MurphyA/hacker-news huggingface dataset:
by SoftTalker | 2026-02-22 05:34:49+00:00
People say that but I think it's gaslighting. I got marked down for using singular "they" in any writing I did in school in the 1980s. I didn't start to see it as a common "gender neutral" pronoun in professional writing (e.g. newspapers) until the last 20 years or so, and really not commonly until the past decade. It still trips me up when I see it used, I have to go back and make sure I didn't miss that more than one person was being discussed.

I suppose one could go back and look at popular style guides from the 1980s and 1990s and see if they endorsed it.
by rfv6723 | 2026-02-22 05:35:11+00:00
Historical records, notably by Herodotus, confirm that the Persian Empire used gold to bribe Greek Oracles, turning "divine prophecy" into a psychological warfare tool.

This mirrors a core flaw in Polymarket: profit maximization is not truth-seeking. Just as Persian bribes manipulated ancient morale, modern "whales" can distort market odds to manufacture narratives or hedge external interests. In both cases, the prediction is a commodity sold to the highest bidder rather than an objective forecast of reality.
by 7734128 | 2026-02-22 05:35:12+00:00
Unless I am completely blind this article doesn't even say how the scam was perpetrated?

I understand that that is not the focus of this article but it would be nice to know at least a little bit. Like, did they skim a card? Perform some kind of online system hijacking?

It really makes a difference of who would be at fault here.
by argee | 2026-02-22 05:35:36+00:00
I don't know about some of those "incantations", but it's pretty clear that an LLM can respond to "generate twenty sentences" vs. "generate one word". That means you can indeed coax it into more verbosity ("in great detail"), and that can help align the output by having more relevant context (inserting irrelevant context or something entirely improbable into LLM output and forcing it to continue from there makes it clear how detrimental that can be).

Of course, that doesn't mean it'll definitely be better, but if you're making an LLM chain it seems prudent to preserve whatever info you can at each step.
by k310 | 2026-02-22 05:35:56+00:00
Not paywalled at these sites. Please stop posting paywalled links and find alternatives. Archive Today and its sister sites are putting up horrible CAPTCHAS that don't even load the requested articles.

https://www.nbcnews.com/news/us-news/dhs-pausing-tsa-prechec...

https://www.reuters.com/world/us/us-dhs-pause-tsa-precheck-g...

https://thehill.com/policy/transportation/5749579-tsa-preche...
by latchkey | 2026-02-22 05:36:11+00:00
I spent a year in high school with this at the top of the chalk board of my history class: "Those who don't study history are destined to repeat it"

The internet has been entwined in my life since 1991, when I got my first email. Before that it was BBS's. The context and parallels that I'm witnessing now very much align with what I've seen before over the last 35 years. I've bet on some history based predictions in this cycle that few else saw, that absolutely have come true.

This isn't a no-code solution, and not even close to that. It is very much of a more code than ever solution.
by Qwertious | 2026-02-22 05:36:56+00:00
It's not that simple.

Batteries are inherently more aerodynamic, because they don't need to suck in oxygen for combustion, and because they need less cooling than an engine that heats itself up by constantly burning fuel. You can getvincredible gains just by improving motor efficiency - the difference between a 98%-efficient motor and a 99%-efficient motor is the latter requires half the cooling. That's more important than the ~1% increase in mileage.

Also, the batteries are static weight, which isn't as nightmarish as liquid fuel that wants to slosh around in the exact directions you want it not to. Static weight means that batteries can be potentially load-bearing structural parts (and in fact already are, in some EV cars).

The math leaves out a lot of important factors.
by wangzhongwang | 2026-02-22 05:37:02+00:00
This resonates with something I've been thinking about a lot. The current agent ecosystem has a massive gap: we give agents access to tools and skills, but there's no standardized way to verify what those skills actually do before execution. It's like running unsigned binaries from random sources.

A human root of trust is necessary but not sufficient — we also need machine-verifiable manifests for agent capabilities. Something like a package.json for agent skills, but with cryptographic guarantees about permissions and data access patterns.

