Source: Nvidia's quarterly earnings releases and Yahoo! Finance historical price data.

Nvidia Stock Has Done This After Its Last Four Quarterly Results. Here's What It May Do After Nov. 20

Discover how Developer Relations (DevRel) is crucial for driving success for open source web3 projects. AlexaBlockchain

Why Developer Relations (DevRel) Is Crucial for Open Source Web3 Projects | AlexaBlockchain

Hong Kong is recovering after a bruising talent drain, with licensed finance jobs hitting a record as banks and asset managers bet on the city’s push to become the world’s biggest wealth management hub. Bloomberg's Jinshan Hong reports. (Source: Bloomberg)

Watch Hong Kong Finance Jobs Jump to Record on Wealth Push - Bloomberg

Prominent artists including Imogen Heap and deadmau5 join KOR Protocol in shaking up the music industry, using blockchain to challenge streaming giants and put power back in artists’ hands. Source link

Mainstream musicians pile into Web3 to revolutionize artist rights with blockchain - StartupNews.fyi - Startup & Technology News

Bloomberg's Tom Giles and Amy Thomson unveil Bloomberg’s second UK Startups to Watch list, which profiles the smartest and most exciting companies in the country, at Bloomberg Tech London 2024. (Source: Bloomberg)

Watch Revealing Bloomberg’s UK Startups to Watch - Bloomberg

Bloomberg's Mark Gurman says the latest iPad mini update could hold a key advantage for Apple's AI ambitions. He speaks on "Bloomberg Technology" with Ed Ludlow and Caroline Hyde. (Source: Bloomberg)

Watch The iPad's Secret AI Advantage - Bloomberg

Carahsoft Technology Corp. is The ... As the Master Government Aggregator® for our vendor partners, we deliver solutions for Law Enforcement, Cybersecurity, MultiCloud, DevSecOps, Artificial Intelligence, Customer Experience and Engagement, Open Source and mor...

Carahsoft and Partners Showcase Law Enforcement, Cybersecurity, Artificial Intelligence and Mobility Innovations at IACP 2024 in Boston, MA, Oct. 19-22, 2024 | The Manila Times

Web3 companies are on the rise again as many focus on the practical features of blockchain technology. Source link

Web3 map reveals blockchain’s strength in SEA - StartupNews.fyi - Startup & Technology News

Auburn redshirt freshman quarterback Hank Brown will start for the Tigers against New Mexico on Saturday, a source confirmed to ESPN's Pete Thamel on Friday.

Source: Auburn to make QB switch, start freshman Hank Brown - ESPN

Source

source

, Source Upon reaching the milestone of (weehee!), here's a brand new set of GIFs that might come in handy for updating your social distancing life on Snapchat or Instagram 😘 4 million views #RemoteWork QUARANTINE & CHILL . Source #WFH - WORK FROM HOME . Source STAY (THE F*CK) HOME . Source More stories on the coronavirus at https://hackernoon.com/tagged/coronavirus More awesome GIFs at https://giphy.com/hackernoon Again, stay safe and sound, and socially distant Designed by Kien Dao

