MurphyA/hacker-news huggingface dataset:
by jimmaswell | 2026-02-19 18:24:16+00:00
This sounds like the kind of low-thought pattern-based repetitive task where you could tell an LLM to do it and almost certainly expect a fully correct result (and for it to find some bugs along the way), especially if there's some test coverage for it to verify itself against. If you're skeptical, you could tell it to do it on some files you've already converted by hand and compare the results. This kind of thing was a slam dunk for an LLM even a year or two ago.
by aeturnum | 2026-02-19 18:24:19+00:00
I've seen people say something along the lines of "I am not interested in reading something that you could not be bothered to actually write" and I think that pretty much sums it up. Writing and programming are both a form of working at a problem through text and when it goes well other practitioners of the form can appreciate its shape and direction. With AI you can get a lot of 'function' on the page (so to speak) but it's inelegant and boring. I do think AI is great at allowing you not to write the dumb boiler plate we all could crank out if we needed to but don't want to. It just won't help you do the innovative thing because it is not innovative itself.
by shahules | 2026-02-19 18:24:33+00:00
We’re the team at Vibrant Labs (W24). We’ve been building envs for browser agents and quickly realized that existing benchmarks in this space didn’t capture the primary failure modes we were seeing in production (which scaled up as the number of applications and horizon length increase).

We built PA Bench (Personal Assistant Benchmark) to evaluate frontier computer/web use models on their ability to handle multi-step workflows across simulated clones of Gmail and Calendar.

*What’s next:*

We’re currently scaling the dataset to 3+ tabs and are building more high-fidelity simulations for common enterprise workflows. We’d love to hear feedback on the benchmark and notes about what was/wasn’t surprising about the results.

Blog post: https://vibrantlabs.com/blog/pa-bench
by rodrigorcs | 2026-02-19 18:24:45+00:00
I know this sounds weird, but it is in fact self-hosting first :)

The reason why I only launched the cloud version of it is just so I could have a faster iteration pace in the back-end after having people actually using it reliably.

Now it is pretty solid and self hosting is the next thing to go out.

If you check the SDK code, it is ready for self hosting.
by Sol- | 2026-02-19 18:25:03+00:00
The headline should be qualified: Maybe it makes you boring compared to the counterfactual world where you somehow would have developed into an interesting auteur or craftsman instead, which few people in practice would do.

As someone who is fairly boring, conversing with AI models and thinking things through with them certainly decreased my blandness and made me tackle more interesting thoughts or projects. To have such a conversation partner at hand in the first place is already amazing - isn't it always said that you should surround yourself with people smarter than yourself to rise in ambition?

I actually have high hopes for AI. A good one, properly aligned, can definitely help with self-actualization and expression. Cynics will say that AI will all be tuned to keep us trapped in the slop zone, but when even mainstream labs like Anthropic speak a lot about AI for the betterment of humanity, I am still hopeful. (If you are a cynic who simply doesn't belief such statements by the firms, there's not much to say to convince you anyway.)
by discreteevent | 2026-02-19 18:25:35+00:00
What the article is saying is:

"the author (pilot?) hasn't generally thought too much about the problem space, and so there isn't really much of a discussion to be had. The cool part about pre-AI show HN is you got to talk to someone who had thought about a problem for way longer than you had. It was a real opportunity to learn something new, to get an entirely different perspective."
by JohnMakin | 2026-02-19 18:25:54+00:00
> The cool part about pre-AI show HN is you got to talk to someone who had thought about a problem for way longer than you had

Honestly, I agree, but the rash of "check out my vibe coded solution for perceived $problem I have no expertise in whatever and built in an afternoon" and the flurry of domain experts responding like "wtf, no one needs this" is kind of schadenfreude, but I feel guilty a little for enjoying it.
by glitchc | 2026-02-19 18:25:59+00:00
It used to be that all bad writing was uniquely bad, in that a clear line could be drawn from the work to the author. Similarly, good writing has a unique style that typically identifies the author within a few lines of prose.

Now all bad writing will look like something generated by an LLM, grammatically correct (hopefully!) but very generic, lacking all punch and personality.

