MurphyA/hacker-news huggingface dataset:
Demo of an indie AI collaboration app – beyond Codex and Claude Code desktop
by seeksky | 2026-02-26 06:40:06+00:00
*golutra — One Person. One AI Squad.*

webs:https://github.com/golutra/golutra
golutra is a next-generation multi-agent workspace that transforms your existing CLI tools into a unified AI collaboration hub. No project migration. No command relearning. No terminal switching. Just parallel execution, automated orchestration, and real-time result tracking.

Click agent avatars to inspect logs, inject prompts directly into terminal streams, or monitor execution while your AI team runs silently in the background. Built with Vue 3 + Rust as a Tauri desktop app for Windows and macOS, golutra upgrades “one person + one editor” into *“one person + an AI squad.”* It replaces single-threaded, manual context switching with coordinated, multi-agent automation.

*Key Highlights*

* Unlimited multi-agent parallel execution
* Automated orchestration from analysis to deployment
* CLI compatibility: Claude, Gemini, Codex, OpenCode, Qwen
* Stealth terminal with context-aware intelligence
* Visual interface combined with command-line power

Keep your familiar commands. golutra wires them into a complete engineering loop.

---

## What’s Next

golutra is only at its beginning.

The next evolution introduces a refactored *OpenClaw* as a true commander layer — a central AI coordinator capable of automatically creating agents, assigning roles, and generating structured collaboration channels based on task complexity. Instead of manually organizing workflows, golutra will dynamically assemble self-structured AI teams on demand.

Upcoming capabilities include:

* *Mobile Remote Control* — monitor agents, review logs, intervene, and redirect tasks directly from your phone.
* *Auto Agent Builder* — quickly generate specialized agents for specific industries or scenarios (e.g., refactoring, compliance audits, trading systems, DevOps automation).
* *Unified Agent Interface* — a standardized agent protocol for seamless integration into the orchestration layer.
* *Deep Memory Layer* — persistent, shared long-horizon memory across agents to strengthen knowledge accumulation and cross-task reasoning.

The mission is clear:
evolve from multi-agent execution to *self-organizing AI teams*, improving overall collaboration efficiency by 30% or more through better coordination, specialization, and memory.

One person. One AI squad.
Soon — one intelligent AI organization.
by jaunt7632 | 2026-02-26 06:40:58+00:00
This is a great demonstration of something that's easy to forget: LLMs don't have a concept of randomness. They pick the most probable next token given the context, and "random name" still activates whatever patterns dominated the training data. Temperature helps but it's not true randomness, just flatter probability distributions.

The real takeaway for me is that any workflow relying on LLMs for uniform sampling is going to produce skewed results. If you need actual randomness, generate it externally and pass it in.
by imiric | 2026-02-26 06:41:31+00:00
Technical concepts can be broken down into ideas anyone can understand if they're interested. Token prediction is at the core of what these tools do, and is a good starting point for more complex topics.

On the other hand, calling these tools "intelligent", capable of "reasoning" and "thought", is not only more confusing and can never be simplified, but dishonest and borderline gaslighting.
by gwern | 2026-02-26 06:41:47+00:00
There is an interesting point here: the US Copyright Office recently tried to argue that images generated by a model could not be copyrighted, no matter how detailed the prompt nor how curated, because the artist did not envision the exact output and thus it is merely the output of an uncreative mechanical process. Clearly OP does not envision the exact way in which glass will shatter or frost or crack, and has to repeatedly update and revise based on what happens; are his glass portraits mere mechanical outputs and uncopyrightable?
by boxed | 2026-02-26 06:42:09+00:00
It's not a linguistic reality in spoken English is what I meant. We Swedish speakers can hear it when English speakers pronounce the space and when they don't, because we're super sensitive to that difference because our language cares. We can easily flip from one state to the other and repeat the word/words back and English speakers will instantly hear that it sounds weird and wrong but often they don't understand why.

