MurphyA/hacker-news huggingface dataset:
by nachocoll | 2026-03-10 11:42:46+00:00
The experience of vibe coding a real project in a specific domain (Emacs/Org-mode workflows) is more instructive than synthetic benchmarks. The observation that AI does well with the "mechanical" parts but struggles with the "why" behind design decisions resonates — and it's structural, not a temporary capability gap.

What strikes me about reflections like this is the recurring theme of the developer spending most of their effort on context management: explaining what already exists, why it exists that way, and what constraints matter. That labor is essentially creating the documentation that should have existed from the start.

The Agile Vibe Coding Manifesto frames this as a first-class concern: "context is explicit and versioned" and "knowledge remains accessible." The point isn't that AI is bad at Emacs Lisp — it's that domain knowledge that lives only in the developer's head is a fragility that vibe coding exposes rather than creates.

Worth reading if you're thinking about how to structure AI-assisted projects in specialized domains: https://agilevibecoding.org
by andy12_ | 2026-03-10 11:43:45+00:00
> Even with continuous backpropagation and "learning"

That's what I said. Backpropagation cannot be enough; that's not how neurons work in the slightest. When you put biological neurons in a Pong environment they learn to play not through some kind of loss or reward function; they self-organize to avoid unpredictable stimulation. As far as I know, no architecture learns in such an unsupervised way.

https://www.sciencedirect.com/science/article/pii/S089662732...
by palata | 2026-03-10 11:44:26+00:00
> Redundancy is a feature. Mismatches are information. Consider this:

Respectfully, if someone wrote code like this, I wouldn't want to work with them. I mean next step is "I copy paste code instead of writing functions, and in the comment above I mention all the other copies, so that it's easy to check that they are all doing the same thing redundantly".

> The arguments against copious inline comments are "but people don't update them" and "I can see less of the code".

Well no, that's not my argument. I have been navigating code for 20 years and in good codebases, comments are rare and describe something "surprising". Good code is hardly surprising.

My problem with "literate programming" (which means "add a lot of comments in the implementation details") is that I find it hard to trust developers who genuinely cannot understand unsurprising code without comments. I am fine with a junior needing more time to learn, but after a few years if a developer cannot do it, it concerns me.
by Grimblewald | 2026-03-10 11:45:02+00:00
no, the bar is accurate and descriptive descriptions. You know how AI words are typically hollow and devoid of meaning? loads of grammatically fine words but not actually
saying anything? well, these repos are the github version of that. Lots of words but so starved of meaning I shut off mentally trying to read half of them. Some descriptions are outright lies.
by hayes969 | 2026-03-10 11:45:30+00:00
Fast Image AI is equipped with the new-generation Nano Banana 2 AI model, comprehensively upgrading the AI Sticker Generator. It achieves leaps in generation speed, image quality, and detail restoration. Supporting one-click generation of multi-style stickers with stronger character consistency and precise text rendering, 4K HD output meets all scenarios of social expression, content creation, and brand marketing.
by cucumber3732842 | 2026-03-10 11:46:14+00:00
>A vehicle will materialize out of nowhere and crash into you.

God I hate these sort of responsibility shirking opinions and their peddlers.

I do this several times a day in a major US city for close to a decade now and I've never had a close call closer than the "two people trying to pass each other in the hallway" routine with a driver trying to take a right on red.

Vehicles and everything else on this rock flying through space obey the same laws of physics.

If the traffic on a road goes X miles per hour, then simply don't cross it where you don't have a sufficiently long line of sight. If crossing where the lines of sight are sufficient is not tractable due to traffic volumes or road construction then cross at a marked crossing, intersection that interrupts traffic flow or use proper body language and someone will stop for you.

Sure, you might get exceptionally unlucky and choose to cross at the exact minute some car that's a few standard deviations above the norm but you might also get hit by lightening.
by qsera | 2026-03-10 11:46:53+00:00
I think clients who care about getting good software will eventually require that LLMs are not directly used during the development.

I think one way to compare the use of LLMs is that it is like comparing a dynamically typed language with a functional/statically typed one. Functional programming languages with static typing makes it harder to implement the solution without understanding and developing an intuition of the problem.

But programming languages with dynamic typing will let you create a (partial) solutions with a lesser understanding the problem.

LLMs takes it even more easy to implement an even more partial solutions, without actually understanding even less of the problem (actually zero understanding is required)..

If I am a client who wants reliable software, then I want an competent programmer to

1. actually understand the problem,

2. and then come up with a solution.

The first part will be really important for me. Using LLM means that I cannot count on 1 being done, so I would not want the contractor to use LLMs.
by embedding-shape | 2026-03-10 11:47:23+00:00
> Are the Apple-owned app windows inconsistent too? I see some inconsistencies but it's generally 3rd party apps mixed with Apple's apps.