#REMOTEWORK, A PIXELATED SET

( ) Source To solve a problem, you first need to understand the problem. Problems exist within systems. So, to understand the problem, you need to understand the system. By systems I mean . They are the most interesting kind, and difficult to deal with. complex adaptive systems Complex, because removing a part destroys the system. Each part is interdependent on other parts. Like removing a heart from the human body. Adaptive, because each part reacts to what the other parts do. Like the heart pumping faster when artery blockages develop. Treating complex systems like a black-box, where you figure out the problem by trying solutions is a terrible idea. Imagine going to a doctor and without listening to you, the doctor gives you aspirin. Do you feel better? No? Then paracetamol. Still no? An antacid. Still no? .. Sigh, okay, tell me what’s wrong? But that’s how we solve most surface problems. And in simple systems, it usually works. The key is, can you distinguish between simple and complex systems? If you don’t know what to call it, you can’t distinguish it. [1] An alternative is white-box thinking. Here, we dissect the system, get into its internals and figure out why things weren’t working, or how the system works. I think there are at least two ways to figure out a system. Simulate the systemExperiment with the system Simulation Simulation is either computational or thought based. One is running a computer simulation, the other is running a thought experiment. In both cases, what you get are insights into how the system works. They go hand in hand. Both need a basic understanding of how things will work — without an existing model, you can’t simulate. These methods are also used to refine your model. For example, you start with a barebones model built by experience. Then, . Then, you improve the model based on the difference in results between your model and the world. you run it One pillar example of simulation is done in 1972. It tracks the demise of humans on the current trajectory of consumption and environment degradation. The Limits to Growth Criticized in the beginning, LTG has come to be accepted as the model predicting reality, now that we have 50 years of living the model behind us. Thought experiments Take a hypothesis, theory or a principle. Think through what will happen if it were true. That’s a thought experiment. Consider an example — (1935). It presents a cat that is indeterminately alive or dead, depending on a random . Figuring out if it’s alive or not is dependent on looking in the box. Till that point, it’s both, alive and dead. Schrödinger’s cat quantum event What makes this thought experiment so great is the way it explains quantum theory. Say, instead, you had a coin inside the box. You can toss this coin by pressing a button outside the box. If it’s heads, the cat will die. Tails, and it lives. With every button press, the probability to live decreases, but at no point is that cat both alive and dead. Is your mind blowing to pieces yet? (ANDRZEJ WOJCICKI/ Science Photo Library/ Corbis) The process You begin with a set of hypothesis about what would happen. You run through them in your mind There are two things that might happen. You arrive at an outcome different from what you expect You arrive at your version of reality Both cases are a cause for concern. You confirm that’s what would happen in the real world, via experience If your model is close enough to the real world, profit. else you refine your hypothesis and restart. Experimentation Here, we enter the territory of real world experiments with real world consequences that might change the state of the system. You’re perturbing the system to try and figure out what will happen. Thus, you want to experiment with small changes first. [2] Want universal basic income? Try experimenting in a few cities first. it’s A/B testing on whatever scale works. The only caveat: things break when you scale up. Experimentation with system is needed when your imagination can’t keep up with what you’re seeing. This usually happens in complex systems. There are too many variables — you don’t know half of them, and you can’t keep the other half in your brain. [3] The main aim, using the above approaches is to build an accurate-enough model. One that behaves the same way the system behaves, given the same input. Input → System → Output Input → Model → Output Fair warning though, knowing this model isn’t the same as controlling the system. That’s the — to control a system, you need a controller at least as complex as the system. Law of Requisite Variety are an excellent tool to build this model. Sometimes sketching things out, looking at it all together helps see the relationships. That’s what BOTDs are for. Behaviour over time diagrams Questions to answer In both approaches, you’re trying to fill in some blanks. Here are some important questions to answer. They exist in every system. If you’re unsure what some of them mean, head over to seeing systems. Find the driving factors What would happen if I do X? Figure out the loop structure If I do more of a driving factor, d , what will happen? Figure out delays in systems How long before the results of changing d occur? Identify the current dominant loop A second from now, towards which driving factor is the system going? System Archetypes Some very common models come into play again and again. If you know these well, the problem switches from trying to figure out what is happening, to which model fits? — This is powerful and dangerous. Dangerous because you get a tendency to beat the current situation into one of these models. Powerful because you don’t have to start from scratch every time. This is how . They are archetypes for life. Mental Models work has an excellent introduction to system archetypes. Wikipedia To dive into available archetypes, The Systems Thinker is an excellent resource. I haven’t explored archetypes enough, yet. What I do know, is that the above archetype list is incomplete. As Charlie Munger found out, sometimes it’s easier to look in an adjacent field than diving deeper in yours. That’s what I’ll do. Into Chemistry — equilibriums. Systems can be thought of as an equilibrium. That’s when we can use results from chemical equilibrium to explain system behaviour. Presenting, . Le Chateliers Principle When a settled system is disturbed, it will adjust to diminish the change that has been made to it. This is precisely what Jay Forrester says happens in social systems. “At any time, a near-equilibrium exists affecting population mobility between different areas of a country. To the extent that there is disequilibrium, it means that some area is slightly more attractive than others and population begins to move in the direction of the more attractive area. Movement continues until rising population drives the more attractive area down in attractiveness to again be in equilibrium with its surroundings. Other things being equal, an increase in population of a city crowds housing, overloads job opportunities, causes congestion, increases pollution, encourages crime, and reduces every component of the quality of life.” — Jay Forrester, Counter intuitive behaviour of social systems [1] Case in point: The Piraha tribe [2] This doesn’t mean the large scale change would mimic the small scale change. Things can go unexpectedly wrong . when you scale up [3] Computer simulations shine on this half. But, still produce crap results because they are just half the variables, and not necessarily the most critical.

