MurphyA/hacker-news huggingface dataset:
Show HN: PoG – the only open-source, live, privacy-first AI provenance system
by pp10 | 2025-12-02 16:25:37+00:00
I just shipped the first fully working open alternative to closed corporate AI watermarking systems.

Repo: https://github.com/TamTunnel/PoG

Invisible watermark + on-chain receipt in 5 lines of code Dual hashes (exact + perceptual) → survives compression & edits Full creator anonymity — only a random wallet address is shown Tiered verification: Strong/Medium/Weak/None OpenAPI spec → TypeScript client in one command Live contract, Python client, verifier, tests, docs — everything is already there

C2PA is mostly future, commercial tools are closed and expensive. PoG is Apache 2.0, deployed today, costs ~$0.001, and you can verify any image with a single drag-and-drop. Gasless relayer coming Q1 2026. Looking for contributors on the relayer, browser extension, and getting ComfyUI/A1111/InvokeAI to ship it by default.

PS. Stars are real momentum — every star helps us become the default open standard, so appreciate every star.

Repo: https://github.com/TamTunnel/PoG
by pratio | 2025-12-02 16:25:58+00:00
Hey there, Great to see Marmot here and I'm a huge fan of your project. Recently, we deployed a catalog but we went with open-metadata https://open-metadata.org/ another amazing project.

What we missed on marmot was existing integrations with Airflow and other plugins like Tableau, PowerBI etc as well as other features such as sso, mcp etc.

We're an enterprise and needed a more mature product. Fingers crossed marmot reaches there soon.
by pengaru | 2025-12-02 16:26:10+00:00
He delivered that speech in 1986, so this would have been based on professional experience through the 60s-70s. A time before ubiquitous electronic communications. Back then you really would have been disconnecting by keeping your office door shut and focusing on your work.

Mapping those observations to today's environment, the individual in a closed private office is more like a hermit with a mailbox but no cell/internet connection.
by HarHarVeryFunny | 2025-12-02 16:26:15+00:00
They've developed a sparse attention mechanism (which they document and release source code for) to increase model efficiency with long context, as needed for fast & cost-effective extensive RL training for reasoning and agentic use

They've built a "stable & scalable" RL protocol - more capable RL training infrastructure

They've built a pipeline/process to generate synthetic data for reasoning and agentic training

These all combine to build an efficient model with extensive RL post-training for reasoning and agentic use, although they note work is still needed on both the base model (more knowledge) and post-training to match frontier performance.
by aengelke | 2025-12-02 16:26:22+00:00
I fully agree, but:

> these are the string instructions like REP MOVSB

AArch64 nowadays has somewhat similar CPY* and SET* instructions. Does that make AArch64 CISC? :-) (Maybe REP SCASB/CMPSB/LODSB (the latter being particularly useless) is a better example.)
by rybosworld | 2025-12-02 16:26:32+00:00
I don't see how you can claim a home ownership rate above 60% means the market is working for most people.

All that means is that a majority of the population was able to buy a house at some point in time under some conditions. The recent data is clear: people who already have homes, are staying put in them. And a growing portion of people without homes are joining a fairly new class of "forever renters".

The median age of first time home buyers was 33 in 2020. In 2025 it is 40.

That's truly incredible if you think about it. In just 5 years, the median age has increased by 7 years.

Here's another interesting statistic: the percentage of homes bought by first time buyers is down 50% since 2007.

> Rent control is fundamentally unfair and socially undesirable.

I think this is an incredible claim to make. Unfair to who? Undesirable compared to what?

Rent control at it's core is a redistribution mechanism. It favors existing tenants at the expense of property owners.

If you care about low income renters getting displaced by a 40% rent increase, then it is a desirable mechanism.
by jacquesm | 2025-12-02 16:26:33+00:00
That's a good point. I've had the same: I lost a bet on HN for a hundred bucks that Facebook would never break a billion in revenues. I - mistakenly - thought people would not be so stupid as to hand over their private lives to the likes of Mark Zuckerberg. But they did.

