ShieldFont: Bludgeoning AI Scrapers That Disrespect Robots.txt

August 14, 2026 by Maya Posch 56 Comments

In the more innocent days of the World Wide Web you could simply put a robots.txt file in the root of your website that search engine indexing bots and similar would consult for the indexing wishes of the site owner. In this brave new world of LLM training data indexing such pleasantries are however rarely respected, leaving site owners to resort to increasingly more involved ways to bludgeon so-called AI scrapers, with ShieldFont being one of the most recent methods.

Its basic functioning is detailed in the white paper, explaining their use of ligatures. These are normally used to join multiple graphemes or letters into a single glyph which are rendered in the final text. By substituting about a quarter of the words in a text with such ligature-based versions in an intelligent, dictionary-based manner, the HTML version – as typically parsed by a scraper – will read as grammatically valid but nonsensical text, while the rendered font version will look normal.

Naturally, there are some disadvantages to this, such as screen readers for the visually impaired needing to also use the rendered font version, and it’s just as effective on legitimate search engine indexing bots. That said, if you apply this to static, archived content, or content marked as ‘do not follow’ in said robots.txt, then it might just be one way to make ChatGPT and friends spit out really funny output in the future now that the novelty of wood glue on pizza and eating rocks has somewhat worn off.

While LLM scrapers can adapt to this by also parsing the rendered text, this makes the scraping effort significantly more expensive. Together with maze traps like Nepenthes and Cloudflare’s offerings that seek to keep these scrapers busy scraping dynamically generated content through infinite linked pages, the tools available to combat the menace of these scrapers keep developing.

Track Bird Visitors With A Raspberry Pi And A USB Mic

August 8, 2026 by Donald Papp 22 Comments

Avian Visitors is a lovely project by [Teddy Warner] that uses a Raspberry Pi and microphone to keep track of which birds have been visiting your home, and creates a colorful illustration of recent visitors on top of it all.

It reports on a web interface of its own making, but what really takes things to a new level is an optional, stylish E-Ink panel that shows the last 24 hours’ worth of visitors at a glance in a collage.

The key to identification is BirdNET (GitHub here), a deep learning classifier from Cornell that can reliably identify and classify more than 11,000 species worldwide based on sound alone.

Based on that information, the system pulls bird images from a reference set for the region and creates a collage representing the breadth and frequency of visitors in a single image. The larger the image of a bird, the more frequently it was heard.

That’s a cool project, but [Teddy] took things one step further by setting up a color E-Ink display to show a running summary of all the avian visitors the system identifies. [Teddy] has a knack for leveraging projects into wall-mounted art, as we saw with his generative art wall plotter. Continue reading “Track Bird Visitors With A Raspberry Pi And A USB Mic” →

Grading Tomatoes With An ESP32 And ML

August 8, 2026 by Al Williams 6 Comments

If you’ve ever worked with produce, you might know about grading. In addition to deciding if, say, a strawberry is good or not, they also have to sort them by color. Turns out, you don’t care if one package of berries is a bit redder than another, but you do care if one package has too much color variation. [Pmalfa31] applied an ESP32 and machine learning to grading tomatoes.

The system knows in advance if you are processing standard tomatoes or cherry tomatoes and uses two different sets of learned data depending on which you select. The program receives raw data from an optical sensor and then processes it to remove empty belt images, compute statistical information, and group readings for a single fruit together.

Continue reading “Grading Tomatoes With An ESP32 And ML” →

Wrangling Datacenter GPUs Into A Desktop

July 30, 2026 by Bryan Cockfield 20 Comments

As we’ve seen many times before, there’s usually some way wrangle a bit more life out of what would otherwise be considered old and obsolete technology. Perhaps one thing that has been passed over by the masses a bit to early is older datacenter GPUs, which is understandable in one sense because of the rate NVIDIA is pumping out new ones, but these cards have plenty of useful life left in them for the average person, as [Andrew] demonstrates.

The cards [Andrew] is using are Tesla V100s of 2017 vintage. Despite being older hardware they have high-speed memory which allows them to run modern LLMs locally, competitively with online models. In this test, Gemma 4 26B and Qwen3 35B are run, with Gemma being a bit faster because it fits entirely in GPU memory and Qwen3 being a bit more capable but more hungry for resources. [Andrew] built a PCI card that can host two V100s, allowing these larger models to fit completely in memory.