A Practical Guide to Understanding Systems

As a participant in this year's Hacktoberfest, I created an AI chatbot called Technical Writer. This AI chatbot, built with Coze, is designed for beginners and technical writing experts. It is also integrated into the Technical Writing Mentorship Program (TWMP) learning site as a contribution to the TWMP community, founded by Nwokocha Wisdom. With his team, they have successfully trained over thirty technical writers, including me, in various technical fields. Technical Writer helps technical writers by: Providing the right answers and career paths in technical writing.
Suggesting projects for technical writers to build their portfolios.
Simplifying technical writing terminologies with real-life examples. Why Technical Writers Matter in Technical Writing As a technical advocate who frequently shares the importance of technical writing on social media, I often receive direct messages from people asking questions like: How can I start in technical writing?
What courses should I take to become a technical writer? And many more questions. Sometimes, I find these questions overwhelming, even though I enjoy answering them. I built this chatbot on available courses on TWMP’s site and an API documentation course by Tom Johnson to help answer and guide technical writers in their journey. Demo https://youtu.be/aVrPfzu0W-s?embedable=true Coze Agent Store: TechnicalWriter Discord Chatbot: TechnicalWriter How I Built Technical Writer These are the steps I took in building this chatbot: Adding the Character and skills I described my chatbot character and skill in the Persona & Prompt field and then optimized the character and skill. Knowledge Resources As mentioned earlier, the chatbot response will be based on the courses available on TWMP and API documentation course. To do this, I added these courses using the Knowledge feature. I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I added these resources using “Add to agent.” The reasons why I used only these resources are: The courses were written by experts in technical writing with proven records.


These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. Chat Experience Welcoming Note I added a friendly welcoming note to the chatbot which gives an overview of what the chatbot is all about using the “opening text” feature. Auto-suggestion To enhance further engagement and clarity for the users, I added the auto-suggestion feature with a custom prompt to keep the chatbot from within the scope of the chatbot. Voice Chat I also added a voice chat feature to the chatbot to ensure that people with visual impairments are not excluded. This promotes accessibility for users worldwide. Furthermore, I enable communication in all available languages such as Arabic, Japanese, French, etc. Discord Integration Since TWMP’s community is on Discord, I integrated this chatbot into a Discord bot so that community members can interact with it directly. I created a New application and named it after the chatbot. Then I copied the Bot token and added it to the chatbot. This link was sent to the group admin and was added to the server. Submitting the Chatbot to the Agent Store To ensure this chatbot is not used solely by the local community, I submitted it to the Coze agent store to help technical writers worldwide attain their skills and expertise in technical writing. It was submitted under the 'Writing’ category. So if you head over to the Writing category, and search for “TechnicalWriter”, you will see it there Don’t forget to drop a comment, like, and share with others Adding the Chatbot to the Learning Site I copied the chatbot Web SDK.



