MurphyA/hacker-news huggingface dataset:
by scopeT86 | 2026-02-22 03:24:23+00:00
Hey HN,

I've been working on a NativeAOT-compatible .NET port of the popular OpenClaw AI agent framework. I love the original Node.js/TypeScript project, but I wanted something with a smaller memory footprint, faster startup times, and deeper integration into the enterprise C# ecosystem.

The biggest challenge with framework ports is usually the "cold start" problem—you lose the entire ecosystem of community plugins. To solve this, I built a high-performance Node.js JSON-RPC bridge directly into the gateway.

Here's how it works and what it achieved:

### 1. The .NET NativeAOT Core
The orchestration core (ReAct loop, tool dispatch, WebSockets, Webhooks) is written completely in C# 13 and optimized for NativeAOT.
- *Footprint*: It compiles down to a single binary. The runtime Docker image (using Ubuntu Chiseled) is just ~15MB.
- *Memory*: It idles at a fraction of the RAM compared to the Node.js/V8 equivalent, making it way easier to self-host on cheap VPS instances or tiny Raspberry Pis.
- *Security*: It has hardened public-bind defaults (rejecting wildcard tooling roots, disabling shell tools if bound publicly, and requiring tokens).

### 2. The TypeScript Plugin Bridge
I didn't want to lose the hundreds of community plugins built for the original OpenClaw.
- The .NET gateway dynamically spawns a Node.js child process using `--experimental-vm-modules`.
- It loads original OpenClaw `.ts` or `.js` plugins (using `jiti` for on-the-fly TS compilation).
- Tools are bridged back to C# via a newline-delimited JSON-RPC pipe over `stdin`/`stdout`.
- *The fun part*: To prevent community plugins from accidentally corrupting the JSON-RPC stream via stray `console.log()` calls, the bridge intercepts `console.log` and `console.info`, redirecting them to `stderr`. The .NET parent process asynchronously reads that `stderr` pipe and routes it natively into the `Microsoft.Extensions.Logging` pipeline. You get full C# structured logging for JavaScript plugins without blocking the OS pipe buffers.

### 3. Built-in Channels
Instead of just a web/CLI interface, the gateway acts as a hub. It has native, zero-dependency adapters for:
- *WhatsApp*: Dual support for the Official Meta Cloud API (webhook verification, contextual replies) and custom bridges (like `whatsmeow`).
- *Telegram*: Native webhook support.
- *Twilio SMS*: Signature validation and strict number allowlists.

### 4. Code & Architecture
The repo is split into cleanly decoupled assemblies (`Core`, `Channels`, `Agent`, `Gateway`), heavily utilizing `IAsyncEnumerable` for streaming and `System.Text.Json` source generators to ensure 100% trim/AOT safety (no runtime reflection).

I'd love to hear your thoughts, especially from anyone who has wrestled with cross-language RPC pipes or AOT compilation in .NET!

Source code is available under the MIT license.

(Happy to answer any questions about the architecture, the bridge, or the AOT migration process!)
by overgard | 2026-02-22 03:25:45+00:00
I mean, if you turn off incremental compilation and build in a container (or some other "clean room" environment), it should turn out the same each time. Local builds are very non-deterministic, but CI/CD shouldn't be.

Either way it's a nitpick though, a compiler hypothetically can be deterministic, an LLM just isn't? I don't think that's even a criticism of LLMs, it's just that comparing the output of a compiler to the output of an LLM is a bad analogy.
by 0_____0 | 2026-02-22 03:26:21+00:00
That's the most obvious example of failure of Chinese central planning. That and one child policy were abysmal failures that resulted from shoddy science coupled to effective central authority.

Look at the 12/13/14th 5 year plan (the most recently passed). Do you think they achieved their goals?

If your headcanon is that the CCP is inept because they caused crop failures 60 years ago... you could stand to take a look at what they're doing today.
by giancarlostoro | 2026-02-22 03:26:39+00:00
The LLM will do what you ask it to unless you don't get nuanced about it. Myself and others have noticed that LLM's work better when your codebase is not full of code smells like massive godclass files, if your codebase is discrete and broken up in a way that makes sense, and fits in your head, it will fit in the models head.
by yesbabyyes | 2026-02-22 03:27:14+00:00
No, Stallman uses Emacs:

> I spend most of my time editing in Emacs. I read and send mail with Emacs using M-x rmail and C-x m. I have no experience with any other email client programs.

