A CLI tool and an API for fetching data from Twitter for free! It is recommended to install the package globally, if you want to use it from the CLI. [...] User Timeline 'user' authentication (logging in) grants access to the following resources/actions: Tweet Details [...] The API_KEY generated by logging in is what allows Rettiwt-API to authenticate as a logged in user while interacting with the Twitter API ('user' authentication). [...] This API uses the cookies of a Twitter account to fetch data from Twitter and as such, there is always a chance (although a measly one) of getting the account banned by Twitter algorithm.

Marker converts PDF to markdown quickly and accurately. Supports a wide range of documents (optimized for books and scientific papers) [...] Here are some known limitations that are on the roadmap to address: Marker will not convert 100% of equations to LaTeX. [...] marker /path/to/input/folder /path/to/output/folder --workers 10 --max 10 --metadata_file /path/to/metadata.json --min_length 10000 --workers is the number of pdfs to convert at once. [...] Then run benchmark.py like this: python benchmark.py data/pdfs data/references report.json --nougat This will benchmark marker against other text extraction methods.

Bun is a fast, all-in-one toolkit for running, building, testing, and debugging JavaScript and TypeScript, from a single file to a full-stack application. Install Bun curl curl -fsSL https://bun.sh/install | bash docker docker run --rm --init --ulimit memlock=-1:-1 oven/bun [...] import { plugin } from "bun"; plugin({ name: "YAML", async setup(build) { const { load } = await import("js-yaml"); const { readFileSync } = await import("fs"); build.onLoad({ filter: /. [...] import { test, expect } from "bun:test"; test("2 + 2", () => { expect(2 + 2).toBe(4); }); You can run your tests with the bun test command. [...] const release = await getRelease(); release.ts export async function getRelease(): Promise { const response = await fetch("https://api.github.com/repos/oven-sh/bun/releases/latest"); const { tag_name } = await response.json(); return tag_name; }

Upscayl lets you enlarge and enhance low-resolution images using advanced AI algorithms. Enlarge images without losing quality, it's almost like magic! [...] 🐧 Linux https://flathub.org/apps/org.upscayl.Upscayl https://appimage.github.io/Upscayl/ https://aur.archlinux.org/packages/upscayl-bin https://snapcraft.io/upscayl/ Upscayl should be available on the Software Store of most Linux operating systems. [...] Right Click AppImage -> Go to Permissions tab -> Check 'allow file to execute' and then double click the file to run Upscayl. [...] Upscayl uses AI models to enhance your images by guessing what the details could be.

https://twitter.com/kdrag0n/status/1638917691036803073Say goodbye to slow, clunky containers and VMs Seamless and efficient Docker and Linux on your Mac. [...] Starts in seconds with turbocharged networking, smooth Rosetta x86 emulation, VirtioFS file sharing, and other optimizations for some workloads. [...] Enjoy Docker as if it were native to macOS, plus CLI integration, file sharing, and remote SSH editing with Linux machines. [...] Connect between Linux machines and Docker containers, and use IPv6 and ICMP painlessly.

https://github.com/j178/chatgpt/releases A CLI for ChatGPT, powered by GPT-3.5-turbo and GPT-4 models. Get or create your OpenAI API Key from here: https://platform.openai.com/account/api-keys 💬 Start in chat mode [...] 💻 Use it in a pipeline cat config.yaml | chatgpt -p 'convert this yaml to json' echo "Hello, world" | chatgpt -p translator | say [...] You can add more prompts in the config file, for example: {"api_key": "sk-xxxxxx", "endpoint": "https://api.openai.com/v1", "prompts": {"default": "You are ChatGPT, a large language model trained by OpenAI. [...] "}, "conversation": {"prompt": "default", "context_length": 6, "model": "gpt-3.5-turbo", "stream": true, "max_tokens": 1024 }} then use -p flag to switch prompt: Note The prompt can be a predefined prompt, or come up with one on the fly.

Unified Model Serving Framework

🏹 Scalable with powerful performance optimizations

abstraction scales model inference separately from your custom code and multi-core CPU utilization with automatic provisioning [...]

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