MuxMaster – a profile-driven video encoder and muxer that treats media conversion as normalization: analyze the source, compare against target specs, convert only what needs to change. Dolby Vision, HDR10, HLG preservation; HDR→SDR tone-mapping, intelligent audio/subtitle selection, and multi-track support.

GitHub action for comparing and detect breaking changes in OpenAPI specs

Parse and compare package versions and ranges. From debian, npm, pypi, ruby and more. Process all the version range specs and expressions. This project is sponsored by an NLnet project https://nlnet.nl/project/vulnerabilitydatabase/ , the Google Summer of Code, nexB and others generous sponsors!

Research comparing 6 spec-driven development tools (Spec-Kit, Spec Kitty, BMad, OpenSpec, Kiro, Tessl) with git worktree analysis and decision frameworks

TUI for querying and comparing cloud pricing across AWS, GCP, Azure, OCI, and LLM APIs — search by spec and by price.

An engine that compares product specs and their prices available on different e-commerce websites

A Ruby gem for parsing, comparing and sorting versions according to the VERS spec.

GitHub action for comparing OpenAPI specs

A benchmark website that tests how well AI coding agents read web content. 10 test pages, each targeting a failure mode from the Agent-Friendly Documentation Spec. Agents self-report what they found; compare against the answer key for a score.

Graphical tool to compare brushless motor specs and performance

Scripts used for the paper 'Deep transfer learning compared to subject-specific models for sEMG decoders'

Input: Traits from PROSAIL inversion/ML retrieval & Original Reflectance Spectra & Land Use. Method: Uses forward modelling of PROSAIL to generate spectra using the trait information. Uses Spectral Angle Mapping to compare simulated spectra with actual spectra. Output: SAM values, comparison of SAM across Land Use through descriptives, KDE plots, ANOVA and Tukey HSD.

AI-powered Python plotting gallery. One spec, nine libraries — matplotlib, plotly, seaborn, bokeh, altair & more. Compare and copy working code.

Interactive EC2 instance type browser with TUI and CLI. Compare specs, pricing (on-demand, spot, savings plans, reserved instances), and find the perfect instance for your workload.

Specs and scaffold of a webapp to develop to compare frameworks implementation

A Go library for parsing, comparing and sorting version ranges according to the VERS spec.

Fancy models.dev wrapper to search, compare, and rank open‑weights model alternatives, with cost estimates and shareable specs.

Compare different renderers of the mapbox-gl spec

Demo of the new(ish) CSS Grid spec as compared to Flexbox

A mobile showroom app allows users to browse, compare, and purchase mobile devices through a user-friendly interface, offering virtual tours, detailed specs, reviews, and secure online transactions for a convenient shopping experience. Build using React.js, Express.js, Node.js, Bootstrap, PrimeReact

Data source • Dataset name: Automobile Data Set • Source: https://archive.ics.uci.edu/ml/datasets/automobile • Data Dictionary: As on link above • Note: Data is slightly modified for training purposes, split into 3 entities • automobile-spec: car specification information • automobile-risk: insurance risk rating • automobile-losses: normalized losses in uses as compared to other cars

🎮 PixelBuddy is a Streamlit app that lets PC gamers quickly check if their system can run any Steam game. It detects your CPU, GPU, RAM, VRAM, storage, and OS, fetches Steam requirements, compares them with your specs, and predicts compatibility, graphics level, and FPS. Perfect for gamers who want to optimize performance and avoid guesswork.

- Analysed the data criticality and locality, i.e. cache lines that read versus the cache lines that are only written and exploited the disparity between the read and the write criticality using the proposed Read Write Partitioning, to divide the LLC into two logical partitions for dirty and clean lines. - Implemented the Read Write Partitioning to predict the best partition sizes to increase the likelihood of the future read hits in Simple-Scalar 3.0 and analysed the results of benchmarks of the SPEC CPU2000 suite. - Attained performance improvement in terms of Miss rate reduction by 10% and memory traffic reduction by 20 %. - Compared RWP with the prior state of the art cache management policies and found that RWP outperforms all of them, apart from few outliers. The analysis of the outliers was also performed.

Spectral Comparison and Analysis

Compare spec/ipmi sdr_sensor data info

Spec-driven development with a deterministic code snapshot. ReOpenSpec is a small CLI (reo) that scans your repo into arch-baseline.json, compares each feature’s api-contracts.json to that baseline (drift), and injects short IDE rules so agents ground structure and contracts in those files.

This is a Visual Studio Code Extension that compares the changes made to a "Swagger 2.0" spec and shows the Breaking and other changes in a table format.

PC Specs Checker is a web app that detects a user’s RAM and CPU using browser APIs and allows manual GPU input. It compares these specs with game requirements to show if games are playable. Built with HTML, CSS, and JavaScript, it demonstrates real-time hardware checking and logic-based results.

This project made used of a 2 level cache - L1: split cache and L2: unified cache. I evaluated the impact of changing the cache configuration, such as L1 and L2 Cache size, Associativity, Block Size and Latency hit time. This was done using a SimpleScalar “sim-outorder” model and the SPEC 2000 benchmark suite. The result was compared using: Number of instructions per cycle (IPC), L1 data cache miss rate L1 instruction cache miss rate L2 unified cache miss rate The number of load instructions executed Total Simulation time

Aequilibrium does love transforming… people, lives, teams, companies. And there’s no better representation of transformation than Hasbro’s Transformers , the classic television series featuring heroic Autobots raging their battle to destroy the evil forces of the Deceptions. Please watch this video: https://www.youtube.com/watch?v=nLS2N9mHWaw And write the following in whatever language you like: The Transformers are at war and you are in charge of settling the score! You’re to evaluate who wins a fight between the Autobots and the Decepticons. Here are the rules. Each Transformer has the following criteria on their tech spec (see http://www.ntfa.net/ntfa/techspecs/index.php?cat=Gen1&group=DeceptPZ&char=Predaking for an example): ● Strength ● Intelligence ● Speed ● Endurance ● Rank ● Courage ● Firepower ● Skill All of these criteria are ranked from 1 to 10. The “overall rating” of a Transformer is the following formula: (Strength + Intelligence + Speed + Endurance + Firepower) Each Transformer must either be an Autobot or a Deception. Your program should take input that describes a group of Transformers and based on that group displays: a. The number of battles b. The winning team c. The surviving members of the losing team The basic rules of the battle are: ● The teams should be sorted by rank and faced off one on one against each other in order to determine a victor, the loser is eliminated ● A battle between opponents uses the following rules: ○ If any fighter is down 4 or more points of courage and 3 or more points of strength compared to their opponent, the opponent automatically wins the face-off regardless of overall rating (opponent has ran away) ○ Otherwise, if one of the fighters is 3 or more points of skill above their opponent, they win the fight regardless of overall rating ○ The winner is the Transformer with the highest overall rating ● In the event of a tie, both Transformers are considered destroyed ● Any Transformers who don’t have a fight are skipped (i.e. if it’s a team of 2 vs. a team of 1, there’s only going to be one battle) ● The team who eliminated the largest number of the opposing team is the winner Special rules: ● Any Transformer named Optimus Prime or Predaking wins his fight automatically regardless of any other criteria ● In the event either of the above face each other (or a duplicate of each other), the game immediately ends with all competitors destroyed For example, given the following input: Soundwave, D, 8,9,2,6,7,5,6,10 Bluestreak, A, 6,6,7,9,5,2,9,7 Hubcap: A, 4,4,4,4,4,4,4,4 The output should be: 1 battle Winning team (Decepticons): Soundwave Survivors from the losing team (Autobots): Hubcap