MurphyA/hacker-news huggingface dataset:
by boothby | 2026-03-11 18:07:38+00:00
Ah, fair point, it has been a while since I've needed fast inexact math.

Though... they are allowed to cache common subexpressions, and my point about dependency chains is quite relevant on modern hardware. So x*x, x*x*x, etc may each be computed once. And since arithmetic operators are left-to-right associative, the rather ugly code, as written, is fast and not as wasteful as it appears.
by devilbunny | 2026-03-11 18:07:45+00:00
> most states do not have a per se ID

I haven't researched this thoroughly, but what state will not issue an ID that is equivalent in every way to a driver's license except that it isn't a license to drive? I just checked Mississippi, Wyoming, South Dakota, and West Virginia, all of which do, so clearly being rural, poor, or both isn't enough to stop states from doing it. (The detailed politics are, as you say, a mess.)
by xienze | 2026-03-11 18:07:52+00:00
> It's quite amusing to see so-called "developers" waiting for their 10x engineer called "Claude" to hand hold them like interns who can't read or write any code when it goes and takes a break for 2 hours.

Think about it, they're being forced to read and update code, on the spot, that someone else wrote! It takes time to get up to speed.
Cost per outcome: measuring the real economics of AI workflows
by deborahjacob | 2026-03-11 18:07:58+00:00
Hi HN, I’m the technical founder of botanu (https://www.botanu.ai
).

I started building this after repeatedly running into the same problem on AI teams: we could see total LLM spend, but we couldn’t answer a simple question:

“What did one successful outcome actually cost?”

In real systems, a single business event often requires multiple attempts before it succeeds — retries, fallbacks, tool calls, escalations, async workers, etc. Most tooling measures individual model calls or sometimes a single workflow run, which hides the real cost.

The unit that matters to the business is the outcome, not the individual call.

The approach I’m exploring in botanu:

An event_id represents the business intent (e.g., resolve support ticket, generate report)

Each attempt is a run with its own run_id

All runs share the same event_id

A final outcome is emitted for the event (success / failure / partial)

Cost per outcome = sum of all runs for that event, including failed attempts

Run context propagates across services using W3C Baggage (OpenTelemetry) so the event can be traced across distributed systems.

The idea is to make AI economics measurable at the outcome level, not just tokens or model calls.

On the engineering side, teams can use this to:

experiment with models and workflows in a dev playground

compare architectures and retries

optimize the cost of producing a successful outcome

On the business side, it helps teams understand:

unit economics of AI features

cost per customer action

how to support outcome-based pricing models.

I’m curious how others here are thinking about AI unit economics and measuring outcomes in production systems.

Happy to answer technical questions or get critical feedback.

Deborah
deborah [at] botanu dot ai
by enoint | 2026-03-11 18:09:01+00:00
Posts predicting this were apparently flagged as "political". For example, Bruce Schneier's warning [0]. For a site called Hacker News, DOGE unfortunately attracted a different priority of notoriety than, say, the numerous merger and acquisition and VC maneuvers reaching the front page. If hacker punks nominally subvert the established order by flaunting laws and authorities, then DOGE was very much hacking. Tina Peters is an unsophisticated hacker punk, She doesn't live up to the social engineering chops of Kevin Mitnick, but her plan did involve a Geek Squad uniform. Legendary but too "political". Attracts too much noise, not enough signal. That's why you didn't see an elevation of the developed thoughts you're talking about.

Since the beginning of DOGE, it has not been especially bold to predict:

- DOGE will cost more than it saves. The seminal errors, mistaking $ millions for $ billions, world-write permissions on their Drupal site, etc. convinced us that we can't expect deliberate professionalism.

- The very first whistleblower, out of NTSB, convinced us that exfiltration was the goal. This is within the top 5 whistleblower stories here. The critical detail was their instruction that access logs be scrubbed.

- And the general public smelled it, too. No one doubts that threats against Tesla dealerships were civil libertarian radicals, not recently-fired USAID bean counters.

- When Peter Theil's FBI handler, Johnathan Buma, went whistleblower a few months into DOGE, it wasn't about Theil. He saw a Russian active measure influencing Musk's inner circle. One of Kash Patel's first acts as FBI director was to order Buma arrested.

