Search Results for "utf-16" - Page 2
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Showing 79 open source projects for "utf-16"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Gen-PixAi
modelAI 100% work Pixel Drawing Free No Limited
...NPCs, monsters, equipment, item drops, and skill effects. Commercial and non-commercial projects with clear licensing rules. ✨ Key Features ✅ AI Pixel Generator: text → sprite/tileset (sizes 16×16, 24×24, 32×32, 48×48, 64×64, custom). ✅ Style Controls: NES/GB/SNES/GENESIS-like palettes, dithering, automatic outlines, tileability. ✅ Batch & Seed: batch generation + seed for reproducibility. -
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Secure VPC Network Generator
Secure VPC Network Generator
...It allows you to quickly scaffold a secure VPC with multiple subnets. Quick Start To use this tool, run the generator: bash # Generate a VPC with 3 subnets python lab_runner.py vpc-gen --cidr 10.10.0.0/16 --subnets 3 --output my_vpc.tf -
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Crossfire RPG game
Cooperative multiplayer graphical RPG and adventure game
Crossfire is a free, open-source, cooperative multi-player RPG and adventure game. Since its initial release, Crossfire has grown to encompass over 150 monsters, 3000 areas to explore, an elaborate magic system, 14 races, 16 character classes, and many powerful artifacts scattered far and wide. Set in a fantastical medieval world, it blends the style of Gauntlet, NetHack, Moria, and Angband. -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on... -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Petoron-P2P-Messenger
minimalistic, secure and autonomous P2P messenger
Petoron P2P Messenger (P-P2P-M) Architecture: Pure P2P - no servers, no databases Key storage: Keys generated in memory, never stored, erased after use Metadata: Only IP and port exist during the session - everything else is encrypted & obfuscated Encryption: PQS v1.2 - PBKDF2-HMAC-SHA256 (200k), BLAKE2s-MAC, custom stream cipher + fake padding Authentication: BLAKE2s-MAC (16 bytes) - instant failure on any data change Packet obfuscation: --stealth mode - padding, hidden structures Connection: Direct peer-to-peer only Anonymity: No accounts, logins, or phone numbers Third-party access: Impossible without physical access to both peers during session History: No storage — all in RAM, wiped on close Message size: Limited only by RAM & MTU — no artificial limits External dependencies: None DPI/blocking resistance: Harder to detect, can be masked Autonomy: Fully offline until peers connect 26.01.26 Add pqs_chat_tor.py github.com/01alekseev/Petoron-P2P-Messenger -
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PQS
Petoron Quantum Standard (PQS)
...PBKDF2-HMAC-SHA256 (200k iterations, adjustable) for password hardening. Key separation via BLAKE2s - independent keys for encryption and MAC. BLAKE2s-MAC authentication - 16-byte tag, any modification = instant rejection. Streaming keystream generator - secure, large-scale XOR without key reuse. Fake padding (HEAD/TAIL) - obfuscates binary boundaries and payload structure. Precise size encoding - restores original payload exactly. Supports files up to 128 MB per encrypted block. No dependencies, fully offline, cross-platform Python implementation. ... -
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Muse: Middleware Universal Scripting idE
Automate: WebSphere; WebLogic; JBoss; Glassfish; Tomcat; Linux, WinRM
...Familiar Eclipse based Jython and Python Development IDE, pre-configured and ready to go. 4-Click Installer. Win x64, Linux WINE x64. Built-In JVM. Java 8/9/10, Amazon Corretto, JETPack13/14/16, IBM SDK Compatible. *** Now with powerful JBoss / GlassFish / Tomcat / Linux Active Auditing Framework. Tomcat / Glassfish 2 Python - Configuration Snapshots *** Infrastructure-as-Code, Code-Writing-Code Designed to Run on JETPack: https://sourceforge.net/projects/jetpack Muse.2026.03.x - Win 10 / Win11 / Linux (WINE) Muse.2025.12.x - Win8 / Win 10 / Win11 / Linux (WINE) -
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ... -
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Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
...On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). Surprise, same code in JavaScript for M2 chip in Safari: 12,5 sec single core and 3,3 sec multi core. Python version with numba and numpy on MacOS with M2: 3,78 sec, Intel Ultra 5 225F Linux Fedora 43 GNOME(*Intel): 3,64 sec., W11Intel: 3,73; Faster style in python, MacOS M2: 1,81 sec, *Intel & W11Intel: 2,02 sec.; Ultra faster style in python, MacOS M2: 1,24 s - 1,26 s - 1,34 s, *Intel: 1,48 s - 1,50 s, W11Intel: 1,53 - 1,63. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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DeepKE
An Open Toolkit for Knowledge Graph Extraction and Construction
...It allows developers and researchers to customize datasets and models to extract information from unstructured texts. DeepKE supports low-resource settings with only a few labeled (e.g., 16/32 shot) instances for widespread information extraction tasks. Since relations are expressed over multiple sentences in real-world applications, DeepKE supports document-level relation extraction. We present a new open-source and extensible knowledge extraction toolkit, called DeepKE, supporting standard fully supervised, low-resource few-shot and document-level scenarios. -
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smolOS
A tiny and simple research operating system
