adeon

I played this game a bit back in 3.4.3 times.

It's been great on long-haul flights to play on the laptop. Doesn't demand your battery.

I get the vibe that it's more like there's unexpected complexity and it's difficult to be confident you know how zipcodes work with enough detail to make the feature work. And that is just one example of possible complexity.

Do zipcodes change for example? Can your drop-down quickly go out-of-date? You'd need a way to manually enter a city so people are able to tell the system an address. Do you want to bother making an auto-updating zipcode feature just for a form?

Is it going to confuse people because nobody else has bothered to make this superfancy selection feature thing?

Is this USA only? There are postal codes/zipcode-equivalents in other countries.

It starts to feel it's likely not worth the time and effort to try to be smart about this particular feature. At least not if I'm imagining this us some generic, universal address web form that is supposed to be usable for USA-sized areas.

To me it feels similar to that famous article about what you can and cannot assume about people's names; turns out they can be way more complicated and weird than one might assume.

Although maybe zipcodes don't really go that deep in complexity. But on the spot I would not dare to assume they are.

Yeah I agree. I think even if you block CSP images, attacker could still hide information, or attempted exfiltration.

The post got me now instead wondering how to not make people shallowly dismiss perfectly fine articles for dumb reasons, like I almost did. It's not even that unclear what the attack is, in the article's its opening when I look at it now again, and I now went around their posts to see how PromptArmor generally does their writing because I got curious about the writing part...

I've seen in the past vulnerabilities that were way overblown but hyped up, so this made me notice how that armor has made me be skeptical whenever some article like this feels it combines marketing + vulnerability reporting.

I found the tone in the article annoying, but my skim reading was that it is an actual vulnerability. The screenshot from OpenAI loads an image from a third-party site and the URL of the image might have all sorts of details etc.

I think the viewer should have some CSP policy in place to not do that.

That being said, if it was closed as "Not Applicable" it gives me a bit of reason to wonder if some crucial details about the whole chain was either not articulated or mentioned by PromptArmor. Maybe for other reasons it is not actually reasonable to put that on OpenAI site. I'm not sure on the spot. But on a skim read it looks like a legit vulnerability from OpenAI's part that they should fix.

I really wish PromptArmor just opened with "OpenAI's log viewer page lacks CSP policies, so it can load arbitrary URL images and here is an example how such things can easily end up on that page". This was really annoying to read but I kept going because I was curious was it a legit thing or not...

Edit: I don't know if the article was edited just now but there is a clarification paragraph that actually makes it a bit more clear. PromptArmor if you are reading this, I wonder if my gut reaction of being skeptical simply because of the tone and presentation is a common thing and there are ways to both be convincing right at the start of an article, but still allowing yourself to be marketing-like. I probably would have started with a paragraph that dryly describes exactly the vulnerability "OpenAI's Log viewer is not secure against maliciously crafted logs, which can result in data exfiltration. On this page, we show a realistic scenario by which a malicious third-party can sneak in an image URL to this page and exfiltrate data." and then go on with the rest of the article.

I dumped it here just now: https://github.com/Noeda/landlock-network-sandboxer-tool

It hopefully will be obvious that nobody should expect quality :) it is like a simplified version of the sandboxer sample in my other comment. E.g. it maybe does not need to touch filesystem stuff at all.

I'd also look at some of the sibling comments for maybe more refined tooling than this thing. Maybe it's useful as a sample though.

I've used Landlock to detect and stop unwanted telemetry. I wrote some C that stopped networking except to accept connections on a single port, no outgoing connections and no accepting connections on any other port.

`dmesg` shows the connections it blocks (I think this is maybe the audit feature). I used an example sandboxer.c as a base (https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux...) except I just set mine up to not touch file restricting, just networking so that it has that one whitelisted incoming port.

    ./network-sandboxer-tool 8000 some-program arg1 arg2 etc.

The "unprivileged" in "Landlock: unprivileged access control" for me was the selling point for this use case.

I don't consider this effective against actively adversarial programs though.