Even though these don’t perform at the same level as the latest top-tier online models, they’re surprisingly capable and also have the benefit of running completely locally. This might be concerning for those looking at the global economy being propped up by companies that essentially have no moat for motivated users, especially as more and more datacenter hardware becomes available on the secondhand market. While this build by [Andrew] goes into detail on getting the software stack up and running, we recently featured another build using the same GPUs that focuses a bit more on hardware for those looking to get started with local hosting.

How Film Industry Data Website The-Numbers.com Got Mauled By Bots

July 25, 2026 by Maya Posch 21 Comments

A lot has been made about the increase of automated traffic on the Internet, with the past years LLM-related crawlers having quite literally swarmed the picture here. Not only does this drive up traffic, it also increases load on web servers, whose owners find themselves faced with increased hosting costs. This recently led to The-Numbers.com going offline for a while as automated traffic was quite literally destroying their bottom line.

This saga is covered by [Stephen Follows], who had a chance to talk with the founder and CEO of the site, [Bruce Nash], after the site went basically offline for a few months. Since the website both licenses data for commercial purposes as well as offering the free access on its website, there were accusations of this being a ‘rug pull’.

The site was started in 1997, as a static HTML site on Geocities where [Bruce] provided box office analyses for investment purposes. Since that beginning traffic was generally polite, with human visitors and usually well-behaved search engine crawlers. Then around 2024 the first wave of scraper bots arrived, followed by a larger wave around December of 2025.

Despite implementing a few mitigations, such as LLM-targeted text, the increased traffic and the resulting load on a site architecture that was never designed for this ultimately led to a collapse. One of the major sources of traffic turned out to be from so-called ‘prediction markets’, like Polymarket, whose bots absolutely hammered the site.

Fortunately for [Bruce] and his team they do not rely on the free website for income, but they have had to massively rework the site’s architecture to bring back a semblance of the original features. As noted in the article, the amount of crawling traffic by these LLMs and ‘agentic AI’ tools is exponentially more than that for search engines, which makes this a major challenge.

Issues like these is why services such as Cloudflare are offering blocking features for such automated traffic. After all, unless such traffic is of use to you, you may as well treat it like a DDoS attack and cut it off at the root.

Thanks to [Ben] for the tip.

Codeberg Bans Cryptocurrency And LLM-Generated Code Projects

July 24, 2026 by Maya Posch 88 Comments

Community-led open source project hosting site Codeberg has formally announced that projects whose code is largely or fully machine-generated through LLMs and other ‘AI’ tools will no longer be welcome. This follows on the heels of a similar ban on cryptocurrency-related projects.

The community vote was on two issues, the first being the notion that scraping of project code for the use in LLMs should be forbidden, which was a motion that easily passed. The second motion was on disallowing projects whose code was substantially generated by LLMs like Claude, OpenAI Codex, and similar. This motion passed with 358 in favor versus 144 against.

In the earlier linked blog post the reasoning behind especially this second issue is expanded upon, covering not only ‘license whitewashing’, but also the direct and indirect hardware costs, with the expanding ‘AI’ datacenter hyperscaling having massively increased hardware costs for Codeberg over the past years, as the costs have been largely externalized.

Also covered is the aspect of these LLM-based tools destroying the OSS community, which is something that is backed up by recent studies. Even if we ignore that such LLM-tools are destroying the cognitive abilities of its users, there’s an argument to be made that if LLM-scraping is disallowed, then it’s consistent to also not allow LLM-generated code.

In the Terms of Use you can see these changes, both for LLMs and for cryptocurrency projects.

Thanks to [mk-fg] for the tip.

A Raspberry Pi Pico 2 W connected to a speaker

Voice Control Toolkit Comes To A Pico Near You

July 23, 2026 by Julian Scheffers 4 Comments

Voice-controlled appliances are nothing new. What might be new, however, is [Moonshine AI] running it all locally on a Raspberry Pi Pico 2 W!

The voice interface is roughly divided into three parts: voice activity detection, SpellingCNN speech-to-text and a neural text to speech. The speech to text supports up to 50 tokens, and can be re-trained to support any specific words you want. It runs a simple loop: detect voice activity, listen for (command) tokens, process them in C++, use the TTS to reply, and repeat.

Now, to be fair, it is a bit of a squeeze: 3.6 MiB of the available 4 MiB FLASH and 468 KiB SRAM on a stock Pi Pico 2 board. It leaves you with just about enough space to write a small amount of extra software, but it’ll be a challenge to fit anything substantial. Still, fitting three different types of AI model needed to make this possible in such a space is quite impressive.