Best AI tools for< Slow Aging >

bigmp4 is an AI-powered video enhancement tool that uses cutting-edge AI models to enhance the quality of videos. It offers features such as lossless video enlargement, video enhancement, AI interpolation to make videos more vivid, black and white video colorization, and smooth slow motion. The tool supports various file formats, including mp4, mov, mkv, m4v, mpg, mpeg, avi, jpg, png, bmp, webp, and gif. It also supports batch mode processing, allowing users to upload multiple files for simultaneous processing.

Cimphony is a legal champion AI tool designed for startups and small businesses. It offers services such as legal counsel, employment/HR support, drafting and reviewing business contracts, and upcoming features like fundraise management. The platform leverages AI to address corporate needs, equity financing, contracts, and blockchain-specific issues with transparency and fair pricing. Cimphony aims to bring corporate legal services into the 21st century by providing a transparent view into legal work, faster delivery of legal advice, and flat-rate pricing for better planning.

Evinced is an AI-powered accessibility tool that helps developers identify and address accessibility issues in websites and mobile apps. By utilizing AI and machine learning, Evinced can automatically find, cluster, and track accessibility problems that would typically require manual audits. The tool provides a comprehensive solution for developers to ensure their digital products are accessible to all users. Evinced offers features such as advanced bug detection, organization of issues, lifecycle tracking, easy integration with existing testing systems, and more.

Basejump AI is an AI-powered data access tool that allows users to interact with their database using natural language queries. It empowers teams to access data quickly and easily, providing instant insights and eliminating the need to navigate through complex dashboards. With Basejump AI, users can explore data, save relevant information, create custom collections, and refine datapoints to meet their specific requirements. The tool ensures data accuracy by allowing users to compare datapoints side by side. Basejump AI caters to various industries such as healthcare, HR, and software, offering real-time insights and analytics to streamline decision-making processes and optimize workflow efficiency.

SinCode AI is an AI-powered copywriting tool that helps businesses create high-quality, engaging content quickly and easily. With SinCode AI, you can generate website copy, blog posts, social media posts, and more in just a few clicks. Our AI engine is trained on a massive dataset of high-performing copy, so you can be sure that your content will be effective and persuasive.

TensorPix is an online AI-powered video enhancer and upscaler that can improve and upscale videos in less than 3 minutes. It is a cloud-based service that can be used to enhance videos from any device, including smartphones and tablets. TensorPix uses AI to enhance video quality, including resolution, framerate, and color correction. It can also remove flickering, film dirt, and interlacing artifacts from old videos. TensorPix is used by thousands of users, including filmmakers, studios, and businesses. It is a powerful tool that can help you improve the quality of your videos and images.

Birdseye is the world's first autonomous email marketing platform that revolutionizes how brands and retailers target customers. It offers hyper-personalized emails on autopilot, analyzing customers' buying and browsing habits to send tailored emails that resonate with them and boost sales. Birdseye's AI engages customers when they want to hear from you, ensuring personalized offers find their perfect home. The platform helps clear slow-moving stock with precision and continues to learn about customers to deliver increasingly personalized offers and drive sales. Birdseye is trusted by leading ecommerce brands for its significant engagement and conversion rates.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

Laravel Slower is a powerful package designed for Laravel developers to optimize the performance of their applications by identifying slow database queries and providing AI-driven suggestions for optimal indexing strategies and performance improvements. It offers actionable insights for debugging and monitoring database interactions, enhancing efficiency and scalability.

FluidFrames.RIFE is a Windows app powered by RIFE AI to create frame-generated and slowmotion videos. It is written in Python and utilizes external packages such as torch, onnxruntime-directml, customtkinter, OpenCV, moviepy, and Nuitka. The app features an elegant GUI, video frame generation at different speeds, video slow motion, video resizing, multiple GPU support, and compatibility with various video formats. Future versions aim to support different GPU types, enhance the GUI, include audio processing, optimize video processing speed, and introduce new features like saving AI-generated frames and supporting different RIFE AI models.

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.

Code Review GPT uses Large Language Models to review code in your CI/CD pipeline. It helps streamline the code review process by providing feedback on code that may have issues or areas for improvement. It should pick up on common issues such as exposed secrets, slow or inefficient code, and unreadable code. It can also be run locally in your command line to review staged files. Code Review GPT is in alpha and should be used for fun only. It may provide useful feedback but please check any suggestions thoroughly.

The frontend repository for Stocknear, an open-source stock analysis and community platform powered by Sveltekit, Tailwindcss, and DaisyUI. The core idea of Stocknear is to be fast and simple, welcoming contributions that focus on refactoring slow code into fast code and increasing simplicity and readability. Users can become Pro Members to access unlimited features or donate money via Ko-fi to support the platform's maintenance costs.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=