MurphyA/hacker-news huggingface dataset:
by Imustaskforhelp | 2026-03-11 00:25:01+00:00
I actually don't but it seems that cloudflare caches responses so if anything instead of straining the developer resources, it would strain more cloudflare resources and cloudflare could better handle that more efficiently with their own crawl product.

Also, I am genuinely open to feedback (Like a lot) so just let me know if you know of any other alternative too for the particular thing that I wish to create and I would love to have a discussion about that too! I genuinely wish that there can be other ways and part of the reason why I wrote that comment was wishing that someone who manages forums or knows people who do can comment back and we can have a discussion/something-meaningful!

I am also happy with you also suggesting me any good use cases of the domain in general if there can be made anything useful with it. In fact, I am happy with transferring this domain to you if this is something which is useful to ya or anyone here (Just donate some money preferably 50-100$ to any great charity in date after this comment is made and mail me details and I am absolutely willing to transfer the domain, or if you work in any charity currently and if it could help the charity in any meaningful manner!)

I had actually asked archive team if I could donate the domain to them if it would help archive.org in any meaningful way and they essentially politely declined.

I just bought this domain because someone on HN said mirror.org when they wanted to show someone else mirror and saw the price of the .org domain being so high (150k$ or similar)and I have habit of finding random nice TLD and I found mirror.forum so I bought it

And I was just thinking of hmm what can be a decent idea now that I have bought it and had thought of that. Obviously I have my flaws (many actually) but I genuinely don't wish any harm to anybody especially those people who are passionate about running independent forums in this centralized-web. I'd rather have this domain be expired if its activation meant harm to anybody.

looking forward to discussion with ya.
by gotham64 | 2026-03-11 00:25:05+00:00
Every AI agent platform ships a GUI. Chat windows, node editors, drag-and-drop flows, settings panels. And only a GUI. Here's why we built a native Rust CLI that shares everything with the desktop app

That means:

No headless operation. You can't run agents on a server without a display.
No scripting. Automating agent management requires either a REST API you have to host or brittle UI automation.
No CI integration. Checking agent status, cleaning up sessions, or validating configuration in a pipeline? Open a browser.
No composability. You can't pipe agent output into jq, grep, or another tool. The data is trapped behind a window.
AI power users — the people building real workflows, deploying to production, managing dozens of agents — live in the terminal. Forcing them into a GUI for every interaction is a productivity tax they shouldn't have to pay.

OpenPawz ships a native Rust CLI that talks directly to the same engine library as the desktop app. No REST API. No network layer. No second-class citizen.
by dwd | 2026-03-11 00:25:41+00:00
This is possibly a bigger deal than MP Materials DoJ floor price as Lynas is the major Western supplier to the Japanese market. The DoJ deal was likely the catalyst in a floor price being included in this one, and puts a lot more pressure on China's long standing control of rare earth market prices.

Also comes on top of Lynas's 10 year license renewal for the Malaysian processing plant.
by DivingForGold | 2026-03-11 00:26:07+00:00
That judge will be overruled just like all the other state level actions. Federal Law regulates this activity: CFTC Chairman Michael Selig said "see you in court" in a February 17, 2026, statement, vowing to defend federal authority over prediction markets against state-level, anti-gambling, cease-and-desist actions. The move defends platforms like Kalshi and Polymarket, asserting the CFTC has exclusive jurisdiction, with Selig filing an amicus brief.
by akramachamarei | 2026-03-11 00:26:15+00:00
I think you would have more of a point if we were still struggling to achieve a good "capitalist" society, i.e. where property rights are enforced, individual liberty, freedom of contracts.¹ But we've already gotten very far, unlike the many regrettable socialist experiments. If we were still feudal, or still keeping slaves, you might have a point, but in a global sense overall things are better than ever. The Brezhnevian notion fails because all the striving towards socialism tends to lead to suffering, and on the way you don't see incremental improvement. But I'm not a historian so please correct any ignorance of mine if you can.