The accountability framework here is a good start. Would love to see it extended with concrete permission models.
by LakshyAAAgrawal | 2026-02-22 05:37:27+00:00
We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architectures, configs, even SVGs. It extends GEPA (our prompt optimizer, discussed here previously: https://arxiv.org/abs/2507.19457) far beyond prompts.
The API is deliberately minimal. You provide what to optimize and how to measure it:

import gepa.optimize_anything as oa

def evaluate(candidate: str) -> tuple[float, dict]:
result = run_my_system(candidate)
return result.score, {"error": result.stderr, "runtime": f"{result.time_ms}ms"}

result = oa.optimize_anything(
seed_candidate="<your artifact>",
evaluator=evaluate,
)

The evaluator returns a score plus diagnostic feedback (we call it "Actionable Side Information" — stack traces, rendered images, profiler output, whatever helps diagnose failures). An LLM proposer reads this feedback during a reflection step and proposes targeted fixes, not blind mutations. Candidates are selected via a Pareto frontier across metrics/examples, so a candidate that's best at one thing survives even if its average is mediocre.

Two ideas distinguish this from AlphaEvolve/OpenEvolve/ShinkaEvolve-style LLM evolution: (1) diagnostic feedback is a first-class API concept rather than a framework-specific mechanism, and (2) the API unifies three optimization modes — single-task search (solve one hard problem), multi-task search (solve related problems with cross-transfer), and generalization (build artifacts that transfer to unseen inputs). Prior frameworks only express mode 1.

We tested across 8 domains. Selected results:

Coding agent skills: Learned repo-specific skills push Claude Code to near-perfect task completion and make it 47% faster
Cloud scheduling: Discovered algorithms that cut costs 40%, topping the ADRS leaderboard over expert heuristics and other LLM-evolution frameworks
Agent architecture: Evolved a 10-line stub into a 300+ line ARC-AGI agent, improving Gemini Flash from 32.5% → 89.5%
Circle packing (n=26): Outperforms AlphaEvolve's published solution
Blackbox optimization: Generated problem-specific solvers matching or exceeding Optuna across 56 EvalSet problems
CUDA kernels: 87% match or beat baseline; multi-task mode outperforms dedicated single-task runs

```
pip install gepa
```

Blog with full results and runnable code for all 8 case studies: https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-o...

GitHub: https://github.com/gepa-ai/gepa
by geocar | 2026-02-22 05:37:42+00:00
> When talking about permacomputing, for example, I don't know why one wouldn't encourage, in any way possible, commercial viability that would lead to the stated goal.

Because capitalism is what destroys the world. Fucking duh.

There's very little point in spending so much time thinking about C compilers in forth that run on scavenged z80s these days if capitalism is actually viable.
by wangzhongwang | 2026-02-22 05:38:58+00:00
Interesting approach. The separation of planning and execution is crucial, but I think there's a missing layer most people overlook: permission boundaries between the two phases.

Right now when Claude Code (or any agent) executes a plan, it typically has the same broad permissions for every step. But ideally, each execution step should only have access to the specific tools and files it needs — least privilege, applied to AI workflows.

I've been experimenting with declarative permission manifests for agent tasks. Instead of giving the agent blanket access, you define upfront what each skill can read, write, and execute. Makes the planning phase more constrained but the execution phase much safer.

Anyone else thinking about this from a security-first angle?
by majormajor | 2026-02-22 05:39:35+00:00
> If it did work, well, the oldest trick in computer science is writing compilers, i suppose we will just have to write an English to pedantry compiler.

"Add tests to this function" for GPT-3.5-era models was much less effective than "you are a senior engineer. add tests for this function. as a good engineer, you should follow the patterns used in these other three function+test examples, using this framework and mocking lib." In today's tools, "add tests to this function" results in a bunch of initial steps to look in common places to see if that additional context already exists, and then pull it in based on what it finds. You can see it in the output the tools spit out while "thinking."

So I'm 90% sure this is already happening on some level.
by rswail | 2026-02-22 05:39:57+00:00
Yes, because relying on google-fu is the way to ensure your build environment is consistent. /s

.gitkeep is explicitly not intended for git, because git doesn't recognize it at all.