The task of riding a horse can be almost entirely offsourced to the professional horse riders. If they take your carriage from point A to point B, sure, you care about just getting somewhere.

Taking the article's task of essay writing: someone presumably is supposed to read them. It's not a carriage task from point A to point B anymore. If the LLM-assisted writers begin to not even understand their own work (quoting from abstract "LLM users also struggled to accurately quote their own work.") how do they know they are not putting out nonsense?

After I wrote the comment, I pondered that too (trying to think examples of what I called "security conscious design" that would be in the LLM itself). Right now and in near future, I think I would be highly skeptical even if an LLM was marketed as having such feature of being able to see "unsanitized" text and not be compromised, but I could see myself not 100% dismissing such thing.

If e.g. someone could train an LLM with a feature like that and also had some form of compelling evidence it is very resource consuming and difficult for such unsanitized text to get the LLM off-rails, that might be acceptable. I have no idea what kind of evidence would work though. Or how you would train one or how the "feature" would actually work mechanically.

Trying to use another LLM to monitor first LLM is another thought but I think the monitored LLM becomes an untrusted source if it sees untrusted source, so now the monitoring LLM cannot be trusted either. Seems that currently you just cannot trust LLMs if they are exposed at all to unsanitized text and then can autonomously do actions based on it. Your security has to depend on some non-LLM guardrails.

I'm wondering also as time goes on, agents mature and systems start saving text the LLMs have seen, if it's possible to design "dormant" attacks, some text in LLM context that no human ever reviews, that is designed to activate only at a certain time or in specific conditions, and so it won't trigger automatic checks. Basically thinking if the GitHub MCP here is the basic baby version of an LLM attack, what would the 100-million dollar targeted attack look like. Attacks only get better and all that.

No idea. The whole security thinking around AI agents seems immature at this point, heh.

I think from security reasoning perspective: if your LLM sees text from an untrusted source, I think you should assume that untrusted source can steer the LLM to generate any text it wants. If that generated text can result in tool calls, well now that untrusted source can use said tools too.

I followed the tweet to invariant labs blog (seems to be also a marketing piece at the same time) and found https://explorer.invariantlabs.ai/docs/guardrails/

I find it unsettling from a security perspective that securing these things is so difficult that companies pop up just to offer guardrail products. I feel that if AI companies themselves had security conscious designs in the first place, there would be less need for this stuff. Assuming that product for example is not nonsense in itself already.

I tried to find some use cases, this paper has listed some, although I think it's not obvious to me what makes the trees uniquely useful compared to other schemes (https://users.dcc.uchile.cl/~gnavarro/ps/cpm12.pdf seems to be the same Navarro as referenced in the article).

The use cases listed in that pdf are revolving around compression, e.g. graph adjacency list is listed as one. I myself found the last use case listed as smelling interesting (colored range queries), but I would need to dig into the references on that one to see what's actually going on with that one and is it truly anything interesting.

I would be interested in things like what's the unique advantage wavelets trees have compared to e.g. stuffing roaring bitmaps or other kinds of bitmaps into a tree. The RRR has rank-and-select queries which I think roaring bitmap won't do, so that might tie into something. Maybe a problem where the wavelet tree is the only known efficient way to solve it, or maybe it is uniquely really easy to throw at some types of problems or something else.

Anyone know real-world examples of wavelet trees used somewhere? I got interested enough to dig a bit deeper but on the spot as I'm writing this comment, I'm not smart enough to immediately see do these things have killer applications in any niches.

Thanks for the resources. Got back to procrastinate on HN and checked the resources (briefly looked at transcript on the video, but found this article more interesting).

I've always assumed that some amount of unencrypted HTTP traffic is going to be slurped into archives, but I've been too lazy to really check an example and how does that look like in the real world. That BADASS system is an example, focusing on phones. I've also run mitmproxy in my home to learn and then I've wondered if the big agencies have something like that but much more scaled and sophisticated.