I added it to the repo. Result: The chatbot icon is displayed on the site. As it responds to the user, it also adds a reference, as shown above. Final Note This process took me one week to complete because I built this chatbot for an open-source community, so it had to go through the proper procedures. I was asked to change the icon to match the community logo. Some of the challenges I faced were: Trying to modify the height of the chatbot.
I couldn't add an avatar or image on behalf of the user. Despite these challenges, I believe more features will be added. Thanks to the documentation and my experience creating a Telegram bot, I also learned how to create a Discord chatbot for the first time. If you find this chatbot helpful, don’t hesitate to add it to your community. As a participant in this year's Hacktoberfest, I created an AI chatbot called Technical Writer . This AI chatbot, built with Coze , is designed for beginners and technical writing experts. It is also integrated into the Technical Writing Mentorship Program (TWMP) learning site as a contribution to the TWMP community, founded by Nwokocha Wisdom . With his team, they have successfully trained over thirty technical writers, including me, in various technical fields. Technical Writer Coze Technical Writing Mentorship Program (TWMP) Technical Writing Mentorship Program (TWMP) Nwokocha Wisdom Technical Writer helps technical writers by: Technical Writer Providing the right answers and career paths in technical writing. Suggesting projects for technical writers to build their portfolios. Simplifying technical writing terminologies with real-life examples. Providing the right answers and career paths in technical writing. Suggesting projects for technical writers to build their portfolios. Simplifying technical writing terminologies with real-life examples. Why Technical Writers Matter in Technical Writing As a technical advocate who frequently shares the importance of technical writing on social media, I often receive direct messages from people asking questions like: How can I start in technical writing? What courses should I take to become a technical writer? How can I start in technical writing? What courses should I take to become a technical writer? And many more questions. Sometimes, I find these questions overwhelming, even though I enjoy answering them. I built this chatbot on available courses on TWMP’s site and an API documentation course by Tom Johnson to help answer and guide technical writers in their journey. API documentation course Tom Johnson Demo https://youtu.be/aVrPfzu0W-s?embedable=true https://youtu.be/aVrPfzu0W-s?embedable=true Coze Agent Store: TechnicalWriter TechnicalWriter Discord Chatbot: TechnicalWriter TechnicalWriter How I Built Technical Writer These are the steps I took in building this chatbot: Adding the Character and skills I described my chatbot character and skill in the Persona & Prompt field and then optimized the character and skill. Persona & Prompt Knowledge Resources As mentioned earlier, the chatbot response will be based on the courses available on TWMP and API documentation course. To do this, I added these courses using the Knowledge feature. Knowledge I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I added these resources using “Add to agent.” I added these resources using “Add to agent.” I added these resources using “ Add to agent.” Add to agent.” The reasons why I used only these resources are: The courses were written by experts in technical writing with proven records. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. The courses were written by experts in technical writing with proven records. The courses were written by experts in technical writing with proven records. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. Chat Experience Welcoming Note I added a friendly welcoming note to the chatbot which gives an overview of what the chatbot is all about using the “ opening text” feature. opening text” Auto-suggestion To enhance further engagement and clarity for the users, I added the auto-suggestion feature with a custom prompt to keep the chatbot from within the scope of the chatbot. Voice Chat I also added a voice chat feature to the chatbot to ensure that people with visual impairments are not excluded. This promotes accessibility for users worldwide. Furthermore, I enable communication in all available languages such as Arabic, Japanese, French, etc. Discord Integration Since TWMP’s community is on Discord, I integrated this chatbot into a Discord bot so that community members can interact with it directly. I created a New application and named it after the chatbot. I created a New application and named it after the chatbot. Then I copied the Bot token and added it to the chatbot. Then I copied the Bot token and added it to the chatbot. Then I copied the Bot token and added it to the chatbot. This link was sent to the group admin and was added to the server. Submitting the Chatbot to the Agent Store To ensure this chatbot is not used solely by the local community, I submitted it to the Coze agent store to help technical writers worldwide attain their skills and expertise in technical writing. It was submitted under the ' Writing’ category. Writing’ So if you head over to the Writing category, and search for “TechnicalWriter”, you will see it there “ TechnicalWriter ”, TechnicalWriter Don’t forget to drop a comment, like, and share with others Don’t forget to drop a comment, like, and share with others Adding the Chatbot to the Learning Site I copied the chatbot Web SDK. I added it to the repo. I copied the chatbot Web SDK. I copied the chatbot Web SDK. I added it to the repo. I added it to the repo. Result: The chatbot icon is displayed on the site. As it responds to the user, it also adds a reference, as shown above. Final Note This process took me one week to complete because I built this chatbot for an open-source community, so it had to go through the proper procedures. I was asked to change the icon to match the community logo. Some of the challenges I faced were: Trying to modify the height of the chatbot. I couldn't add an avatar or image on behalf of the user. Trying to modify the height of the chatbot. I couldn't add an avatar or image on behalf of the user. Despite these challenges, I believe more features will be added. Thanks to the documentation and my experience creating a Telegram bot, I also learned how to create a Discord chatbot for the first time. If you find this chatbot helpful, don’t hesitate to add it to your community. If you find this chatbot helpful, don’t hesitate to add it to your community.

