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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

Obyte is a big ecosystem with smart contracts, Autonomous Agents (AAs), attestations, oracles, internal assets, chatbots, and many Decentralized Finance (DeFi) applications. As such, there are a lot of aspects, items, and resources you may want to check, depending on your own activities inside the platform. Except for stats and public transactions of the privacy coin Blackbytes (GBB) and other private tokens, for obvious reasons, the rest of the network is transparent for everyone to consult whatever they need. However, not every resource is directly available on the main website but on complementary sites. From some general data to liquidity providers (LP) reward lists, let’s explore some handy Obyte-related portals. Non-Financial Data & Stats The main website of our network is Obyte.org, but there’s also an Obyte.io page. It’s not phishing or any other trap, but an open-source non-financial data explorer for Obyte, created by Fabien and currently maintained by tarmo888. Here, you can find information about all available assets (search bar included), attestors, oracles (data feeds), Autonomous Agents (Dapps), chatbots, Order Providers (previously witnesses), polls, and a ledger timeline. All this data could help in the creation of smart contracts and conditional payments without coding, from the same Obyte wallet. Likewise, we have a specific page for Obyte Stats, where it’s possible to check data like connected wallets, nodes, richest addresses, order providers, and transaction history. There’s also a specific site for Autonomous Agent Stats, showing Total Value Locked (TVL) in AAs, 24-hour turnover, most active agents, and activity. Outside Obyte, CoinMarketCap (CMC) and CoinGecko have listed the Obyte native coin, GBYTE, and they show stats like historical price and market capitalization, available markets, supply, trading volume, and TVL from internal DeFi platforms. For the latter purpose, Obyte is also present on DeFi Llama, where anyone can consult the specific charts and TVLs of our different DeFi applications. That includes the Counterstake Bridge, the Oswap.io DEX, the prediction market Prophet, our Pythagorean Perpetual Futures, and even the donation platform Kivach. Rewards for LPs Liquidity providers from Oswap.io can check all details about the distribution of rewards on a dedicated website. Every seven days, 100 GBYTEs are distributed among all of them in proportion to their contributed liquidity. They can consult here the involved Autonomous Agent address (and its activity on the Obyte Explorer), eligible pools, TVL, distribution dates, yield percentage, and all LP addresses with their respective rewards. It’s also possible to consult previous distributions, or directly search by address. There’s asimilar site for the Kava Rise Program rewards. In 2023, Obyte registered two of its DeFi apps (Counterstake and LINE Token) to receive rewards from this program, in proportion to their TVL. To earn a part of the total rewards, users must simply hold imported tokens from the Kava network in their Obyte wallets. On this site, users can consult every distribution by date, including wallet addresses, rewards in USD and LINE, and wallet Annual Percentage Yield (APY). Research & Devs For developers and researchers, the main website lists several useful resources, including our whitepaper, blog, and Wiki. There’s also a separate developer website that includes a quick start, tutorials for newcomers, libraries, scripts, and all kinds of tools to build custom apps on the network. Of course, this is paired with our open-source repositories, available on GitHub. AnObyte testnet with its own wallet and explorer is also available to developers and regular users alike to explore and experiment with most of the network features without financial risk. Developers can integrate it into their Dapp creation process, safely test functionalities, and provide feedback for improvements. Users, on the other hand, can use the wallet as a learning tool, simulating real transactions with valueless coins while exploring features like chatbots, merchant demos, and conditional payments. Finally, you can find news, discussions, and guidance on our social media, especially on our Discord server. Here, you can discover channels for general discussions, help for any problems, grants proposals, platform statistics, announcements, and new projects for the entire community, developers, and the Obyte team to discuss their issues and ideas. Ready to try it? Featured Vector Image by rawpixel/Freepik Obyte is a big ecosystem with smart contracts, Autonomous Agents (AAs), attestations, oracles, internal assets, chatbots, and many Decentralized Finance (DeFi) applications. As such, there are a lot of aspects, items, and resources you may want to check, depending on your own activities inside the platform. Except for stats and public transactions of the privacy coin Blackbytes (GBB) and other private tokens, for obvious reasons, the rest of the network is transparent for everyone to consult whatever they