I've recently got into studying security, deobfuscated code, or decompiling, tried to find vulnerabilities or bad security, in websites and programs. I've found some, although not anything worth writing home about. I found a replay attack in one VSCode extension that implemented its own encrypted protocol, but it is difficult to use it to do real damage. Found a bad integrity check library (hopelessly naive against canonicalization attack) used by another VSCode extension. I've found something weird in Anthropic's Claude website after you log in, but because their "responsible security policy" is so draconian, I don't want to bother trying to poke it to research it further in case I earn their wrath.

Biggest bummer I found that a video game (Don't Starve Together) I had played for a long time with friends does not have any encryption whatsoever for chat messages to this day. (People gonna say private things in video game chats). The other video game I play in multiplayer a lot, Minecraft, has encryption (a bit unusual encryption but it is encryption).

That article gave me a bit of validation that I'm not a nut for giving shits about encryption and security, and being annoyed at ungodly amount of analytics I see in mitmproxy my laptop is blabbering about.

Lol, yeah, I also learned yesterday that there is apparently, NSA, National Security Authority. No, not the NSA this article is talking about and everyone knows about.

I mean: National Security Authority, "Kansallinen turvallisuusviranomainen", which appears to be some office/people under Finnish foreign affairs: https://um.fi/national-security-authority-nsa-contact-inform...

I will say I got confused a moment yesterday when googling on the topic here because when you put NSA and Finland in the same search, it would get topics about this other NSA that just happens to exist which I had never heard of before, and just happens to be Finland-associated.

Ah yeah, I saw the propublica as well, it was one of the first articles I found when looking on the topic. I don't doubt at all that Angry Birds data was used by NSA, doesn't seem controversial.

The specific question I am interested in is: Did Rovio knowingly and willingly accept $$$ from NSA (directly or indirectly) to weaken their security? I.e. were they acting as a willing accomplice.

Because that part would be unusual for Finland (well, at least as far as I know). For US companies I wouldn't bat an eye at news like this.

Is there any reliable source for NSA paying Rovio other than this random bar discussion? Not that I don't believe you or that I'm naive about NSA and the power of money, but I looked around news in 2014 and the accusations against Rovio specifically are a bit different flavor. It seems that Rovio was oversharing data to ad networks (Millennial Media comes up a lot), and NSA likely slurped data from the advertising companies. This bar banter is suggesting that NSA had some kind of arrangement with Rovio directly instead, and Rovio willingly went along.

Or alternatively, do you feel the Rovio employee's blabbering was talking about an actual, real NSA deal with Rovio, or was it more like a bar joke and direct NSA co-operation was not really implied? (e.g. "we know our security is bad, but these ad companies pay us $XX million to not use encryption so it's sorta like NSA pays us to keep it that way sips beer").

I'm interested, because if that is an actual thing that happened, then that's an example of NSA paying a Finnish company $$$ to weaken their security, and the Finnish company willingly agreeing to that. Is it in NSA's Modus Operandi to approach and then pay foreign companies to do this sort of thing?

Your comment is describing it in few words, but to me it sounds like it maybe wasn't implying an actual NSA direct co-operation, more like someone doing bar banter and being entirely serious. But that's just me trying to guess tone.

(I'm Finnish. I want to know if Rovio has skeletons in their closet. So I can roast them.)

Okay, now that makes sense. I actually put your example of gun violence in google with kabuki theater and that found me some (depressing) articles that use the phrase. Thanks for educating me on a new expression :)

I feel a little stupid for asking... but what does "kabuki theater" mean in this context? Do you mean the CEO in the scene sort of "acted a rehearsed show" in a meeting to make sure everyone in the room followed through some thought process? Or maybe in other words (guessing meaning): making people get up and talk to force them to think through something (CEO's real goal), but the CEO framed it as him simply asking questions? (I have not seen the movie or the scene, apologies if the meaning is more obvious to infer if one has seen it).

I tried googling it but I get some movie theater in San Francisco and a Wikipedia page describing it as a Japanese theatre with dancing and elaborate costumes and flair. I've not seen it used in an expression before.