English spelling does NOT line up with pronounced English when it comes to what we in Swedish calls "särskrivning" which is a word that roughly means "separateness writing".
by ej88 | 2026-02-26 06:43:38+00:00
"CEO Dario Amodei predicted last March that in six months AI would be writing 90% of code, and when that didn’t happen"

I mean, a lot of developers have 90% of their code being written by AI (myself and my friends at the labs included). Obviously YMMV depending on your codebase and individual skill.

"Software engineers will at times overestimate their capabilities, as demonstrated by the METR study that found that developers believed they were 24% faster when using LLMs, when in fact coding models made them 19% slower.
This, naturally, makes them quite defensive of the products they use, and whether or not they’re actually seeing improvements."

I wonder what he thinks about the new METR update that showed a net speedup as a lower bound (due to participants literally not wanting to even tackle tasks with AI due to how slow it would be), with the returning devs having the greatest improvements in speedup?

"for one of Anthropic’s greatest lies: that AI can “work uninterrupted” for periods of time, leaving the reader or listener to fill in the (unsaid) gap of “...and actually create useful stuff.”"

We're probably at the beginning of the S curve for long-running tasks that create useful stuff (https://ladybird.org/posts/adopting-rust/) but it clearly needs hand-holding and a way to self-verify work.

"No amount of DarioMath about how a model “costs this much and makes this much revenue” changes the fact that profitability is when a company makes more money than it spends."

Feels like he's being dishonest here because the economics of the labs are unique (and precarious). Each model (revenue - cost to train and serve) is profitable. Labs invest in the next model to maintain their advantage, otherwise people will stop using their latest models. This probably doesn't go on in perpetuity (which is what Ed should've analyzed more). To his benefit, he's right that CC subscriptions are currently being subsidized.

[Insert quotes of Dario saying models will be smarter than most humans or Nobel laureates]

I mean, he's not wrong in certain definitions of "smart". They're already well above the average human in terms of testable world knowledge, math, coding, science, etc... but obviously fall short in other ways compared to humans.
by measurablefunc | 2026-02-26 06:43:49+00:00
I don't know, that's why I asked b/c I always see a lot of empty platitudes when it comes to LLM praise so I'm curious to see if people can actually back up their claims.

I haven't done any 3D modeling so I'll take your word for it but I can tell you that I am working on a very simple interpreter & bytecode compiler for a subset of Erlang & I have yet to see anything novel or even useful from any of the coding assistants. One might naively think that there is enough literature on interpreters & compilers for coding agents to pretty much accomplish the task in one go but that's not what happens in practice.
by dakolli | 2026-02-26 06:43:58+00:00
Not everything can be understood through p values and studies with 13 PARTICIPANTS undergoing a ONLY TWO EXPERIMENTS each, nor should the observed effects on 13 PEOPLE (half placebo) be extrapolated out to justifying safety for hundreds of millions of people. Next time you want to prove a point, take the time to research good sources, and not just ask an llm use your brain for once.

Please go put your kid on amphetamines for 15 years and let me know how that works out for them..

Do you care how much incidents of psychosis it causes on a yearly basis?

I said that the Ruling class (aka these professors) are pushing an agenda, and then you choose to provide evidence published by the ruling class to support that agenda.
by selridge | 2026-02-26 06:45:24+00:00
Ok cool cool. Instead of pretending you need to teach me, you could engage with what I'm saying or even the OP!

"I don't know how you get here from "predict the next word"" is not really so much a statement of ignorance where someone needs you to step in but a reflection that perhaps the tech is not so easily explained as that. No magic needs to be present for that to be the case.
by intended | 2026-02-26 06:45:38+00:00
The article talks about LLMs reviewing Econ papers.

I’m hesitant to call this an outright win, though.

Perhaps the review service the author is using is really good.

Almost certainly the taste, expertise and experience of the author is doing unseen heavy lifting.

I found that using prompts to do submission reviews for conferences tended to make my output worse, not better.

Letting the LLM analyze submissions resulted in me disconnecting from the content. To the point I would forget submissions after I closed the tab.

I ended up going back to doing things manually, using them as a sanity check.

On the flip side, weaker submissions using generative tools became a nightmare, because you had to wade through paragraphs of fluff to realize there was no substantive point.