How I Built Technical Writer: An AI Chatbot For an Open Source Project using Coze

In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently.


Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface.


Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file).


Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more.


Available for various OS: available to download for Windows, MacOS, and Linux. Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql 🌎 Website: stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org 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. In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Available for various OS: available to download for Windows, MacOS, and Linux. SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying : unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: Available for various OS: available to download for Windows, MacOS, and Linux. Available for various OS : available to download for Windows, MacOS, and Linux. Available for various OS Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql Star StackQL on GitHub: github.com/stackql/stackql github.com/stackql/stackql 🌎 Website: stackql.io 🌎 Website: stackql.io stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org 🌎 Website: docsify.js.org 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 Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter LinkedIn 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 Also, published here . here

9 Open Source Projects Every Developer Needs to Bookmark for Their Workflow

Ensuring Reproducibility in AI Research: Code and Pre-trained Weights Open-Sourced

Bluemarz is a new python written AI framework; it’s also an open-source platform specifically designed for managing and orchestrating multiple AI agents. It brings a measure of scalability and flexibility that’s been lacking in the Ai open-source industry. From its stateless architecture to its support for multiple language models (such as OpenAI, Anthropic Claude, and Google Gemini), Bluemarz offers a robust solution to meet enterprise needs around scalability, security, and privacy — all crucial for projects/organizations handling sensitive data and complex workflows. Let’s briefly dive into what makes Bluemarz a unique and powerful tool for developers looking to deploy Ai agents at scale. Getting Started with Bluemarz Installation: You can get Bluemarz running by installing it from GitHub using pip: pip install git+https://github.com/StartADAM/bluemarz.git Basic Workflow: Bluemarz introduces three main concepts: Agent, Session, and Assignment. These allow developers to set up a flexible workflow where multiple agents can interact within the same session, pulling from different LLMs as needed. Here’s an example of a simple session from its repo: https://github.com/StartADAM/bluemarz): import bluemarz as bm
import asyncio

async def procedural_example():
    # Initialize an agent using OpenAI
    agent = bm.openai.OpenAiAssistant.from_id(api_key, assistant_id)
    
    # Start a session
    session = bm.openai.OpenAiAssistantNativeSession.new_session(api_key)
    
    # Assign the agent to the session
    task = bm.Assignment(agent, session)
    task.add_message(bm.SessionMessage(role=bm.MessageRole.USER, text="What can you do?"))
    
    # Run the task and display the result
    res = await task.run_until_breakpoint()
    print(res)

asyncio.run(procedural_example()) Key Benefits of Bluemarz Bluemarz addresses significant limitations that other platforms like LangChain, LangGraph, and Chainlit haven’t fully solved, especially around multi-agent, multi-LLM support, and session scalability. What Sets Bluemarz Apart Stateless and Scalable: Running Bluemarz in a Kubernetes cluster or on any cloud platform is simple due to its stateless design, which doesn’t require session retention, thus enhancing scalability.
Multi-Agent Flexibility: You can assign multiple agents in a single session and add or remove agents dynamically. This means that if an AI agent for translation is needed mid-session, it can be added on the fly without interrupting ongoing conversations.
Enterprise-Ready Security: Bluemarz is built to fit into corporate environments, with compliance and privacy controls already accounted for.
Dynamic Agent Selection: Either through code or with an AI-powered Selector (coming soon), developers can manage agent workflows with ease, adding a layer of control over task assignment and performance. Core Components Providers: These are the LLMs Bluemarz supports, including OpenAI, Anthropic Claude, and Google Gemini, with the flexibility to work with on-premises models. Sessions: Sessions represent interactions that are entirely stateless in Bluemarz, running and storing sessions within your LLM provider’s infrastructure. Agents and Assignments: Agents can be defined and assigned to sessions dynamically. Bluemarz supports both manual and programmatic agent assignments, allowing real-time changes during active sessions. Extending Bluemarz with Tools One of the most powerful features of Bluemarz is the ability to define reusable tools. Tools extend the capabilities of LLMs by connecting them to external systems, data sources, or services. Here’s an example of a tool that converts Celsius to Kelvin: class TemperatureConverter(bm.SyncTool):
    def __init__(self):
        self._spec = bm.ToolSpec.model_validate({
            "id": "convert_celsius_to_kelvin",
            "name": "Convert Celsius to Kelvin",
            "description": "Converts a temperature from Celsius to Kelvin",
            "toolType": "sync",
            "variables": {
                "temperature": {
                    "type": "string",
                    "description": "Temperature in Celsius",
                },
            },
        })

