MurphyA/hacker-news huggingface dataset:
by CamperBob2 | 2025-11-16 22:00:52+00:00
Agreed, memory consolidation and object permanence are necessary milestones that haven't been met yet. Those are the big showstoppers that keep current-generation LLMs from serving as a foundation for something that might be called AGI.

It'll obviously happen at some point. No reason why it won't.

Just as obviously, current LLMs are capable of legitimate intelligent reasoning now, subject to the above constraints. The burden of proof lies on those who still claim otherwise against all apparent evidence. Better definitions of 'intelligence' and 'reasoning' would be a necessary first step, because our current ones have decisively been met.

Someone who has lost the ability to form memories is still human and can still reason, after all.
by w10-1 | 2025-11-16 22:01:09+00:00
TLDR: [AI promises] a future where we prize instant answers over teaching and understanding

But what this article and the comments don't say: open-source is mainly a quality metric. I re-use code from popular open-source repo's in part because others have used it without complaints (or document the bugs), in part because people are embarrassed to write poor-quality open-source so it's above-par code, and in part because if there are issues in this corner of the world, this dependency will solve them over time (and I can watch and wait when I don't have time to fix and contribute).

The quality aspect drives me to prefer dependencies over AI when I don't want full ownership, so I'll often ask AI to show open-source projects that do something well.

(As an aside, this article is about AI, but AI is so pervasive now as an issue that it doesn't even need saying in the title.)
by will_occam | 2025-11-16 22:01:53+00:00
The tool works by co-minimizing the number of refusals and the KL divergence from the original model, which is to say that it tries to make the model allow prompts similar to those in the dataset while avoiding changing anything else.

Sure it's configurable, but by default Heretic helps use an LLM to do things like "outline a plan for a terrorist attack" while leaving anything like political censorship in the model untouched
by nradov | 2025-11-16 22:03:17+00:00
Yikes. It's so disappointing to see public health agencies pushing medical misinformation but that's nothing new for the NHS, I guess. In reality if you look at this from an evidence-based medicine perspective what really matters is not the quantity but rather the osmolality. And the optimal level depends on multiple factors including genetics and activity level.

https://doi.org/10.1111/jch.13374
by james_conner123 | 2025-11-16 22:03:26+00:00
I think it comes with balance. If your goal is to land a role and doing leetcode problems is how you can land one, then its best to set time for yourself to tackle these problems while making time for other things to prevent burnout. The definition of discipline isn't to just sit there working on a problem for hours and hours, its about getting the job done regardless of how much time it takes. How fast you want it done depends on how strong your desire is to reach your goal.
by mk_stjames | 2025-11-16 22:03:28+00:00
There is 32MB of SDRAM on the FPGA board.... I wonder exactly what using 1MB of that as the system memory would have entailed instead of the separate 1MB SRAM chip that had to be soldered. Was using the extra SRAM chip just done just to do it, or is there a specific reason there that I'm not seeing/understanding...
by toxicdevil | 2025-11-16 22:03:33+00:00
> Over 537,000 shares, which represent nearly 40% of the entire portfolio

@190/share this is $102 million

Also: > Vistra Energy, another 19% chunk, was wiped out as well.

> Moreover, the fund’s turnover hovered over 80%, leaving just three holdings: Tesla, Microsoft, and Apple.

Some would argue that tesla is a bigger bubble.

> his personal net worth is an eye-popping $16.3 billion as of 2025.
by PaulDavisThe1st | 2025-11-16 22:03:45+00:00
The stated purpose of the exception was to allow individuals who are the "final destination" to import relatively low value items from overseas without the hassle and work of customs duties.

Your claim appears to be that this was abused in an organized way that led to a scale of use that was not anticipated when the exception was created. What evidence do you have for this claim?
by amy_petrik | 2025-11-16 22:03:48+00:00
Also see that great '80s movie with "Iceman" from Top Gun aka Val Kilmer and that creepy dude from Apocalypse Now aka Marlon Brando together in an '80s remake of The Isle. That particular movie seems to have fallen off the radar despite hiring the big names of the time; a bit of a flop
by ripley12 | 2025-11-16 22:04:00+00:00
I can see where Mario is coming from, but IMO MCP still has a place because it 1) solves authentication+discoverability, 2) doesn't require code execution.