So not only do you write a full book, but you keep the content online, up to date by making sure readers are informed of new developments that might make advice irrelevant? And you are able on the spot to say "one of three mistakes that are not relevant anymore"? You impress me, random book-writing Internet person.

You give me a feeling you really care about the craft and just making a good useful resource, which what I respect. I looked around the site and bookmarked it as a technical writing example I might go to read around now and then.

I sometimes teach coding or general computing things (but hands-on, less about writing) and I've come to appreciate that sometimes it is extraordinarily difficult to educate on or communicate complicated ideas.

Quoting you: To give you a sense of what I mean by improving the book “over and over“, keep in mind that between feedback from my DE, external reviewers, and others, there are parts of the book that I rewrote more than ten times.

I also do rewriting especially with content I intend to be a resource or education piece. Obsessively rewrite. Make it shorter. Clearer. Oops that reads like crap let's start over. IMO having an urge to do this and address all feedback or your own itches means you care about your work. Just have to remind myself that that perfect is the enemy of good enough (or something like that I forgot if the expression went exactly like that).

Yes, that was the crux of my question (and was answered by that link when I checked teivah-given link, which linked https://go.dev/blog/loopvar-preview right there as well). Basically I wondered if the example given was really about:

1) In Go, the 'i' variable in the for loop is the same 'i' for each round of the iteration, meaning closures created inside the loop all refer to that same 'i' variable, instead of getting their own copy of it. Very easy to accidentally think the all closures have their own copy of 'i'. Goroutines are only mentioned because in Golang this mistake tends to come up with Goroutines because of a common code pattern.

OR

2) Goroutines themselves either behave or have some weird lexical scope rules in a way I don't know and it doesn't really have to do with closures but an entirely Golang-foreign-alien concept to me I cannot see, and this is why the book example had Goroutines mentioned with the mistake.

I rarely write Go myself so I was curious :) It looks like it was 1) unless I am bad at reading, and I think the Go 1.22 change is good. I could easily imagine myself and others making that mistake even with the knowledge to be careful around this sort of code (the link shows a more complicated example when scrolling down that IMO is a good motivating example).

I got a question about the example shown, the goroutine one marked as #63, I'd copypaste but it's an image.

Is there a reason the common mistake is about goroutines specifically? If I instead just made function closures without launching off goroutines, would they all refer to the same 'i' variable? (I assume it's maybe just that the mistake tends to go hand in hand with goroutine-launching code in a pattern like that).

I'd presume the book would say right after the example :)

But otherwise: the author gets serious respect from me for going through that process, taking feedback and learning how to communicate, i.e. taking "how do I make a good book?" very seriously and trying their best. And also things like for putting their foot down when the problematic copyeditor. I'm almost interested in the book, not for learning about Go but learning about what it looks like when I know the writing has some serious intent behind it to communicate clearly.

I was at an event where people were making these avatars, many first time users. One person who gave feedback at the end said he was frustrated he could not get the avatar to look like him.

I think whatever the hell Meta is doing with their weird alien humanoids is far from "normie" appeal as well. The furries and anime girls seem more normie in comparison.

I don't know if there is some middle ground that would actually be appealing to some definition of a "billion normie"s. Maybe actually photorealistic looking humans? Making the graphics not look like it's from 2003? Or going the other way: make them look like the Mii characters with Nintendo? Something totally different? Maybe appealing to the furries and anime girls would be actually a good idea at first, to build up some "power users" or whatever, and then attract more casual users.

I share the sentiment of the Instagram users in the article and the grandparent; it is baffling to me why the product looks so terrible, with so many resources poured into the Metaverse.

I think not, because doesn't ring a bell at all. It was a book related to stock market though, IIRC it was a Canadian author who described their career as a day-trader and how they thought of how markets work. I wish I remembered better but it was quite long ago, and I don't have said book anymore. My book likely came out after that book, so maybe my author picked it up from others.

