Blazingly 🔥 fast 🚀 memory vulnerabilities, written in 100% safe Rust. 🦀 - Speykious/cve-rs

GitHub - Speykious/cve-rs: Blazingly 🔥 fast 🚀 memory vulnerabilities, written in 100% safe Rust. 🦀

Contribute to huggingface/cosmopedia development by creating an account on GitHub.

GitHub - huggingface/cosmopedia

I-SOON/Anxun leak related stuff. Contribute to soufianetahiri/Anxun-isoon development by creating an account on GitHub.

GitHub - soufianetahiri/Anxun-isoon: I-SOON/Anxun leak related stuff

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Farewell to the .NET Foundation · AvaloniaUI/Avalonia · Discussion #14666

Detect file content types with deep learning. Contribute to google/magika development by creating an account on GitHub.

GitHub - google/magika: Detect file content types with deep learning

Contribute to dymensionxyz/staker-snapshots development by creating an account on GitHub.

GitHub - dymensionxyz/staker-snapshots

A modern dashboard exploit for the original Microsoft Xbox. - XboxDev/endgame-exploit

GitHub - XboxDev/endgame-exploit: A modern dashboard exploit for the original Microsoft Xbox.

Contribute to carlini/yet-another-applied-llm-benchmark development by creating an account on GitHub.

GitHub - carlini/yet-another-applied-llm-benchmark

Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. - karpathy/minbpe

GitHub - karpathy/minbpe: Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

An extremely fast Python package installer and resolver, written in Rust. - astral-sh/uv

GitHub - astral-sh/uv: An extremely fast Python package installer and resolver, written in Rust.

Get the most recent info and news about GitHub on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over 83 million people use GitHub to build amazing things together across 200+ million repositories. With all the collaborative features of GitHub, it has never been easier for individuals and teams to write faster, better code.


GitHub

GitHub is more than just a platform for hosting repositories—it’s a highly scalable distributed system that processes millions of transactions daily. From handling git push requests to computing file differences efficiently, GitHub relies on robust algorithms and architecture to ensure high performance and reliability. This article explores how GitHub processes vast amounts of data, scales to handle millions of transactions, and employs diff algorithms to track file changes efficiently. The article also includes detailed JavaScript implementations of core algorithms used in version control systems. 1. The Challenges of Large-Scale Version Control Systems. Modern version control systems must deal with several key challenges: Processing millions of transactions per day, including commits, merges, and pull requests.
Efficiently computing differences between files, which is critical for version tracking and merging.
Scaling storage and processing power, ensuring fast response times for developers worldwide. These principles are not exclusive to GitHub. Similar architectures and algorithms are used in GitLab, Bitbucket, and other platforms that deal with version control at scale. 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) When tracking changes in a file, GitHub (and Git itself) uses diff algorithms to compute the minimum number of edits needed to transform one version of a file into another. A widely used algorithm for this is Myers’ Diff Algorithm. 2.1. How Myers’ Algorithm Works. Myers’ algorithm finds the shortest sequence of insertions and deletions required to convert one file into another. It operates by iterating through possible edit distances (d) and computing possible transformations along “diagonals” in the edit graph. 2.2. JavaScript Implementation of Myers’ Algorithm. /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
 const N = a.length;
 const M = b.length;
 const maxD = N + M;

 let v = {
 1: 0
 };

 for (let d = 0; d <= maxD; d++) {
 for (let k = -d; k <= d; k += 2) {
 let x;
 
 if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
 x = v[k + 1] || 0;
 } else {
 x = (v[k - 1] || 0) + 1;
 }

 let y = x - k;
 while (x < N && y < M && a[x] === b[y]) {
 x++;
 y++;
 }

 v[k] = x;
 if (x >= N && y >= M) {
 return d;
 }
 }
 }
 return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); Breakdown of the code: Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph.


Loop through possible edit distances (d): Iterates through each possible number of edits needed.


Computing the optimal path: Determines whether to insert or delete based on v[k] values.


“Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. 3. GitHub’s Transaction Processing Architecture. To handle millions of transactions, GitHub employs a multi-layered architecture. Here’s how a typical transaction flows: Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.).
Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing.
Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates.
Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. This architecture allows GitHub to scale efficiently, ensuring that no single component becomes a bottleneck 4. JavaScript Implementation of GitHub-Like Transaction Processing. GitHub processes transactions asynchronously to handle high traffic. The following JavaScript code simulates parallel processing of transactions using Promises. /**
* Simulates a transaction in a version control system.
*/

class Transaction {
 constructor(id, action, payload) {
 this.id = id;
 this.action = action;
 this.payload = payload;
 }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

 return new Promise((resolve) => {
 console.log(`Processing transaction ${tx.id}: ${tx.action}`);
 setTimeout(() => {
 console.log(`Indexing ${tx.id}...`);
 setTimeout(() => {
 console.log(`Computing diff for ${tx.id}...`);
 setTimeout(() => {
 console.log(`Updating database for ${tx.id}...`);
 resolve("success");
 }, 100);
 }, 50);
 }, 100);
 });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
 const transactions = [
 new Transaction("tx001", "commit", "Modified file A"),
 new Transaction("tx002", "commit", "Fixed bug in file B"),
 new Transaction("tx003", "merge", "Merged branches"),
 ];

 const promises = transactions.map(async (tx) => {
 const result = await processTransaction(tx);
 console.log(`Transaction ${tx.id} result: ${result}`);
 });

