MurphyA/hacker-news huggingface dataset:
by fooker | 2026-03-10 17:00:02+00:00
Anti patterns are great, they act as escape hatches or pressure release valves. Every piece of mechanical equipment has some analogue for good reason.

Without things like null pointers, goto, globals, unsafe modes in modern safe(r) languages you often get yourself into a corner by over designing everything, often leading to complex unmaintainable code.

With judicious use of these anti-patterns you get mostly good/clean design with one or two well documented exceptions.
by dudeinhawaii | 2026-03-10 17:00:03+00:00
I don't see why we can't have AI powered reviews as a verification of truth and trust score modifier. Let me explain.

1. You layout policy stating that all code, especially AI code has to be written to a high quality level and have been reviewed for issues prior to submission.

2. Given that even the fastest AI models do a great job of code reviews, you setup an agent using Codex-Spark or Sonnnet, etc to scan submissions for a few different dimensions (maintainability, security, etc).

3. If a submission comes through that fails review, that's a strong indication that the submitter hasn't put even the lowest effort into reviewing their own code. Especially since most AI models will flag similar issues. Knock their trust score down and supply feedback.

3a. If the submitter never acts on the feedback - close the submission and knock the trust score down even more.

3b. If the submitter acts on the feedback - boost trust score slightly. We now have a self-reinforcing loop that pushes thoughtful submitters to screen their own code. (Or ai models to iterate and improve their own code)

4. Submission passes and trust score of submitter meets some minimal threshold. Queued for human review pending prioritization.

I haven't put much thought into this but it seems like you could design a system such that "clout chasing" or "bot submissions" would be forced to either deliver something useful or give up _and_ lose enough trust score that you can safely shadowban them.
by _wire_ | 2026-03-10 17:00:10+00:00
> because the time will just go into tests that wouldn't otherwise have been written

Writing tests to ensure a program is correct is the same problem as writing a correct program.

Evaluating conformance is a different category of concern from ensuring correctness. Tests are about conformance not correctness.

Ensuring correct programs is like cleaning in the sense that you can only push dirt around, you can't get rid of it.

You can push uncertainty around and but you can't eliminate it.

This is the point of Gödel's theorem. Shannon's information theory observes similar aspects for fidelity in communication.

As Douglas Adams noted: ultimately you've got to know where your towel is.
by Sean-Der | 2026-03-10 17:00:16+00:00
I built a Open Source tool to help people debug/understand their live stream quality better. I would really appreciate people's feedback! https://streamsniff.com/

I see a constant stream of people saying 'my video is blurry' or 'why do I have so much latency' and I wanted to build something that helped people understand/diagnose. Also wanted to make something that runs in the browser so you can share/send a link to a friend (or on discord) to make debugging easier.
by FrasiertheLion | 2026-03-10 17:00:40+00:00
The model is running in a secure enclave that spans the GPU using NVIDIA Confidential Computing: https://www.nvidia.com/en-us/data-center/solutions/confident.... The connection is encrypted with a key that is only accessible inside the enclave.

Within the enclave itself, DRAM and PCIe connections between the CPU and GPU are encrypted, but the CPU registers and the GPU onboard memory are plaintext. So the computation is happening on plaintext data, it’s just extremely difficult to access it from even the machine running the enclave.
by D_ashe | 2026-03-10 17:00:46+00:00
Inspired by the original Million Dollar Homepage (2005 classic), but with a twist: instead of pixels, there's one single spot for the current message. To post, you outbid whoever's currently on the board (min bid just over the current price, e.g. $1.03 right now to beat $1.02). Each new bid overwrites the previous message and becomes the new "king of the hill."

Or skip the auction entirely and lock in the final, unbeatable message for a flat $999,999.99.