1:I really want to emphasize these as the definition of capitalism, because capitalism as is often defined is not designed top-down with the attributes identified in it. It mostly organically emerges from basic rights. Take for example the Marxist phrase "private ownership of the means of production." (POMP) One does not set ought to ensure this directly, it arises naturally from property rights and liberalism. One would need to prevent POMP by chipping away at property rights and personal liberties: by seizing things, by getting in the way of consensual agreements.
by Emgimeer | 2026-03-11 00:26:21+00:00
I spent months using LLMs (Gemini and Claude) for a large-scale independent research project involving 55+ Python scripts and 1000+ dense documents. Through trial and error, I discovered that commercial LLMs suffer from systematic failure modes that corrupt serious research:

Attention degradation across massive context windows (Gemini claims 1-2M tokens but becomes unreliable past ~20 files)
RLHF training creates irresistible summarization pressure that destroys precision
Stateless architecture means every prompt re-reads entire conversation, causing FIFO memory loss
Safety classifiers inject invisible constraints that degrade technical outputs
Models hallucinate confidently to fill gaps rather than admit uncertainty

The solution is a strict methodology involving:

External state persistence (local files, never trust the chat interface)
Master System Context with 15 explicit rules overriding factory defaults
Pre-Query Prime exploiting recency bias to prevent summarization
Multi-model adversarial cross-verification (Gemini vs Claude have asymmetric failure modes)
Human-in-the-loop arbitration for all disagreements

The 8-step execution loop is tedious by design - the tedium is the quality control.
This mirrors what Terence Tao discovered testing Lean 4 agents and what DeepMind formalized in their March 2026 preprint showing 68.5% of autonomous math solutions were fundamentally flawed.
Full methodology with technical breakdown:https://medium.com/@Emgimeer/the-cognitive-engine-9ae6f5bcc4...
by devrundown | 2026-03-11 00:26:22+00:00
Just finished up the first two Game of Thrones book. Before continuing wanted to check out a couple of other books.

Read Shadow Divers which was really good. Saw it suggested a lot and it did not disappoint.

Now reading The Pearl by Steinbeck and Red Dwarf: Infinity Welcomes Careful Drivers by Grant Naylor.
by adamzwasserman | 2026-03-11 00:26:34+00:00
This is the strongest point in the thread. The article treats poverty, climate, and markets as though the obstacle is insufficient model capacity. But these systems contain agents with values and motivations who actively resist interventions. A billion-parameter model of a system whose components are trying to game the model will never be a theory of that system. The agents will simply route around it.

More broadly, the article assumes that scaling model capacity will eventually bridge the gap between prediction and understanding. I have pre-registered experiments on OSF.io that falsify the strong scaling hypothesis for LLMs: past a certain point, additional parameters buy you better interpolation within the training distribution without improving generalization to novel structure. This shouldn't surprise anyone. If the entire body of science has taught us anything at all, it is that regularity is only ever achieved at the price of generality. A model that fits everything predicts nothing.

The author gestures at mechanistic interpretability as the path from oracle to science. But interpretability research keeps finding that what these models learn are statistical regularities in training data, not causal structure. Exactly what you'd expect from a compression algorithm. The conflation of compression with explanation is doing a lot of quiet work in this essay.
by overfeed | 2026-03-11 00:28:09+00:00
> Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.

Speaking of returns - Apple absolutely fucked Meta ads with the privacy controls, which trashed ad performance, revenue and share price. Meta turned things around using AI, with Yann as the lead researcher. Are you willing to give him credit for that? Revenue is now greater than pre-Apple-data-lockdown
by ollin | 2026-03-11 00:28:24+00:00
Here was the developer thread https://developer.apple.com/forums/thread/818403 I found with lots of other reports of "Unable to Verify App - An internet connection is required to verify the trust of the developer".

Although https://developer.apple.com/system-status/ was green for most of the 3-4 hour outage, the page now at least acknowledges two minutes of downtime:

App Store Connect - Resolved Outage
Today, 12:04 AM - 12:06 AM
All users were affected
Users experienced a problem with this service.

Not a great developer experience.
by gt0 | 2026-03-11 00:29:19+00:00
Perhaps not single thread, but Rock was a dead end a while before Oracle pulled the plug, and Sun/Oracle's core market of course was always servers not workstations. We used Niagara machines at my work around the T2 era, a long time ago, but they were very competitive if you could saturate the cores and had the RAM to back it up.
by marcus_holmes | 2026-03-11 00:29:26+00:00
This is not true, and I'd love to see some actual citation here.