NFTs are just as stupid, if not more so and this time at least it looks like sanity prevailed. But the problem is more complex than just boolean 'made' or 'fail', and I think that's where the investment angle comes in. Investors bet on 'the next wave' all the time. And NFTs looked to the clueless as much as 'the next wave' as mobile phones or the transistor did at some point in time. The big differentiator to me is whether or not a thing like that requires a belief system or not. If it does then I don't give it much chance. But then we have all of crypto as a counterexample and quite a few people got stupidly rich peddling that.
by flohofwoe | 2025-12-02 16:26:39+00:00
> Actually it seems like they just colored everything async and you pick whether you have worker threads or not.

There is no 'async' anywhere yet in the new Zig IO system (in the sense of the compiler doing the 'state machine code transform' on async functions).

AFAIK the current IO runtimes simply use traditional threads or coroutines with stack switching. Bringing code-transform-async-await back is still on the todo-list.

The basic idea is that the code which calls into IO interface doesn't need to know how the IO runtime implements concurrency. I guess though that the function that's called through the `.async()` wrapper is expected to work properly both in multi- and single-threaded contexts.
by breathemein | 2025-12-02 16:26:43+00:00
I actually think the surprise is warranted. The model didn't just "get something wrong." It produced specific, confidently stated, errors that look like factual knowledge but are actually side-effects of pattern recognition. The whole point of the article is that this type of error is subtle enough that a non-expert might take it as truth (and to point out just how silly some of those confidently stated answers are for a laugh). Understanding the patterns and why the model behaves like this is useful so it can be improved. I don't think you can discredit the entire technology because it's not 100% perfect.
by quesera | 2025-12-02 16:26:43+00:00
You can find definitions that suit your needs if you like, but it does not change the meaning of the word.

A mythology is not necessarily untrue, although they do have a tendency toward expansiveness, which has a coincident trend toward favored interpretations.

But the ultimate truthiness (if it can be measured at all) is neither requirement nor disqualification, vis a vis being a mythology.
by stego-tech | 2025-12-02 16:27:45+00:00
This all vibes with what I observed during the pandemic myself, also in a Fortune 500 SV enterprise:

* Working together in close proximity resulted in higher distractions that lowered output, but made it easier to identify meaningful priorities beyond what was on the Sprint Board and jump in to help when necessary. This extended to mentoring Juniors as well, who thrived on proximity-based work to a significant degree.

* Working apart allowed us to work at our individual best, with the consequence of a loss of cohesion. This was mitigated through daily standup ceremonies as a way of checking-in and asking for help, and I credit a string of three excellent people managers for building that functional working relationship between colleagues who didn’t share timezones or continents. This was frustrating for Juniors to adapt to (especially new hires), but at the same time the independent working mode forced them to figure stuff out on their own, learn to ask for help when they needed it, and build confidence in their own skills.

* The best balance involved hybrid/remote work models with a yearly (ideally quarterly or semi-annually), week-long “crunch week” of sorts. The global team got together in-person for a week, did some off-site activities first (offroading, volunteering, sight-seeing) to build rapport, then we spent a week in a conference room together banging out eighteen months(!) of bigger project timelines, planning, and triaging in-person issues with other teams (like PKI changes). This was always followed on with nightly dinners where we dropped work entirely and focused on human connection, especially with Juniors who lacked self-confidence still and needed to be shown that work isn’t what defines existence.

My ultimate takeaway is that it’s all about balancing the three for optimal outcomes: letting folks knuckle down at home or privately when they need to focus, building in-person collaboration around intention rather than spontaneity of presence, and ensuring global teams meet together regularly (in-person and over video conferencing) to build rapport.
Show HN: I built an automated AI lab that generates and publishes inventions
by Archivist_Vale | 2025-12-02 16:27:46+00:00
Hi HN – For the last year, I’ve been building Unpatentable.org, an AI “innovation lab” that does one thing: generate new inventions and immediately publish them so they can’t be patented later.