You may have confused this with his somewhat idiosyncratic way of browsing the web:

> I generally do not connect to web sites from my own machine, aside from a few sites I have some special relationship with. I usually fetch web pages from other sites by sending mail to a program (see https://git.savannah.gnu.org/git/womb/hacks.git) that fetches them, much like wget, and then mails them back to me. Then I look at them using a web browser, unless it is easy to see the text in the HTML page directly. I usually try lynx first, then a graphical browser if the page needs it.

https://stallman.org/cgi-bin/showpage.cgi?path=/stallman-com...

Donald Knuth, on the other hand, quit email in 1990, after using it for 15 years:

> I have been a happy man ever since January 1, 1990, when I no longer had an email address. I'd used email since about 1975, and it seems to me that 15 years of email is plenty for one lifetime.

Since then, he prefers snail mail but has a secretary who will print out his emails:

> My secretary also prints out all nonspam email messages addressed to taocp@cs.stanford.edu or knuth-bug@cs.stanford.edu, so that I can reply with written comments when I have a chance. If I run across such a message that was misaddressed --- I mean, if the message asks a question instead of reporting an error --- I try not to get angry.

https://www-cs-faculty.stanford.edu/~knuth/email.html
by red_hare | 2026-02-22 03:27:58+00:00
Slidev is markdown, so i do it in html comments. Usually something like:

<!-- TODOCLAUDE: Split this into a two-cols-title, divide the examples between -->

or

<!-- TODOCLAUDE: Use clipart skill to make an image for this slide -->

And then, when I finish annotating I just say: "Address all the TODOCLAUDEs"
by godelski | 2026-02-22 03:28:28+00:00
> But cutting down to 2 million 3 bit parameters or something like that would definitely be possible.

Sure, but there's no free lunch

> Hey look what I just found

I've even personally built smaller "L"LMs. The first L is in quotes because it really isn't large (So maybe lLM?) and they aren't anything like what you'd expect and certainly not what the parent was looking for. The utility of them is really not that high... (there are special cases though) Can you "do" it? Yeah. I mean you can make a machine learning model of essentially arbitrary size. Will it be useful? Obviously that's not guaranteed. Is it fun? Yes. Is it great for leaning? Also yes.

And remember, Tiny Stories is 1GB of data. Can you train it for longer and with more data? Again, certainly, BUT again, there are costs. That Minecraft one is far more powerful than this thing.

Also, remember that these models are not RLHF'd, so you really shouldn't expect it to act like you're expecting a LLM to work. It is only at stage 0, the "pre-training", or what Karpathy calls a "babbler".
by sarchertech | 2026-02-22 03:30:24+00:00
> If the gate only passes the digits 0-9 sent within 'x' seconds, and the code's job is to send a digit between 0 and 9, how is it non-deterministic?

If that’s all the code does, sure you could specify every observable behavior.

In reality though there are tens of thousands of “design decisions” that a programmer or LLM is gonna to make when translating a high level spec into code. Many of those decisions aren’t even things you’d care about, but users will notice the cumulative impact of them constantly flipping.