Current board (as of submit): "I ate 6 Big Arches" by Bergsy (bid $1.02)

Built this as a fun weekend experiment to see how people behave with escalating bids and silly/expensive internet real estate. No pixels, no ads—just one message at a time getting more and more expensive.
by skeeter2020 | 2026-03-10 17:01:00+00:00
>> but also make it available for others in case other people want to use it for the extra features and polish as well.

this feels like the place where your approach breaks down. I have had very poor results trying to build a foundation that CAN be polished, or where features don't quickly feel like a jenga tower. I'm wondering if the success we've seen is because AI is building on top of, or we're early days in "foundational" work? Is anyone aware of studies comparing longer term structural aspects? is it too early?
by angelorc | 2026-03-10 17:01:01+00:00
Hi HN, I'm Angelo. I built vmsan because I wanted Firecracker's isolation
without Firecracker's complexity.

If you've tried using Firecracker directly, you know the pain: setting up
TAP devices, configuring the jailer, writing iptables rules, wiring up
network namespaces, building rootfs images by hand. It's 30+ steps before
you can boot a VM and run a command inside it. vmsan reduces that to:

curl -fsSL https://vmsan.dev/install | bash
vmsan create --connect

You're in a shell inside a Firecracker microVM. Sub-3 second boot. No SSH —
a Go agent inside the VM handles exec, PTY, and file transfer over HTTP.

Some technical choices worth discussing:

- *nftables over iptables.* As of v0.2.0, all networking uses nftables
via the google/nftables Go library (netlink, no shelling out). Each VM
gets its own nftables table — teardown is one atomic flush instead of
tracking dozens of rules. This also means ICMP and non-DNS UDP are
blocked by default.

- *Network namespaces, not bridges.* Each VM lives in its own network
namespace with a veth pair. The host routes traffic. This gives per-VM
egress policies (allow-all, deny-all, or custom domain/CIDR allowlists)
without iptables spaghetti.

- *Docker images as rootfs.* `--from-image node:22-alpine` pulls the
image, extracts the filesystem, and builds an ext4 rootfs. You don't
need to maintain custom AMIs or rootfs images.

- *Why not just Docker?* Firecracker VMs run their own kernel behind
KVM. A container escape gives you the host; a VM escape gives you...
another VM boundary. If you're running your own trusted code, Docker is
fine. If you're running untrusted code, user-submitted workloads, or
multi-tenant anything, the isolation model matters. vmsan tries to make
the better isolation model just as easy to use.

Requirements: Linux with KVM support (/dev/kvm).

Feedback welcome — especially on the CLI UX and networking
model.
by kypro | 2026-03-10 17:01:12+00:00
> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions.

No hate, but this is just your opinion.

The definition of "text" here is extremely broad – an SVG is text, but it's also an image format. It's not incomprehensible to imagine how an AI model trained on lots of SVG "text" might build internal models to help it "visualise" SVGs in the same way you might visualise objects in your mind when you read a description of them.

The human brain only has electrical signals for IO, yet we can learn and reason about the world just fine. I don't see why the same wouldn't be possible with textual IO.
by jms703 | 2026-03-10 17:01:28+00:00
It sounds like a great policy. Good news is that you can choose to be on time or pay the penalty or choose another provider who hasn't decided to implement this...yet.

I'm shocked by your question. I honestly would like to hear why you think this should not be acceptable. Why should they continue working overtime and cut into their own personal/family time because of the parent's failure?
by jacquesm | 2026-03-10 17:01:57+00:00
That microcontroller costs a small fraction of the precision ground crystal it is boxed in with.

You need a way to calibrate the device after the package is sealed, that implies some smarts or you're going to end up with a whole raft of extra pins and that would be costlier than the microcontroller!

I'm sure there are alternative ways but in this day and age cpus and small amounts of flash + memory are priced a little bit above the sand they're made of. I have whole units packaged and with far larger capabilities for $3 Q1, and that's with a whole lot of assembly and other costly detailing.

Microchip, one particular embedded controller manufacturer lists their SMD packaged PIC16F15213-I/SN which is much more powerful than what you need here for $0.33, Q100 that drops to $ 0,27400. This is a complete device, not an unpackaged die, which would retail for a small fraction of that.

