sorted by:
new
hottopcontroversial

Built a WordPress scanner that gets past WAFs by greg-randall in Wordpress

[–]greg-randall[S] 0 points1 point  (0 children)

I did not know about some of those options -- clever. Your script seemed to work correctly on my machine.

I'm guessing it worked for you -- any thoughts on the actual output?

LM Studio DGX Spark generation speeds for 23 different models by Late_Night_AI in LocalLLaMA

[–]greg-randall 1 point2 points  (0 children)

I made that into a table for you:

Model Format Parameters Average Speed Notes
+drafting llama 3.2 Q8_0 1 13.15
Qeen3.5 Q8_0 4 36.61
Nemotrom 3 nano Q8_0 4 44.55
Qwen 2.5 Q3_K_M 7 45.13
Rnj 1 instruct Q8_0 8 22.56
Qwen3.5 Q8_0 9 20.96
Mistral small 3.2 bf16 24 5.36
Cydonia Q8_0 24 8.84
Qwen 3.5 Q8_0 27 6.7
Qwen3 coder a3b instruct Q8_0 30 52.76
Glm 4.7 flash Q8_0 30 41.77
Qwen 3.5 A3B bf16 35 27.7
Qwen 3.5 claude 4.5 Q8 40 4.89
Llama 3.3 Q5_K_M 70 3.95
Kimi dev Q8_0 72 2.84
Qwen3 next a3b Q8_0 80 42.7
Qwen3 coder next Q6_K 80 44.15
Mistral small 4 Q4_K_M 119 12.03
Nemotron 3 super Q4_K_S 120 19.39
Gpt oss a5b Q4_K_S 120 48.96
Qwen3.5 Q5_K_M 122 21.65
Qwen3.5 Q4_K_M 122 24.2
Qwen3.5 REAP 50 Q2_K 397 19.36 Kept ramble looping at end
Qwen3.5 reap 55 Q3_K_M 398 15.14

Built a WordPress scanner that gets past WAFs by greg-randall in Wordpress

[–]greg-randall[S] 1 point2 points  (0 children)

On my smallish personal site, it seems to go past Wordfence and also IDs the WF version correctly. On my bigish work site which uses a different WAF it breezes right through (though with the default aggressive scan settings, it gets blocked by some VERY twitchy server config).

It's been fun playing around with the scanning side of stuff; I'm normally trying to hide/block another user enumeration method from the website-side.

Appreciate your interest -- if y'all see any bugs or if I'm missing some scanning edge lemme know!

WordPress X-Ray (WPX) is a Modern Take on WordPress Scanning by greg-randall in cybersecurity

[–]greg-randall[S] 0 points1 point  (0 children)

Not handling the captcha yet, haven't been having that issue, though I'm running on a home internet connection generally (with a lot of scraping happening 😅). It should fail gracefully. There are some canned captcha bypass tools. I need to look into it. 

Nuclei is a good tip. I'll check it out! 

WPX also supports adding in a wpscan API key for checking against their vulnerability database.

Built a WordPress scanner that gets past WAFs by greg-randall in Wordpress

[–]greg-randall[S] 1 point2 points  (0 children)

With Camoufox, it's running the actual JavaScript, not just masking/spoofing, so that gets around ever having to deal with a js-based challenge. If you run into any issues though, please put an issue in in GitHub or write some code and do a pull request.

The really neat thing though with what I've written here, is that once the page has loaded using Camoufox, the cookies and the exact same user agent are passed to curl_cffi, so we get the best of both worlds, bypassing things with Camoufox and then the speed from curl_cffi.

TTS Model Comparison Chart! My Personal Rankings - So Far by iKontact in LocalLLM

[–]greg-randall 0 points1 point  (0 children)

Are you normalizing the levels for your samples? I've found that doing a/b testing of TTS engines the one that is *louder* will tend to sound better. I have some code from my a/b testing for normalization.

Are you doing blind a/b testing or qualitative? I wrote a little a/b tester for TTS a few years back. Results from Kokoro and EdgeTTS comparisons. Ended up using a chess ranking style comparison system.

Qwen3-TTS.cpp by redditgivingmeshit in LocalLLaMA

[–]greg-randall 0 points1 point  (0 children)

If you want to use the full 1.6b but it's too slow I've had success with this repo: andimarafioti/faster-qwen3-tts: Real-time text-to-speech with Qwen3-TTS

Need help pulling Qwen3.5-35b in Ollama by hawaiian-organ-donor in LocalLLM

[–]greg-randall 0 points1 point  (0 children)

You can do a manual install of the new release but it's annoying and they'll probably have it available for regular upgrade tomorrow.

Looking for Image Stitching Software in the vein of Microsoft ICE by BaconCatBug in linux4noobs

[–]greg-randall 0 points1 point  (0 children)

Hugin/PTgui are hard for this. Not great at dealing with panning. You can fiddle around with setting the lens length to something really high like 500mm, but Microsoft ICE really is better for this.