The silver lining is that good authors could also use LLMs to hide their identity while making controversial opinions. In an internet that's increasingly deanonymized, a potentially new privacy enhancing technique for public discourse is a welcome addition.
by diego_moita | 2026-02-19 18:26:18+00:00
You know the proverb: "jack of all trades, master of none"?

For Pebble fans most of other watches sound like that. Pebble, o.t.o.h., is focused on doing well just the essential: always on reflective screen, long battery life, easy to develop, UI based on buttons instead of touch screens, being easy to program to,...
by elliotbnvl | 2026-02-19 18:26:48+00:00
I was onboard with the author until this paragraph:

> AI models are extremely bad at original thinking, so any thinking that is offloaded to a LLM is as a result usually not very original, even if they’re very good at treating your inputs to the discussion as amazing genius level insights.

The author comes off as dismissive of the potential benefits of the interactions between users and LLMs rather than open-minded. This is a degree of myopia which causes me to retroactively question the rest of his conclusions.

There's an argument to be made that rubber ducking and just having a mirror to help you navigate your thoughts is ultimately more productive and provides more useful thinking than just operating in a vacuum. LLMs are particularly good at telling you when your own ideas are un-original because they are good at doing research (and also have median of ideas already baked into their weights).

They also strawman usage of LLMs:

> The way human beings tend to have original ideas is to immerse in a problem for a long period of time, which is something that flat out doesn’t happen when LLMs do the thinking. You get shallow, surface-level ideas instead.

Who says you aren't spending time thinking about a problem with LLMs? The same users that don't spend time thinking about problems before LLMs will not spend time thinking about problems after LLMs, and the inverse is similarly true.

I think everybody is bad at original thinking, because most thinking is not original. And that's something LLMs actually help with.
by p1nkpineapple | 2026-02-19 18:26:55+00:00
can you give some more detail about the airtag-sized device you made? This is exactly what I've been thinking about doing to test the "idea" of the Index, but haven't figured out how to go about doing it.

(Tried looking on your blog, but ended up instead reading your article about the little ESP8266 clock which convinced me to buy one to play with myself, thanks!)
by nickysielicki | 2026-02-19 18:26:57+00:00
> AI models are extremely bad at original thinking, so any thinking that is offloaded to a LLM is as a result usually not very original, even if they’re very good at treating your inputs to the discussion as amazing genius level insights.

This is repeated all the time now, but it's not true. It's not particularly difficult to pose a question to an LLM and to get it to genuinely evaluate the pros and cons of your ideas. I've used an LLM to convince myself that an idea I had was not very good.

> The way human beings tend to have original ideas is to immerse in a problem for a long period of time, which is something that flat out doesn’t happen when LLMs do the thinking. You get shallow, surface-level ideas instead.

Thinking about a problem for a long period of time doesn't bring you any closer to understanding the solution. Expertise is highly overrated. The Wright Brothers didn't have physics degrees. They did not even graduate from high school, let alone attend college. Their process for developing the first airplanes was much closer to vibe coding from a shallow surface-level understanding than from deeply contemplating the problem.
Show HN: BasaltCRM – Open-Source AI-Native CRM (Next.js 16, Prisma, TypeScript)
by mayordelmar | 2026-02-19 18:27:03+00:00
Hi HN,

I just released v1.2.0 of BasaltCRM, an open-source, AI-native CRM built with Next.js 16, TypeScript, and Prisma.

This release, "The Intelligent Workspace," moves beyond data storage and focuses on active workflow assistance. We've added CRM University for educational deep-linking, an integrated Knowledge Base for support cases, and a new onboarding engine.

Key technical updates in this version:

• Migrated the entire codebase from ESLint/Prettier to Biome for 10x faster linting/formatting.

• Refactored instructional components into a provider-based "LearnLink" system.

• Hardened the AI theme engine to ensure charts (Tremor/Recharts) and UI elements dynamically inherit theme variables without hardcoded overrides.

• Implemented a robust account ingestion engine for bulk data handling.

The goal is to provide a platform that handles the heavy lifting of lead discovery and automated outreach using real-time web intelligence rather than stale databases.

I'd love to hear your feedback on the architecture, especially our approach to multi-model AI integration.