     def call(self, tool_call: bm.ToolCall) -> bm.ToolCallResult:
        temp_celsius = int(tool_call.arguments.get("temperature", "0"))
        kelvin = temp_celsius + 273.15
        return bm.ToolCallResult(tool_call=tool_call, text=str(kelvin)) Once defined, this tool can be used across different agents and sessions, providing a single point of configuration for any agent needing temperature conversions. Real-World Use Cases for Bluemarz Customer Support Automation: Bluemarz’s multi-agent support allows for agents specialized in different domains to collaborate in real-time within a single session, improving response times and relevance.
R&D: Devs can use Bluemarz to configure research sessions where agents access documents or datasets dynamically.
Cost Control and Optimization: Bluemarz’s agent flexibility means only the necessary agents are deployed, reducing compute costs for organizations. Conclusion If you’re looking to contribute to a new, powerful, and flexible, open-source solution check it out: https://github.com/StartADAM/bluemarz. Since its stateless, adaptable, and ready for corporate-grade deployments, it should be a great project on a portfolio and an easy win into contributing to the Ai band-wagon. Whether you’re orchestrating a single, complex task across multiple agents or need to ensure scalability and security, Bluemarz can provide the infrastructure to support and grow the AI agent ecosystem. Bluemarz is a new python written AI framework; it’s also an open-source platform specifically designed for managing and orchestrating multiple AI agents. It brings a measure of scalability and flexibility that’s been lacking in the Ai open-source industry. From its stateless architecture to its support for multiple language models (such as OpenAI, Anthropic Claude, and Google Gemini), Bluemarz offers a robust solution to meet enterprise needs around scalability, security, and privacy — all crucial for projects/organizations handling sensitive data and complex workflows. Let’s briefly dive into what makes Bluemarz a unique and powerful tool for developers looking to deploy Ai agents at scale. Getting Started with Bluemarz Installation : You can get Bluemarz running by installing it from GitHub using pip: Installation pip install git+https://github.com/StartADAM/bluemarz.git pip install git+https://github.com/StartADAM/bluemarz.git Basic Workflow : Bluemarz introduces three main concepts: Agent , Session , and Assignment . These allow developers to set up a flexible workflow where multiple agents can interact within the same session, pulling from different LLMs as needed. Here’s an example of a simple session from its repo: https://github.com/StartADAM/bluemarz ): Basic Workflow Agent Session Assignment https://github.com/StartADAM/bluemarz https://github.com/StartADAM/bluemarz import bluemarz as bm
import asyncio

async def procedural_example():
    # Initialize an agent using OpenAI
    agent = bm.openai.OpenAiAssistant.from_id(api_key, assistant_id)
    
    # Start a session
    session = bm.openai.OpenAiAssistantNativeSession.new_session(api_key)
    
    # Assign the agent to the session
    task = bm.Assignment(agent, session)
    task.add_message(bm.SessionMessage(role=bm.MessageRole.USER, text="What can you do?"))
    
    # Run the task and display the result
    res = await task.run_until_breakpoint()
    print(res)

asyncio.run(procedural_example()) import bluemarz as bm
import asyncio

async def procedural_example():
    # Initialize an agent using OpenAI
    agent = bm.openai.OpenAiAssistant.from_id(api_key, assistant_id)
    
    # Start a session
    session = bm.openai.OpenAiAssistantNativeSession.new_session(api_key)
    
    # Assign the agent to the session
    task = bm.Assignment(agent, session)
    task.add_message(bm.SessionMessage(role=bm.MessageRole.USER, text="What can you do?"))
    