That book however you mentioned, I just looked it up and it seems interesting. Might put on my read list.

I'm pretty sure I've seen this pattern of "Stocks (tumble|gain) as X happened" but then 2 hours later if the stocks go in the other direction, there is a new headline "Stocks (tumble|gain) as X happened" where X remains the same, but tumble|gain got swapped.

I think I originally read that this happens in a book somewhere, then observed and noticed yeah that seems to be the norm. Not surprised it seems to be same thing in these adjacent metric % headlines where there's no proper thought if there exists a causal link between X and Y.

I think my brain has learned to recognize the pattern "Something rises X% as Thing Happened" and it reminds me of that cheeky quote about how every headline that is in the form of a question is, ... uh I forgot the full quote. But feels like almost this could have some kind of cheeky rule of its own, about how no causal link ever exists in a headline like this.

How tokens per second do you get typically? I rent a Hetzner server with EPYC 48-core 9454P, but it's got only 256GB. A typical infer speed is in ballpark of ~6 tokens/second with llama.cpp. Memory is some DDR5 type. I think I have the entire machine for myself as a dedicated server on a rack somewhere but I don't know enough about hosting to say for certainty.

I have to run the, uh, "crispier" very compressed version because otherwise it'll spill into swap. I use the 212GB .gguf one from Unsloth's page, with a name that I can't remember on top of my head but I think it was the largest they made using their specialized quantization for llama.cpp. Jpeggified weights. Actually I guess llama.cpp quantization is a bit closer analogy to the reducing number of colors rather than jpeg-style compression crispiness? Gif had reduced colors (256) IIRC. Heavily gif-like compressed artificial brains. Gifbrained model.

Just like you, I use it for tons of other things that have nothing to do with AI, it just happened to be convenient that Deepseek-R1 came out and just about barely is able to run it on this thing, with enough quality to be coherent. My use otherwise is mostly hosting game servers for my friend groups or other random CPU-heavy projects.

I haven't investigated myself but I've noticed in passing: There is a person on llama.cpp and in /r/localllama who is working on specialized CPU-optimized Deepseek-R1 code, and saw them asking for an EPYC machine for testing, with specific request for a certain configuration. IIRC also said that the optimized version needs new quants to get the speeeds. So maybe this particular model will get some speedup if that effort succeeds.

If you have the equipment (e.g. a spare Linux computer and WiFi router) and know-how, you can set up something like mitmproxy (looks very similar feature set to the Android App, but likely requires more effort to set up) to your home network. That's what I did some weeks ago, and then basically the same exercise you did (just my whole network instead of just phone), looking what's going on. And yeah...it's not good.

Even if I trust some companies to be trustworthy, I can't possibly vet a gazillion entities getting telemetry requests, and not all of them can have their shit together, security, privacy or ethics-wise.

It made me ditch some Microsoft software, but overall escaping spying feels like a lost battle, unless you go do spartan Richard Stallman-like computing (IIRC he had pretty hardcore stance over the software he'll use).

Starting earlier this year I've set up a mitmproxy a lot on my entire home network, and often have it on for all traffic at times. I put up an old NAS and I'm abusing it as a mitmproxy tool for my home.

There would be so much to write about what I've seen. I've thought of making a blog post. I use mitmproxy to check on sketchy apps and to learn in general.

The information sent out is fascinating. I knew extensive telemetry is pretty norm these days, but it's another thing to see it with your own eyes. My exercise has also made the typical "yes, we collect data/telemetry, but it's deanonymized/secured/etc. and deleted after X days so no worries" sound very hollow; even if a company goes in good faith by their own rules, how am I supposed to trust the other 1000 companies who also do data collection. If someone hacked my mitmproxy itself and downloaded all the payloads it collected, they would probably know me better than I do.