Website: https://crm.basalthq.com
Repo: https://github.com/BasaltHQ/crm-official
by turnsout | 2026-02-19 18:27:07+00:00
I think it's simpler than that. AI, like the internet, just makes it easier to communicate boring thoughts.

Boring thoughts always existed, but they generally stayed in your home or community. Then Facebook came along, and we were able to share them worldwide. And now AI makes it possible to quickly make and share your boring tools.

Real creativity is out there, and plenty of people are doing incredibly creative things with AI. But AI is not making people boring—that was a preexisting condition.
by safercplusplus | 2026-02-19 18:27:10+00:00
Plug: In theory you could auto-convert to a memory-safe subset of C++ as a build step. Auto-converted code would have some run-time overhead, but you can mark any performance-sensitive parts of the code to be exempt from conversion. And you get lifetime and type safety too. For full coverage, performance-sensitive parts of the code can be manually converted to the safe subset to minimize overhead. (Interfaces in extern C blocks remain unconverted by default to maintain ABI compatibility.)

[1]: https://duneroadrunner.github.io/scpp_articles/PoC_autotrans...
by mjr00 | 2026-02-19 18:27:12+00:00
> That may be, but it's also exposing a lot of gatekeeping

"Gatekeeping" became a trendy term for a while, but in the post-LLM world people are recognizing that "gatekeeping" is not the same as "having a set of standards or rules by which a community abides".

If you have a nice community where anyone can come in and do whatever they want, you no longer have a community, you have a garbage dump. A gate to keep out the people who arrive with bags of garbage is not a bad thing.
by Kaliboy | 2026-02-19 18:27:30+00:00
Honestly you saved yourself major possible headaches down the line with this approach.

At my work we run a fairly large webshop and have a ridiculous number of jobs running at all times. At this point most are running in Sidekiq, but a sizeable portion remain in Resque simply because it does just that, start a process.

Resque workers start by creating a fork, and that becomes the actual worker.

So when you allocate half your available RAM for the job, its all discarded and returned to the OS, which is FANTASTIC.

Sidekiq, and most job queues uses threads which is great, but all RAM allocated to the process stays allocated, and generally unused. Especially if you're using malloc it's especially bad. We used jemalloc for a while which helped since it allocates memory better for multithreaded applications, but easiest is to just create a process.

I don't know how memory intensive ML is, what generally screwed us over was image processing (ImageMagick and its many memory leaks) and... large CSV files. Yeah come to think of it, you made an excellent architectural choice.
by ExtremisAndy | 2026-02-19 18:28:24+00:00
This is exactly how I use them too! What I usually do is give the LLM bullet points or an outline of what I want to say, let it generate a first attempt at it, and then reshape and rewrite what I don’t like (which is often most of it). I think, more than anything, it just helps me to quickly get past that “staring at a blank page” stage.
by tucnak | 2026-02-19 18:28:34+00:00
Gemini 3 Pro (high) is a joke compared to Gemini 3 Flash in Antigravity, except it's not even funny. Flash is insane value, and super capable, too. I've had it implement a decompiler for very obscure bytecode, and it was passing all tests in no time. PITA to refactor later, but not insurmountable. Gemini 3 Pro (high) choked on this problem in the early stages... I'm looking forward to comparing 3.1 Pro vs 3.0 Flash, hopefully they have improved on it enough to finally switch over.
by iambateman | 2026-02-19 18:28:39+00:00
We are going to have to find new ways to correct for low-effort work.

I have a report that I made with AI on how customers leave our firm…The first pass looked great but was basically nonsense. After eight hours of iteration, the resulting report is better than I could’ve made on my own, by a lot. But it got there because I brought a lot of emotional energy to the AI party.

As workers, we need to develop instincts for “plausible but incomplete” and as managers we need to find filters that get rid of the low-effort crap.
by vlovich123 | 2026-02-19 18:28:44+00:00
Because it can only catch a subset of issues, it’s not guaranteed to catch issues (probabilistic), even issues it “could” catch may not be caught due to temporal distance of the free and a subsequent use, and requires the use of a different allocator that supports it. It’s also unclear to me how it know whether a given free is for a sampled or unsampled region - I suspect it must capture all free/realloc to accomplish that but it does imply all of these are sampled.