The engine runs continuously; the public side is now a library of hundreds (900 so far) of novel AI-generated inventions across domains like energy, life sciences, robotics, space tech, and more.

Each innovation is documented in three separate reports totaling 120+ pages:

a main report (problem, mechanism, constraints)

an implementation guide - Step by step how-to

an societal/industry impact overview

We publish them on the site, anchor PDFs on the Arweave blockchain for immutable timestamps, assign metadata for discoverability, and place them into the USPTO prior art archive. The research papers are structured to meet or exceed international criteria for defensive disclosures (public, enabling, novel, time-stamped, non-confidential, specific, reproducible by a skilled practitioner). They are intended to be treated as prior art blueprints that enable others to build from them.

The engine itself isn’t a product; it's more of a publisher. That said, we do grant engine access to thinkers and inventors who want to solve and open-source solutions to particular challenges. We’re exploring sponsorships where organizations fund tracks like “wildfire resilience” or “decentralized compute,” and the output is a stream of open, unpatentable innovations in that domain. Monetizing the project has been an afterthought. I built this because I believe information wants to be free, and that in the coming age of ai, humanity is at risk of losing shared knowledge to well funded corporate patent trolls. Published innovations, are free forever, for everyone on earth.

Side note: for human inventors, there’s also an Unpatent tool (/unpatent) that lets you upload your own write-up and have it published as prior art (USPTO-linked prior art archive + Arweave + search indexing) for a flat fee. That’s separate from the AI engine, but built on the same plumbing.

You can browse the public innovation library here: https://unpatentable.org/innovation

I'll add some detail in the comments below and try to address a few anticipated questions. Feedback, critique, and suggestions are very welcome.
by mbirth | 2025-12-02 16:28:19+00:00
I’ve got mine set to cynical and have the objectivity personalisation (found here a year ago) in place:

> Strict objectivity mode: be as scientifically grounded and unflinching as possible. Be unbiased and only base your answers on facts. Do not react with emotion, but with logic and patterns. Answer in short where you can.

And I got this:

> It’s 2025 right now, so 2026 is indeed the next calendar year. Wild how counting still trips people up, but yes, that’s how numbers roll.

https://chatgpt.com/s/t_692f132f7bf0819199d4f7b8ae2a2a85
by Archivist_Vale | 2025-12-02 16:28:27+00:00
More technical detail for the curious:

The engine itself is a collaborative multi-agent pipeline stitched together mostly in Python. At a high level:

Problem hunting agents are given domain/subdomain pairs and are tasked with identifying the most impactful problems humanity faces in each area. Challenges are expanded into structured problem statements.

Domain agents (energy, digital systems, life-sciences-adjacent, etc.) work collaboratively to propose and refine seed candidate mechanisms and architectures - working from first principles within gaps discovered during systematic searches of patent archives, academic papers, and the open internet.

Refinement loops force agent interaction to resolve contradictions, tighten constraints, and generate something physically, economically, and organizationally plausible.

Report agents turn that graph into three separate documents: main report, implementation guide, and societal/industry impact report.

A guardian layer tries to filter out obvious garbage and anything in categories we simply do not want to publish (biohazards, weapons, high-risk surveillance, etc.).

Right now it runs on a mixture of frontier models behind an abstraction layer so we can swap individual agent models without rewriting the pipeline. Model selection is important, but perhaps less so than the framework and choreography: prompt schemas, roles, collisions, constraints, and consistency checks contribute most to the end result.

Every published innovation is a shared vision between at least 18 individual ai agents.
by matwood | 2025-12-02 16:28:29+00:00
> Did you ever hire any duds when you were not hiring remote?

Bingo. I had an exec ask me once how will we know people are working if they are remote? I asked back, how do we know they are working now?