Control loops and analog stuff works well, but not if you also want to be able to do calibration after the fact package is sealed, I'm not aware of any tech that would be fully analog but that would have the same flexibility and long term stability, never mind mechanical stability (microphony, talking to a crystal is probably the cheapest and easiest way to get FM modulation!). Note that this is different precision wise from a device that simply measures the temperature and does a compensation based on that, the device you are looking at in this article is easily an order of magnitude better.
by bigstrat2003 | 2026-03-10 17:02:39+00:00
Agreed. I think that sometimes people on HN lose sight of what is actually important, which is human flourishing. The other day there was someone arguing that the best thing to do to fix loneliness problems in society is to remove the human need for socializing. Which... is certainly one way to fix the problem, I guess, but completely missed the point. The point is not to fix a mismatch between essential human desires and what we can attain, the point is to work on fulfilling those desires! Just something goes with nerd autism, I guess.
by alhwyn | 2026-03-10 17:02:51+00:00
i barely use google anymore now it's mostly claude or chatgpt for search when looking for restaurants nearby with friends, finding gifts for birthdays. so i built a scuffed mvp that track queries chatgpt and if your site being cited by an LLM (chat gpt for now)

i want to build something open-source that anyone can self-host whether you're prompting claude code to clone the repo locally or just one-clicking it onto railway or vercel and just start using it. there is definitely great aeo or geo tool out there at the moment mine just only has one feature because I started to work on this yesterday.

started to build this in typescript and rust, want to make sure my tool is ai-native easy to integrate with clawpify and claude code and runs the ai queries in parrallel.

as chatgpt and gemini integrate commerce prob this year through protocols like ucp.dev (https://ucp.dev/) for AI payments, e-commerce brands are going to need their sites actually ready with meta tags, structured data, and citations all optimized for ai crawlers to easily read product descriptions so they can be suggested. longer term, agent-to-agent payments are prob coming so ai can crawl through commerce sites or b2b and just pay for tools on its own to do its task

curious and what you think of this project and what tools do you guys already use to optimize ai search availability?
by nr378 | 2026-03-10 17:03:14+00:00
Dario has made a specific cohort argument here. His numbers (from various interviews) are: you train a model in 2023 for $100M, deploy it, and it earns $200M over its lifetime. Meanwhile you train the 2024 model for $1B, which goes on to earn $2B. Each vintage returns 2x on its training cost.

However, the GAAP P&L tells the opposite story. You book $200M revenue in the same year you spend $1B training the next model, so you report an $800M loss. Next year you book $2B against $10B in training spend, reporting an $8B loss. The business looks like it's dying when every individual model generation actually generates a healthy profit.

That's actually Dario's answer to your depreciation question. If each cohort earns back its training cost within its natural lifespan (however short that lifespan is), the depreciation schedule is already baked in. The model doesn't need to live forever, it just needs to return more than it cost before the next one replaces it. Whether that's actually happening at Anthropic is a different question, and one we can't answer without audited financials, but it's the claim Dario makes (and seems entirely reasonable from a distance).
by graemep | 2026-03-10 17:03:20+00:00
We are not really better. Chat control being pushed in the EU. The Online Safety Act already passed in the UK, and now legislation to give politicians the power to decide what websites need age verification. Crony capitalism/technofedualism/whatever all over the place. Hate speech laws that are often politicised and give the police and prosecutors a lot of room to target people they dislike (something the US has constitutional protections against). Extremist parties such as PVV and AfD getting a significant proportion of the vote.
by projektfu | 2026-03-10 17:03:50+00:00
The marginal cost of the next token. That can include the power, the operating cost of the facility, repair costs, etc.

The API price should hopefully incorporate the capitalized cost of the hardware, the facility rent, the cost to train the model, the r&d, cost of sales, etc., to make it profitable.

Claude Code Max may be able to offer a good price by having a mix of higher and lower utilization of users and ignoring the fixed costs, treating it as a driver of API sales. But it doesn't make sense to essentially pay people to use it.
by miroljub | 2026-03-10 17:03:56+00:00
> The fact that many independent national newspapers (including this article from CNBC) are openly calling-out the surveillance state and entering the debate into the public conscience should tell everyone that USA (and the West) is very different from Russia or China or Dubai.