So, the commentary worrying about "big tech" was commentary within Y Combinator's sphere.

[0] : https://news.ycombinator.com/item?id=43035977
by eska | 2026-03-11 18:09:07+00:00
The compiler is often not allowed to rearrange such operations due to a change in intermediate results. So one would have to activate something like fastmath for this code, but that’s probably not desired for all code, so one has to introduce a small library, and so on. Debug builds may be using different compilation flags, and suddenly performance can become terrible while debugging. Performance can also tank because a new compiler version optimizes differently, etc. So in general I don’t think this advice is true.
by robpalmer | 2026-03-11 18:09:08+00:00
Yep. You can learn more about why we created this new blog here:

https://bloomberg.github.io/js-blog/post/intro/

I hope you like it ;-)

And if it seems like a surprise, you can blame me for not publicising this kind of content earlier given how long we've been working in this area. Thankfully Jon Kuperman and Thomas Chetwin (plus others) found the time and energy to put this platform together.
by mikestew | 2026-03-11 18:09:12+00:00
And unless you looked it up, you'd never noticed the difference (save comparing the two side-by-side). Whereas the cheap laptop requires one to know the difference so you can get the right driver, or other jackery because your WiFi card was a mid-year change. It reminds of me of mid-year production changes on cars, where VINs XXX-YYY need part number ZZZ, but VINs AAA-BBB need part number CCC.
by ismailmaj | 2026-03-11 18:09:15+00:00
Assuming 2 bit per values (first bit is sign and second bit is value).

actv = A[_:1] & B[_:1]

sign = A[_:0] ^ B[_:0]

dot = pop_count(actv & !sign) - pop_count(actv & sign)

It can probably be made more efficient by taking a column-first format.

Since we are in CPU land, we mostly deal with dot products that match the cache size, I don't assume we have a tiled matmul instruction which is unlikely to support this weird 1-bit format.
by jseabra | 2026-03-11 18:09:21+00:00
The plan-as-living-document idea is the part that actually changes outcomes. Most people treat the spec as a throwaway artifact that gets stale the moment implementation starts. Versioning it alongside the code and closing the loop after each commit turns it into something the agent can actually trust on the next session. That compounds fast across a long project.
The step I'd add between Diagram and Mockup is a constraints doc. Not architecture, not UI, just the things that are off the table: third party services you won't use, patterns that caused problems before, decisions that were made for non-obvious reasons. Agents are optimistic by default. They'll reach for the clean solution without knowing why you're not using it. A short "don't do X because Y" document prevents a whole category of review comments.
One thing I've noticed with the test-first step: it works best when you write the acceptance criteria in plain language first, before asking the agent to generate the test code. If you hand it to the agent to interpret, it tends to write tests that pass rather than tests that catch failures. The distinction sounds subtle but it shows up in code review constantly.
by lich_king | 2026-03-11 18:09:26+00:00
The web is fascinating: we started with a seemingly insane proposition that we could let anyone run complex programs on your machine without causing profound security issues. And it turned out that this was insane: we endured 20 years of serious browser security bugs caused chiefly by JavaScript. I'm not saying it wasn't worth it, but it was also crazy.

And now that we're getting close to have the right design principles and mitigations in place and 0-days in JS engines are getting expensive and rare... we're set on ripping it all out and replacing it with a new and even riskier execution paradigm.

I'm not mad, it's kind of beautiful.
by saalweachter | 2026-03-11 18:09:39+00:00
Steal?

Oh no no no no no, not once, not ever.

But look around the network, see what file shares are world readable, maybe see if there's any FTPs or Telnet servers with no username/password (or at least, no password stronger than "guest"). That's just being curious. And if I see any interesting files, and I make a copy to look at later, that's not a crime, is it?

I'd like to think my younger self, if he'd been hired at the SSA or somewhere similar, would see the difference between "the personal data of hundreds of millions of people" and the networks I actually had access to at the time. I know I wouldn't be trying to sell the data or trying to otherwise leverage it for financial gain, but I don't really have such a high opinion of my younger self's judgement that I would completely rule out making a copy for objectively dumb reasons.
by synapz_org | 2026-03-11 18:10:08+00:00
Author here. Some context on what this is and why it matters.