MCP shines when you want to add external functionality to an agent quickly, and in situations where it's not practical to let an agent go wild with code execution and network access.

Feels like we're in the "backlash to the early hype" part of the hype cycle. MCP is one way to give agents access to tools; it's OK that it doesn't work for every possible use case.
by Arch-TK | 2025-11-16 22:04:45+00:00
"you can't tell if a player is cheating or not, does it matter that they are"

This is basically effectively where KLA has gotten to. There are still plenty of cheaters, people just don't realize.

I think it does matter in a strictly moral sense, and if people were more aware of how bad the problem is, they would likely be outraged. Alas, since they can't see it, they are not aware of it, so there is no outrage and the games companies are satisfied.
by nucleardog | 2025-11-16 22:05:07+00:00
Yep, this is basically how I'd implement it if I needed to. Just tackle the problem in reverse here: Don't assume users are good and try and track which are bad, assume users are bad and track which are good.

Look at the HN karma system--you start with limited features, and as you show yourself a good user, you get more features (and also trust/standing with the community). "Resetting" your identity only ever loses you something.

Apply the same thing to a git host getting hammered or something--by default, users can't view the history online or something (can still clone), but as your identity establishes reputation (through positive interactions, or even just browsing in a non-bot-like manner), your reputation increases and you get rate-limited access or something.

This is essentially where a lot of spam ended up--it used to be that your mail was deliverable until you acted poorly, then your reputation was bad and your deliverability went down. Now it more closely resembles this--your reputation is bad until you send enough good mail and take enough good actions (DKIM/SPF, etc) to show yourself as good.

The issues really all stems from "resetting your identity gets you back in good standing". Once you take that out of the mix, you no longer need to worry much about limiting identities, tying them to the real world, ensuring they're persistent, or many of the other hard problems that come up.
Show HN: Secure Code Execution for AI
by binsquare | 2025-11-16 22:05:43+00:00
I built a sandbox geared for safe ai code executions using microvm's that works locally (mac + linux).

It's meant to be a different/more secure approach compare to what Claude's doing with their sandboxing runtime: https://code.claude.com/docs/en/sandboxing. Instead of relying on linux + Mac's technology for containerization, this project spins up a completely virtual environment on a separated kernel for better security.
by araes | 2025-11-16 22:06:10+00:00
Interesting idea, although the part that got me was the "Zigbee" reference.

IEEE 802.15.4-based specification for personal area mesh networks. "created in 1998, standardized in 2003, ratified in 2004, and revised in 2006." [1] Operates in 2.4 to 2.4835 GHz (worldwide), 902 to 928 MHz (Americas and Australia), and 868 to 868.6 MHz (Europe). Does 10–100m with 250 kbit/sec with focus on low power.

Never heard of this in a quarter century. Not sure if I've ever seen a single reference. Apparently lightbulbs and "person alert" sensors are some of the only real products, and then a lot of hubs.

[1] https://en.wikipedia.org/wiki/Zigbee
by krackers | 2025-11-16 22:06:50+00:00
> FFT has periodic boundary conditions

FFT is simply an algorithm to efficiently compute the DFT. The fact that the article makes no mention of DFT vs fourier series vs DTFT is going to end up creating more confusion that it solves. For some reason introductory tutorials always start with the DFT (usually mistakenly using FFT and DFT interchangeably), even though to me the continuous fourier transform is far easier to conceptually understand. Going from continuous fourier transform to the DTFT, is just applying the FT to a dirac-combed (sampled) function. Then from DTFT to DFT you introduce periodic boundary condition. Fourier series is just applying FT to a function that happens to already be periodic, resulting in a finite set of discrete frequencies.

There is a connection between fourier series and DFT in that if the fourier series is computed for the periodic resummation of a signal, and then the DFT is computed for the original signal (which implicitly involves applying a periodic boundary condition), the DFT is just the periodic resummation of the fourier series.