So, the only benefit of the USA is that some media can still complain. And the regime just ignores and does what they want. Regardless dems or reps, they criticize the reduction of freedoms when they are in opposition, but as soon as they grab power, they keep reducing freedoms. It's like they are all just puppets of someone you can't even name without being called names.

> USA is not perfect, but at least is has active public discourse. We can openly (and legally) debate these things, and if we convince enough people, then we can change them.

Yep, they convinced you you are free because you can argue while keeping more and more freedoms and rights from you.

Today, the only difference between Western and Eastern regimes is that one side chooses the "Brave New World" way and the other the "1984" way. But eventually, they'll all converge into Zamyatin's "We" kind of dystopia that inspired both of these.
by rramadass | 2026-03-10 17:04:05+00:00
I think it is even more relevant today.

Hoare Logic + Dijkstra's weakest precondition + Meyer's Design-by-Contract is what should be used to get LLMs to generate proof with code in a correctness-by-construction approach to implementation.

References:

Correctness-by-Construction (CbC) - https://www.tu-braunschweig.de/en/isf/research/cbc

A Course on Correctness by Construction - https://wp.software.imdea.org/cbc/
by fredgrott | 2026-03-10 17:04:13+00:00
Curious question, how many Amazon Engineers flunk basic CS?

If you know CS you know two things:

1. AI can not judge code either noise or signal, AI cannot tell.
2. CS-wise we use statistic analysis to judge good code from bad.

How much time does it take to take AI output and run the basic statistic tools for most computer languages?

Some juniors need firing outright
by pjerem | 2026-03-10 17:04:19+00:00
What tool did you use ?

I routinely generate applications for my personal use using OpenCode + Claude Sonnet/Opus.

Yesterday I generated an app for my son to learn multiplication tables using spaced repetition algorithm and score keeping. It took me like 5 minutes.

Of course if you use ChatGPT it will not work but there is no way Claude Code/Open Code with any modern model isn't able to generate a one hundred line script on the first try.
by jmclnx | 2026-03-10 17:04:31+00:00
I do not understand this question.

Is there a reason you cannot install Linux ? Linux can be installed from a USB and should work on any system running windows 10.

If Linux is too heavy for your system, there is always NetBSD. I have NetBSD on an AMD 300MHz (= PII) with 512 MB memory and it runs fine.
by Strilanc | 2026-03-10 17:05:07+00:00
No, 15 is unique in that all multiplications by a known constant coprime to 15 correspond to bit rotations and/or bit flips. For 2047 that only occurs for a teeny tiny fraction of the selectable multipliers.

Shor's algorithm specifies that you should pick the base (which determines the multipliers) at random. Somehow picking a rare base that is cheap to do really does start overlapping with knowing the factors as part of making the circuit. By far the biggest cheat you can do is to "somehow" pick a number g such that g^2=1 (mod n) but g isn't 1 or N-1. Because that's exactly the number that Shor's algorithm is looking for, and the whole thing collapses into triviality.
by graemep | 2026-03-10 17:05:28+00:00
> I'm not really concerned with the law, here. I think it is ethically dubious to use someone else's work without compensating them in the manner they declared.

OK, that is reasonable. I do not think copyright is a good mechanism though, and I think the need to compensate depends on multiple factors depending on what you use a work for and under what circumstances.
by carlosinfantes | 2026-03-10 17:05:35+00:00
I built Berth because AI writes code in seconds but deploying it still takes times of Docker/YAML/config/cron monitoring. Berth auto-detects the runtime and deploys to your Mac or any Linux server with one command. Works as an MCP server so Claude Code can deploy for you. Free, open source, macOS native app + CLI. Feedback is welcomed :)
by hnthrow0287345 | 2026-03-10 17:06:10+00:00
>requires an amount of time approaching the time spent if they had just done it themselves.

I would actually say having at least 2 people on any given work item should probably be the norm at Amazon's size if you also want to churn through people as Amazon does and also want quality.