 await Promise.all(promises);
 console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); Key takeaways from this code: Asynchronous processing: Transactions run in parallel using Promise.all().
Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage.
Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. 5. Conclusion: GitHub’s Scalable Approach to Version Control. GitHub’s ability to process millions of transactions per day relies on a combination of: Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes.
Scalable distributed architecture, using microservices and message queues to balance workload.
Parallel transaction processing, ensuring fast responses even under heavy loads. These techniques aren’t exclusive to GitHub—they are widely used in GitLab, Bitbucket, and large-scale data processing systems. Understanding these principles helps developers build efficient, scalable applications for handling high volumes of transactions. GitHub is more than just a platform for hosting repositories—it’s a highly scalable distributed system that processes millions of transactions daily. From handling git push requests to computing file differences efficiently, GitHub relies on robust algorithms and architecture to ensure high performance and reliability. This article explores how GitHub processes vast amounts of data, scales to handle millions of transactions, and employs diff algorithms to track file changes efficiently. The article also includes detailed JavaScript implementations of core algorithms used in version control systems. 1. The Challenges of Large-Scale Version Control Systems. Modern version control systems must deal with several key challenges: Processing millions of transactions per day, including commits, merges, and pull requests. Efficiently computing differences between files, which is critical for version tracking and merging. Scaling storage and processing power, ensuring fast response times for developers worldwide. Processing millions of transactions per day, including commits, merges, and pull requests. Efficiently computing differences between files, which is critical for version tracking and merging. Scaling storage and processing power, ensuring fast response times for developers worldwide. These principles are not exclusive to GitHub. Similar architectures and algorithms are used in GitLab, Bitbucket, and other platforms that deal with version control at scale. 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) 2. How GitHub Computes File Differences (Diff Algorithm Implementation in JavaScript) When tracking changes in a file, GitHub (and Git itself) uses diff algorithms to compute the minimum number of edits needed to transform one version of a file into another. A widely used algorithm for this is Myers’ Diff Algorithm. 2.1. How Myers’ Algorithm Works. Myers’ algorithm finds the shortest sequence of insertions and deletions required to convert one file into another. It operates by iterating through possible edit distances (d) and computing possible transformations along “diagonals” in the edit graph. 2.2. JavaScript Implementation of Myers’ Algorithm. 2.2. JavaScript Implementation of Myers’ Algorithm. /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
 const N = a.length;
 const M = b.length;
 const maxD = N + M;

 let v = {
 1: 0
 };

 for (let d = 0; d <= maxD; d++) {
 for (let k = -d; k <= d; k += 2) {
 let x;
 
 if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
 x = v[k + 1] || 0;
 } else {
 x = (v[k - 1] || 0) + 1;
 }

 let y = x - k;
 while (x < N && y < M && a[x] === b[y]) {
 x++;
 y++;
 }

 v[k] = x;
 if (x >= N && y >= M) {
 return d;
 }
 }
 }
 return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); /**
* Computes the minimum edit distance between two arrays using Myers' Diff Algorithm.
* @param {Array} a - The original array (e.g., characters of a file)
* @param {Array} b - The modified array
* @returns {number} The minimum number of edit operations required
*/

function myersDiff(a, b) {
 const N = a.length;
 const M = b.length;
 const maxD = N + M;

 let v = {
 1: 0
 };

 for (let d = 0; d <= maxD; d++) {
 for (let k = -d; k <= d; k += 2) {
 let x;
 
 if (k === -d || (k !== d && (v[k - 1] || 0) < (v[k + 1] || 0))) {
 x = v[k + 1] || 0;
 } else {
 x = (v[k - 1] || 0) + 1;
 }

 let y = x - k;
 while (x < N && y < M && a[x] === b[y]) {
 x++;
 y++;
 }

 v[k] = x;
 if (x >= N && y >= M) {
 return d;
 }
 }
 }
 return maxD;
}

// Example usage:
const oldVersion = Array.from("Hello World");
const newVersion = Array.from("Hello GitHub World");
const operations = myersDiff(oldVersion, newVersion);

console.log(`Minimum number of edits: ${operations}`); Breakdown of the code: Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Computing the optimal path: Determines whether to insert or delete based on v[k] values. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Initialization: The algorithm initializes an array v to store the maximum x values for each diagonal in the edit graph. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Loop through possible edit distances (d): Iterates through each possible number of edits needed. Computing the optimal path: Determines whether to insert or delete based on v[k] values. Computing the optimal path: Determines whether to insert or delete based on v[k] values. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. “Greedy match” step: Moves diagonally as long as characters match, minimizing unnecessary operations. 3. GitHub’s Transaction Processing Architecture. To handle millions of transactions, GitHub employs a multi-layered architecture. Here’s how a typical transaction flows: Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.). Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing. Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates. Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. Receiving the request: API and Webhooks receive transactions (git push, git pull, etc.). Queueing the request: Transactions are placed in a distributed queue (Redis/Kafka) for parallel processing. Processing in microservices: Dedicated services handle indexing, diff computation, and statistics updates. Updating storage: Results are committed to a database (SQL/NoSQL) and cached for quick access. This architecture allows GitHub to scale efficiently, ensuring that no single component becomes a bottleneck 4. JavaScript Implementation of GitHub-Like Transaction Processing. GitHub processes transactions asynchronously to handle high traffic. The following JavaScript code simulates parallel processing of transactions using Promises. /**
* Simulates a transaction in a version control system.
*/