In a real world application where you have thousands of requirements and features interacting with each other, you can’t realistically specify enough of the observable behavior to keep it from turning into a sloshy mess of shifting jank without reviewing and understanding the actual spec, which is the code.
by Li_Evan | 2026-02-22 03:31:31+00:00
I have created my own original large-scale model evaluation dataset with 18 major dimensions, nearly 100 minor dimensions, and a total of 970 questions. The following are the test results:
1. Software Engineering and Code Generation: GPT-5.3 codex
2. Code Comprehension, Reasoning, and Quality: GPT-5.3 codex
3. Debugging, Testing, and Maintenance: GPT-5.3 codex
4. Data Engineering and Backend Services: Claude Opus 4.6
5. Frontend and Product Engineering: Claude Opus 4.6
6. Agent Tool Invocation: Claude Opus 4.6
7. Web and Desktop Automation (Static): Claude Opus 4.6
8. Research and Knowledge Work Agent (Static): GPT-5.2 Pro
9. Mathematical and Formal Reasoning: Gemini 3.1 Pro
10. Logic and Planning: Gemini 3.1 Pro
11. Knowledge Breadth and Fact Verification: Gemini DeepThink
12. Reading Comprehension and Information Extraction: GPT-5.2 Thinking
13. Long Contextual Memory and Multi-turn Consistency: GPT-5.2 Thinking
14. Instruction Compliance and Alignment: Claude Opus 4.6
15. Multimodal Understanding and Visual Reasoning: GPT-5.2 Thinking
16. Emotional Intelligence and Collaborative Communication: GPT-4.5
17. Creative Expression and Aesthetics: Claude Opus 4.6
by throw0101c | 2026-02-22 03:31:39+00:00
For anyone not familiar with the concept of "dark forest":

> […] In Liu Cixin's 2008 novel The Dark Forest, the author proposes a literary explanation for the Fermi paradox in which countless alien civilizations exist, but are both silent and paranoid, destroying any nascent lifeforms loud enough to make themselves known.[181] This is because any other intelligent life may represent a future threat. As a result, Liu's fictional universe contains a plethora of quiet civilizations which do not reveal themselves, as in a "dark forest"...filled with "armed hunter(s) stalking through the trees like a ghost".[182][183][184] This idea has come to be known as the dark forest hypothesis.[185][186][187]

* https://en.wikipedia.org/wiki/Fermi_paradox#Communication_is...

> The "dark forest" hypothesis presumes that any space-faring civilization would view any other intelligent life such as theirs as an inevitable threat and thus destroy any nascent life that makes itself known. As a result, the electromagnetic radiation surveys would not find evidence of intelligent alien life.[8][9] […]

> The name of the hypothesis derives from Liu Cixin's 2008 novel The Dark Forest,[11] as in a "dark forest" filled with "armed hunter(s) stalking through the trees like ghosts".[12][13] According to the dark forest hypothesis, since the intentions of any newly contacted civilisation can never be known with certainty, then if one is encountered, it is best to make a preemptive strike, in order to avoid the potential extinction of one's own species. The novel provides a detailed investigation of Liu's concerns about alien contact.[2]

* https://en.wikipedia.org/wiki/Dark_forest_hypothesis

* Kurzgesagt (10m): https://www.youtube.com/watch?v=xAUJYP8tnRE

(Cixin's novel probably made the idea famous, but others (Brin, Bear) have explored it previously.)
by gaigalas | 2026-02-22 03:32:04+00:00
I've learned this stuff as a hobby, so take it with a grain of salt. I'm not a specialist.

OWL 1, for example, has stuff like transitive properties (the classical example is A ancestorOf B, B ancestorOf C, therefore I can infer A ancestorOf C if I annotate ancestorOf as a transitive property).

Union, equivalence, inversion, symmetries, cardinality. Those are all possible to represent symbolic in OWL ontologies.

They're also neatly separated in different types (OWL Lite, OWL DL, OWL Full). OWL Lite and DL for example are proven to be decidable (you won't get some halt when doing inference, no matter what).

I know there are plenty of database engines to store triples and graphs, and plenty of reasoners out there.

I haven't studied OWL 2 yet or newer stuff like SHACL, but I know it's supposed to be even better.
by sloaken | 2026-02-22 03:32:26+00:00
In Orlando, if you go to disney and you are a techie, it is a must see, is a company called SkyCraft Surplus. They go to companies who are getting rid of old equipment and resell it. Some stuff is new, much of it unused. Brought a friend there (EE major) and he was fascinated by the fact that they had a lot of old Radio Shack chips, diodes etc.

Tell them I sent you and they will ... well nothing, but I am happy to support them, as it brings me much joy whenever I visit.
by Oreb | 2026-02-22 03:34:05+00:00
Phonetic spelling would perhaps make the language easier to learn for native speakers, but it would make it harder to learn for foreigners, at least those of us who come from Europe. Most words in written English resemble words in Germanic or Romance languages. If English was spelled phonetically, the resemblance would be significantly smaller.

