The Command pattern wraps the request into a specific object that has all the information necessary to perform its task. You can think of it as the next stage of refactoring, where at first we extract the code to a separate method, and then to a separate object, taking the arguments needed to execute the request in the constructor.

Este blog ha sido creado con una finalidad muy simple, aprender. Todos podemos aprender de todos, compartir puntos de vista y mejorar nuestro código. Y una estupenda forma de aprender, es enseñar.

Editor y recortador de fondos por IA es una nueva aplicación de edición de fotos para Android, todo lo que vas a necesitar durante mucho tiempo para crear tus composiciones por puro entretenimiento. De modo que podrás fácilmente, por ejemplo, poner texto en fotos, donde podrás elegir entre muchas fuentes de tipos de letra, recortar imágenes y, lo más importante, recortar el fondo de la imágenes con ayuda de la inteligencia artificial (IA). Aunque es mucho más.

Let's start above the fold. To quote Donald Miller, a caveman should be able to glance at it and immediately grunt back what you offer. Here’s my formula:

I decided to try out as many prompt variations as I could think of using the Discord-based AI art/image generation tool Midjourney

Midjourney and DALL-E are AI systems that generate realistic visuals and arts from natural language descriptions – Text-to-Image Prompts. Using a dataset of text-image pairs, a 12-billion parameter version of GPT-3 was trained to produce images from text descriptions. Here is list of prompts to help beginners to start using AI Art Generators.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality, etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing.

🤖 Sencillo script en python que dado un conjunto de keywords:

1⃣ Crea títulos para esas keys por IA
2⃣ Con ese título la IA genera un artículo de hasta 1.500 palabras
3⃣ Publica el artículo en wordpress

A Simple Lyrics Finder That Just Works

You can now get lyrics for a song just by passing in the song name without passing an artist name and this library will do the rest of the job for you.

It fetches, extracts and returns the song's title and song lyrics from various websites autocorrecting the song names for the misspelled names along the way.

Here, we have compared Flutter vs Native Technologies on essential factors like performance, development cost & time, maintenance, app size, etc. Moreover, we have given scenarios when to choose a specific technology.

These rules refer to Run Apps on a Hardware Device - Android Studio and include many suggestions from the Archlinux and Github Communities.