need. However, not every resource is directly available on the main website but on complementary sites. From some general data to liquidity providers (LP) reward lists, let’s explore some handy Obyte-related portals. Blackbytes Blackbytes Non-Financial Data & Stats The main website of our network is Obyte.org , but there’s also an Obyte.io page. It’s not phishing or any other trap, but an open-source non-financial data explorer for Obyte, created by Fabien and currently maintained by tarmo888. Here, you can find information about all available assets (search bar included), attestors, oracles (data feeds), Autonomous Agents (Dapps), chatbots, Order Providers (previously witnesses), polls, and a ledger timeline. Obyte.org Obyte.org Obyte.io Obyte.io It’s not phishing or any other trap, but an open-source non-financial data explorer for Obyte, created by Fabien and currently maintained by tarmo888. All this data could help in the creation of smart contracts and conditional payments without coding, from the same Obyte wallet. Likewise, we have a specific page for Obyte Stats , where it’s possible to check data like connected wallets, nodes, richest addresses, order providers, and transaction history. There’s also a specific site for Autonomous Agent Stats , showing Total Value Locked (TVL) in AAs, 24-hour turnover, most active agents, and activity. Obyte Stats Obyte Stats Autonomous Agent Stats Autonomous Agent Stats Outside Obyte, CoinMarketCap (CMC) and CoinGecko have listed the Obyte native coin, GBYTE, and they show stats like historical price and market capitalization, available markets, supply, trading volume, and TVL from internal DeFi platforms. Outside Obyte, CoinMarketCap (CMC) and CoinGecko have listed the Obyte native coin, GBYTE, CoinGecko CoinGecko For the latter purpose, Obyte is also present on DeFi Llama , where anyone can consult the specific charts and TVLs of our different DeFi applications. That includes the Counterstake Bridge , the Oswap.io DEX, the prediction market Prophet , our Pythagorean Perpetual Futures , and even the donation platform Kivach . on DeFi Llama on DeFi Llama Counterstake Bridge Counterstake Bridge Oswap.io Oswap.io Prophet Prophet Pythagorean Perpetual Futures Pythagorean Perpetual Futures Kivach Kivach Rewards for LPs Liquidity providers from Oswap.io can check all details about the distribution of rewards on a dedicated website . Every seven days, 100 GBYTEs are distributed among all of them in proportion to their contributed liquidity. They can consult here the involved Autonomous Agent address (and its activity on the Obyte Explorer ), eligible pools, TVL, distribution dates, yield percentage, and all LP addresses with their respective rewards. It’s also possible to consult previous distributions, or directly search by address. dedicated website dedicated website Every seven days, 100 GBYTEs are distributed among all of them in proportion to their contributed liquidity. Obyte Explorer Obyte Explorer There’s a similar site for the Kava Rise Program rewards. In 2023, Obyte registered two of its DeFi apps (Counterstake and LINE Token ) to receive rewards from this program, in proportion to their TVL. To earn a part of the total rewards, users must simply hold imported tokens from the Kava network in their Obyte wallets. similar site similar site LINE Token LINE Token On this site, users can consult every distribution by date, including wallet addresses, rewards in USD and LINE, and wallet Annual Percentage Yield (APY). Research & Devs For developers and researchers, the main website lists several useful resources , including our whitepaper, blog, and Wiki. There’s also a separate developer website that includes a quick start, tutorials for newcomers, libraries, scripts , and all kinds of tools to build custom apps on the network. Of course, this is paired with our open-source repositories, available on GitHub . useful resources useful resources There’s also a separate developer website that includes a quick start, tutorials for newcomers, libraries, scripts developer website developer website on GitHub on GitHub An Obyte testnet with its own wallet and explorer is also available to developers and regular users alike to explore and experiment with most of the network features without financial risk. Developers can integrate it into their Dapp creation process, safely test functionalities, and provide feedback for improvements. Users, on the other hand, can use the wallet as a learning tool, simulating real transactions with valueless coins while exploring features like chatbots, merchant demos, and conditional payments. Obyte testnet Obyte testnet Finally, you can find news, discussions, and guidance on our social media, especially on our Discord server . Here, you can discover channels for general discussions, help for any problems, grants proposals, platform statistics, announcements, and new projects for the entire community, developers, and the Obyte team to discuss their issues and ideas. Ready to try it? Discord server Discord server Featured Vector Image by rawpixel/Freepik Featured Vector Image by rawpixel/ Freepik Freepik Freepik