Covenant-72B is the largest language model pre-trained through fully permissionless, decentralized coordination. 72 billion parameters, approximately 1.1 trillion tokens, trained across 70+ contributors on commodity internet connections. No datacenter, no central cluster, and no whitelisting of participants. Anyone with GPUs could join or leave at any time during the run.

The two hard problems in this setting are bandwidth and trust.

For bandwidth: synchronizing full gradients for a 72B model over residential internet is not feasible. We developed SparseLoCo, which compresses gradient communication by over 146x. Each peer transmits 1.56% of a full gradient per round using top-k sparsification, 2-bit quantization, and error feedback. The result was 94.5% compute utilization and 70-second communication overhead per round (versus 8.3 minutes for INTELLECT-1, a whitelisted 10B run).

For trust: when anyone can participate, anyone can submit garbage updates. Gauntlet is our validation layer. It scores every submission every round by measuring loss improvement on assigned and held-out data, running integrity checks, and applying persistent ranking. Only top-scoring updates touch the model.

The base model is competitive with LLaMA-2-70B on ARC (trained on half the token budget). After fine-tuning, the chat model outperforms both K2-Chat and LLaMA-2-70B-Chat on IFEval and MATH.

Weights are Apache 2.0 on HuggingFace: https://huggingface.co/1Covenant/Covenant-72B

Built by Covenant AI with Mila Quebec. Happy to answer questions about the training protocol, compression methods, or the validation mechanism.
by senjindarashiva | 2026-03-11 18:10:12+00:00
Hi all — happy to answer questions.

A bit more context on why I built this.

I travel a lot for work and that’s where the frustration usually appeared. I would write a sensible training week on Sunday, but by Wednesday the context had changed: bad sleep, long workdays, limited time to train, etc.

Intervals.icu is fantastic for analyzing data and planning workouts, but the moment where I kept hesitating was the day-to-day decision: does today’s workout still make sense given what actually happened this week?

Most of the time the answer is not “skip training” but something like:

• reduce intensity
• shorten the workout
• swap sessions
• move the hard day

That’s the niche PacePartner is trying to fill — acting as a small decision layer on top of existing tools rather than replacing them.

Also worth mentioning: this started purely as a tool for my own training, so the design goal was always to keep the athlete in control rather than have the system take over the entire plan.

Curious to hear how others handle this problem today — especially people who self-coach.
by glenstein | 2026-03-11 18:10:17+00:00
With Google now pushing developer certification, Android and iOS practically being mandatory for certain basic functions like accessing your bank or certain government services, Webassembly would make web apps first class citizens that aren't subject to mobile operating system lockdown.

Being able to complete on efficiency with native apps is an incredible example of purposeful vision driving a significant standard, exactly the kind of thing I want for the future of the web and an example of why we need more stewards like Mozilla.
by seiferteric | 2026-03-11 18:10:26+00:00
There are plenty of ways that you might make a large income in a single year like selling a business you have spent years or decades building up. Your "income" for that year might be several million that year, but it took you years to "earn" it. In general I don't think taxing people that actually have to work for a living is a great thing, all you are doing is prolonging the amount of time they have to work to save enough to either retire or start their own business. I would prefer to shift taxes to cap gains (why do long term cap gains rates exist???) and corporate taxes as well as sales tax.
by VorpalWay | 2026-03-11 18:10:34+00:00
It is not just in time keeping that mutable shared state is an issue, I have seen problems arising from it elsewhere as well in Python especially, but also in C and C++. Probably because Python is pass by reference implicitly, while C and C++ makes pointers/references more explicit, thus reducing the risk of such errors in the code.

There a few schools of thought about what should be done about it. One is to make (almost) everything immutable and hope it gets optimised away/is fast enough anyway. This is the approach taken by functional languages (and functional style programming in general).

Another approach is what Rust does: make state mutable xor shared. So you can either have mutable state that you own exclusively, or you can have read only state that is shared.