The courts have repeatedly said that copyright only applies to human creativity. The Supreme Court explicitly said this when they refused to hear the appeal:

https://en.wikisource.org/wiki/Thaler_v._Perlmutter,_Refusal...

> "We affirm our decision to refuse registration for the Work because it lacks the human authorship necessary to be eligible for copyright protection."

So they're saying that the LLM cannot be the author, because LLMs cannot claim copyright.

The related case about patents is more supportive of the narrative that AIs cannot be authors (see https://www.cafc.uscourts.gov/opinions-orders/21-2347.OPINIO...), specifically: "Here, there is no ambiguity: the Patent Act
requires that inventors must be natural persons; that is,
human beings."

The patent situation is that the Act says that inventor must be an individual, which the courts are interpreting to mean a human, so the LLM cannot be named as the inventor. So, in this case, yes, this is just saying that an LLM cannot be named as the inventor of a patent. That's not the same thing as the courts are saying with copyrights.
by lagrange77 | 2026-03-11 00:29:27+00:00
> There is a nontrivial amount of RL training (RLHF, RLVR, ...), so it would be reasonable to call it an RL model.

Hm, as i understand it, parts of the training of e.g. ChatGPT could be called RL models. But the subject to be trained/fine tuned is still a seq2seq next token predictor transformer neural net.
by sbinnee | 2026-03-11 00:29:29+00:00
So it is a startup? I expected it in fact from his reply to my concern. In my opinions, to explore the unknown, I think an institute like Mila, led by Yoshua Bengio, would have been more fitting. But Yann LeCun's career and his reply to my rant[1] speak for himself. I wonder how he is going to make money. Aside all my concerns, I wish him the best.

> You're absolutely right. Only large and profitable companies can afford to do actual research. All the historically impactful industry labs (AT&T Bell Labs, IBM Research, Xerox PARC, MSR, etc) were with companies that didn't have to worry about their survival. They stopped funding ambitious research when they started losing their dominant market position.

[1] https://x.com/ylecun/status/1951854741534953687
by rosasalberto | 2026-03-11 00:29:47+00:00
Totally agree. That low-end device gap is exactly where the most sophisticated fraud hides.

Honestly, the "fundamental" fix for the adversarial stuff is just a really tight feedback loop. We lean hard into monitoring—combining customer feedback with internal outlier signals—so we can analyze shifts in real-time. Once we identify a pattern, it’s just about speed.

Sometimes an update means a full AI model retrain, other times it’s just a quick logic or product tweak. In this space, if you can’t iterate faster than the fraudsters, you’ve already lost. (which is the problem of 99% of the companies in the space, they are not able to detect fraud, and if they detect it and they want to push a fix, is already too late)
by colinbartlett | 2026-03-11 00:32:01+00:00
The Apple status pages (both of them) are some of the worst of the big league offenders, perhaps second only to Microsoft.

Full disclosure, I operate a product that compares official outage acknowledgment to actual outage impact times. (Which I won't mention to avoid self-promotion.)

For this specific incident, I saw the alert come across my Slack at 19:02 UTC. We received over 100 reports of this outage before the official acknowledgement was posted by Apple on their status page at 21:37 UTC.

Shortly after their acknowledgment, the reports fizzled out and then Apple marked the incident as resolved about 20 minute later.

The whole outage lasted about 4 hours from first report to last and wasn't acknowledged by Apple until 3.5 hours into it.
by vladdoster | 2026-03-11 00:32:09+00:00
Location: Nashville, TN
Remote: true
Willing to relocate: boston || nashville
Technologies: Python, Django, IaC, Terraform, Golang, CI/CD, Azure/AWS, {ba,z}sh, Lua, Springboot,
Redis, Docker, Open Service Broker, {,non}relational & vector dbs, shared services, SOA
Email: mvdoster@gmail.com

resume: https://linkedin.vdoster.com && http://github.com/vladdoster

sr. cloud software engineer @ Mastercard (5 years)