class Transaction {
 constructor(id, action, payload) {
 this.id = id;
 this.action = action;
 this.payload = payload;
 }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

 return new Promise((resolve) => {
 console.log(`Processing transaction ${tx.id}: ${tx.action}`);
 setTimeout(() => {
 console.log(`Indexing ${tx.id}...`);
 setTimeout(() => {
 console.log(`Computing diff for ${tx.id}...`);
 setTimeout(() => {
 console.log(`Updating database for ${tx.id}...`);
 resolve("success");
 }, 100);
 }, 50);
 }, 100);
 });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
 const transactions = [
 new Transaction("tx001", "commit", "Modified file A"),
 new Transaction("tx002", "commit", "Fixed bug in file B"),
 new Transaction("tx003", "merge", "Merged branches"),
 ];

 const promises = transactions.map(async (tx) => {
 const result = await processTransaction(tx);
 console.log(`Transaction ${tx.id} result: ${result}`);
 });

 await Promise.all(promises);
 console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); /**
* Simulates a transaction in a version control system.
*/

class Transaction {
 constructor(id, action, payload) {
 this.id = id;
 this.action = action;
 this.payload = payload;
 }
}

/**

* Simulates processing a transaction step-by-step.
* @param {Transaction} tx - The transaction to process
* @returns {Promise<string>} The result of processing
*/

function processTransaction(tx) {

 return new Promise((resolve) => {
 console.log(`Processing transaction ${tx.id}: ${tx.action}`);
 setTimeout(() => {
 console.log(`Indexing ${tx.id}...`);
 setTimeout(() => {
 console.log(`Computing diff for ${tx.id}...`);
 setTimeout(() => {
 console.log(`Updating database for ${tx.id}...`);
 resolve("success");
 }, 100);
 }, 50);
 }, 100);
 });
}

/**
* Simulates processing multiple transactions in parallel.
*/
async function processTransactions() {
 const transactions = [
 new Transaction("tx001", "commit", "Modified file A"),
 new Transaction("tx002", "commit", "Fixed bug in file B"),
 new Transaction("tx003", "merge", "Merged branches"),
 ];

 const promises = transactions.map(async (tx) => {
 const result = await processTransaction(tx);
 console.log(`Transaction ${tx.id} result: ${result}`);
 });

 await Promise.all(promises);
 console.log("All transactions processed.");
}

// Run transaction processing

processTransactions(); Key takeaways from this code: Asynchronous processing: Transactions run in parallel using Promise.all(). Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage. Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. Asynchronous processing: Transactions run in parallel using Promise.all(). Step-by-step simulation: Each transaction follows the same processing flow—indexing, computing diffs, and updating storage. Scalability: Similar principles apply in real-world systems like GitHub, where Redis/Kafka queues help distribute workloads across microservices. 5. Conclusion: GitHub’s Scalable Approach to Version Control. GitHub’s ability to process millions of transactions per day relies on a combination of: Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes. Scalable distributed architecture, using microservices and message queues to balance workload. Parallel transaction processing, ensuring fast responses even under heavy loads. Optimized diff algorithms, like Myers’ Algorithm, to efficiently compute file changes. Scalable distributed architecture, using microservices and message queues to balance workload. Parallel transaction processing, ensuring fast responses even under heavy loads. These techniques aren’t exclusive to GitHub—they are widely used in GitLab, Bitbucket, and large-scale data processing systems. Understanding these principles helps developers build efficient, scalable applications for handling high volumes of transactions.

Unveiling GitHub's Secret Blueprint—How to Handle Millions of Transactions Daily

የGitHubን ሚስጥራዊ ንድፍ ማውጣት—በቀን በሚሊዮን የሚቆጠሩ ግብይቶችን እንዴት ማስተናገድ እንደሚቻል

Desvelando el plan secreto de GitHub: cómo gestionar millones de transacciones diarias

Dévoilement du plan secret de GitHub : comment gérer des millions de transactions quotidiennes

A GitHub titkos tervének bemutatása – Hogyan kezeljük a tranzakciók millióit naponta

GitHub の秘密の青写真を公開 - 毎日何百万ものトランザクションを処理する方法

GitHub의 비밀 청사진 공개 - 매일 수백만 건의 거래를 처리하는 방법

„GitHub“ slapto plano atskleidimas – kaip kasdien atlikti milijonus operacijų

Go Utolla Blueprint ya Sephiri ya GitHub—Kamoo o ka Swarago Dimilione tša Dikgwebišano Letšatši le Letšatši

Odsłaniamy tajny plan GitHub — jak obsługiwać miliony transakcji dziennie

GitHubpa pakasqa planonta riqsichiy —Imaynata sapa p'unchaw millonnintin ruwaykunata ruwayta

GitHub-yň gizlin meýilnamasyny açmak - Her gün millionlarça amallary nädip çözmeli

Pagbubunyag ng Lihim na Blueprint ng GitHub—Paano Pangasiwaan ang Milyun-milyong Transaksyon Araw-araw