It’s to the point that I dread reviewing.

I am going to guess that this is relatively useful for experts, who will submit stronger submissions, than novices and journeymen, who will still make foundational errors.
by OneMorePerson | 2026-02-26 06:46:24+00:00
Your case sounds complicated so I'm not sure, but two things to note:

1. US is one of only a few countries where children emigrating with parents don't officially declare intent to immigrate, they do it automatically with their parents. This means that your grandfather (whether he was aware or not) was still German, since German law says you only give it up if you "take action to immigrate" or something like that. Likewise every child since then (your mother and you) were born as US citizens "involuntarily" (as in you didn't choose) so you also retained your citizenship.

2. In 2021 Section 5 of the StAG law was updated to say that people born to German mothers between 1949-1975 are now eligible, it was updated since male only was seen as discriminatory. So theoretically say grandfather -> mother (born to male) -> you (post 1949). Not an expert so double check this.

Im not an expert but my understanding of your case would be that you are not even needing to apply for status, you are literally German now, and just need to request a passport (check this with the resources on Reddit I mention below).

I'd recommend checking Reddit "German Citizenship by Descent" resources. There's a couple profile names you will see there really frequently who are German citizens who can help you in finding paperwork from German government resources if needed (old birth certificates, etc.) for a small fee.

You can also see public threads where people explain their case and you can see if you find one similar to yours. Example: https://www.reddit.com/r/Genealogy/comments/scvkwb/german_ci...
by abustamam | 2026-02-26 06:48:07+00:00
One of the first projects I made while learning to code was a calculator.

It wasn't very sophisticated. But it was fast and it handled commas and periods. It wasn't localized, but it could be.

Sad to think that me having a month of coding experience made a better product than MSFT, yet whoever coded the calculator is probably making ten times what I am right now.
by chii | 2026-02-26 06:49:13+00:00
> People who produced the works LLMs are trained on are not compensated for the value they are now producing

the value being extracted via LLM techniques is new value, which did not previously exist. The producer(s) of the old data had an asking price, which was taken by the LLM trainers. They cannot make the argument that since the LLM is producing new value, they should retroactively update their old asking price for their works.

They could update their asking price for any new works they produce. They also have the right to ask their works not be used for training, etc. But they cannot ask their old works to be paid for by the new uses in LLM in a retroactive way.
by eucyclos | 2026-02-26 06:49:47+00:00
I suspect it's a left vs right issue because the right is the cheerleader of the military industrial complex, which is only justified by protecting oil interests. If oil becomes a curiosity useful for making certain consumer products and little else, the military industrial complex is oversized by any metric, hence the right is against that.

Or to oversimplify even more: it's the Bush dynasty's fault.
by shreyaspapi | 2026-02-26 06:49:50+00:00
I built Onera because I was uncomfortable sending sensitive code to LLM APIs in plaintext. Most hosted inference today requires trusting the provider not to log or inspect prompts, but technically the infrastructure still has access. I wanted a setup where the server operator itself could not read user data.

Onera uses AMD SEV-SNP trusted execution environments to run inference inside a hardware encrypted VM, where memory is encrypted and isolated from the host. The client first performs remote attestation to verify the enclave, and then establishes an encrypted channel directly into it. Prompts are sent through this secure channel and processed entirely inside the enclave, so even the machine running the workload cannot inspect them.

The API is OpenAI compatible, so it works with existing tools like OpenClaw, OpenWebUI, Cursor, Claude Code, or anything using the OpenAI SDK, without requiring changes to the client architecture.

The entire client and enclave runtime are open source here: https://github.com/onera-app/onera

Happy to answer any technical questions or feedback.
by c22 | 2026-02-26 06:49:54+00:00
> If you train a model that f(x) = 5 and f(x+1) = 6, the number 7 doesn't have to exist in the training data for the model to give you a correct answer for f(x+2)

This is an interesting claim to me. Are there any models that exist that have been trained with a (single digit) number omitted from the training data?