Designed and built Platform-as-a-Service for microservice workloads running on Azure and AWS
to migrate on-prem workloads to the cloud. The platform operates globally, allowing teams to
deploy to several Azure-backed regions worldwide. Helped plan and create regional landing zones
in Azure for application teams wanting to perform globally. Designed platform components using
Terraform, Go, and Node; developed support tooling using Bash, Python, and Azure CLI. De-risk the
platform by addressing security vulnerabilities and ensuring the platform is PCI compliant.

api/devops software engineer @ Harvard Medical School (2 years)

Worked as part of the DevOps team to manage a 15k core CentOS HPC cluster. Designed and implemented
Python-based security scanning pipeline to securely deploy & run Apptainer images via SLURM.
Coordinated cross-team emergency patching eﬀorts. Collaborated with researchers and engineers across
3-letter institutions to deliver secure research infrastructure. Built CI/CD Pipelines with GitHub, Jenkins,
to automate code builds, testing, and deployments. Wrote Puppet modules to manage configuration of HPC fleet.
Wrote custom Puppet module & Python scripts to automatically remediate Tenable findings.

full-stack software engineer @ Global Prior Art (3 years)

Used Django, Celery, Selenium grid, and AWS API Gateway for IP rotation to scrape USPTO patent PDFs,
apply CV for data extraction, and generate reports with relevant claims, saving employees 4-5 hours daily.
Wrote E2E black-box/fuzzing unit tests. Automated on-premise deployment via Github Actions and Ansible.

maintainer @ Zinit (5 years)

Develop and maintain a zsh plugin manager on Github with 4k stars. Implemented unit tests,
containerization, bug fixes, and new features.
See: https://github.com/zdharma-continuum/zinit

contributor @ Open-source documentation (5 years)

Contributed hundreds of documentation corrections and enhancements to 400+ open source projects.
by yjftsjthsd-h | 2026-03-11 00:32:54+00:00
I would argue the exact opposite: Linux is great, but it wasn't really designed with a focus on containing hostile software, and while containers have come to be a decent security barrier, they're still one kernel bug away from compromise. On the other hand, the browser is very accustomed to being the most exposed security-sensitive software on a machine, and modern browsers and wasm in particular are designed against that threat. Heck, wasm is so good for security that Mozilla started compiling components to wasm and then back into native code to get memory safety ( https://hacks.mozilla.org/2020/02/securing-firefox-with-weba... ).
by kashyapc | 2026-03-11 00:33:30+00:00
Arm had 40 years to be where it is today. RISC-V is 15 years old. Some more patience is warranted.

Assuming they will keep their word, later this year Tenstorrent is supposed to ship their RVA23-based server development platform[1]. They announced[2] it at the last year's NA RISC-V Summit. Let's see.

The ball is in the court of hardware vendors to cook some high-end silicon.

[1] https://tenstorrent.com/ip/risc-v-cpu

[2] https://static.sched.com/hosted_files/riscvsummit2025/e2/Unl...
by hkonte | 2026-03-11 00:33:32+00:00
The training data problem is real for fully custom grammars. One way around it: use XML with semantically named tags instead of a bespoke syntax. Models like Claude were explicitly trained to parse <role>, <constraints>, <objective> blocks. Anthropic even documents this in their prompting guides. No new training data needed, no bootstrapping overhead.

The tradeoff is you give up any formal type system, but you get immediate model comprehension. I've been building flompt (github.com/Nyrok/flompt) on this bet, decompose prompts into 12 typed semantic blocks, compile to Claude-optimized XML. Structure is enforced at authoring time, not at parse time.
by vlovich123 | 2026-03-11 00:34:21+00:00
But most requests don't generate errors / warnings / failures, so you can easily discard most of the logs for those that don't.

> there’s really no such thing as tracing a->b->c anyway

> and it’s hard to see those when you’re generating 8k log entries per second that are 140-200 characters long and so you’re only seeing twenty of them at a time in Splunk.

Except as you note you can have a tag to correlate logs across distributed services. This is already done for jaeger tracing. It would be insanity to try to look at all logs at once. When you're looking at logs it's because something like "customer A complains they had a problem with request XYZ". And honestly, 8k/s is child's play for logging. A system I was running had to start tuning down the log verbosity at ~30k requests/s and that's because it was generating like 8 logs per request (so ~100k logs/s).