The History of GitHub Awesome-Lists

Гісторыя GitHub Awesome-Lists

Historien om GitHub Awesome-lister

La historia de las Awesome-Lists de GitHub

ההיסטוריה של GitHub Awesome-Lists

GitHub Awesome-Lists の歴史

GitHub Awesome-Lists의 역사

GitHub Awesome тизмелеринин тарыхы

Ny tantaran'ny GitHub Awesome-Lists

Histori ya GitHub Awesome-Lists

Nhoroondo yeGitHub Awesome-Lists

GitHub Awesome-List'lerin Tarihi

Matimu ya GitHub Awesome-Lists

Fetch Commits Like a Pro With This Rust Meets GitHub Guide

Hello JavaScript Enthusiasts! Welcome to another edition of "This Week in JavaScript"! Today, we’ll be looking at the State of JavaScript 2024 report, unpacking Tailwind CSS 4 updates that’ll make your styles fly, and serving up fresh tools to streamline your dev grind. Oh, and did we mention that GitHub Copilot is now free? Buckle up! The Rise of TypeScript The State of JavaScript 2024 survey results are in, and TypeScript is officially dominating the JavaScript landscape. 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption.
Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. It’s clear that while JavaScript itself remains stable, TypeScript and modern build tools like Vite are shaping the future of how we build apps. GitHub Copilot is now Free GitHub has made a major move by offering its Copilot AI for free—well, with a few limits. If you have a GitHub account, you can now access 2,000 code completions and 50 chat messages per month, all within Visual Studio Code and GitHub. Here's how you can take advantage of it: Speed up your coding: Copilot offers AI-driven code completion that saves time and effort.
Multi-file edits: Easily make edits across multiple files.
Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results.
Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. This new offering is amazing for streamlining your workflow and boosting productivity. New Tailwind CSS 4 Developments We’ve already talked about the Tailwind CSS 4 Beta Release before, but you need to know about these features! Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations.
CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config.
Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges.
Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML.
Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. While there’s a lot to love about the new update, be mindful of potential pitfalls with maintainability! Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems.
Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects.
Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem.
pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security.
ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Hello JavaScript Enthusiasts! Welcome to another edition of " This Week in JavaScript "! This Week in JavaScript Today, we’ll be looking at the State of JavaScript 2024 report, unpacking Tailwind CSS 4 updates that’ll make your styles fly, and serving up fresh tools to streamline your dev grind. Oh, and did we mention that GitHub Copilot is now free? Buckle up! The Rise of TypeScript The Rise of TypeScript The State of JavaScript 2024 survey results are in, and TypeScript is officially dominating the JavaScript landscape. The State of JavaScript 2024 TypeScript 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption. Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. 67% of developers now use TypeScript more than traditional JavaScript. Type safety, better tooling, and cleaner code are driving its widespread adoption. 67% of developers Vite is leading the pack when it comes to build tools, with developers loving its simplicity and speed over older tools like webpack. Vite It’s clear that while JavaScript itself remains stable, TypeScript and modern build tools like Vite are shaping the future of how we build apps. GitHub Copilot is now Free GitHub Copilot is now Free GitHub has made a major move by offering its Copilot AI for free —well, with a few limits. If you have a GitHub account, you can now access 2,000 code completions and 50 chat messages per month, all within Visual Studio Code and GitHub. GitHub Copilot AI free Here's how you can take advantage of it: Speed up your coding: Copilot offers AI-driven code completion that saves time and effort. Multi-file edits: Easily make edits across multiple files. Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results. Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. Speed up your coding: Copilot offers AI-driven code completion that saves time and effort. Speed up your coding: Multi-file edits: Easily make edits across multiple files. Multi-file edits: Model options: Choose from models like Anthropic’s Claude 3.5 Sonnet for tailored results. Model options: Copilot Chat: Ask questions, generate code snippets, and get real-time help with your codebase. Copilot Chat: This new offering is amazing for streamlining your workflow and boosting productivity. New Tailwind CSS 4 Developments New Tailwind CSS 4 Developments We’ve already talked about the Tailwind CSS 4 Beta Release before, but you need to know about these features! Tailwind CSS 4 Beta Release Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations. CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config. Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges. Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML. Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. Lightning CSS: Tailwind has moved to Lightning CSS, which speeds up builds and reduces the need for PostCSS configurations. Lightning CSS: CSS-first configuration: With native CSS variables for theming, styling has become more streamlined, although you might miss the type safety from the JavaScript-based config. CSS-first configuration: Descendant Variants: A new double-star feature lets you style elements deep in the hierarchy. However, while this might help with CMS content, it could bring back maintainability challenges. Descendant Variants: Radial and Conic Gradients: The new gradients are great but come with complex syntax, better suited for design systems than HTML. Radial and Conic Gradients: Class Name Changes: Class names like grow (instead of flex-grow) are making tools like Tailwind Merge less efficient. Class Name Changes: While there’s a lot to love about the new update, be mindful of potential pitfalls with maintainability! Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems. Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects. Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem. pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security. ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. JerryScript 3.0: A super lightweight JavaScript engine for IoT with 84% Test262 compliance. It introduces ES2020 features like optional chaining and dynamic imports—great for embedded systems. JerryScript 3.0: Transformers.js v3.2: Bringing machine learning to the browser! Features include real-time speech recognition with Moonshine, image analysis with Phi-3.5 Vision, and bilingual text generation with EXAONE. Perfect for integrating AI directly into web projects. Transformers.js v3.2: Bun 1.1.39 & 1.1.40: With bun.lock for better tooling compatibility, 30% faster cached installations, and WebSocket improvements, Bun keeps optimizing the JavaScript ecosystem. Bun 1.1.39 & 1.1.40: pnpm Update: The pnpm link command now adds dependency overrides to package.json, simplifying global and workspace linking. SHA256 hashing has been introduced, improving security. pnpm Update: ESLint v9.17.0: A major update! The no-unused-vars rule now auto-fixes unused variables, and crashes from nullish values are resolved. This update will keep your codebase cleaner and more efficient. ESLint v9.17.0: If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