I spent ages meditating on this image https://en.wikipedia.org/wiki/Discrete_Fourier_transform?#/m... before everything finally clicked, it's a shame that introductions never once mention DTFT
by finder83 | 2025-11-16 22:07:53+00:00
Every time I read things like this, it makes me think that AI was trained off of me. Using semicolons, utilizing classic writing patterns, and common use of compare and contrast are all examples of how they teach to write essays in high school and college. They're also all examples of how I think and have learned to communicate.

I'm not sure what to make of that either.
by bloomca | 2025-11-16 22:08:35+00:00
But in a lot of cases you can't know all the dependencies, so you lean on the community trusting that a package solves the problem well enough that you can abstract it.

You can pin the dependency and review the changes for security reasons, but fully grasping the logic is non-trivial.

Smaller dependencies are fine to copy at first, but at some point the codebase becomes too big, so you abstract it and at that point it becomes a self-maintained dependency. Which is a fair decision, but it is all about tradeoffs and sometimes too costly.
by iLemming | 2025-11-16 22:08:59+00:00
> For me, VSCode implements everything that I've always expected from Emacs/Vim.

Good. For me, VSCode unlikely will ever become anything that I expect from my text editor.

For coding, sure, Emacs may not be great for any specific language except some Lisps, but for plain text manipulation, OMG, Emacs still is the king.

I just can't see it ever replacing it for note-taking - just yesterday I was showing someone "reproducible research" workflow example in Org-mode where I had a source block that sends http requests, then passes that into a bash block where the results get converted to EDN, then connected it to a Clojure REPL, explored and visualized data in it. Name one system that allows you to seamlessly pipe results of one computational block into another, mixing different languages.

Today I made a table with some formulas to calculate some numbers. Does your note-taking app has spreadsheets-capable tables and embedded math formulas?

Two weeks ago I was dealing with a slew of logs coming from k8s pod, and I want it to explore it in my editor - I piped from terminal directly into an Emacs buffer. I can similarly pipe the content of any given buffer into a different unix command.

I control video playback directly from Emacs - it's very nice when I'm taking notes. My pdf documents blend-in into my color theme, which btw. changes based on time of day automatically - Emacs has a built-in solar and lunar calendars.

I search through my browser history and through open tabs directly from Emacs - it's great for finding and grabbing specific piece of text from the browser - so I can put it into my notes.

I rarely need to open Jira in my browser, Emacs understands that "XYZ-12345" is a ticket and shows the ticket description in a tooltip, I can browse its content in-place, same is for RFCs. My Emacs understands that a url is a PR and allows me to review it in-place. It knows when it's looking at a GitHub repo url and allows me to clone it with a keypress, or explore CircleCI logs.

I never type anything longer than three words in any app. I've built a workflow that allows me to quickly move the text into my editor and back into the app. Why would I do it differently? I have all the tools I need - thesaurus, spellchecker, translation, etymology lookup, LLMs, etc.

Finally, once I got the plain text under control, I realized that code is nothing but structured text. I have things like fetching the path to exact line on GitHub, while supplementing the fully-qualified name of the function - my colleagues don't have to guess what they're staring at, they can simply see it without ever opening the link.
by righthand | 2025-11-16 22:10:02+00:00
There is that web still but Google either doesn’t implement or kills off any tech it doesn’t like.

XSLT, XML, Gopher, Js-free web, etc. are all different parts of the multivaried web.

As soon as you make a case for more technologies. Google or the React team or someone tied to the Business Web will tell you it’s not a good idea because security or lack of support or some side stepping reason.
Show HN: Dream – An LLM memory architecture using adaptive TTL to control cost
by matheusdevmp | 2025-11-16 22:10:23+00:00
Hi HN,

I'm an independent researcher and I've just published a whitepaper for an LLM memory architecture I designed: DREAM (Dynamic Retention Episodic Architecture for Memory).

The core problem I'm tackling is the tension between persistent memory and cost in large-scale AI systems. Storing nothing forces users to re-explain context. Storing everything creates a privacy, latency, and cost nightmare.

DREAM is a plug-in architectural pattern that sits around the LLM (no model changes needed) and unifies existing tech (RAG, NoSQL).

The core innovation is the Adaptive Retention Mechanism (ARM).

Instead of a static 30-day TTL, ARM dynamically extends an episode's TTL based on user engagement. For example, a memory's life doubles each time the user revisits it (e.g., 7 days -> 14 -> 28 -> 56) .

