This is Robin, bringing you Salty Talk's Daily AI Stew.
Of today's three stories, one lands on authorization for training data, one on the moment a vulnerability gets opened in the wild, and one on how two matrix multiplications get arranged inside a GPU. Each maps to a specific point of friction in the AI business: where the material comes from, how fragile systems are, and how to save compute.
Elvis's Songs, and an Argument Over "Fair Use"
The music rights holders of Elvis Presley have sued Anthropic, alleging it trained AI models on 274 songs without permission, infringing copyright. According to the accompanying reporting, the dispute in this case centers on the "fair use" defense — the very question of whether copyrighted songs can be used to train AI. Source: Los Angeles Times
The specifics of the allegation are clearly laid out in the reporting: the rights holders of this song catalog say the songs were used to train the online agent Claude. NBC News places the case within a broader wave of litigation against Anthropic and other AI companies. One line has to be held here — the available material consists only of news headlines and these reports; the court where the suit was filed, the details of the complaint, Anthropic's official response, and any ruling are all still unknown. So all that can be confirmed at this point is that the lawsuit has been filed; it cannot be said that Anthropic has committed infringement. Source: Google News Source: NBC News
Why does this deserve the space? The common question in these generative-AI training-data copyright cases is whether developers fed copyrighted works into model training without permission or compensation. The figure of 274 songs stings because it is not an incidental quotation inside some user request — the catalog was treated as training material. If the case moves forward, its progress could shape how music copyright applies to model training, the exposure to damages, and licensing models, and AI developers who train on unauthorized music will keep carrying litigation and compliance pressure.
Hand a Server to the Whole Web and See Who Withdraws the Coins First
A developer, cloudlandsdev, has put online a PHP site deliberately built to be exploitable — it is called Steal My Bitcoin — to test whether a recent PHP remote code execution vulnerability is still exploitable in a real environment. A Bitcoin key worth about 502 US dollars is hidden on the server; PHP users can claim it through an executable called print-flag, and the first person to take the key wins. The PHP version the site runs is one from before the PHP2SHELL series of CVEs was published. Source: Steal My Bitcoin
To make this clear, start with the vulnerability itself. According to the security sources cve.tools and watchTowr, PHP2Shell is a set of high-severity remote code execution vulnerabilities, including CVE-2026-109618 and CVE-2026-109619, affecting PHP 5.4 through 8.5; the PHP team released fixes on October 21, 2026 — versions 8.5.12, 8.4.27, 8.3.36, and 8.2.35. The security firm watchTowr has observed the vulnerabilities being exploited in the wild, meaning this is not a laboratory exercise on paper but a real risk that needs patching promptly — deliberately leaving a site on a pre-disclosure version puts that risk on the public internet for anyone to try. Source: Hacker News discussion Source: cve.tools Source: watchTowr
The on-the-ground details are interesting. The poster says the team had spent the previous days scrambling to deal with the RCE vulnerability; screenshots show the challenge had run for 0.65 hours with no successful claim, and comments mention the server briefly overloaded. One commenter who claims to hold a working RCE deliberately declined to demonstrate it, saying that publishing the exploitation method would shorten everyone else's patching window; another commenter used "challenge still open, no one had succeeded for a while" to push back on the claim that thousands of autonomous AI agents were breaching websites at scale. The objections are concrete too: pushing an exploitable site to the Hacker News front page affects more than the challenger's own 502 dollars of Bitcoin — it endangers visitors and could expose zero-day details; others asked whether the exploitation process could be logged and publicly verified.
llama.cpp's CUDA Kernels: Two Multiplications and a GLU, Twisted Into One
One level deeper still. llama.cpp PR #29948 fuses the FFN gate/up matrix multiplications with the GLU on CUDA's MMQ path, organizing the computation by interleaving gate/up rows. Source: Reddit r/LocalLLaMA
To see where the saving comes from, look at the original path first. The MMQ path handles quantized matrix multiplication; a typical gated FFN runs the gate and up projections separately, then combines the results element-wise with a GLU. This PR interleaves the gate/up rows in storage, so the two matrix multiplications and the GLU travel one computational path, and the intermediate results save a round trip of reads and writes. There is a cost: the fused path needs a compatible projection layout and a safe buffer, and it must fall back to the unfused implementation whenever overlaps exist.
The performance numbers were obtained like this. The contributor ran llama-bench on a DGX Spark (GB10) and an RTX 5090, configured with full GPU offload, Flash Attention enabled, a 16384-token prompt, a 2048 batch, and 5 repetitions, comparing master against the PR. Across several Qwen models and Llama-3.1-8B in the Q4_K_M, Q5_K_M, Q6_K, Q8_0, and IQ4_XS quantizations, throughput improved by roughly 1.9% to 10.7%; the DGX Spark gained about 4.6% to 10.7%, and the RTX 5090 about 1.9% to 4.7%. These figures come from benchmarks the contributor ran themselves and have not been independently verified; the actual gains on other models, quantizations, and hardware still need re-measurement — for users running local models with full GPU offload on a DGX Spark GB10 or an RTX 5090, that percentage range is the expected ceiling and floor.
Put the three stories side by side and they are really telling different sides of the same thing: a model's capability boundary is never decided by the model alone. Whether Elvis's songs can enter a training set depends on licensing and a legal ruling; how long a deliberately vulnerable server survives before someone takes its 502 dollars depends on patches outrunning exploits; and how many percentage points a fused kernel saves depends on whether the data of two matrix multiplications was arranged into the same round of reads and writes. Material, security, compute — each friction point runs on its own clock.
🎧 This episode is also available as a podcast: listen to Daily AI Stew · 2026-10-25.