December Brings Free GitHub Copilot

How to Automatically Publish Your NPM Package Using GitHub Actions

Hoe om u NPM-pakket outomaties met GitHub-aksies te publiseer

Jak automaticky publikovat svůj balíček NPM pomocí akcí GitHub

Cómo publicar automáticamente su paquete NPM mediante GitHub Actions

Como publicar automaticamente o teu paquete NPM usando accións de GitHub

Ինչպես ավտոմատ կերպով հրապարակել ձեր NPM փաթեթը՝ օգտագործելով GitHub գործողությունները

Come pubblicare automaticamente il tuo pacchetto NPM utilizzando le azioni GitHub

כיצד לפרסם באופן אוטומטי את חבילת ה-NPM שלך באמצעות פעולות GitHub

GitHub Actions を使用して NPM パッケージを自動的に公開する方法

Cara Menerbitkan Pakej NPM Anda Secara Automatik Menggunakan Tindakan GitHub

Como publicar automaticamente seu pacote NPM usando ações do GitHub

Ako automaticky publikovať svoj balík NPM pomocou akcií GitHub

Hur du automatiskt publicerar ditt NPM-paket med hjälp av GitHub-åtgärder

While GitHub is primarily known for version control and hosting code, it also hides some of the best resources for tech jobs and internships. I manually curated 9 of my favorite repositories with amazing career opportunities, divided across categories and with application links for each. From software engineering roles to AI-focused internships, remote jobs, and open-source opportunities, both beginners and seasoned devs are welcome! I hope that this article will be useful for you to land your dream job or secure an internship role! Wishing you the best of luck in your tech career journey! 1. Summer Internships 2025 ⭐ GitHub stars: 35.2K+ A curated list of computer science, software, tech, and quant internships for Summer 2025, updated collaboratively by the community. 👉 https://github.com/SimplifyJobs/Summer2025-Internships Who is it for: College students and recent graduates in technical fields seeking internship opportunities across various companies. How to apply: Browse the list, identify roles of interest, and follow the application links provided for each opportunity. 2. Open Source Internships Programs ⭐ GitHub stars: 3.8K+ A comprehensive directory of open-source internship programs, with timeline, eligibility, stipend, and tech stack focus details. 👉 https://github.com/deepanshu1422/List-Of-Open-Source-Internships-Programs Who is it for: Beginners and seasoned developers interested in contributing to open-source projects and gaining industry experience. How to apply: Check the deadlines and program details listed, and follow the official application instructions provided for each program. 3. SWE College Jobs 2025 ⭐ GitHub stars: 1.8K+ A collection of software engineering internships and new graduate positions. Daily updated roles for both the US and international scope. 👉 https://github.com/speedyapply/2025-SWE-College-Jobs Who is it for: Perfect for students pursuing computer science or related degrees looking for hands-on software engineering roles. How to apply: Use the provided apply links and follow the instructions to submit your application for the role. 4. AI College Jobs 2025 ⭐ GitHub stars: 400+ A dedicated list of artificial intelligence, machine learning, and data science jobs. Available roles for both the US and internationally, and updated daily. 👉 https://github.com/speedyapply/2025-AI-College-Jobs Who is it for: Well suited for college students interested in AI technologies and looking for internships in this growing field. How to apply: Find the company and niche of your interest and follow the direct application links for AI internship positions. 5. Remote Jobs ⭐ GitHub stars: 30K+ A comprehensive list of remote-friendly companies that offer tech roles globally. 👉 https://github.com/remoteintech/remote-jobs Who is it for: Developers, designers, and other tech professionals of various experience levels looking for fully remote job opportunities. How to apply: Explore the companies to read more about their tech stacks, size, and mission, and apply via the links provided. 6. Tech Jobs with Relocation ⭐ GitHub stars: 3.2K+ A collection of tech jobs and internships that offer relocation support, helping you find roles in cities or countries of your choice. 👉 https://github.com/AndrewStetsenko/tech-jobs-with-relocation Who is it for: Professionals open to moving internationally for tech roles, looking to expand their horizons, and seeking new adventures. How to apply: Follow the links to company career pages, and apply directly for relevant roles. Also, lots of valuable info about work permits, visas, etc. 7. Easy Application ⭐ GitHub stars: 7.8K+ A collection of job applications that require minimal to apply. This lets candidates focus on their application, rather than meticulously fill out lengthy forms. 👉 https://github.com/j-delaney/easy-application Who is it for: Job seekers who prefer simple application processes by uploading their resume and experience straight-forward UX. How to apply: Leverage the provided links to the company career pages. 8. Hiring Without Whiteboards ⭐ GitHub stars: 46.4K+ A directory of companies that evaluate technical candidates through non-traditional methods, often avoiding whiteboard interviews. 👉 https://github.com/poteto/hiring-without-whiteboards Who is it for: Developers who value practical coding challenges and real-world problem-solving over theoretical interviews. How to apply: Use the list to find companies, read more about their interview process, and follow their specific application guidelines. 9. Community Writer Programs ⭐ GitHub stars: 3.9K+ A list of programs that reward technical writers for creating content, including blog posts in the IT field, tech reviews, and tutorials. 👉 https://github.com/malgamves/CommunityWriterPrograms Who is it for: Developers and writers looking to earn money and recognition through technical writing by sharing their expertise and insights. How to apply: Select a program, read the guidelines, and submit content proposals directly. You might be required to provide a blog link or sample work. Did You Like The Resources? Here Is More 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter! Also, connect with me on Twitter, LinkedIn, and GitHub! Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch! Also published here. While GitHub is primarily known for version control and hosting code, it also hides some of the best resources for tech jobs and internships. I manually curated 9 of my favorite repositories with amazing career opportunities, divided across categories and with application links for each. From software engineering roles to AI-focused internships, remote jobs, and open-source opportunities, both beginners and seasoned devs are welcome! I hope that this article will be useful for you to land your dream job or secure an internship role! Wishing you the best of luck in your tech career journey! 1. Summer Internships 2025 Summer Internships 2025 ⭐ GitHub stars: 35.2K+ ⭐ GitHub stars: 35.2K+ A curated list of computer science, software, tech, and quant internships for Summer 2025, updated collaboratively by the community. 👉 https://github.com/SimplifyJobs/Summer2025-Internships https://github.com/SimplifyJobs/Summer2025-Internships Who is it for: College students and recent graduates in technical fields seeking internship opportunities across various companies. Who is it for: How to apply: Browse the list, identify roles of interest, and follow the application links provided for each opportunity. How to apply: 2. Open Source Internships Programs Open Source Internships Programs ⭐ GitHub stars: 3.8K+ ⭐ GitHub stars: 3.8K+ A comprehensive directory of open-source internship programs, with timeline, eligibility, stipend, and tech stack focus details. 👉 https://github.com/deepanshu1422/List-Of-Open-Source-Internships-Programs https://github.com/deepanshu1422/List-Of-Open-Source-Internships-Programs Who is it for: Beginners and seasoned developers interested in contributing to open-source projects and gaining industry experience. Who is it for: How to apply: Check the deadlines and program details listed, and follow the official application instructions provided for each program. How to apply: 3. SWE College Jobs 2025 SWE College Jobs 2025 ⭐ GitHub stars: 1.8K+ ⭐ GitHub stars: 1.8K+ A collection of software engineering internships and new graduate positions. Daily updated roles for both the US and international scope. 👉 https://github.com/speedyapply/2025-SWE-College-Jobs https://github.com/speedyapply/2025-SWE-College-Jobs Who is it for: Perfect for students pursuing computer science or related degrees looking for hands-on software engineering roles. Who is it for: How to apply: Use the provided apply links and follow the instructions to submit your application for the role. How to apply: 4. AI College Jobs 2025 AI College Jobs 2025 ⭐ GitHub stars: 400+ ⭐ GitHub stars: 400+ A dedicated list of artificial intelligence, machine learning, and data science jobs. Available roles for both the US and internationally, and updated daily. 👉 https://github.com/speedyapply/2025-AI-College-Jobs https://github.com/speedyapply/2025-AI-College-Jobs Who is it for: Well suited for college students interested in AI technologies and looking for internships in this growing field. Who is it for: How to apply: Find the company and niche of your interest and follow the direct application links for AI internship positions. How to apply: 5. Remote Jobs Remote Jobs ⭐ GitHub stars: 30K+ ⭐ GitHub stars: 30K+ A comprehensive list of remote-friendly companies that offer tech roles globally. 👉 https://github.com/remoteintech/remote-jobs https://github.com/remoteintech/remote-jobs Who is it for: Developers, designers, and other tech professionals of various experience levels looking for fully remote job opportunities. Who is it for: How to apply: Explore the companies to read more about their tech stacks, size, and mission, and apply via the links provided. How to apply: 6. Tech Jobs with Relocation Tech Jobs with Relocation ⭐ GitHub stars: 3.2K+ ⭐ GitHub stars: 3.2K+ A collection of tech jobs and internships that offer relocation support, helping you find roles in cities or countries of your choice. 👉 https://github.com/AndrewStetsenko/tech-jobs-with-relocation https://github.com/AndrewStetsenko/tech-jobs-with-relocation Who is it for: Professionals open to moving internationally for tech roles, looking to expand their horizons, and seeking new adventures. Who is it for: How to apply: Follow the links to company career pages, and apply directly for relevant roles. Also, lots of valuable info about work permits, visas, etc. How to apply: 7. Easy Application Easy Application ⭐ GitHub stars: 7.8K+ ⭐ GitHub stars: 7.8K+ A collection of job applications that require minimal to apply. This lets candidates focus on their application, rather than meticulously fill out lengthy forms. 👉 https://github.com/j-delaney/easy-application https://github.com/j-delaney/easy-application Who is it for: Job seekers who prefer simple application processes by uploading their resume and experience straight-forward UX. Who is it for: How to apply: Leverage the provided links to the company career pages. How to apply: 8. Hiring Without Whiteboards Hiring Without Whiteboards ⭐ GitHub stars: 46.4K+ ⭐ GitHub stars: 46.4K+ A directory of companies that evaluate technical candidates through non-traditional methods, often avoiding whiteboard interviews. 👉 https://github.com/poteto/hiring-without-whiteboards https://github.com/poteto/hiring-without-whiteboards Who is it for: Developers who value practical coding challenges and real-world problem-solving over theoretical interviews. Who is it for: How to apply: Use the list to find companies, read more about their interview process, and follow their specific application guidelines. How to apply: 9. Community Writer Programs Community Writer Programs ⭐ GitHub stars: 3.9K+ ⭐ GitHub stars: 3.9K+ A list of programs that reward technical writers for creating content, including blog posts in the IT field, tech reviews, and tutorials. 👉 https://github.com/malgamves/CommunityWriterPrograms https://github.com/malgamves/CommunityWriterPrograms Who is it for: Developers and writers looking to earn money and recognition through technical writing by sharing their expertise and insights. Who is it for: How to apply: Select a program, read the guidelines, and submit content proposals directly. You might be required to provide a blog link or sample work. How to apply: Did You Like The Resources? Here Is More 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter! my newsletter ! my newsletter Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter Twitter LinkedIn LinkedIn GitHub GitHub Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch ! get in touch get in touch Also published here . here