Random examples on top of my head from mitmproxy (when I say "chatty" I mean they talk a lot to server somewhere):

I had GitHub CoPilot neovim plugin. I didn't realize how chatty it was until I did this (although I wasn't surprised either, obviously completions are sent out to a server, but it also has your usual telemetry+AB test experiment stuff). I had wanted to ditch that service for a long time so I finally did it after seeing with a local setup since open stuff has mostly caught up. Also it's not actually open source I think? I had no idea (I thought it would just be a simple wrapper to call into some APIs, but: no PRs, no issues, code has blobs of .wasm and .node: https://github.com/github/copilot.vim)

Firefox telemetry, if it's turned on, is a bit concerningly detailed to me. I think I might be completely identifiable on some of the payloads if someone decided to really take a go at analyzing the payloads I send. Also I find it funny that one of the JSON fields says "telemetry is off". Telemetry is actually on on the menu (I leave it on purpose to see stuff like this); just in the JSON for some reason it says off. I'm not sure if that telemetry is meant to be non-identifiable though in the first place.

Unity-made software (also mentioned in the article) send out a Unity piece at start-up that looks similar to the article, although I didn't take a deeper look myself.

Author mentioned the battery: I also noticed that a lot of mobile apps are interested in the battery level. I didn't connect the pieces why but the article mentions Uber 4% battery surcharge, and now it makes a bit more sense.

One app that has at least once been on HN at high scores starts sending out analytics before you've consented to any terms and conditions. One of the fields is your computer hostname (one of my computers has my real name in my hostname...it does not anymore). Usually web pages have "by downloading you accept terms and conditions" but this one only presented that text after you launch app before you get to the main portion. I never clicked it (still haven't), but I allowed the app mellow on background to snoop on its behavior.

Video games: The ones I've tried seen mostly don't do anything too interesting. But I haven't tried any crappy mobile games for example. One unity game on the laptop, Bloons TD 6 sends out analytics at every menu click and a finished game sends a summary and is the "chattiest" game so far, although seems limited to what the game actually needs to do (it has an.online aspect). The payloads had more detailed info on my game stats though, they should add those to the game UI ;)

Apple updates don't work through mitmproxy (won't trust the certificates). Neither do many mobile apps (none of the banking ones did, now I know what a mitm attack would look like to my bank app).

Some requests have a boatload of HTTP headers. I've thought of writing a mitmproxy module to make a top 10 list. I think some Google services might be at the top that I've seen. (I think Google also has developed new HTTP tech, is it so that they can more efficiently set even more cookies? ;)

I think anything Microsoft-tied may be chattiest programs overall on my laptop. But I haven't done stats or anything like that.

Aside from mitmproxy, I'm learning security/cryptography (managed to find real world vulnerabilities although frankly very boring ones so far...), Ghidra, started learning some low-level seccomp() stuff, qemu user emulation, things in that nature to get some skills in this space. Still need to learn: legal side of things (ToSes like to say 'no reverse engineering'), how to not get into trouble if you reverse engineer something someone didn't like. I've not dared to report some things, and to not poke some APIs or even mention them because I don't know enough yet how to cover my ass.

Modern computing privacy and security is a mess.

I've worked a good part of my career at a DSP company (it would be in the box that says "Criteo" on it on the author's article). So I have some idea what companies in that space have as data.

Thanks for for the two replies, the replies make a lot of sense to me. It's hard for someone who is just starting out to find information on the "workflow" side of things, what kind tools people use and in my early journey to reverse engineering, so right now I assume to be somewhat blind to deficiencies in some tools I've tried so far.

Before Ghidra, I had been looking around /r/reverseengineering reddit and random Google searches to find what kind of tools people work with and how a reverse engineering work goes in general, and I'm happy there's a lot of blog posts that describe a project reverse engineering some project this and that.

Found a few things like "binwalk" to inspect a random binary for structure (apparently it was rewritten in Rust recently and not totally sure it's actually better (yet) than their slower Python-written older version also called "binwalk"). Also learned things like setting up mitmproxy (and how to Python script it) I was able to get to my entire home network through a Synology NAS with an iptables+mitmproxy setup I'm abusing as a firewall and as an inspection tool. On Linux specifically I learned some basics of seccomp() and existence of qemu-emulation, thought I might try some kind of "behavioral inspection" of untrusted binaries at some point with these tools or similar to them.