If such a model does exist, how does it represent the answer? (What symbol does it use for the '7'?)
by toast0 | 2026-02-26 06:50:11+00:00
> Regarding your links, I’ve seen you post about 1 before and have read about it - it looks very cool. I don’t recall seeing 2 or 3 before and look forward to reading more about those. Thanks as always for your insights!

So #1 now has dist connection stuff as of a few hours ago. Not super obvious, but you can load two (or more) nodes and call nodes() and see they're connected.

#2 is a pretty neat way to diagnose path mtu problems. And I've been seeing people use it and link to it on networking forums all over, even forums in other languages. Which is pretty awesome. Maybe a few links in forums over the past year, but it's always cool to see people using stuff I built mostly for me. :)

#3 is like I dunno, probably not that useful, I think you could do a lot of similar stuff already, but it felt like a tool that was missing... but I also got some feedback that maybe there's other ways to do it already too, so shrug. But pxe booting is always fun.
by gck1 | 2026-02-26 06:50:40+00:00
If OSS models are 6-12 months behind, it means sometime during 2026, we'll see a model that is on par with the likes of GPT 5.2/Opus 4.5.

For code generation specifically, the performance level of this is going to be more than enough for this customer base. What does Anthropic do then to justify $200/mo price sticker? Better model? Just how much better? Better tools? Single company can't compete with the tools entire OSS can produce.

I would be unable to sleep if I was running OAI / Anthropic.
by pianom4n | 2026-02-26 06:51:41+00:00
Do you think the submitter intended this as an ad? His post history doesn't seem suspicious.

Or do you think article's author wrote this an an ad? He's a reputable academic who seems impressed with an AI tool he used and is honestly sharing his thoughts.

For reference he publish the 80 page inflation mini-book 2 weeks ago asking for feedback:
https://www.grumpy-economist.com/p/inflation
by ildar | 2026-02-26 06:51:51+00:00
This thread captures the exact tension: executives want AI agents, security teams say no, nobody has a middle ground.

Microsoft's security blog last week was explicit: "OpenClaw should be treated as untrusted code execution with persistent credentials. Not appropriate for standard workstations."

Their solution (dedicated VMs) is technically correct but practically useless. The exec with the Mac Mini isn't running a VM.

I built an open-source tool to bridge this gap: ClawMoat (https://github.com/darfaz/clawmoat). Host-level security between the agent and your file system: permission tiers, forbidden zones for sensitive dirs (~/.ssh, ~/.aws, browser data), full audit trails, real-time alerts. One npm install, zero dependencies, MIT licensed.

Not a silver bullet - you still want prompt injection scanning (LlamaFirewall) and conversation guardrails. But it's the only open-source tool I know of that protects the host FROM the agent rather than the other way around.

The answer to "should we ban OpenClaw?" is probably "no, but you should see what it's doing and stop it from touching your credentials."
by Rebelgecko | 2026-02-26 06:52:00+00:00
How much more do we need? 2x the capacity? 5x? 10x? The state has added a ridiculous amount of battery over the last couple years so it doesn't seem intractable

I remember looking at CAISO's graphs a few years ago and when the sun goes down we were basically completely dependent on imports and natty gas, especially for scaling up to demand around 6-7pm. Looking at today's graph, batteries were actually our top electricity source for a while around peak electricity time. Natty has been able to stay pretty flat, and even now its neck and neck with batteries
by ildar | 2026-02-26 06:52:21+00:00
Really like the process-level isolation approach. Moving credentials out of the agent's address space is fundamentally sound.

I've been building something complementary: ClawMoat (https://github.com/darfaz/clawmoat) - host-level security that enforces permission tiers and forbidden zones on file system access and shell commands. Where ClawShell isolates the credentials themselves, ClawMoat restricts what the agent can do with the host: which directories it can read, which commands it can run, network egress control, plus full audit trails.

The "Lethal Trifecta" framing is spot on. I think the defense stack is going to be layered:

1. Process isolation (ClawShell) - credentials never in agent memory
2. Host-level policy (ClawMoat) - agent can't touch ~/.ssh, ~/.aws even if compromised
3. Prompt-level scanning (LlamaFirewall) - catch injection before it reaches the agent
4. Conversation guardrails (NeMo) - keep the agent on-topic

No single layer is sufficient. Microsoft's security blog last week basically confirmed this - they recommend "defense in depth" for agent deployments.