How to Find a Job or Internship in Tech With These 9 GitHub Repositories (2025 Guide)📚🔥

এই 9টি গিটহাব রিপোজিটরি (2025 গাইড) 📚🔥 দিয়ে কীভাবে প্রযুক্তিতে চাকরি বা ইন্টার্নশিপ খুঁজে পাবেন

So finden Sie mit diesen 9 GitHub-Repositories einen Job oder ein Praktikum im technischen Bereich (Leitfaden 2025)📚🔥

Cómo encontrar un trabajo o una pasantía en tecnología con estos 9 repositorios de GitHub (guía 2025)📚🔥

Comment trouver un emploi ou un stage dans le domaine des technologies avec ces 9 référentiels GitHub (Guide 2025)📚🔥

इन 9 GitHub रिपॉजिटरी के साथ टेक में नौकरी या इंटर्नशिप कैसे खोजें (2025 गाइड)📚🔥

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Como encontrar um emprego ou estágio em tecnologia com estes 9 repositórios do GitHub (guia 2025)📚🔥

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Cách tìm việc làm hoặc thực tập trong lĩnh vực công nghệ với 9 kho lưu trữ GitHub này (Hướng dẫn năm 2025)📚🔥

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Recently, I haven’t been updating my open-source articles as frequently — not because I’ve stopped writing, but because the progress on open-source commercialization has been great, and the endless task of drafting proposals has consumed my time. As a native open-source commercial company, WhaleOps employs mostly engineers. Asking these open-source contributors to write proposals wastes their development time, and their proposals don’t always meet the quality standard. Unlike managing in a big company, being a startup CEO means stepping into any role the company needs. After every strategic meeting, I’m the first to roll up my sleeves and tackle the most urgent tasks. As a result, I haven’t had time to write articles, as my limited time is mostly taken up with creating proposals that follow the formal template style. Especially recently, with one bid after another, I’ve found myself questioning my sanity. The existing large models can’t solve my problem, so I’ll build my own As a tech person, I always look to tools to solve my problems. Naturally, I thought of large models, but they can’t fully grasp the nuances of our products and often produce unreliable outputs. Plus, you’re the one delivering the work, not the model. So, I decided to develop a tool for proposal generation based on large models using Python and a compatible large model (compatible with ChatGPT). This tool automatically generates a proposal from your product documentation, breaking down product manuals into feature points. Based on a human-created mapping of these points to the requirements, it generates a Word version of the proposal and an Excel deviation table. The model can condense or expand content or simply copy relevant functionality as needed. Features of the open-source proposal tool The functionality of this tool is simple, with the most challenging part being the Word formatting (formatting in Word is always a pain). I experimented with several methods to make sure it follows the correct Title 1, Title 2, Title 3, body text, table, and image formats in the Word template. Staying true to the open-source spirit, I’ve uploaded the tool to my personal GitHub under an Apache License, so feel free to use it if you need. Here’s what it does: Get it here: https://github.com/William-GuoWei/ProposalLLM Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”).
Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”).
For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed).
Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. With this tool, you can quickly modify and generate proposals at will. Proposal generation process Start by running Extract_Word.py to generate your product feature points as a Word document, then run Generate.py. If a feature point is missing, you can enter “X” in the Excel file, and the model will generate content that meets the requirements. However, I strongly recommend a manual review. After generation, you’ll see the proposal formatted with a table of contents, body text, images, tables, and bullet points, all automatically organized. The entire proposal is structured according to the client’s requirements in sequential format, with subheadings, content, images, and point-to-point responses. This takes care of all the repetitive work. The automatically generated deviation table includes everything, whether the content was generated by the model or not, along with the corresponding section numbers. You only need to finalize the deviation table with “&” symbols based on the final requirements — the model has written it all for you. For me, this tool has reduced what used to be 8 hours of work to around 30 minutes, and even our business team can generate the business proposal sections using the template. In total, this has cut down our time for a week-long proposal to 1–2 days, reducing the manpower required by 80%. How to use it? First, download all the code to a single directory from GitHub at https://github.com/William-GuoWei/ProposalLLM: Proposal Large Model (Chinese version). Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx.
Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section.
Copy your product manual to Template.docx. Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues.
Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly.
Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically.
Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors.
Adjust the parameters in Generate.py: API_KEY and SECRET_KEY: Baidu Cloud large model keys.
MAX_WIDTH_CM: Maximum image width; images larger than this will be resized automatically.
The prompts for generating content have been customized for large data scenarios, but feel free to modify them.
MoreSection=1 will read Column C to generate detailed tertiary headings (default is on).
ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off).
DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on).
key_flag=1 adds the importance level of each requirement to the proposal headings (default is on).