Doing code reviews are not as highly valued in terms of incentives to the employees and it blocks them working on things they would get more compensation for.
by qnleigh | 2026-03-10 17:06:12+00:00
I seriously doubt that they think senior reviewers will meticulously hunt down and fix all the AI bugs. Even if they could, they surely don't have the time. But it offers other benefits here:

1. They can assess whether the use of AI is appropriate without looking in detail. E.g. if the AI changed 1000 lines of code to fix a minor bug, or changed code that is essential for security.

2. To discourage AI use, because of the added friction.
by wearebilig | 2026-03-10 17:06:17+00:00
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by egoode007 | 2026-03-10 17:06:55+00:00
Hi HN,

I've been experimenting with ways AI can help small businesses think more strategically about growth and automation.

Many small businesses know AI could help them operate more efficiently, but figuring out where to start is often the hardest part.

As an experiment, I built a small collection of free tools that help with things like:
- analyzing business strategy
- identifying automation opportunities
- exploring growth ideas

They're free to use and don't require signup.

I'd really appreciate feedback from the HN community on whether tools like this are actually useful in practice.

You can try them here:
https://cornerstonebusiness.solutions/free-tools
by adrian_b | 2026-03-10 17:07:04+00:00
If you implement a temperature-calibration curve by analog means, it will drift in time, unless you use very high-quality and expensive components.

Calibrations done with a microcontroller have replaced those done with analog components in most applications, because the total cost is reduced in this way.

Even a relatively powerful 32-bit ARM microcontroller costs a fraction of a dollar. Good analog components, with guaranteed behavior in temperature and in time, are usually more expensive than microcontrollers.
by raw_anon_1111 | 2026-03-10 17:07:19+00:00
Yes because I didn’t check to see if Claude code used a for loop instead of a while loop? Or that it didn’t use my preferred GOF pattern and didn’t use what I read in “Clean Code”?

Guess what? I also stopped caring how registers are used and counting clock cycles in my assembly language code like it’s the 80s and I’m still programming on a 1Mhz 65C02
by aerodexis | 2026-03-10 17:07:30+00:00
The desire to anthropomorphize LLMs is super interesting. People naturally anthropomorphize technology (even printers: "why are you not working!?"). It's a natural and useful heuristic. However, I can easily see how chatGPT would want to intensify this tendency in order to sell the technology's "agency" and the promise that it can solve all your problems. However, since it's a heuristic, it papers over a lot of details that one would do well to understand.

(as an aside - this reminds me of the trend of Object Oriented Ontology that specifically /tried/ to imbue agency onto large-scale phenomena that were difficult to understand discretely. I remember "global warming" being one of those things - and I can see now how this philosophy would have done more to obscure the dominion of experts wrt that topic)
by zdragnar | 2026-03-10 17:07:39+00:00
Slax (https://www.slax.org/) runs entirely off of a USB stick. There's other options that have been around for awhile aimed at simple experiences for older and weak hardware.

Back in the netbook era, there were quite a few that tried to be internet browser focused like peppermint OS, though I don't know how well any of them faired with the rise of chromeos.
by ACCount37 | 2026-03-10 17:08:07+00:00
It's the difference between raw LLM output vs LLM output that was tweaked, reviewed and validated by a competent developer.

Both can look like the same exact type of AI-generated code. But one is a broken useless piece of shit and the other actually does what it claims to do.

The problem is just how hard it is to differentiate the two at a glance.
by rectang | 2026-03-10 17:08:22+00:00
You're not telling me anything I don't know already. Only a person who accepts that they're fallible can execute this methodology anyway, because that's the kind of mentality that it takes to think through potential failure modes.

Yes, code produced this way will have bugs, especially of the "unknown unknown" variety — but so would the code that I would have written by hand.

I think a bigger factor contributing to unforeseen bugs is whether the LLM's code is statistically likely to be correct:

* Is this a domain that the LLM has trained on a lot? (i.e. lots of React code out there, not much in your home-grown DSL)

* Is the codebase itself easy to understand, written with best practices, and adhering to popular conventions? Code which is hard for humans to understand is also hard for an LLM to understand.