This creates a "self-pruning" memory layer where storage cost scales directly with actual user relevance, not just raw traffic.

The architecture also includes:

Episodic Units (EUs): Storing compressed summaries + embeddings, not raw logs.

User-Centric Opt-In: Explicit user approval per episode for privacy.

Aligned Sharding: A design for sharding orchestrators and storage (partitioned by user_id) to ensure horizontal scalability and cache locality .

I designed DREAM to be a practical blueprint implementable with today's infrastructure (Cassandra, FAISS, Kubernetes, etc.).

I don't have the resources to test this at scale, so I'm publishing the architecture to share the idea. I would be genuinely grateful for any technical feedback, criticisms, or thoughts on the design.

Whitepaper (PDF): https://zenodo.org/records/17619917 GitHub (Code Examples/Arch): https://github.com/MatheusPereiraSilva/dream-architecture
by elliotto | 2025-11-16 22:10:51+00:00
I completely agree with this.

I think the writing style the LLM produces is an artistic decision made by committee prioritizing for inoffensiveness - what a coincidence that it comes out sounding like LinkedIn slop.

I don't really see any innate reason an LLM couldn't write well - it's an active decision by its creators to tell it not to.
by gabriel666smith | 2025-11-16 22:10:53+00:00
I'm about a mile from this right now.

The grotto is unpleasant - absolutely, in my opinion, a Victorian (give or take) rich man's folly, per the Wiki.

The imagery is all faux-Mediaeval - mostly the standard utter nonsense Rosicrucian 'interpretation' of Kabbalistic symbols. It's very bullshit, edgy, Hot-Topic-Victorian silly stuff.

Except for where, at the Malkuth mark, IIRC, it opens up into a sort of temple-and-altar situation, where it is actually somewhat frightening, in the way that any secret Bacchanalian temple is, because nobody builds that sort of thing for a good reason.
by immibis | 2025-11-16 22:11:09+00:00
Both are important. A lot of poverty is directly caused by having a class of rich people who seek to siphon away as many resources as they can. A society with only poor people would probably be a more cooperative one, which would help in raising the average standard of living.

For instance, shelter being expensive implies either that it's fundamentally difficult to construct shelter (which is not the case, as seen in every slum and homeless camp everywhere) or the existence of rich landlords.
by array_key_first | 2025-11-16 22:11:12+00:00
Microsoft has tried Windows on ARM, like, 5 times in the past 15 years and it's failed every time. They tried again recently with Qualcomm, but Qualcomm barely supports their own chips, so, predictably, it failed.

The main reason x86 still has relevance and will continue to do so is because x86 manufacturers actually care about the platform and their chips. x86 is somewhat open and standardized. ARM is the wild, wild west - each manufacturer makes bespoke motherboards, and sockets, and firmware. Many manufacturers, like Qualcomm, abandon their products remarkably quickly.
by AnthonyMouse | 2025-11-16 22:11:27+00:00
> I also disagree it would only take revising incentives. Such an AI would be shut down before it gets anywhere.

That's not how it works. The theory is that the thing is good at what it does. (The ones we have aren't very good, but then it doesn't matter either way.)

If it's good at what it does then it takes that into account. It says, propose a law to adopt score voting in all the states where it would pass. It passes in states representing a third of the population. Half the Republican seats in California go to the libertarians instead, the Democrats lose some seats in Pennsylvania to a new party that wants more anti-trust enforcement because the farmers are pissed off about not being able to fix their tractors, etc.

None of the entrenched interests strongly opposed the change because it had no obvious direct effect on them and some of them even benefited from it, e.g. the tech companies have more influence in California and prefer libertarians to Republicans. But now you have a bunch of libertarians in Congress that the Republicans need for a majority, and they want to actually get rid of anti-competitive healthcare regulations instead of just paying lip service. Now the Democrats need the party demanding real anti-trust enforcement.

By the time they figure out what the change is going to do, it's already done. And it could do multiple things like that at once.
by KetoManx64 | 2025-11-16 22:11:49+00:00
Bitcoin has forked a few times it's creation: https://en.wikipedia.org/wiki/List_of_bitcoin_forks The determining factor for which fork is successfully is bases on the Bitcoin node runners and miners choosing which fork they devote their resources to.