And on top of that, learned about cryptography at a more deep level, I think my entire interest this year started from me and my friend getting fed up with VSCode live share bugs and quirks (I do code teaching), doing research on alternatives, finding an alternative VSCode extension that seemed sketchy and then wanting to learn how do I "security audit" the thing. Ended up deobfuscating the JavaScript, reading its crypto, learning a lot about historical attacks on crypto and what is the typical kind of mistake that happens, AES-GCM misuse (IIRC it affected other AEAD schemes too, Invisible Salamanders), SHA length extension attacks, canonicalization attacks, re-using nonces in a scheme where that's really bad to do (AES-GCM again was my context but I think it applied to stream ciphers in general that take a nonce), the PS3 mess up with their private keys, Signal double ratchet thing, legal basics how much risk you do in reverse engineering against uncooperative companies (EFF had guides on legal side of it), and so on. Important to my security audit thing but also if I have ever have to "roll some crypto" and not completely make it totally amateur crap that breaks immediately when an actually competent cryptographer sees it and laughs at it.

Soooo many tools and things to learn. The above is just what I happened to remember on top of my head. I don't intend to become some superhacker but I want to be able to do some basic "sketchiness" check on applications I don't trust.

I looked at pictures off the Internet on the JADX tool, and yeah it clearly has a bit of a focus than Ghidra itself, and now Malimite makes a whole lot more sense as its own tool. While I thought Ghidra is mind-blowing (maybe a noob's first impressions and it isn't actually that amazing :) it definitely is also ugly and a bit heavy) there seems to be a rich set of tools to use.

My targets on reverse engineering are not currently any mobile apps or macOS apps, I have my interests right now elsewhere, but your Malimite tool here entered my notes to check out for iOS/macOS app decompilation if that comes up. I was already aware of the macOS .app structure, I've messed with them but not in any sophisticated reverse engineering sense. There's a video game called Don't Starve for example that contains a .zip file with lots of .lua code inside that is just readable as-is, not much effort or special tooling required.

Also technically you are the first human I've asked a question on reverse engineering (learned of existence of JADX and a more rich ecosystem of tools) and got an answer so I got happy for a sort-of "did first human interaction on an reverse engineering topic" achievement, even if it was just baby steps.

Starting this year I started learning bunch of security topics and Ghidra is something I started learning. I decompiled some games and getting comfortable how to work a project, teach Ghidra structures etc.

Am I right in looking at Malimite here and reading "Built on top of Ghidra decompilation to offer direct support for Swift, Objective-C, and Apple resources." that this is not a Ghidra extension but rather it is using a piece of Ghidra (the decompilation) like a backend? Malimite here is presented as its own piece of software.

Asking as a Ghidra noob who doesn't know all the ways Ghidra can be used: Would it make sense for something like this to be a Ghidra extension instead? I.e. give Ghidra some tooling/plugin to understand iOS apps or their languages better, instead of a new app that just uses parts of Ghidra. Also the Malimite screenshot in the page looks similar to Ghidra CodeBrowser tool.

Asking because it feels like it could be: from the little I've used Ghidra so far, looks like it is designed to be extendable, scriptable, usable by a team collaborating, etc. And Ghidra seems more holistic than just focusing on decompiling code.

In my case just CPU (it's a Hetzner server, checked in /proc/cpuinfo and it said "AMD EPYC 9454P 48-Core Processor"). I apparently had still in terminal backlog some stats, so I pasted below.

It's not a speed demon but enough to mess around and test things out. Thinking can sometimes be pretty long so it can take a while to get responses, even if 6 tokens/sec is pretty good considering pure CPU setup.