Having the .gitignore, which is actually recognized by the git tools, means you can rely on the .gitignore functionality, including ensuring that things other than the .gitignore cannot be added to the repo.
by wangzhongwang | 2026-02-22 05:40:11+00:00
The tool-use explosion is real, but I worry we're building on sand. Every new "layer" added to LLM agents (tools, skills, plugins, MCPs) increases the attack surface without a corresponding increase in security guarantees.

Right now most agent frameworks trust tools implicitly — if a tool is installed, the agent can call it with whatever parameters it wants. There's no manifest saying "this tool can only read from /tmp" or "this skill needs network access to exactly these domains."

We need something like Android's permission model but for agent skills. Declare capabilities upfront, enforce them at runtime, and let users audit before granting access. Otherwise we're one malicious MCP server away from a supply chain attack on millions of agent deployments.
by Starman_Jones | 2026-02-22 05:40:40+00:00
You're both partially right, and that highlights the difference between nonviolent and violent resistance. You are incorrect in saying that a resistance is always trying to disincentivize the counterparty. Even in your example, the NVA didn't overrun their counterparty (the US military); they convinced enough of the US voting public (which is very much a separate entity from the US military) that "Peace with honor" was a viable, preferable option.
by 6ak74rfy | 2026-02-22 05:42:07+00:00
Are there collaborative versions of these *claws today? Like, if an "admin" could self-host one on their home server and the whole family could use it? IIRC, OpenClaw has some version of "profiles" but does it allow, say, couple of family members to collaborate with the bot in a shared chat but each has individual/private chats as well.
by raw_anon_1111 | 2026-02-22 05:42:27+00:00
Who knows? It’s part of an enterprise plan. I work for a consulting company. There are a number of fallbacks, the first fallback if we are working on an internal project is just to use our internal AWS account and use Claude code with the Anthropic hosted on Bedrock.

https://code.claude.com/docs/en/amazon-bedrock

The second fallback if it is for a customer project is to use their AWS account for development for them.

The rate my company charges for me - my level as an American based staff consultant (highest bill rate at the company) they are happy to let us use Claude Code using their AWS credentials. Besides, if we are using AWS Bedrock hosted Anthropic models, they know none of their secrets are going to Anthropic. They already have the required legal confidentiality/compliancd agreements with AWS.
by ekr | 2026-02-22 05:43:41+00:00
That's strange (i.e. different from my experience). I've been living in the Netherlands since 2021, speak some (~ B1) Dutch, but good English and German. Dutch language was from day one comprehensible due to German similarity. Many/most words either sound like the German equivalent to the point where you naturally match them in your thought, or they are written (mostly) like the German equivalent.

The connection between Dutch and English languages is far more minimal in comparison. In fact, when I first faced the language, I would have said it was a combination of ~80% German, 10% English, 5% French, +5% Others.
by PacificSpecific | 2026-02-22 05:44:44+00:00
Discord could have been a lack of resources as I previously said. They weren't a billion dollar company when the application was conceived.

Regardless the only thing keeping those millions of people at this point is lock-in. Even then people are actively looking for ways to move away from it. I'm witnessing the migration now and am looking forward to the day I don't have to hard restart the client 2-3 times a day.
by AngryData | 2026-02-22 05:45:04+00:00
But hydrogen is also so easy to produce on demand that you can design your balloon to be at small positive pressure all the time and always leaks outwards into the open air. If oxygen is allowed to leaked in undetected, yeah that's a death trap. The same if hydrogen leaked into semi contained oxygen enclosures. But leaking through the skin of the balloon to open sky even with decent size holes and a bit of positive pressure doesn't ignite particularly well, despite hydrogen's wide range of ignition conditions.

It is not such a fool proof technology that everybody should have one, but to me building and operating a hydrogen balloon isn't dissimilar to running a steam locomotive. It can be dangerous if done badly or incorrectly, but it can also be done safely with pretty well known and understood technologies and methods and practices. And considering the massive efficiency of lighter-than-air transport I find it hard to dismiss its potential even so long after their heyday and previous problems.
Show HN: X-Ray – Filter your X (Twitter) timeline by country
by batudotpy | 2026-02-22 05:45:32+00:00
I built a Chrome extension that lets you filter tweets by country.