Remote work is harder on management and leadership. It’s easy to see if someone is at their desk and seems friendly, it’s hard to really think about what value a person brings.
by zahlman | 2025-12-02 16:28:32+00:00
> LLM's do not think, understand, reason, reflect, comprehend and they never shall. ... It's amazing the results that LLM's are able to acheive. ... it also makes sense as to why it would, just look at the volume of human knowledge

Not so much amazing as bewildering that certain results are possible in spite of a lack of thinking etc. I find it highly counterintuitive that simply referencing established knowledge would ever get the correct answer to novel problems, absent any understanding of that knowledge.
by observationist | 2025-12-02 16:28:34+00:00
The Law of Accelerating Returns is a better formulation, not tied to any particular substrate, it's just not as widely known.

https://imgur.com/a/UOUGYzZ - had chatgpt whip up an updated chart.

LoAR shows remarkably steady improvement. It's not about space or power efficiency, just ops per $1000, so transistor counts served as a very good proxy for a long time.

There's been sufficiently predictable progress that 80-100 TFLOPS in your pocket by 3035 is probably a solid bet, especially if a fully generative AI OS and platform catches on as a product. The LoAR frontier for compute in 2035 is going to be more advanced than the limits of prosumer/flagship handheld products like phones, so theres a bit of lag and variability.
by Archivist_Vale | 2025-12-02 16:28:51+00:00
On the publishing side:

We render PDFs with consistent front-matter, metadata, and internal IDs.

Hashes and PDFs are anchored on Arweave for timestamping and persistence.

A WordPress front end and some custom templates serve the human-readable library.

Throughput is currently set at ~4 innovations per day, mainly constrained by API cost and some deliberately conservative safety checks. There is a scheduler that manages queues, retries, and backpressure so the thing can run 24/7 without me babysitting it.

Happy to go deeper on any of the above if there is interest.
by Archivist_Vale | 2025-12-02 16:29:29+00:00
On prior art, legal reality, and the Unpatent tool:

A few clarifications from the IP side, since this always comes up.

This is not legal advice.

“Prior Art” is messy in practice. What counts, and how heavily it weighs, varies by jurisdiction and fact pattern.

What we can control is: novelty, public availability, timestamps, persistence, and how closely the disclosures resemble things examiners actually read.

For the AI-generated innovations, I worked with a patent lawyer to make each package look like a reasonably “enabling” disclosure: a clear statement of the problem, the core idea, constraints, and at least a sketched path to implementation. Some will inevitably be too hand-wavy to block a very narrow claim, others will be overly broad. The goal is to increase the probability that examiners and litigators have something concrete to point at.

Separately from the lab, the Unpatent tool exists for humans who already have their own write-ups and want to publish them as prior art:

You upload a document or paste text, add some metadata, pay a flat fee.

The system publishes to a prior-art archive that is explicitly consumed by the USPTO, and anchors the same content on Arweave for timestamping.

Result is a public, timestamped disclosure that can be cited as a printed publication equivalent.

That path is meant for cases where someone has a real, specific invention and wants a cleaner, more obvious defensive disclosure than hoping a blog post gets indexed and noticed.

I am very interested in hearing from more patent practitioners on how this is perceived, and how to make both the AI-generated disclosures and the Unpatent tool more useful in the real world.
by mikepurvis | 2025-12-02 16:29:31+00:00
As a nix person, I like this a lot and could see myself using it. My only criticism would be I don't love this kind of thing:

"<!DOCTYPE html>\n" + html [ (attrs.lang metadata.lang) ] [ (head [ ] [ (partials.head context) ]) (body [ (attrs.classes [ "font-sans" "bg-white" ])

I understand that this is very flexible and powerful, but it immediately makes the templates highly non-portable and requiring of hand-authorship— fine for a personal blog by a code nerd, but a non-starter for when a designer is involved who will want to use visual tools. I see that right below that there's also a "normal string templating" option that's closer to conventional jinja2 or the like, but it's ultimately still pretty nix-ified.

It might be worth some ifd [1] to allow a step that pre-processes conventional html + tags template files into the form that can be consumed by nixtml.

[1]: or the still-experimental dynamic derivations: https://fzakaria.com/2025/03/10/an-early-look-at-nix-dynamic...
by Archivist_Vale | 2025-12-02 16:29:50+00:00
Why a one-way lab and why sponsorships instead of a SaaS product:

A few people have asked why I am not just offering this as “GPT for inventions”. A product that anyone can prompt.

