medium companies, often alone and sometimes with small ad-hoc teams. I also do the sales myself, in person. For me, not using LLMs would mean giving up a lot of productivity. But the way I use them is very structured. Work on an application starts with requirements appraisal: identifying actors, defining use cases, and understanding the business constraints. Then I design the objects and flows. When possible, I formalize the system with fairly strict axioms and constraints.

Only after that do LLMs come in, mostly to help with the mechanical parts of implementation. In my experience it's still humans all the way down. The thinking, modeling, and responsibility for the system are human. The LLM just helps move the implementation faster.

I also suspect the segment I work in will be among the last affected by LLM-driven job displacement. My clients are small to medium companies that need tailored internal systems. They're not going to suddenly start vibe-coding their own software. What they actually need is someone to understand the business, define the model, and take responsibility for the system. LLMs help with the implementation, but that part was never the hard part of the job.

These scans seem perfect for fabrication experiments.

I’ve been trying a workflow where the mesh is inverted and used to generate a 3D-printed mold, then I gelcast zirconia ceramic into it and sinter it. The result is a dense ceramic version of the sculpture.

If you downscale the models they work well as small desktop statues or relief friezes, and ceramic casting can preserve surprisingly fine detail from the scan.

I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper question: what is the moral status of such systems? If they have experiences or agency, treating them purely as tools could start to look uncomfortably close to slavery.

In the 90s people hoped Unified Modeling Language diagrams would generate software automatically. That mostly didn’t happen. But large language models might actually be the realization of that old dream. Instead of formal diagrams, we describe the system in natural language and the model produces the code. It reminds me of the old debates around visual web tools vs hand-written HTML. There seems to be a recurring pattern: every step up the abstraction ladder creates tension between people who prefer the new layer and those who want to stay closer to the underlying mechanics.

Roughly: machine code --> assembly --> C --> high-level languages --> frameworks --> visual tools --> LLM-assisted coding. Most of those transitions were controversial at the time, but in retrospect they mostly expanded the toolbox rather than replacing the lower layers.

One workflow I’ve found useful with LLMs is to treat them more like a code generator after the design phase. I first define the constraints, objects, actors, and flows of the system, then use structured prompts to generate or refine pieces of the implementation.