Educational Byte: Checking Obyte Stats and Resources

Byte educativo: consulta de estadísticas y recursos de Obyte

Hezkuntza Byte: Obyte Estatistikak eta Baliabideak egiaztatzea

Koulutustavu: Obbyte-tilastojen ja -resurssien tarkistaminen

Oktatási bájt: Obyte-statisztikák és erőforrások ellenőrzése

教育バイト: Obyte の統計とリソースの確認

საგანმანათლებლო ბაიტი: Obyte სტატისტიკისა და რესურსების შემოწმება

Образовательный байт: проверка статистики и ресурсов Obyte

Uburezi Byte: Kugenzura Imibare ya Obyte

Образовни бајт: Провера Обите статистике и ресурса

Elimu Byte: Kuangalia Takwimu na Rasilimali za Obyte

Byte ya Dyondzo: Ku kambela Tistats ta Obyte na Switirhisiwa

Ta'lim bayti: Obyte statistikasi va manbalarini tekshirish

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

BTC.COM Reshapes To Open-Source Computing Power Platform

BTC.COM Açıq Mənbəli Hesablama Güc Platformasına Yenidən Forma Verir

BTC.COM ওপেন-সোর্স কম্পিউটিং পাওয়ার প্ল্যাটফর্মে নতুন আকার দেয়

Το BTC.COM αναδιαμορφώνεται σε πλατφόρμα υπολογιστικής ισχύος ανοιχτού κώδικα

BTC.COM se transforma en una plataforma de potencia informática de código abierto

BTC.COM がオープンソースのコンピューティング パワー プラットフォームに生まれ変わる

BTC.COM pārveido par atvērtā pirmkoda skaitļošanas jaudas platformu

Ny BTC.COM dia mamolavola indray ho amin'ny sehatra herinaratra Open-Source Computing

BTC.COM Reshapes Go Open-Mohlodi Khomphuthara Matla Platform

BTC.COM преобразуется в платформу вычислительной мощности с открытым исходным кодом

БТЦ.ЦОМ се преобликује у платформу за рачунарску моћ отвореног кода

BTC.COM Yi Vumba Hi Vuntshwa Ku Ya Eka Pulatifomo Ya Matimba Ya Khomphyuta Ya Xihlovo Lexi Pfulekeke