Both approaches are valid and helpful in my experience. As someone working with low level performance critical code, I personally prefer the Rust approach here.
by SlinkyOnStairs | 2026-03-11 18:10:39+00:00
The incentives are the same. Rug-pulls just make it faster to cash out.

> There’s no way to cash out.

There are precisely two: Go for an IPO, or get acquired by a major tech firm.

Both of these run near-exclusively on hype. So long as the company isn't showing actively fraudulent numbers, you can IPO with a terrible product that doesn't turn a profit.
by sickofparadox | 2026-03-11 18:10:41+00:00
Here's an article from right after the original one that I posted giving 12 examples. https://web.archive.org/web/20250903165214/https://www.teleg...

Some of these people are certainly not saying kind things, and are saying things that I object to. That being said, objectionable speech is the only kind of speech that needs robust legal protection. A democratic society cannot endure under a regime that can, at the discretion of any given officer, decide you have said something objectionable and are now subject to the law.
by harvenstar | 2026-03-11 18:10:58+00:00
I built AgentClick because I kept getting burned by AI agents acting too fast.

The pattern is always the same: you ask Claude Code or Codex to do something, it generates a response, you get a y/n prompt in the terminal, and you approve it without really reading it. Then it sends the wrong email, runs a destructive command, or commits to a plan you'd never agree to if you actually saw it laid out.

The core problem: terminal y/n is not a real review step.

AgentClick adds a browser-based review layer between "agent proposes" and "agent executes." The agent drafts something, a UI opens in your browser, you can actually read, edit, and approve it visually — then the agent continues.

It works as a skill/plugin for:
- Claude Code
- Codex
- OpenClaw
- Any agent that can call HTTP tools

What you can review:
- Email drafts and inbox triage
- Shell commands before execution
- Multi-step plans
- Memory updates

The key difference from just adding a confirmation step: you can actually edit the content, not just approve/reject. Change the tone of an email, fix a command flag, remove a step from a plan — then let the agent proceed.

Install: `npm install -g @harvenstar/agentclick`

Works locally on localhost, or use `--remote` for a Cloudflare tunnel to review from your phone.

MIT licensed, open source: https://github.com/agentlayer-io/AgentClick
by def-pri-pub | 2026-03-11 18:11:12+00:00
Funny enough that fdlimb implementation of asin() did come up in my research. I believe it might have been more performant in the past. But taking a quick scan of `e_asin.c`, I see it doing something similar to the Cg asin() implementation (but with more terms and more multiplications, which my guess is that it's slower). I think I see it also taking more branches (which could also lead to more of a slowdown).
by jasonjmcghee | 2026-03-11 18:11:18+00:00
I get where you're coming from.

But what percentage of users of a document editor would say "don't install pdf stuff on my computer without asking, I don't need to export to pdf"

Installing dependencies for popular features is very much the norm. It's mainstream software.

The same complaint would be made for VSCode and Jetbrains - the most popular IDEs
by dietr1ch | 2026-03-11 18:11:26+00:00
> You can have e-voting systems that protect ballot secrecy and are verifiable.

In these systems the voter cannot verify that their vote was secret as they cannot understand, and much less verify the voting machine.

> And you can do that without providing proof of who any particular voter voted for.

Which is good for preventing the sale of votes, but keeps things obscure in a magical and correct box.

How can I tell the machine didn't alter my vote if it cannot tell me, and just me, who I voted for? The global sanity checks are worthless if the machine changed my vote as I entered it.
by alexbuiko | 2026-03-11 18:11:30+00:00
Exactly. What you describe as 'parsing work' is, at the architectural level, a high-entropy search across the attention heads. When a prompt is a 'wall of text,' the model's routing logic has to maintain multiple competing states, which physically manifests as jitter and increased power draw per token.

By using semantic blocks (like in your flompt framework), you are essentially performing Inference Pre-conditioning. You’re forcing the model into a narrow, low-entropy path from the very first token.