Would be interesting to explore integration - ClawMoat could detect when an agent tries to bypass ClawShell by accessing credential files directly.
by grey-area | 2026-02-26 06:52:40+00:00
Given their failure on novel logic problems, generation of meaningless text, tendency to do things like delete tests and incompetence at simple mathematics, it seems very unlikely they have built any sort of world model. It’s remarkable how competent they are given the way they work.

Predict the next word is a terrible summary of what these machines do though, they certainly do more than that, but there are significant limitations.

‘Reasoning’ etc are marketing terms and we should not trust the claims made by companies who make these models.

The Turing test had too much confidence in humans it seems.
by libraryofbabel | 2026-02-26 06:53:18+00:00
Obviously you don’t just say those words and leave it at that. Both those things can be explained in understandable terms. And even having a superficial sense of what they are gives people a better picture of what modern LLMs are all about than tired tropes from three years ago like “they’re just trained to predict the next token in the training data, therefore…”
by h33t-l4x0r | 2026-02-26 06:53:23+00:00
10 seconds to buy my wife a Pepsi? Why that estimate seems quite absurd.

First I would have to walk 10 miles into town. Then I would have to locate a purveyor of goods that carried Pepsi-Cola products...

Then I reckon we would spend a fort-minute dickering over price.

And finally trudging back home with my Pepsi product in tow.

Why, I'd be lucky to accomplish this herculean task in the very same evening.
by kristiandupont | 2026-02-26 06:53:23+00:00
I had Claude help me get a program written for Linux to compile on macOS. The program is written in a programming language the author invented for the project, a pretty unusual one (for example, it allows spaces in variable names).

Claude figured out how the language worked and debugged segfaults until the compiler compiled, and then until the program did. That might not be magic, but it shows a level of sophistication where referring to “statistics” is about as meaningful as describing a person as the statistics of electrical impulses between neurons.
Show HN: Bing Webmaster CLI for Agents and LLMs
by Gregoryy | 2026-02-26 06:54:18+00:00
Created this CLI client for Bing Webmaster to let my agents automate SEO reporting and improvements on Bing.

Works via API key auth (from ENV of CLI argument).

Repo includes a skill that can be installed in your OpenClaw agent.

I also have a CLI for Google Search Console: https://github.com/NmadeleiDev/google-search-console-cli

- from the developer of https://openclaw-setup.me
by floren | 2026-02-26 06:54:59+00:00
You're right, you posted a lot about LLM style but only pasted LLM output twice. I apologize for misrepresenting your posting in that fashion.

I do think you would do well to revisit the thread you linked at https://news.ycombinator.com/reply?id=46986446, because I saw the OP's comment when it was posted, I agreed with it then and I kind of still do.
by iwontberude | 2026-02-26 06:55:04+00:00
Your critique of Apple and Tim Cook is unsubstantiated and misleading. That same Tim Cook stood up to the FBI and refused to participate in breaking into phones for them when they were pressed in 2015-2016. The same Apple that later fought against the government forcing document scanning in iCloud and was able to keep them out off device. They have been fighting the whole time. Apple is was first to normalize whole disk encryption on commercial machines, they have made Safari a weapon against tracking which is abused by governments.
by Rebelgecko | 2026-02-26 06:55:07+00:00
Best bet is probably a 240v inverter, ground mounted used panels (can often be found ridiculously cheap)and a Docan or similar battery pack. 240v inverters made for the US tend to be pricier but you may be able to get an EU model to work. 120v won't be able to recharge 100mi/day. If you're in the US $1000 is prob not enough though
by bryanrasmussen | 2026-02-26 06:55:37+00:00
reminds me of "Memoir from Antproof Case"
https://en.wikipedia.org/wiki/Memoir_from_Antproof_Case
which wikipedia really does not describe well, as the plot really details the protagonist's life long war against coffee drinking, the following review handles that part better:

https://web.archive.org/web/20121008201138/https://www.nytim...