BTC.COM Tái Cấu Trúc Thành Nền Tảng Điện Toán Nguồn Mở

100x Simpler -  The Ultimate Open-Source Guide

In the world of software development, every developer is familiar with the trusty TODO comment. It's often used as a quick reminder to revisit code, refactor, or complete a task at a later stage. But over time, these TODO comments can pile up, becoming silent accumulations of technical debt — a hidden burden that can impact a project's health and maintainability. If left unchecked, these scattered reminders can weigh down the codebase, making it harder for developers to navigate, debug, and extend. In my own work, I encountered this issue firsthand and eventually decided to address it with a tool that makes technical debt visible and actionable: Todoctor. https://github.com/azat-io/todoctor?embedable=true The Hidden Problem of TODO Comments At first, a TODO comment may seem harmless or even helpful. It's a signal for developers to revisit something — perhaps optimize an algorithm, refactor a tricky section, replace an outdated dependency, or complete a feature that’s still in progress. However, with busy schedules, changing priorities, and tight deadlines, these TODOs are often postponed indefinitely. Instead of being addressed, they become artifacts of things left unresolved. Over time, TODO comments can create several issues: Invisible Technical Debt: An accumulation of TODO comments gives the illusion that the code is well-maintained, while, in reality, it hides incomplete tasks and unresolved issues.
Decreased Code Quality: Left unaddressed, these comments can contribute to bugs, hard-to-maintain code, and unexpected behaviors.
Loss of Context: As time passes, developers may forget the purpose of each TODO, especially when team members come and go. This leads to a growing disconnect between the code and the team’s understanding of it. In larger projects or those with multiple contributors, the problem only grows. Without regular attention, TODOs can be scattered throughout the codebase, making it challenging to manage and track technical debt. The Solution: Making Technical Debt Visible with Todoctor As I grappled with this problem, I realized the need for a tool to bring TODO comments out of hiding and into an actionable space where teams can easily assess and address them. This led to the creation of Todoctor, a CLI utility designed to analyze and track TODO comments in JavaScript and TypeScript codebases. With Todoctor, developers and teams can visualize and manage their technical debt over time, helping them make informed decisions about prioritizing cleanup and refactoring. Here’s how it works: Identify TODO Hotspots: Todoctor scans the codebase to locate every TODO comment, collecting information on each comment's age, author, and content.
Visualize Technical Debt: It then generates a report with a graph that illustrates how the number of TODOs has evolved over time, giving teams a clear picture of their technical debt.
Encourage Accountability: With Todoctor, you can track the age of each TODO, see who created it, and view a sortable list of all pending tasks in the codebase. Through these metrics, Todoctor turns technical debt into a visible, manageable element of the project. This transparency can motivate teams to take action on outstanding tasks, and it helps foster a culture of code ownership and quality. A Glimpse into Todoctor’s Technical Implementation Todoctor was developed with simplicity and usability in mind, allowing it to seamlessly integrate into any development workflow. The tool is written in JavaScript and operates as a CLI utility, which makes it easy to set up and run with a single command. For each TODO comment, Todoctor collects metadata such as: Age of the Comment: How long the TODO has been in the codebase.


Total Number of TODOs: A straightforward count to track debt size.


Average Age of TODOs: A snapshot of how long tasks have been pending.