X recently added "Account based in [Country]" to profiles, so the location data is publicly available — X-Ray uses it
to show location badges on every tweet and lets you blur or block tweets from selected countries.

There's no native way to do this on X yet (though they're reportedly building one). So I built it.

Tech: Chrome Extension (Manifest V3), Node.js backend, PostgreSQL. The extension intercepts X's GraphQL API responses
to extract location data, caches it to avoid rate limits.

It's free, 170+ countries, 9 languages.

Happy to answer technical questions.
by k310 | 2026-02-22 05:46:07+00:00
Your post is toxic in itself.

"similar to" and "pushing to" are weasel words.

I skim EVERYTHING. And in light of this, anything fishy is easy for me to just ignore.

HN has strong moderation. I only use two sites daily, both of which are heavily moderated. If something is really offensive, I trust the mods to deal with it appropriately.
by selridge | 2026-02-22 05:46:52+00:00
Please just read what I wrote. Please. You and I are talking about different things. You showed me a source for your claim and then acted like I was somehow misreading your source when I just wasn't talking about it.

We're basically in agreement, but you want to act like you're teaching me something. It's irritating.

One can fully understand that the goal is to write provable programs and yet we do not, we write programs that need debugging. So therefore, I don't think it's hard to imagine that if we get along with that, we may get along with natural language in the control channel, despite that being also proscribed in that vaunted essay you linked to me.
by dawnerd | 2026-02-22 05:49:57+00:00
We used to go weekly. Then sometime in 2018-2019 service went downhill fast. It would take forever to get a seat despite not being busy. It would take forever to get service once seated. The endless fries never came or when they did they looked like they were grabbed from another table.

Then their rewards were gutted. We've only been back a handful of times and every time was worse and now we go elsewhere entirely.
No LLM, No training data, No cloud – Engine that understands architecture
by twoelf | 2026-02-22 05:50:12+00:00
Everyone's throwing LLMs at code. Billions of dollars on next-token prediction. And the best they can do is autocomplete and chat.

They can't tell you what's actually happening in your codebase. They can't map the architecture. They can't detect drift. They can't explain why a change in one file breaks something three layers deep. They guess. Confidently. And they're wrong often enough that you still have to verify everything.

I took a completely different approach. No language model. No embeddings. No training data. No cloud. No GPU.

I built a system that reads raw source code and understands the architecture. Automatically. Deterministically. Every decision is traceable and explainable.

*45,476 functions. 6 real-world open-source codebases. 4 languages. 89.3% accuracy.*

It runs on your laptop in under a second. It doesn't phone home. And it gets better with every scan — without human labeling.

The AI industry has a blind spot. Everyone's chasing generation. Nobody's building understanding. You can't govern what you can't see, and right now, nobody can see their own codebase clearly.

I'm building the system that sees it.

Everyone's throwing LLMs at code. Billions of dollars on next-token prediction. And the best they can do is autocomplete and chat.

They can't tell you what's actually happening in your codebase. They can't map the architecture. They can't detect drift. They can't explain why a change in one file breaks something three
layers deep. They guess. Confidently.

I took a completely different approach. No language model. No embeddings. No training data. No cloud. No GPU.

I built a system that reads raw source code and understands the architecture. Automatically. Deterministically. Every decision is traceable and explainable.

45,476 functions. 6 real-world open-source codebases. 4 languages. 89.3% accuracy.

It runs on your laptop in under a second. It doesn't phone home. And it gets better with every scan — without human labeling.

The AI industry has a blind spot. Everyone's chasing generation. Nobody's building understanding. You can't govern what you can't see, and right now, nobody can see their own codebase
clearly.