    # Run the task and display the result
    res = await task.run_until_breakpoint()
    print(res)

asyncio.run(procedural_example()) Key Benefits of Bluemarz Bluemarz addresses significant limitations that other platforms like LangChain, LangGraph, and Chainlit haven’t fully solved, especially around multi-agent, multi-LLM support, and session scalability. What Sets Bluemarz Apart Stateless and Scalable: Running Bluemarz in a Kubernetes cluster or on any cloud platform is simple due to its stateless design, which doesn’t require session retention, thus enhancing scalability. Multi-Agent Flexibility: You can assign multiple agents in a single session and add or remove agents dynamically. This means that if an AI agent for translation is needed mid-session, it can be added on the fly without interrupting ongoing conversations. Enterprise-Ready Security: Bluemarz is built to fit into corporate environments, with compliance and privacy controls already accounted for. Dynamic Agent Selection: Either through code or with an AI-powered Selector (coming soon), developers can manage agent workflows with ease, adding a layer of control over task assignment and performance. Stateless and Scalable : Running Bluemarz in a Kubernetes cluster or on any cloud platform is simple due to its stateless design, which doesn’t require session retention, thus enhancing scalability. Stateless and Scalable Multi-Agent Flexibility : You can assign multiple agents in a single session and add or remove agents dynamically. This means that if an AI agent for translation is needed mid-session, it can be added on the fly without interrupting ongoing conversations. Multi-Agent Flexibility Enterprise-Ready Security : Bluemarz is built to fit into corporate environments, with compliance and privacy controls already accounted for. Enterprise-Ready Security Dynamic Agent Selection : Either through code or with an AI-powered Selector (coming soon), developers can manage agent workflows with ease, adding a layer of control over task assignment and performance. Dynamic Agent Selection Core Components Providers : These are the LLMs Bluemarz supports, including OpenAI, Anthropic Claude, and Google Gemini, with the flexibility to work with on-premises models. Providers Sessions : Sessions represent interactions that are entirely stateless in Bluemarz, running and storing sessions within your LLM provider’s infrastructure. Sessions Agents and Assignments : Agents can be defined and assigned to sessions dynamically. Bluemarz supports both manual and programmatic agent assignments, allowing real-time changes during active sessions. Agents and Assignments Extending Bluemarz with Tools One of the most powerful features of Bluemarz is the ability to define reusable tools. Tools extend the capabilities of LLMs by connecting them to external systems, data sources, or services. Here’s an example of a tool that converts Celsius to Kelvin: class TemperatureConverter(bm.SyncTool):
    def __init__(self):
        self._spec = bm.ToolSpec.model_validate({
            "id": "convert_celsius_to_kelvin",
            "name": "Convert Celsius to Kelvin",
            "description": "Converts a temperature from Celsius to Kelvin",
            "toolType": "sync",
            "variables": {
                "temperature": {
                    "type": "string",
                    "description": "Temperature in Celsius",
                },
            },
        })

     def call(self, tool_call: bm.ToolCall) -> bm.ToolCallResult:
        temp_celsius = int(tool_call.arguments.get("temperature", "0"))
        kelvin = temp_celsius + 273.15
        return bm.ToolCallResult(tool_call=tool_call, text=str(kelvin)) class TemperatureConverter(bm.SyncTool):
    def __init__(self):
        self._spec = bm.ToolSpec.model_validate({
            "id": "convert_celsius_to_kelvin",
            "name": "Convert Celsius to Kelvin",
            "description": "Converts a temperature from Celsius to Kelvin",
            "toolType": "sync",
            "variables": {
                "temperature": {
                    "type": "string",
                    "description": "Temperature in Celsius",
                },
            },
        })