last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” 8. Run Generate.py. Summary With this tool, you only need to check the product features against the proposal requirements, and most of the proposal content is generated automatically. The business proposal section can also be generated similarly, so creating a 1,000-page proposal now only takes a few minutes. Recently, I haven’t been updating my open-source articles as frequently — not because I’ve stopped writing, but because the progress on open-source commercialization has been great, and the endless task of drafting proposals has consumed my time. As a native open-source commercial company, WhaleOps employs mostly engineers. Asking these open-source contributors to write proposals wastes their development time, and their proposals don’t always meet the quality standard. Unlike managing in a big company, being a startup CEO means stepping into any role the company needs. After every strategic meeting, I’m the first to roll up my sleeves and tackle the most urgent tasks. As a result, I haven’t had time to write articles, as my limited time is mostly taken up with creating proposals that follow the formal template style. Especially recently, with one bid after another, I’ve found myself questioning my sanity. The existing large models can’t solve my problem, so I’ll build my own As a tech person, I always look to tools to solve my problems. Naturally, I thought of large models, but they can’t fully grasp the nuances of our products and often produce unreliable outputs. Plus, you’re the one delivering the work, not the model. So, I decided to develop a tool for proposal generation based on large models using Python and a compatible large model (compatible with ChatGPT). This tool automatically generates a proposal from your product documentation, breaking down product manuals into feature points. Based on a human-created mapping of these points to the requirements, it generates a Word version of the proposal and an Excel deviation table. The model can condense or expand content or simply copy relevant functionality as needed. Features of the open-source proposal tool The functionality of this tool is simple, with the most challenging part being the Word formatting (formatting in Word is always a pain). I experimented with several methods to make sure it follows the correct Title 1, Title 2, Title 3, body text, table, and image formats in the Word template. Staying true to the open-source spirit, I’ve uploaded the tool to my personal GitHub under an Apache License, so feel free to use it if you need. Here’s what it does: Get it here: https://github.com/William-GuoWei/ProposalLLM Get it here: https://github.com/William-GuoWei/ProposalLLM https://github.com/William-GuoWei/ProposalLLM Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”). Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”). For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed). Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”). Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”). For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed). Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. With this tool, you can quickly modify and generate proposals at will. Proposal generation process Start by running Extract_Word.py to generate your product feature points as a Word document, then run Generate.py . If a feature point is missing, you can enter “X” in the Excel file, and the model will generate content that meets the requirements. However, I strongly recommend a manual review. After generation, you’ll see the proposal formatted with a table of contents, body text, images, tables, and bullet points, all automatically organized. Extract_Word.py Generate.py The entire proposal is structured according to the client’s requirements in sequential format, with subheadings, content, images, and point-to-point responses. This takes care of all the repetitive work. The automatically generated deviation table includes everything, whether the content was generated by the model or not, along with the corresponding section numbers. You only need to finalize the deviation table with “&” symbols based on the final requirements — the model has written it all for you. For me, this tool has reduced what used to be 8 hours of work to around 30 minutes, and even our business team can generate the business proposal sections using the template. In total, this has cut down our time for a week-long proposal to 1–2 days, reducing the manpower required by 80%. How to use it? First, download all the code to a single directory from GitHub at https://github.com/William-GuoWei/ProposalLLM : Proposal Large Model (Chinese version). https://github.com/William-GuoWei/ProposalLLM Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx. Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section. Copy your product manual to Template.docx. Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues. Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly. Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically. Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors. Adjust the parameters in Generate.py: Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx . pip install openpyxl, docx, openai, requests, docx, python-docx Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section. Copy your product manual to Template.docx . Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues. Template.docx Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly. Extract_Word.py Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically. Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors. Adjust the parameters in Generate.py : Generate.py API_KEY and SECRET_KEY: Baidu Cloud large model keys. MAX_WIDTH_CM: Maximum image width; images larger than this will be resized automatically. The prompts for generating content have been customized for large data scenarios, but feel free to modify them. MoreSection=1 will read Column C to generate detailed tertiary headings (default is on). ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off). DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on). key_flag=1 adds the importance level of each requirement to the proposal headings (default is on). last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” API_KEY and SECRET_KEY : Baidu Cloud large model keys. API_KEY SECRET_KEY MAX_WIDTH_CM : Maximum image width; images larger than this will be resized automatically. MAX_WIDTH_CM The prompts for generating content have been customized for large data scenarios, but feel free to modify them. MoreSection=1 will read Column C to generate detailed tertiary headings (default is on). MoreSection=1 ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off). ReGenerateText=0 DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on). DDDAnswer=1 key_flag=1 adds the importance level of each requirement to the proposal headings (default is on). key_flag=1 last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” last_heading_1=2 8. Run Generate.py . Generate.py Summary With this tool, you only need to check the product features against the proposal requirements, and most of the proposal content is generated automatically. The business proposal section can also be generated similarly, so creating a 1,000-page proposal now only takes a few minutes.

I Built An Automatic Proposal Generation Large Language Model and Open-Sourced It on GitHub

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