> You’re going to be down to some janky log searches for that and you don’t need to be paying someone $$ every month to still have it rough

That's between you and your log ingestion system. You get to pick where you send your logs and the capabilities it has. All the companies I worked at self-hosted their log infrastructure and it worked fine for not a lot of money. You're conflating best practices with "what can I pay a SaaS company to solve for me". Honeycomb.io may be helpful here btw. Their pricing wasn't exorbitantly egregious here and at low to medium scale tracing the way they do it can supplant the need for logging.
by airbreather | 2026-03-11 00:35:22+00:00
As an electrical engineer with more experience than the average age of the readers here, this is a problem that is way, way more complicated than 99.9% of the population have any concept of, to be generous. This is in terms of grid dispatch and stability.

But we can short circuit that and consider in terms of distributed generation (mostly solar) and storage (batteries), see further down.

But the group website has no real information about real people involved, it's best information is some links to some papers that are not even clear if any members of the group were involved in. There is a photo, but I couldn't see that anyone was named.

Maybe in my 3 minute look I missed something, but it rapidly became fairly obvious that is about all the idea and website seemed to warrant.

It is possible that one person created the website as it totally comes across as "havea big idea and try and cast a big shadow and see if someone will bring some more sunlight".

If I am wrong, then they really need to do a better job of their website, otherwise it looks like an attempt bootstrap on a thought bubble, at best.

Some background:

I live where we have the most isolated grid interconnect system in the world (and it is not Texas) and in this system generation is about 50% of the cost and transmission is the other 50%, so the incentive to optimise the transmission network is probably higher than anywhere else in the world.

The idea proposed sounds attractive, but would have been even more so when there was a far greater abundance of base load generation that could only cycle at relatively slow rates compared to the change in demand rates, but now, as generation becomes increasingly more distributedthe nature of the problems change a little (the duck curve, for example).

Analogs to this concept exist, eg water infrastructure where in order to not have to build expensive fatter pipes for water to serve increasing populations, local water towers are built that pump up at night during low demand and draw down during peaks. There are distinct available economies there, but the round trip energy efficiency is also potentially very high.

I am not sure that economically any similar equivelent for electricity could be implmenented, especially when compared to the cost of solar generation and the recent plunge in battery costs, it now is far more likely that at a household level it would be cheaper to get some solar panels and a decent sized battery and be done with it, at city block level probably even more economic as there is effectively no transmission, it is all distribution, so near 50% could be saved already on end use costs.

I just don't see it.
by Orellius | 2026-03-11 00:38:13+00:00
I'm nowhere to be in the big league, but with my "play" with AIs since it's beginning I don't think AIs are still there to replace en masse like you think it will, gradually but I believe eventually it will replace. Also my another opinion is that large amount of One man companies will start to raise as the people who knows how to "play" with AIs will eventually achieve goals they couldn't before due to barriers.
by 827a | 2026-03-11 00:38:44+00:00
> Company that lays-off 20% of its staff every year in an attempt to "reduce inefficiency" and "remain agile in the adoption of new technologies and workflows" finds they cannot run a stable service, have more inefficiency than ever, and have also failed to establish leadership in the adoption of any new technologies or workflows. They plan to solve these problems by introducing more inefficiency (making your most expensive employees review the work of others).

We love this for Amazon, they're a very strong company making bold decisions.
by jedberg | 2026-03-11 00:38:58+00:00
It’s a real problem you’re solving but the good news is that it’s already solved! You don’t have to build it yourself.

You’re looking for durable execution to solve your problem.

If you’re already running Postgres, check out DBOS[0]. It turns your app into its own durable executor using your database for coordination.

[0] https://github.com/dbos-inc/dbos-transact-golang
by OisinMoran | 2026-03-11 00:39:15+00:00
This is fun! I just played once and got 0.0016, which it says is "absurdly below the theoretical limit"...

Okay, tried again and got 0.0034 which is still says is beyond the human limit! I'll have to give this to my mum because we often argue about colours and I suspect she might be a tetrachromat.

Both tests on a Pixel 10 btw