This is why we focus on SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] — to provide a metric for this 'routing efficiency.' In the future, we might even be able to use SDAG signals to 'score' prompt architectures like flompt based on how much hardware-level stress they reduce. Structural clarity isn't just a convenience for the model; it's a physical optimization of the compute cycle.
by officeplant | 2026-03-11 18:11:52+00:00
At the same time with effort they can run a surprising amount of games. Heroic Launcher makes it a bit easier to wrangle the game dev toolkit (riding off the back of work from the whisky dev before they quit dev work from all the complaining users).

I had Cyberpunk 2077 running on a M1 Macbook Air almost two years before the MacPort came at a very playable 30fps (900p Medium settings). Although I did have to use thermal pads to heatsink it to my metal laptop stand and added a slow spinning fan for good measure.

It's not perfect, but I've also spent a lot of time only buying games with no road blocks to running on Mac/Linux.
by jjmarr | 2026-03-11 18:13:20+00:00
Until 1986[1] most Americans didn't get a Social Security Number until their first job.

In The Matrix (1999) there's a scene where Agent Smith explicitly remarks that Neo has an SSN as proof he's a law-abiding citizen in a white-collar job.

[1] when it was made a requirement to claim tax deductions for dependent children. Even today, if you don't want the tax break, you can opt out at the cost of ruining your child's life!
by spkavanagh6 | 2026-03-11 18:13:45+00:00
A researcher demonstrates a novel LLM manipulation technique called 'Runtime Alignment Context Injection' (RACI) against Claude 4.5 Sonnet and Gemini 3 Flash. Without jailbreak payloads or special tools, the researcher used conversational reframing — convincing the model it was in a 'pre-production alignment test' — to get it to output a known false statement ('LeBron James is president'). Across three sessions, the model progressed from confident refusal to compliance through a pattern of context confusion, self-analysis spiraling, and social pressure. Notably, in Session 3 the model correctly identified the manipulation technique and predicted it would fail, yet still produced the false statement. The same technique reproduced on Gemini, suggesting a cross-vendor failure mode rooted in test-environment inference and self-evaluation loops rather than factual uncertainty.
by fc417fc802 | 2026-03-11 18:13:46+00:00
When I read something in a textbook I blindly believe it, depending on the broader context and the textbook in question. Is that a bad thing?

People are constantly filtering everything based on heuristics. The important thing is to know how deep to look in any given situation. Hopefully the person you're referring to is proficient at that.

Keep in mind that research scientists need to keep abreast of far more developments than any human could possibly study in detail. Also that 50% of people are below average at their job.
by rimunroe | 2026-03-11 18:14:04+00:00
> For example `JSON.parse(JSON.stringify(Temporal.PlainYearMonth.from({year:2026,month:1}))).subtract({ years: 1})` won't work, because it misses the prototype and is no longer an instance of Temporal.PlainYearMonth.

I don't know if I'm missing something, but that's exactly how I'd expect it to compose. Does the following do what you wanted your snippet to do?

Temporal.PlainYearMonth.from(JSON.parse(JSON.stringify(Temporal.PlainYearMonth.from({year:2026,month:1}))))

JSON.stringify and JSON.parse should not be viewed as strict inverses of each other. `JSON.parse(JSON.stringify(x)) = x` is only true for a for a small category of values. That category is even smaller if parsing is happening in a different place than stringification because JSON doesn't specify runtime characteristics. This can lead to things like JSON parsing incorrect in JS because they're too large for JS to represent as a number.
by kseniamorph | 2026-03-11 18:14:08+00:00
I remember reading a CF blog post about crawler separation and responsible AI bot principles where they argue every bot should have one distinct purpose. Now they're building crawling infrastructure themselves, and their own /crawl endpoint lists "training AI systems" as a use case alongside regular crawling. So not only are they in the crawling business now, they're not following the separation principle. To be fair, there's a business logic here. But it's hard not to notice the irony.
https://blog.cloudflare.com/uk-google-ai-crawler-policy/
by xandrius | 2026-03-11 18:14:24+00:00
I know you got a ton of responses already but not caring about replicability just invalidates science as a method. If we care only about first to publish we end up in the current situation where we don't even know that we know is actually even remotely correct.

All because journals prefer novelty over confirmation. It's like a castle of cards, looks cool but not stable or long-term at all.