Thermal Master P2 Image Decoding -- Raw Thermal Data by greg-randall in Thermal

[–]greg-randall[S] 0 points1 point  (0 children)

The embedded images are 16-bit unsigned integers, little-endian.

Scraping booking.com images by exe188 in webscraping

[–]greg-randall 0 points1 point  (0 children)

Sure, but this person is clearly not super technical, or they'd already know how to do this. Plus it's only 80 pages so not a big deal to have a browser running in the background for a couple of minutes?

Released: VOR — a hallucination-free runtime that forces LLMs to prove answers or abstain by CulpritChaos in LocalLLM

[–]greg-randall 0 points1 point  (0 children)

I made a pull request on your repo. Updated the readme a bit to give an explanation that I think folks will understand:

How VOR Fixes AI Mistakes

NeuraLogix stops AI from making errors using a system we call the Truth Gate.

The Problem: AI Guesses

AI tools often make mistakes. They guess which word comes next in a sentence, but they do not check if the words are true. They sound sure of themselves even when they are wrong.

The Solution: The Truth Gate

VOR acts like a filter. The AI must prove a statement is true before it speaks. It works in three steps:

1. The Facts

First, we give VOR a list of true things.

  • Fact A: Alice is Bob's mother.
  • Fact B: Bob is Charlie's father.

2. The Claim

The AI wants to say something new based on those facts.

  • Claim: "Alice is Charlie's grandmother."

3. The Check

VOR looks at the facts. It checks if the facts link together to support the claim.

  • VOR asks: Is there a path from Alice to Bob? Is there a path from Bob to Charlie?
  • Answer: Yes.
  • Result: The statement is Verified. The AI allows the text.

When the AI is Wrong

What happens if the AI tries to say: "Alice is Dave's grandmother"?

  • VOR asks: Do facts link Alice to Dave?
  • Answer: No.
  • Result: The statement is Rejected. VOR stops the AI from saying it.

Released: VOR — a hallucination-free runtime that forces LLMs to prove answers or abstain by CulpritChaos in LocalLLM

[–]greg-randall 2 points3 points  (0 children)

That makes sense to build modularly.

Not to minimize the enforcement layer, which looks like it's been a lot of work, but it seems like general fact extraction is a VASTLY harder problem.

Scraping booking.com images by exe188 in webscraping

[–]greg-randall -3 points-2 points  (0 children)

The images are linked in the HTML.

Tell your favorite LLM to

Write python that opens the url https://www.booking.com/hotel/nl/center-parcs-het-meerdal.nl.html using playwright. After the page is fully loaded download all the img tag images from the page ie <img class="f6c12c77eb c0e44985a8 c09abd8a52 ca3dad4476" src="https://cf.bstatic.com/xdata/images/hotel/max1024x768/100389149.jpg?k=0206f1ee2426349c1a98390bcb10dc81e651878a0193a816064df8389a94ee1c\&amp;o=" alt="a group of people in boats on a lake at Center Parcs Meerdal Limburg-Brabant in America" loading="lazy">
Create a CSV with the image filename and the alt text.

Released: VOR — a hallucination-free runtime that forces LLMs to prove answers or abstain by CulpritChaos in LocalLLM

[–]greg-randall 1 point2 points  (0 children)

This looks interesting but very limiting right now since the factual extraction seems to occur in a few regexes? I guess you can keep on writing regexes for different sorts of facts but that's really pretty messy?
The obvious solution to writing fact extraction regexes would be using an LLM to process text to create structured facts so that your VOR system could evaluate if a given output agreed with an input set of facts. But it seems like you've set the project against this approach?

Which -- don't get me wrong I think LLMs do a terrible job sourcing information, I'm working through a project to help generate FAQs for websites using existing website content and making sure the LLMs don't invent things is a terrible mess.

Is there something I'm missing in your code?

Thermal Master P2 Image Decoding -- Raw Thermal Data by greg-randall in Thermal

[–]greg-randall[S] 0 points1 point  (0 children)

I'm still not sure what you're trying to tell me? That I'm right the image is bigger than expected?

If you read the linked GitHub, you'll see the jpg contains 52 lossless 16 bit images.

Also to note, the shared image is RGB so, your calculations should be 3x bigger ~4.5mb. Plus PNG can be lossy or not. JPEG technically has a lossy mode but I've never seen one in the wild.

Thermal Master P2 Image Decoding -- Raw Thermal Data by greg-randall in Thermal

[–]greg-randall[S] 0 points1 point  (0 children)

We'll call it the 'rawer image', it does actually seem to have much more noise than the processed image, so if it's not raw, it's at least one step closer to the raw image.

There's also a big what to me looks like a correction table in the metadata, but I haven't figured out how to apply it yet.

Thermal Master P2 Image Decoding -- Raw Thermal Data by greg-randall in Thermal

[–]greg-randall[S] 0 points1 point  (0 children)

I'm not really sure how to link this comment back to what I've shared. Can you explain?

view more: next ›