smolOS is a minimalist 16-bit operating system written in pure Assembly for x86 real mode. Its goal is to demonstrate the fundamentals of how an OS boots, handles input, and displays output using only the most essential code. smolOS fits in a single boot sector and runs directly on BIOS-compatible hardware or emulators like QEMU. It’s ideal for those seeking to understand bootloaders, BIOS interrupts, and ultra-low-level system programming through a practical and tiny project. -
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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
...Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math). -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
...The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. We can also feed in an image as an optimization goal, instead of only priming the generator network. ... -
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q - Text as Data
Run SQL directly on CSV or TSV files
...Files with BOM: Files which contain a BOM (Byte Order Mark) are not properly supported inside python's csv module. q contains a workaround that allows reading UTF8 files which contain a BOM - Use -e utf-8-sig for this. The plan is to separate the BOM handling from the encoding itself, which would allow to support BOMs for all encodings. -
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HashApp
python software for cracking and creating SHA and MD5 hashes
...If you would like to change the online dictionary URL for hash decryption, use CTRL+F in your text editor and search for this line: LIST_OF_WORDS = str(urlopen('https://raw.githubusercontent.com/dwyl/english-words/master/words.txt').read(), 'utf-8') Change the url in the urlopen function and replace all instances with your version of the above line. If you want to use a txt dictionary stored in your computer, then, well, nothing. Unless the word search fails with the online dictionary, you can't use a txt dictionary. But don't worry; you just have to store your txt on github (like the default dictionary above). -
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FARM
Fast & easy transfer learning for NLP
...With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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BeaEngine 5
BeaEngine disasm project
BeaEngine is a C library designed to decode instructions from 16-bit, 32-bit and 64-bit intel architectures. It includes standard instructions set and instructions set from FPU, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, VMX, CLMUL, AES, MPX, AVX, AVX2, AVX512 (VEX & EVEX prefixes), CET, BMI1, BMI2, SGX, UINTR, KL, TDX and AMX extensions. If you want to analyze malicious codes and more generally obfuscated codes, BeaEngine sends back a complex structure that describes precisely the analyzed instructions. ... -
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Exif UserComment Editor
Dialog box to edit Exif comment into images
Display a simple graphical dialog box to add or edit Exif UserComment contained into images. Supports Unicode UTF-8 characters. Originally developed as a plugin for Geeqie image viwer. It requires Python GTK+ and exiv2 tool. -
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Rdbtools
Parse Redis dump.rdb files, Analyze Memory, and Export Data to JSON
...Every run of RDB Tool requires to specify a command to indicate what should be done with the parsed RDB data. Valid commands are JSON, diff, justkeys, justkeyvals and protocol. The JSON command output is UTF-8 encoded JSON. By default, the callback try to parse RDB data using UTF-8 and escape non 'ASCII printable' characters with the \U notation, or non-UTF-8 parsable bytes with \x. Attempting to decode RDB data can lead to binary data corruption, this can be avoided by using the --escape raw option. Another option is to use -e base64 for Base64 encoding of binary data. -
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Tux Math Scrabble
12÷16×3=9÷4
Latest version 0.9.1 runs on Python3. Date: June 15, 2023 Online/touch-screen version: https://www.asymptopia.com/tuxmathscrabble Encourages kids to construct compound equations and consider multiple abstract possibilities. -
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Exposure
Learning infinite-resolution image processing with GAN and RL
...ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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SmartMuseum
Software for work with Corpus of Everyday life history Sources
...In this project, we consider the problem of creating software infrastructure for collaborative semantic annotation, information relation, and personalized access to corpus of everyday life history sources. Project financially supported from Department for Humanities of Russian Fund for Basic Research according to project # 16-01-12033. Authors: Vdovenko A., Marchenkov S., Petrina O., Korzun D. -
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NTK RTMP SERVER
Naam Tamilar Web TV Live Streamer
...ForFirst UAT - User Acceptance Test has been conducted @ Pollachi Veerathamilatchi Sengodi Nenaivendal @ Sandaipettai Thidal, Pollachi on 19 August 2017 #3146 Viewed Simultaneously Streamed with 16 GB Ram, 500 GB SSD, Ubuntu 16.04 LTS Server with NTK RTMP Server -
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cwbot
KoL chat bot
...IMPORTANT UPDATE INFORMATION: Version 0.15.0 fixes various bugs, including an error that the bot thinks it is out of items when it really isn't. Due to a KoL server upgrade, versions older than 0.14.2 will not be able to log in to the server as of 4/12/16. Version 0.14.0 obsoletes the FaxModule. Be sure to set up the FaxModule2 to properly get faxes. Also introducing the PeriodicAnnouncementModule! Set up those daily/hourly announcements (temporary and permanent!) Also the bot shouldn't crash anymore when sent items that can't be returned. For full documentation, see the readme.txt file in the /doc folder. ...
