@INCIBE como se puede reportar este SEO poisoning? parece un agujero de seguridad de la libreria krpano usada por https://t.co/QAsqGmOWJl
los hackers son capaces de colar sus urls scam en el query parameter krpano carga imagenes o datos
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
Si estás usando npm install, estás en peligro.
¡Así de crudo te lo cuento para que reacciones!
Ayer se comprometieron paquetes de TanStack en npm.
De las bibliotecas más usadas en el mundo JavaScript.
Y de ahí saltó a Mistral, OpenSearch, UiPath, PyPI...
Porque muchos ataques no necesitan que importes nada. Basta con una instalación para infectarte.
¿Cómo?
Colando scripts como preinstall o postinstall que se ejecutan durante la instalación.
Lo importante es que tiene solución:
① Usa pnpm 11
Viene con defensas por defecto contra este tipo de ataques.
② Si sigues usando pnpm 10, npm, yarn o bun
Activa minimumReleaseAge y ponle 1440.
Evita instalar versiones publicadas el mismo día.
③ Bloquea scripts de instalación por defecto
pnpm evita que cualquier dependencia ejecute código en tu máquina solo por instalarla.
Por favor, comparte esto para que le llegue al máximo número de personas y paremos la cadena de ataques.
Arguably the most brilliant engineer in FFmpeg left because of this. He reverse engineered dozens of codecs by hand as a volunteer.
Then security "researchers" and corporate employees came along repeatedly insisted "critical" security issues were fixed immediately waving their CVEs.
This was hugely demotivating to the fun and enjoyment of reverse engineering.
Wait, don't go, it's really simple.
It’s a large language model, self-hosted on your own rig. You grab a GGUF file—think distilled neural net weights, quantized to the nth degree, like Q4_K_M with 4-bit precision or Q8_0 with 8-bit integer ops, packed tight with GGML optimizations for minimal memory footprint. Snag it off Hugging Face, maybe a 7B parameter model, 7 billion weights, fits in about 4-6GB of VRAM if you’re lucky. Then you compile llama.cpp—straight C++ inference engine, leverages SIMD instructions, single instruction multiple data, for parallel crunching. Point it at the GGUF, and it’s live, no cloud, no nonsense.
Hardware’s key. You need a beefy GPU—say an NVIDIA RTX 4090 with 24GB GDDR6X VRAM, tensor cores screaming at 16-bit float precision, pushing 30 tokens per second on a 13B model. CPU fallback’s doable, Intel i9-13900K with 24 cores, 32 threads, AVX-512 support for vectorized math, but it’ll crawl at 5 tokens per second tops. RAM’s non-negotiable—64GB DDR5 at 5600 MT/s, because context spills into system memory past 8k tokens. Storage? NVMe SSD, Samsung 990 Pro, 2TB, 7450MB/s read, keep those weights streaming.
Settings are a playground. Temperature’s a float, 0.65 for tight coherence, 1.8 if you want it spitting chaotic embeddings. Context length—4096 tokens, 4k word fragments, needs 16GB VRAM or it swaps to RAM and stutters. Tokenization’s baked into the GGUF, BPE algo, byte pair encoding, splits text into subword units, 50k vocab size typical. Tuning? LoRA’s your ticket—low-rank adaptation, slap a 16-rank delta on the weight matrix, fine-tune on a 3080 Ti in half a day if you’ve got the dataset.
Crazy thought—could you cram it on a Raspberry Pi 5? 8GB LPDDR4X, ARM Cortex-A76, no CUDA, so you’re stuck with CPU inference. Maybe a 1B parameter model, Q2 quantization, 2-bit weights, 500MB footprint. Chugs at 1 token per second if the thermals don’t throttle it to death. Overclock it, liquid cool it, who knows? I’d benchmark it just to see the bus bandwidth choke. Stock’s fine for most, though—13B on a 3090, call it a day.
...
Ladies?
now i have a reason to send love (simp) letters
prompt in image's ALT. i stole it from sowehere but i don't remember where. images were stolen from pepelangelo
Prompt:
"Create a detailed pixel art frame animation for a game, where the final image is divided into multiple sub-images, each serving as a continuous animation keyframe. Design the sequence to depict [a wizard casting a spell: begin with intricate hand motions, then show the wizard conjuring a vibrant fireball, and finally capture the moment of casting the fireball.] Ensure the keyframes transition smoothly and continuously, and include as many frames as possible to achieve a high level of fluidity and detail in the animation."
Replace this part with your character + animation description: [a wizard casting a spell: begin with intricate hand motions, then show the wizard conjuring a vibrant fireball, and finally capture the moment of casting the fireball.]
Credit to https://t.co/PXjvkVEEtw for the original prompt this was refined from.