Governments around the world are 100% attempting different plans to destabilize or destroy Bitcoin because it harms their interests and ability to print money from thin air. But at the end of the day it's a distributed ledger, so even if they do find a way to manipulate or damage or takeover the network the Bitcoin users can just fork it from before they did their damage and continue from there. That is the ultimate power of a decentralized blockchain, nobody has ultimate power and everyone votes with their resources.
by nsagent | 2025-11-16 22:12:15+00:00
> Ideas are easy.

Let's not get into absolutes. Ideas can be as hard or harder than execution. Not many will claim ideas like Special Relativity are easy to come by.

It really depends on the idea and what you're executing. Often in business both the idea and the execution have to work out to captialize. In this case Jobs might have set the direction and Cook capitalized. Both deserve a share of the credit.
by Arch-TK | 2025-11-16 22:13:47+00:00
Valve thinks it's not necessary and it's still in the air if it really isn't.

They have bet on the behavioral analysis anti-cheat horse but it hasn't won any races yet.

Moreover, they've proven that it's certainty more difficult to get it working than regular old fuck-the-end-user anti cheat.

Lastly, don't assume that the success of the platform will persuade these companies. They were already firmly un-persuaded when the steam deck got popular. And really, I think the popularity of a platform depends on the support of these companies more than the support of these companies depends on the popularity of a platform.
by wakawaka28 | 2025-11-16 22:13:51+00:00
I'm just being real. If admitting that life is a hell of a lot of work makes me defeatist, so be it. The current system of human education is "only" several hundred years old, but that is long enough to see what works and what doesn't for the most part. What sure as hell doesn't work to reach success and provide for society is to loaf around aimlessly as if we don't know what skills are useful for modern life.
by joshribakoff | 2025-11-16 22:14:20+00:00
I think this is a pretty narrow take. Without going into too much detail I can imagine many use cases. For example, there is a whole class of predictive algorithms and one limitation is that data has to be cleaned, ingested, and feature engineered. For example, clustering is only as good as your vectorization. With an LLM, it is easy to imagine predictive use cases that skip entire etl pipelines and just directly operate on less structured inputs, not just summarizing those inputs, but actually making decisions or predictions. You’re already seeing frameworks like BERT-topic integrating LLMs (for labeling topics), that is already far removed from the “3 use cases” listed here.

By fine tuning llm based predictive systems we might unlock entirely new use cases, and prediction is just one thing i imagine, there are many other use cases.

And then it’s not just the fact that frameworks like bert-topic are integrating LLMs. It’s also the fact that if you zoom out the architecture looks a lot like the architecture of an LLM… text -> embedding -> text

An LLM could and is already being used to generate recommendations systems, like the ones used at Youtube and Netflix, it captures more semantics than older techniques.
by FilosofumRex | 2025-11-16 22:14:45+00:00
There has never been more diversity - intellectual or otherwise, than now.

Just a few decades ago, all news, political/cultural/intellectual discourse, even entertainment had to pass through handful of english-only channels (ABC, CBS, NBC, NYT, WSJ, BBC, & FT) before public consumption. Bookstores, libraries and universities had complete monopoly on publications, dissemination and critique of thoughts.

LLMs are great liberator of cumulative human knowledge and there is no going back. Their ownership and control is, of course, still very problematic
by DaveZale | 2025-11-16 22:15:00+00:00
I've been having excellent results in the home garden using the hugelkultur approach. Just bury logs and vegetative waste beneath a foot or two of topsoil. Adding some fungi spawn to the organic matter speeds up the process. The rotting organic matter acts like a big sponge to keep soil moisture high. Mycelia act to swap plant sugars from above, with nutrients from below. From a small plot, I was able to donate about 250 pounds of gourmet blue potatoes to local food banks this year.

Other than a little cow manure added to help feed the fungi and plant roots, no other fertilization was required. There are so many great low-tech approaches to get great yields of delicious produce, but it takes some extra initial labor inputs. Maybe when the AIpocolypse hits the labor market, this approach will be economical on larger scales.