Does it matter if it's 3rd party apps or not? Wasn't a huge part of the sell with Apple's own GUI toolkits that all native apps work uniquely, but look familiar and like part of one and the same? The consistency and "all apps look and work great" I seem to recall being one of the "features" people used to tout about OSX.

FWIW; TFA compares the border radius of TextEdit and Calculator, both two Apple apps, built-in nonetheless.
by nachocoll | 2026-03-10 11:47:38+00:00
Using formal verification as the target for vibe coding is a fascinating stress test — if AI can produce verified correct code, it forces the question of what "understanding" means in this context. But I think the verification angle reveals something the article might understate: verified correctness of isolated functions doesn't address correctness of design intent.

A formally verified compiler component is correct with respect to its specification. But who wrote the specification, and does it capture the right semantics? In vibe coding contexts, the specification itself often emerges through iteration, which means you can have a chain of verified components implementing something the developer didn't fully intend.

This is the accountability gap the Agile Vibe Coding Manifesto addresses: "humans remain accountable for software systems" even when each component is technically verified. Verification guarantees implementation matches spec; it doesn't guarantee spec matches intent. That gap requires explicit, traceable decision-making that vibe coding workflows often skip.

Interesting experiment at the frontier of what AI-assisted formal methods can do: https://agilevibecoding.org
by iamcalledrob | 2026-03-10 11:47:54+00:00
This is an issue on Linux too, especially when Wayland is used and applications are responsible for drawing their own chrome. Ugh.

HOWEVER, due to the open nature of the platform, you can install an extension to clean this up. Now, all my windows have identical corner radii, strokes, shadows etc. My Linux desktop is, surprisingly, more consistent now than macOS in this regard.

https://extensions.gnome.org/extension/7048/rounded-window-c...
by hkonte | 2026-03-10 11:47:54+00:00
Point 4 is the key insight. The raw vs. structured prompt gap isn't just about code — it generalizes across all LLM tasks. Once you separate role/objective/constraints/output-format into discrete named blocks instead of prose, models follow every instruction rather than probabilistically dropping the less salient ones under attention pressure.

AGENTS.md is essentially this at the repo level. The same principle applied at the prompt level yields similar gains.

Built https://github.com/Nyrok/flompt around exactly this — decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Basically AGENTS.md for prompt structure itself.
by lproven | 2026-03-10 11:48:41+00:00
> Bodies acclimate.

Absolutely -- and at different rates.

I have had severe asthma since puberty. After a decade of suffering, good drugs arrived around the turn of the century, and I've been well controlled since and I've been pretty active.

But I am better at altitude. I have narrow obstructed tubes in my lungs. Thinner air flows through them more easily. The higher I am (on land, obviously) the more easily and efficiently I can breathe.

In the old days, asthma clinics were located up mountains for this reason.

I like spending time at altitude. Thinner air means more oxygen for me, which means I feel better and can be more active.

If I could live 3-4 km up all the time, without vast expense in somewhere tiny, I would.
by embedding-shape | 2026-03-10 11:49:26+00:00
> There are people who have OCD and can’t help but seeing these things. It’s great for coding and seeing minor changes but its shit for real life - trust me.

I don't have OCD, but easily notice inconsistencies in various design choices these mega-corporations continue to fumble.

It's less "OMG I can't focus on coding because Calculator and TextEdit aren't sharing the same border radius" but more "The UX/UI department seems like they're on perpetual vacation if Apple is letting simple things like this slip through", and this specific case is just an example, every version of macOS seems to get worse when it comes to consistency.
by A_D_E_P_T | 2026-03-10 11:51:21+00:00
Suno is transformer-based; in a way it's a heavily modified LLM.

You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible.

The same goes for LLMs and invention generally, which is why they've made no important scientific discoveries.

You can learn a lot by playing with Suno.
by hackermailman | 2026-03-10 11:52:34+00:00
I like how these listings have the mandatory 'must have 3 years experience' using models that have only been viable for 3 months.

I'm going in a different direction breaking all the imperative code slop being generated. It's like the early 2000s all over again if you're interested in security. The peak will be when some corps start selling SECURITY.md skill files that we will be able to load up in Alloy or Forge and easily break the logic https://forge-fm.github.io/book/2026/
by noemit | 2026-03-10 11:53:01+00:00
A counter example:

I've been wearing an Apple Watch for close to 10 years. I've tracked my weight as well along those years but nothing crazy like OP. The Apple watch tracked plenty.