     def call(self, tool_call: bm.ToolCall) -> bm.ToolCallResult:
        temp_celsius = int(tool_call.arguments.get("temperature", "0"))
        kelvin = temp_celsius + 273.15
        return bm.ToolCallResult(tool_call=tool_call, text=str(kelvin)) Once defined, this tool can be used across different agents and sessions, providing a single point of configuration for any agent needing temperature conversions. Real-World Use Cases for Bluemarz Customer Support Automation: Bluemarz’s multi-agent support allows for agents specialized in different domains to collaborate in real-time within a single session, improving response times and relevance. R&D: Devs can use Bluemarz to configure research sessions where agents access documents or datasets dynamically. Cost Control and Optimization: Bluemarz’s agent flexibility means only the necessary agents are deployed, reducing compute costs for organizations. Customer Support Automation : Bluemarz’s multi-agent support allows for agents specialized in different domains to collaborate in real-time within a single session, improving response times and relevance. Customer Support Automation R&D : Devs can use Bluemarz to configure research sessions where agents access documents or datasets dynamically. R&D Cost Control and Optimization : Bluemarz’s agent flexibility means only the necessary agents are deployed, reducing compute costs for organizations. Cost Control and Optimization Conclusion If you’re looking to contribute to a new, powerful, and flexible, open-source solution check it out: https://github.com/StartADAM/bluemarz . Since its stateless, adaptable, and ready for corporate-grade deployments, it should be a great project on a portfolio and an easy win into contributing to the Ai band-wagon. Whether you’re orchestrating a single, complex task across multiple agents or need to ensure scalability and security, Bluemarz can provide the infrastructure to support and grow the AI agent ecosystem. https://github.com/StartADAM/bluemarz https://github.com/StartADAM/bluemarz

An Open-Source Platform for Multi-Agent AI Orchestration

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

Embracing the Open-Source Revolution: A New Pathway for MSP Growth

Рэвалюцыя адкрытага кода: новы шлях для росту MSP

Adoptar la revolución del código abierto: un nuevo camino para el crecimiento de los MSP

Adopter la révolution de l'open source : une nouvelle voie pour la croissance des MSP

A nyílt forráskódú forradalom felkarolása: Új út az MSP növekedéséhez

Merangkul Revolusi Open-Source: Jalan Baru untuk Pertumbuhan MSP

Abbracciare la rivoluzione open source: un nuovo percorso per la crescita degli MSP

オープンソース革命の受け入れ: MSP 成長への新たな道

ღია წყაროს რევოლუციის მომხრე: MSP ზრდის ახალი გზა

Përqafimi i Revolucionit me Burim të Hapur: Një rrugë e re për rritjen e MSP

ምሕቋፍ ክፉት ምንጪ ሰውራ፡ ሓድሽ መንገዲ ንዕብየት MSP

Açyk çeşme ynkylaby kabul etmek: MSP ösüşi üçin täze ýol

اوپن سورس انقلاب کو اپنانا: MSP گروتھ کے لیے ایک نیا راستہ

SINGAPORE, Singapore, November 5th, 2024, Chainwire/-BTC.com officially announced the completion of its upgrade and the launch of an open-source computing power platform. The platform continues to embody the community spirit and adopts the motto “Embrace Open Source, Gather Computing Power, Create the Future Together” to support developers and computing power providers in jointly promoting the development of a decentralized ecosystem. The platform will serve as a bridge for computing power suppliers, enabling them to achieve efficient management and flexible trading through its one-click deployment tools. Additionally, it will provide a free trading environment for those in need of computing power, allowing on-demand access to achieve a balance between cost and efficiency. About BTC.com BTC.com is committed to connecting computing power for open-source projects, providing infrastructure for the development of the blockchain and AI industries, and promoting the flourishing growth of industry applications. Contact PR team contactPR@btc.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SINGAPORE, Singapore, November 5th, 2024, Chainwire/-BTC.com officially announced the completion of its upgrade and the launch of an open-source computing power platform. The platform continues to embody the community spirit and adopts the motto “Embrace Open Source, Gather Computing Power, Create the Future Together” to support developers and computing power providers in jointly promoting the development of a decentralized ecosystem. SINGAPORE, Singapore, November 5th, 2024, Chainwire/-BTC.com The platform will serve as a bridge for computing power suppliers, enabling them to achieve efficient management and flexible trading through its one-click deployment tools. Additionally, it will provide a free trading environment for those in need of computing power, allowing on-demand access to achieve a balance between cost and efficiency. About BTC.com BTC.com is committed to connecting computing power for open-source projects, providing infrastructure for the development of the blockchain and AI industries, and promoting the flourishing growth of industry applications. BTC.com BTC.com Contact PR team contactPR@btc.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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