Looking for early design partners and investors who understand that the next wave of developer tooling isn't "AI writes your code" — it's "AI understands your code."

twoelf47@gmail.com
by throwaway290 | 2026-02-22 05:50:27+00:00
> you'll often get a few meetings and then they will go silent and then what you were trying to sell them will be offered by them as a new product about a year later

This is table stakes in any business anywhere. any time you talk to investors or suppliers you risk them using this information so you better be careful have lawyers and set up correctly.

The caveat is of course when Chinese companies do this your lawyers can do nothing while in a developed country you can have some recourse

But even if Samsung was super predatory business wise it is beside the point. They both get subsidies and de facto are close to their governments so you have to look at what their governments do. Maybe you like what CCP is doing, it's your choice
by CjHuber | 2026-02-22 05:50:37+00:00
I think your point doesn't hold up really. Telling an LLM to summarize something losslessly will loose so much more nuance than updating the plan directly every time when some useful information is gained.

That file is not even a plan but effectively a compaction as well, just better as its done on the fly only processing the last message(s) rather than expecting an LLM to catch all nuances at once over a 100-200k+ conversation.
by vunderba | 2026-02-22 05:51:13+00:00
Yeah that could definitely work. I think most of the ones I've seen are pretty Western-centric.

Personally I'd drop the signup (or at least make it optional), add in a room link / invitation system, and then setup some packs by country - maybe even autodetecting incoming IP address of visitor and suggesting a pack for them (famous Japanese movies, famous French films, etc).
by caminante | 2026-02-22 05:51:15+00:00
Hype and confusion.

OpenClaw is hyped for running local/private LLMs and controlling your data, but these people don't realize the difference between

(1) running local open source LLMs

(2) and API calls to cloud LLMs.

The vast majority will do #2. To your point, a Raspberry Pi is sufficient.

For the former, you still need a lot of RAM (+32GB for larger models) so most minis are underpowered despite having unified memory and higher efficiency.
by benmanns | 2026-02-22 05:51:49+00:00
They were only touched on (and just barely) in my CS education, so don’t feel too left out. Spend an evening or two on the Wiki for Probabilistic data structures[0]. With a CS education you should have the baseline knowledge to find them really fascinating. Enjoy!

Oh, and
I don’t find myself actually implementing any of these very often or knowing that they are in use. I occasionally use things like APPROX_COUNT_DISTINCT in Snowflake[1], which is a HyperLogLog (linked in the Wiki).

[0]: https://en.wikipedia.org/wiki/Category:Probabilistic_data_st...

[1]: https://docs.snowflake.com/en/sql-reference/functions/approx...
by guiambros | 2026-02-22 05:52:17+00:00
If you read the transformer paper, or get any book on NLP, you will see that this is not magic incantation; it's purely the attention mechanism at work. Or you can just ask Gemini or Claude why these prompts work.

But I get the impression from your comment that you have a fixed idea, and you're not really interested in understanding how or why it works.

If you think like a hammer, everything will look like a nail.
by sky2224 | 2026-02-22 05:53:56+00:00
Your em-dashes make me think this is an AI generated post but whatever.

My company uses Github Copilot. We have a very specific enterprise agreement that states that data does go to Microsoft's servers where it gets processed in an ephemeral environment and wiped after 3 months.

I'm guessing Anthropic has something similar in their agreements. Now, if you have some proof that Anthropic is stealing highly confidential and/or trade secrets, that'd be good to see, but also whomever is throwing that kind of information into an off-premises and non airgapped model is just asking for a data leak.
by zmmmmm | 2026-02-22 05:54:16+00:00
It seems to be premised on the idea we would vibe code a replica of what we get from SaaS. But the real point is, we would not do that. We would vibe code something that exactly fits our business.

We have products we're paying $100k a year for and using 3% of the functionality. And they suck. This is the target.
by selridge | 2026-02-22 05:54:27+00:00
"maybe you don't have the necessary perspective to understand what you're asking for"

this is in no way a personal attack. It's just a statement that's true. I didn't imply anything about them or their character or limitations, but they might not have the necessary perspective if that's the question they are asking.

I think it's critically important people figure out what they want to learn from what's being shared.

What do you need from submitters here? Even setting aside the burden of supplying it, what do you hope to learn?