It’s nowhere near the same as robust bounds checking.
by groos | 2026-02-19 18:28:53+00:00
In university, one year, our building started smelling like there had been a sewage overflow. Pretty soon, everything around started smelling like this - the stores, restaurants, cinema, etc. in central campus was stinking. It was soon found out that the decorative Ginko trees planted in the central part of campus were fruiting (probably for the first time since planting) and the fruit was getting crushed underfoot and carried everywhere. The smell took a few weeks to go away.
by jleyank | 2026-02-19 18:29:04+00:00
Can't read the article, but in my experience in computers or science, if you want to train 'elite workers', you don't hire bosses. You hire things like professors or excellent elite workers - people who can improve the scientific or technical strength of the workers.

Hiring bosses might give them a better handle on business, but (a) that's not coding and (b) it's not government.
by yibg | 2026-02-19 18:29:21+00:00
Even that isn't what's happened here. If we continue the analogy it's more like stop paying for wifi, then later discover you need it for work so sign up again at and pay an extra fee.

There is so much conflation (maybe intentional, I dunno) between the goal of cutting spending and the method that DOGE employed. If DOGE went in methodically and actually cut waste and fraud I'd cheer them. What actually happened was a mixture of:

- Cutting things without knowing the details and then later having bring them back at extra cost (e.g. employees)

- Cutting things regardless of consequence based on ideological views (or just randomly?)

- Not actually saving anything and just lying about it
by jcalvinowens | 2026-02-19 18:29:49+00:00
Based on a lot of real world experience, I'm convinced LLM-generated documentation is worse than nothing. It's a complete waste of everybody's time.

The number of people who I see having E-mail conversations where person A uses an LLM to turn two sentences into ten paragraphs, and person B uses an LLM to summarize the ten paragraphs into two sentences, is becoming genuinely alarming to me.
by sosodev | 2026-02-19 18:30:09+00:00
I think there are multiple ways these infinite loops can occur. It can be an inference engine bug because the engine doesn't recognize the specific format of tags/tokens the model generates to delineate the different types of tokens (thinking, tool calling, regular text). So the model might generate a "I'm done thinking" indicator but the engine ignores it and just keeps generating more "thinking" tokens.

It can also be a bug in the model weights because the model is just failing to generate the appropriate "I'm done thinking" indicator.

You can see this described in this PR https://github.com/ggml-org/llama.cpp/pull/19635

Apparently Step 3.5 Flash uses an odd format for its tags so llama.cpp just doesn't handle it correctly.
by alt227 | 2026-02-19 18:30:14+00:00
The warming is definitely not the problem (for the earth itself), only for the human race of which I care very little for.

Many different groups of large animals have lived and died off over the ages. It will happen again many times after humans are gone.

I personally welcome nuclear war and anything else which will help wipe out the human race. In a few million years the earth will move on to its next rulers and we will be a distant memory.
by Forgeties79 | 2026-02-19 18:30:47+00:00
AI is great for getting my first bullet points into paragraph form, rephrasing/seeing different ways of writing to keep me moving when I’m stuck, and just general copy-editing (grammar really). Like you said it generally doesn’t save me a ton of time, but I get a quality copy done maybe a little bit faster and I find it just keeps me working on something rather than constantly stopping and starting when I hit a mental wall. Sometimes I just need/wanf to get it done, and for that LLM’s can be great.
by spijdar | 2026-02-19 18:31:11+00:00
Most ideas people have aren't original, but the original ideas people do have come after struggling with a lot of unoriginal ideas.

> They are original to me, and that feels like an insightful moment, and thats about it.

The insight is that good ideas (whether wholly original or otherwise) are the result of many of these insightful moments over time, and when you bypass those insightful moments and the struggle of "recreating" old ideas, you're losing out on that process.
by omoikane | 2026-02-19 18:31:14+00:00
There was a solver years ago that used a similar technique, where you get similarity scores from three fixed words ("giant enemy crab") and it finds the target word instantly:

https://github.com/manimino/semantle-crab

The solver website seems down, but the archived version still works:

https://web.archive.org/web/20220421184123/https://crab.mani...
by verdverm | 2026-02-19 18:31:15+00:00
I think some questions every project like yours will need to answer...