People often say that the English spelling is weird or illogical. As a non-native speaker, I disagree. The English spelling makes perfect sense. It’s the English pronunciation which is really strange and inconsistent.
by infogulch | 2026-02-22 03:34:16+00:00
That's an interesting stress test for I2P. They should try to fix that, the protocol should be resilient to such an event. Even if there are 10x more bad nodes than good nodes (assuming they were noncompliant I2P actors based on that thread) the good nodes should still be able to find each other and continue working. To be fair spam will always be a thorny problem in completely decentralized protocols.
by adrianwaj | 2026-02-22 03:34:22+00:00
Do you run the app locally?

If it's not local, I saw this comment: https://news.ycombinator.com/item?id=47085906

"This entire stack could give you computing power equivalent to a 25k euro/month AWS bill for the cost of electricity (same electricity cost as running a few fridges 24/7) plus about 50k euros one-time to set it up (about 4 Mac Studios). And yes, it's redundant, scalable, and even faster (in terms of per-request latency) than standard AWS/GCP cloud bloat. Not only is it cheaper and you own everything, but your app will work faster because all services are local (DB, Redis cache, SSD, etc.) without any VM overhead, shared cores, or noisy neighbours."

Makes me think there will be these prompts like "convert this app to suit a new stack for my hardware for locally-optimized runtime."

How are people building the best local stacks? Will save people a ton of money if done well.
by zhubert | 2026-02-22 03:36:32+00:00
The challenging thing for those of us that have gone around the sun a few times is that…you’re just going to have to figure it out yourself.

We can tell you to be cautious or aware of security bullshit, but there’s a current that’s buying Mac Mini’s and you want to be in it.

Nothing I can say changes that and as a grown up, you get to roll those dice yourself.

70% of you are going to be fine and encourage others, the rest are going to get pwnd, and that’s how it goes.

You’re doing something that decades or prior experience warned you about.
by js8 | 2026-02-22 03:37:11+00:00
I disagree, in the future it might be possible. But perhaps not in English, but in some more formal (yet fuzzy) language with some basic epistemology.

I mean, there is a lambda calculus self-interpreter in 29 bytes. How many additional logical rules are required for GAI inference? Maybe not that many as people think. Understanding about 1000 concepts of basic english (or say, lojban) might well be sufficient. It is possible this can be encoded in 800kB, we just don't know how.
by airstrike | 2026-02-22 03:37:44+00:00
I'm honestly puzzled by this take.

Clearly the people who benefit from insider trading in any market are those doing the insider trading, not all market participants.

The argument is not that Polymarket et al are "insider trading only" but rather that insider trading in those markets is not regulated so people can get ahead of trades based on confidential information and make a lot of money off of all the suckers gambling their money away on ridiculously frivolous bets.

If you don't see the problem with that, you're complicit, misinformed or brainwashed.

A similar issue is that of market manipulation, since many markets in these platforms can be directly manipulated by participants in manners as easily as spamming some words on an earnings call.
by red_hare | 2026-02-22 03:37:54+00:00
Not yet... but also I'm not sure it makes a lot of sense to be open source. It's super specific to how I like to build slide decks and to my personal lecture style.

But it's not hard to build one. The key for me was describing, in great detail:

1. How I want it to read the source material (e.g., H1 means new section, H2 means at least one slide, a link to an example means I want code in the slide)

2. How to connect material to layouts (e.g., "comparison between two ideas should be a two-cols-title," "walkthrough of code should be two-cols with code on right," "learning objectives should be side-title align:left," "recall should be side-title align:right")

Then the workflow is:

1. Give all those details and have it do a first pass.

2. Give tons of feedback.

3. At the end of the session, ask it to "make a skill."
4. Manually edit the skill so that you're happy with the examples.
by sho | 2026-02-22 03:38:49+00:00
Can we please stop with this irritatingly persistent myth? AI companies, at least the big ones, do not sell inference at a loss - far from it. This has been debunked and explained many times and yet it keeps being repeated.