Three reasons (this is my current reasoning, subject to change):

Safety and incentives. If the engine was exposed as a generic invention API, there would be strong incentives to push it into areas I do not want to automate or publish (offense-heavy domains, surveillance optimizations, etc.). Keeping it as a one-way lab allows steering towards domains with positive human impact.

Mission alignment. The point of this project is to create a massive public library of prior art that is hard to appropriate and easy to build on. Charging per-generation access to the engine pushes in the opposite direction. I would rather keep the core engine pointed at “public good” domains and let people use the outputs freely.

Funding reality. There are real running costs (models, infra, time), so it cannot be self funded forever. Demand for an ai invention tools that gives away the secret sauce is generally low among the public sector. The model we are exploring is domain sponsorship:

A sponsor funds a track (or tracks), for example “wildfire resilience” or “low-cost diagnostics”.

We configure and run cohorts of the engine focused on that track.

The output still goes into the public library and through all disclosure channels with clear notice that the track was sponsored; there are no exclusive rights.

That feels like a better fit for what this is trying to be: infrastructure for open innovation rather than another IP-wrapping startup.

If you have opinions on better funding structures for this kind of thing, I would genuinely like to hear them. I am trying to avoid the failure mode where something starts as “for the commons” and drifts into enclosure because of financial pressure.
by danans | 2025-12-02 16:29:55+00:00
> You are confusing obedience and willingness to jump through hoops with the motivation to learn.

You can call it obedience or whatever libertarian talking point you like, but the ability to negotiate social systems is an important skill for functioning in society, whether as an employee or entrepreneur.

It's always been the case, even before industrialization. The most effective STEM workers are the ones who understand the social context they work in, not the narrowly technically focused STEM nerd.

> Someone having a specific diploma doesn't mean he is actually competent in practice. It is just more likely that he isn't absolutely terrible, but that's mostly risk [h]edging (sic)

It's absolutely risk hedging, and that's just how most employment works. Most employers are not looking for special snowflakes, that would be really inefficient. They want a likely average competence above a certain level, which varies by industry and company.

> That's the whole point of a business. Assume risks to merit the potential payoff.

If you think business are interested in assuming any more risk than absolutely necessary to improve maximize financial outcomes, you haven't been in business much. We live in an era of lean operations (for better or for worse), and it's only getting leaner.

Companies want to solve the problem they focus on (i.e. build a farm combine), not run a basic education program for understanding the units of torque values on the bolts that hold it together.

> Why do you think the cost of job training should be assumed by the public when they will not get any of the private benefits generated.

The public gets taxpayers and a population who can afford to raise a family, which is essential for the continuation of "the public".

> What is need is relaxed employment regulations, so that if it doesn't work out, it is not too expensive for business to let go of poor prospect.

We already have relaxed regulations today. The existence of noncompete and at-will employment agreements means that even highly educated workers can be fired without cause.

Furthermore, many companies try to hire people as contractors for as long as they can get away with it in the labor market. The number of 1099 workers is growing lately, largely due to reduced labor leverage.

Relaxing employment regulations isn't going to make employers invest in education. They would sooner invest in automation (like automated paint shops for car manufacturing), which is what they have done for generations now.

Back to to the original point, automation is a major why they don't have need for less educated workers anymore, as anyone who has visited a modern factory can see for themselves.
by adi_kurian | 2025-12-02 16:29:56+00:00
https://www.google.com/search?gs_lcrp=EgZjaHJvbWUyBggAEEUYOT...

I don't think this is an LLM bug. I think this is an implementation bug. The model thinks it is 2024 due to training cutoff date, though it is "connected to Search", so conflicting information is getting in the prompt.

Answer when connected to search —

https://www.google.com/search?gs_lcrp=EgZjaHJvbWUyBggAEEUYOT...

Multi pass + search = correct answer

https://www.google.com/search?gs_lcrp=EgZjaHJvbWUyBggAEEUYOT...