I had some strange symptoms and two doctors insisted I had a weak heart and potential heart failure. This was shocking! Turns out I do have a really "weak" rhythm, but heart failure is when your heart is progressively getting worse in it's pumping. I don't even remember which metric he looked at in my Apple health - but basically my heart has always been this way. A doctor looking at a single data point might think I have abnormally low blood pressure/heart rate, but if I've had this for 10 years with no change, the medical assessment is very different - it means nothing. Sometimes boring data is exactly what you need. For this reason, I will probably always wear an Apple watch (or equivalent) moving forward.

Data can feel useless for 10 years until one day it becomes critical. The benefit is spiky and uneven.
by AnnaHP | 2026-03-10 11:53:04+00:00
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by AyyEye | 2026-03-10 11:53:16+00:00
I understand the desire to act holier than thou and pretend that going through a red light with no traffic is murder in the making, but the situation they advocate for (running when clear) is even written into law in some states (at least for motorcycles/bicycles). Some vehicles don't trigger the sensors and the lights never change, so you are allowed to go after a full stop. I would not be surprised in the least if there were some states where the wording of the law applied to cars as well.
by remembradev | 2026-03-10 11:53:39+00:00
Nice work on the hierarchy approach. The 5-level lazy loading is clever for keeping context small.

We took a different path with Remembra - hybrid search (vector + BM25 keyword) instead of hierarchical summaries. The tradeoff: you lose the explicit levels but gain better fuzzy matching when agents don't remember exact terms.

Entity extraction has been the bigger win for us - automatically linking "Mr. Kim" to "David Kim" without the agent needing to know they're the same person. Hit 100% on the LoCoMo benchmark with that.

Curious about the no-vector-embeddings choice. Does full-text search miss anything in practice when agents use different phrasing across sessions?

Self-host: docker run -d -p 8787:8787 remembra/remembra if anyone wants to compare: remembra.dev
by avanticc | 2026-03-10 11:54:23+00:00
The shift toward Waymo mentioned in the comments is a perfect example of how 'low-trust' environments drive automation. People aren't just choosing autonomous vehicles for the tech; they’re choosing them to remove the human variable entirely. If a platform like Uber can't guarantee safety through its vetting process, the market will naturally move toward solutions that eliminate the need for trust in the first place. It’s a sad commentary on the state of the gig economy when the 'killer feature' is just the absence of another person.
by nachocoll | 2026-03-10 11:54:47+00:00
The "Deployment Hell" section is the most instructive part of this writeup. The AI could write the code but couldn't navigate the deployment environment — and you found that it was only by understanding the "why" behind the failures that you could actually get unstuck.

This is a pattern that shows up consistently in vibe coding experiences: AI excels at generating implementation within a well-understood context, but breaks down at system boundaries where implicit knowledge matters most. Deployment, authentication flows, third-party API quirks — these are places where the gap between "code that looks right" and "code that works in your specific environment" is largest.

What you discovered intuitively — that technical fundamentals are the "safety net" — is exactly what the Agile Vibe Coding Manifesto articulates: "humans remain accountable for software systems" and "generated systems remain understandable and maintainable." The beginner who learns why things fail, not just how to prompt around them, ends up with something genuinely theirs.

Great example of vibe coding used as a learning accelerator rather than a shortcut: https://agilevibecoding.org
by abraxas | 2026-03-10 11:55:19+00:00
Than you for Tiled Words. It quickly became a morning ritual to complete the daily puzzle. I wish there were more mobile games that are not obnoxious. The idea and the execution are top notch.

My only concern is that there is a buzzing noise if the app is in the foreground and some audio is playing in the background. This is on pixel 9a
by pwndByDeath | 2026-03-10 11:55:32+00:00
An interesting experiment, I think I'm too uncomfortable leaking data I don't yet know why someone would curate to me free of charge until I knew.
If there was a FOSS suite like Home Assistant that would do a few of those things I might try it out, especially the weather (I would add air quality) correlation to mood and other subjective states.
by 1718627440 | 2026-03-10 11:55:37+00:00
> But people have had formal language for millennia, so why don't we use it?

In case you don't refer to the mathematical notion of formal, then we use formal language all the time. Every subject has its formal terms, contracts are all written in a formal way, specifications use formal language. Anything that really matters or is read by a large audience is written in formal language.
by jlarcombe | 2026-03-10 11:55:41+00:00
FidoNet was great fun. Despite finding it difficult to remember any useful numbers in my life (credit card, NI etc) I can still remember my FidoNet addresses from when I was a youngster.

I'm not sure how I'd feel about an archive though, I'm sure I wrote a lot of childish nonsense on it! like a lot of things, perhaps best left as a happy memory...
by DavidHaerer | 2026-03-10 11:55:44+00:00
I've started a side business to support local artisans.

I want to help people making a living from what they've created with their hands. A major focus to do so is to connect with local artisans and learning from their perspective.