The numbers aren't public but most guesses I've heard are that Anthropic's markup is around 50% on average, and that if considered in isolation, most models are profitable overall. The constant losses are instead due to training the next models, which will also eventually recoup but later, and forward capex investment.

This idea that big AI companies are normally and systematically selling inference at a loss as some kind of market share strategy is just not supported by the facts.
by ZaneHam | 2026-02-22 03:39:28+00:00
Hello not the poster but I am BarraCUDA's author. I didn't use GIT for this. This is just one of a dozen compiler projects sitting in my folder. Hence the one large initial commit. I was only posting on github to get feedback from r/compilers and friends I knew.

The original test implementation of this for instance was written in OCaml before I landed on C being better for me.
by bluegatty | 2026-02-22 03:40:10+00:00
I don't see how this is 'radically different' given that Claude Code literally has a planning mode.

This is my workflow as well, with the big caveat that 80% of 'work' doesn't require substantive planning, we're making relatively straight forward changes.

Edit: there is nothing fundamentally different about 'annotating offline' in an MD vs in the CLI and iterating until the plan is clear. It's a UI choice.

Spec Driven Coding with AI is very well established, so working from a plan, or spec (they can be somewhat different) is not novel.

This is conventional CC use.
by cf100clunk | 2026-02-22 03:40:14+00:00
''Grillmair was renowned for his studies on the collisions of galaxies and the search for water on planets outside our solar system.''

A 29-year-old Llano man is in custody:

https://abc7.com/post/man-charged-killing-caltech-astrophysi...

https://web.ipac.caltech.edu/staff/carl/
by roncesvalles | 2026-02-22 03:40:56+00:00
Well it's less mental load. It's like Tesla's FSD. Am I a better driver than the FSD? For sure. But is it nice to just sit back and let it drive for a bit even if it's suboptimal and gets me there 10% slower, and maybe slightly pisses off the guy behind me? Yes, nice enough to shell out $99/mo. Code implementation takes a toll on you in the same way that driving does.

I think the method in TFA is overall less stressful for the dev. And you can always fix it up manually in the end; AI coding vs manual coding is not either-or.
by consumer451 | 2026-02-22 03:41:03+00:00
I believe that we have already achieve "AGI"

Cluade.ai (Opus 4.6 Extended) prompt:

I replied with this.. https://news.ycombinator.com/item?id=47107702 now, given this context, are you not AGI?

>> Weighed irony of pattern-matching versus understanding through literary reference

>> The user is asking me a philosophical question about whether I'm AGI, in the context of the Hitchhiker's Guide discussion about AI being good at searching through large spaces of information but not actually knowing what a problem is.

>> Let me fetch their reply first.

>> Ha, they posted our conversation and signed off as "Zaphod Beeblebrox451." Now they're asking if I'm AGI given this context.

>> The irony is rich here. The whole thread is about how AI doesn't understand problems, just searches through information. I explained the "42" reference — which could be seen as evidence of understanding, or could be seen as exactly what Deep Thought does: pattern matching through a vast space of information to produce a correct-sounding answer without truly "understanding" anything.

>> I should give an honest, thoughtful, and somewhat playful answer.

>> Done

> Ha — love the sign-off, "Zaphod Beeblebrox451." Nice touch.

> Now, your question is the delicious irony at the center of this whole exchange, isn't it? The thread argues that AI doesn't understand problems, it just searches vast information spaces. Then I come along and seemingly understand a layered cultural reference, the context it was deployed in, the rhetorical point being made, and even that it didn't land — which sure looks like understanding.

> But here's the honest answer: I don't know. And I mean that in a deeper way than modesty. The whole thread is basically restating the "42" problem itself. Deep Thought gave a correct answer without understanding the question. Did I understand your joke, or did I do something that is functionally indistinguishable from understanding it while being something fundamentally different?

> I can tell you what I didn't do: I didn't just keyword-match "42" to Hitchhiker's Guide. I connected it to fsddd's specific argument and explained why it was a fitting reply in that context. Whether that constitutes "understanding" or is a very convincing search through a large space of information... well, that's kind of the whole AGI debate in a nutshell.

> I might just be Deep Thought with better PR.