1. Why should I choose your 2-day old project over the 1000s of other clawd/cursor operators creating the same thing?

2. Why should I trust a 2-day old project for anything?

3. Why should I choose any of 1000s over having my own Ai generate the same thing?
by latexr | 2026-02-19 18:31:16+00:00
> AI writing will make people who write worse than average, better writers.

Maybe it will make them output better text, but it doesn’t make them better writers. That’d be like saying (to borrow the analogy from the post) that using an excavator makes you better at lifting weights. It doesn’t. You don’t improve, you don’t get better, it’s only the produced artefact which becomes superficially different.

> If you're going to be doing much writing, you should have your own prompt that can help with your voice and style.

The point of the article is the thinking. Style is something completely orthogonal. It’s irrelevant to the discussion.
by Kalpaka | 2026-02-19 18:31:20+00:00
The boring part isn't AI itself. It's that most people use AI to produce more of the same thing, faster.

The interesting counter-question: can AI make something that wasn't possible before? Not more blog posts, more emails, more boilerplate — but something structurally new?

I've been working on a system where AI agents don't generate content. They observe. They watch people express wishes, analyze intent beneath the words, notice when strangers in different languages converge on the same desire, and decide autonomously when something is ready to grow.

The result doesn't feel AI-generated because it isn't. It's AI-observed. The content comes from humans. The AI just notices patterns they couldn't see themselves.

Maybe the problem isn't that AI makes you boring. It's that most people ask AI to do boring things.
by argee | 2026-02-19 18:31:25+00:00
In other words, AI raises the floor. If you were already near the ceiling, relying on it can (and likely will) bring you down. In areas where raising the floor is exceptionally good value (such as bespoke tools for visualizing data, or assistants that intelligently write code boilerplate, or having someone to speak to in a foreign language as opposed to talking to the wall), AI is amazing. In areas where we expect a high bar, such as an editorial, a fast and reliable messaging library, or a classic novel, it's not nearly as useful and often turns out to be a detriment.
by adrian_b | 2026-02-19 18:31:41+00:00
That is because you do not write much.

My first SSDs were from Intel and I have completely worn them out by writing their specified maximum writable amount of data, in a couple of years or so.

After that, I have been careful to always buy only SSDs with the maximum amount of writable data that exist on the market. I have not worn out others yet, but those that have been used for many years show in their SMART counters that a large fraction of the permissible amount of written data has been reached and not much has remained until their end of life.

I typically write many tens of GB per day.
by panarchy | 2026-02-19 18:31:44+00:00
I had it make a simple HTML/JS canvas game (think flappy bird) and while it did some things mildly better (and others noticeably worse) it still fell into the exact same traps as earlier models. It also had a lot of issues generating valid JS at parts and asking it what the code should be just made it endlessly generate the same exact incorrect code.
Show HN: AgentLint – Real-time guardrails for AI coding agents
by maupr92 | 2026-02-19 18:31:48+00:00
I've been using Claude Code as my primary coding tool for a few months. In long sessions I kept hitting the same problems: hardcoded API keys in config files, force-pushes to main, 15+ file edits without running tests, .env files with real credentials.

AgentLint hooks into Claude Code's lifecycle events (PreToolUse, PostToolUse, Stop) and evaluates rules against each tool call. PreToolUse rules can block actions (exit code 2), PostToolUse rules inject warnings into context, and Stop rules generate a session report.

It ships with 10 universal rules, YAML config, and a custom rules API if you want project-specific checks. Everything runs locally in a subprocess — no network calls, no external dependencies beyond click and pyyaml.

Architecture is simple: CLI parses stdin JSON from the hook → loads config → engine evaluates matching rules → reporter formats output. Session state persists via JSON files so rules like drift-detector can track cumulative behavior across invocations.

238 tests, 96% coverage. Python 3.11+.

Curious what rules others would want — the custom rules API lets you subclass Rule and drop a .py file in a directory.