On the technical side, I'm developing a static site[0] with Astro / React / Shadcn. The main interface is a OSM leaflet map with a custom calendar timeline.

As a software developer, this is my main entry in the world of agentic engineering. I'm using OpenCode with models from OpenRouter. One of the surprising insights is that there are often free quotas for new coding models (e.g. MiniMax). It's a very interesting time for building.

[0] https://ains.art
by davidfarrell | 2026-03-10 11:55:55+00:00
W Brian Arthur's book "The Nature of Technology" provides a framework for classifying new technology as elemental vs innovative that I find helpful. For example the Huntley-Mcllroy diff operates on the phenomenon that ordered correspondence survives editing. That was an invention (discovery of a natural phenomenon and a means to harness it). Myers diff improves the performance by exploiting the fact that text changes are sparse. That's innovation. A python app using libdiff, that's engineering.
And then you might say in terms of "descendants": invention > innovation > engineering. But it's just a perspective.
by butILoveLife | 2026-03-10 11:56:00+00:00
Academia is so cult-ish. I'm convinced its some sort of way to distinguish themselves from their higher paid, higher demand, industry peers. (if you can call them peers)

My experience with Academia is that its full of people who couldn't get jobs after undergrad, so they stayed in school. Their parents are at least middle class and support this route so they can harvest some of the status of having a PhD or professor.

I'd be less bitter if so much 'science' that came out in the last 100 years didn't fail to replicate... Yet we were expected to learn it like it was fact, while signing the praises of the authors. Literal waste of resources.

Meanwhile, industry is sending the world forward with life changing improvements. I think academia would hope they had some impact on it, but I think its wishful thinking.
by iroddis | 2026-03-10 11:56:08+00:00
+1 for vim slime. It’s not only amazing for programming in REPL languages. Since you can send anything from the buffer to another pane, it can be used to execute commands (send some rows from a cookbook to a remote shell), copy and paste segments of a local file to a remote source, and lots of other things. It’s a great example of doing something simple (send selections to another tmux/screen pane) that can be used in all kinds of useful ways. Very much the unix philosophy.
by readthemanual | 2026-03-10 11:56:38+00:00
I think the main issue I have with the article is that author whole argument is based on 'Qwen wouldn't run at a loss'. But why wouldn't it?
Depsite it being a business, there might be a number of arguments why they decide to run without profit for now: from trying to expand the user base, to Chinese government sponsoring Chinese AI business.
by georgehopkin | 2026-03-10 11:56:47+00:00
It sounds like pure science fiction, but researchers have figured out how to tap into the visual cortex of a living mouse and see exactly what the animal is seeing. In a major breakthrough, a team at University College London (UCL) has successfully reconstructed 10-second video clips purely by decoding the activity of the animal’s brain cells.

The findings, published in the journal eLife, demonstrate a powerful new way to decode how the brain processes visual information on a pixel-by-pixel basis.

While previous research has used fMRI machines to attempt to decode human brain activity, the UCL team utilised single-cell recordings in mice to achieve unprecedented precision. By analysing data from approximately 8,000 individual neurons in the mouse visual cortex, the researchers reconstructed the high-quality movie clips at 30 frames per second.
by _giorgio_ | 2026-03-10 11:57:23+00:00
I can’t reconcile this dichotomy: most of the landmark deep learning papers were developed with what, by today’s standards, were almost ridiculously small training budgets — from Transformers to dropout, and so on.

So I keep wondering: if his idea is really that good — and I genuinely hope it is — why hasn’t it led to anything truly groundbreaking yet? It can’t just be a matter of needing more data or more researchers. You tell me :-D
by macleginn | 2026-03-10 11:57:26+00:00
However, these services revenues (App Store tax, iCloud storage, even the deal with Google) are still anchored to users' loyalty to the platform. So Apple needs users to (1) stay on the platform and (2) buy new devices. Making user experience painful everywhere except for on the newest devices works against (1) and for (2).

That said, Microsoft's trade offs re quality of their software are rather different and their solution is even weirder: high-quality user-facing software is not in competition with their b2b sales, so ok, no reason to spend too many resources on it, but absolutely no evident reason to make it noticeably worse either.

As a result, Microsoft's approach of regressive evolution probably lets Apple get away with almost not caring or even going the path of slower regressive evolution.
by avanticc | 2026-03-10 11:57:59+00:00
Claiming it takes 5 minutes of manual work per entry while also saying it would take 45 days to build a software solution is a strange contradiction. In any private sector logistics setup, if you have 53 million records to untangle, you don't even consider the manual route; you spend the first 30 days building the parser and the next 15 days running edge-case tests. The fact that they are even presenting the '4.4 million hours' figure as a viable metric for the court shows how disconnected the administrative process is from modern data engineering.