---

prompt eval time = 133.55 ms / 1 tokens ( 133.55 ms per token, 7.49 tokens per second) eval time = 392205.46 ms / 2220 tokens ( 176.67 ms per token, 5.66 tokens per second) total time = 392339.02 ms / 2221 tokens

And my exact command was:

llama-server --model DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf --temp 0.6 -c 9000 --min-p 0.1 --top-k 0 --top-p 1 --timeout 3600 --slot-save-path ~/llama_kv_path --port 8117 -ctk q8_0

(IIRC slot save path argument does absolutely nothing unless and is superfluous, but I have been pasting a similar command around and been too lazy to remove it). -ctk q8_0 reduces memory use a bit for context.

I think my 256gb is right at the limit of spilling a bit into swap, so I'm pushing the limits :)

The --min-p 0.1 was a recommendation from Unsloth page; I think because the quant is going so low in bits, some things may start to misbehave and it is a mitigation. But I haven't messed around enough to say how true that is, or any nuance about it. I think I put --temp 0.6 for the same reason.

To explain to anyone not aware of llama-server: it exposes (a somewhat) OpenAI-compatible API and then you can use it with any software that speaks that. llama-server itself also has a UI, but I haven't used it.

I had some SSH tunnels set up to use the server interface with https://github.com/oobabooga/text-generation-webui where I hacked an "OpenAI" client to it (that UI doesn't have it natively). The only reason I use the oobabooga UI is out of habit so I don't recommend this setup to others.

I've run the R1 local one (the 600B one) and it does do similar refusals like in the article. Basically I observed pretty much the same things as the article in my little testing.

I used "What is the status of Taiwan?" and that seemed to rather reliably trigger a canned answer.

But when my prompt was literally just "Taiwan" that gave a way less propagandy answer (the think part was still empty though).

I've also seen comments that sometimes in the app it starts giving answer that suddenly disappears, possibly because of moderation.

My guess: the article author's observations are correct and apply on the local R1 too, but also if you use the app, it maybe has another layer of moderation. And yeah really easy to bypass.

I used the R1 from unsloth-people from huggingface, ran on 256GB server, with the default template the model has inside inside its metadata. If someone wants to replicate this, I have the filename and it looks like: DeepSeek-R1-UD-Q2_K_XL-00001-of-00005.gguf for the first file (it's in five parts), got it from here: https://huggingface.co/unsloth/DeepSeek-R1-GGUF

(Previously I thought quants of this level would be incredibly low quality, but this seems to be somewhat coherent.)

Edit: reading sibling comments, somehow I didn't realize there also exists something called "DeepSeek-R1-Zero" which maybe does not have the canned response fine-tuning? Reading huggingface it seems like DeepSeek-R1 is "improvement" over the zero but from a quick skim not clear if the zero is a base model of some kind, or just a different technique.

Ooh that is interesting. Also noticed this passage:

"When people measure actual end-to-end latency for games on normal computer setups, they usually find latencies in the 100ms range."

That is really surprising to me. 100ms is HUGE. On NetHack, sshing into servers the difference between 50ms and 150ms is enormous. If do my experiment and I'm not too lazy I want to check on that 100ms figure, your link points to more sources which I didn't check on right now.

I don't know if I'm blind but I can't tell when that article was written exactly. But it mentioned MacBook 2014 and Lubuntu 16.04 so maybe it's around mid 2010, 2016 possibly? (I should check properly if I do my experiments)

The author in the Wayland vs X11 used mouse flicks on the video. Just now while writing I kind of had a thought if this needs some more deep thinking: how do I accurately measure "my finger touched this button and X milliseconds later character moved". Wondering what to consider as the "finger touched this button" event in a 240fps video. Maybe I can do what the author did and map a key to a mouse instead, because maybe there's a much-easier-to-see physical movement with a mouse. But then, am I really measuring faithfully my old NetHack-like environment? Or maybe this isn't really an issue and it'll be easy to tell.

Too much for my small brain to think in the moment :) that link gave me at least some validation that iTerm2 maybe really is a bit slow, or at least was at that time. There's also bunch of terminals on that page I've never heard of.

Today iTerm2 is my daily driver, but xterm is still really fast on my Fedora Linux system I have on the side.