Top Contributors: A list showing which team members authored the most TODOs, allowing teams to follow up on specific tasks. Using this data, Todoctor generates a comprehensive HTML report that can be added to a codebase or shared with the team. To start using Todoctor, simply install it and run the following command in the root of your project: npx todoctor Within seconds, Todoctor will analyze your codebase and generate a report, giving you a full view of your technical debt and actionable insights. Why Visualizing Technical Debt Matters Technical debt may seem like a small issue until it begins to slow down development and affect code quality. TODO comments, while useful in the short term, can easily become forgotten liabilities that ultimately need to be addressed. By making these reminders visible and measurable, Todoctor empowers teams to reclaim control over their codebase, ensuring it remains maintainable and ready for future growth. Try Todoctor: https://github.com/azat-io/todoctor?embedable=true In the world of software development, every developer is familiar with the trusty TODO comment. It's often used as a quick reminder to revisit code, refactor, or complete a task at a later stage. But over time, these TODO comments can pile up, becoming silent accumulations of technical debt — a hidden burden that can impact a project's health and maintainability. TODO TODO If left unchecked, these scattered reminders can weigh down the codebase, making it harder for developers to navigate, debug, and extend. In my own work, I encountered this issue firsthand and eventually decided to address it with a tool that makes technical debt visible and actionable: Todoctor . Todoctor https://github.com/azat-io/todoctor?embedable=true https://github.com/azat-io/todoctor?embedable=true The Hidden Problem of TODO Comments TODO At first, a TODO comment may seem harmless or even helpful. It's a signal for developers to revisit something — perhaps optimize an algorithm, refactor a tricky section, replace an outdated dependency, or complete a feature that’s still in progress. However, with busy schedules, changing priorities, and tight deadlines, these TODO s are often postponed indefinitely. Instead of being addressed, they become artifacts of things left unresolved. TODO TODO Over time, TODO comments can create several issues: TODO Invisible Technical Debt: An accumulation of TODO comments gives the illusion that the code is well-maintained, while, in reality, it hides incomplete tasks and unresolved issues. Decreased Code Quality: Left unaddressed, these comments can contribute to bugs, hard-to-maintain code, and unexpected behaviors. Loss of Context: As time passes, developers may forget the purpose of each TODO, especially when team members come and go. This leads to a growing disconnect between the code and the team’s understanding of it. Invisible Technical Debt : An accumulation of TODO comments gives the illusion that the code is well-maintained, while, in reality, it hides incomplete tasks and unresolved issues. Invisible Technical Debt TODO Decreased Code Quality : Left unaddressed, these comments can contribute to bugs, hard-to-maintain code, and unexpected behaviors. Decreased Code Quality Loss of Context : As time passes, developers may forget the purpose of each TODO , especially when team members come and go. This leads to a growing disconnect between the code and the team’s understanding of it. Loss of Context TODO In larger projects or those with multiple contributors, the problem only grows. Without regular attention, TODO s can be scattered throughout the codebase, making it challenging to manage and track technical debt. TODO The Solution: Making Technical Debt Visible with Todoctor As I grappled with this problem, I realized the need for a tool to bring TODO comments out of hiding and into an actionable space where teams can easily assess and address them. This led to the creation of Todoctor , a CLI utility designed to analyze and track TODO comments in JavaScript and TypeScript codebases. TODO Todoctor TODO With Todoctor, developers and teams can visualize and manage their technical debt over time, helping them make informed decisions about prioritizing cleanup and refactoring. Here’s how it works: Identify TODO Hotspots: Todoctor scans the codebase to locate every TODO comment, collecting information on each comment's age, author, and content. Visualize Technical Debt: It then generates a report with a graph that illustrates how the number of TODOs has evolved over time, giving teams a clear picture of their technical debt. Encourage Accountability: With Todoctor, you can track the age of each TODO, see who created it, and view a sortable list of all pending tasks in the codebase. Identify TODO Hotspots: Todoctor scans the codebase to locate every TODO comment, collecting information on each comment's age, author, and content. Identify TODO TODO Visualize Technical Debt : It then generates a report with a graph that illustrates how the number of TODO s has evolved over time, giving teams a clear picture of their technical debt. Visualize Technical Debt TODO Encourage Accountability : With Todoctor, you can track the age of each TODO , see who created it, and view a sortable list of all pending tasks in the codebase. Encourage Accountability TODO Through these metrics, Todoctor turns technical debt into a visible, manageable element of the project. This transparency can motivate teams to take action on outstanding tasks, and it helps foster a culture of code ownership and quality. A Glimpse into Todoctor’s Technical Implementation Todoctor was developed with simplicity and usability in mind, allowing it to seamlessly integrate into any development workflow. The tool is written in JavaScript and operates as a CLI utility, which makes it easy to set up and run with a single command. For each TODO comment, Todoctor collects metadata such as: TODO Age of the Comment: How long the TODO has been in the codebase. Total Number of TODOs: A straightforward count to track debt size. Average Age of TODOs: A snapshot of how long tasks have been pending. Top Contributors: A list showing which team members authored the most TODOs, allowing teams to follow up on specific tasks. Age of the Comment: How long the TODO has been in the codebase. Age of the Comment : How long the TODO has been in the codebase. Age of the Comment TODO Total Number of TODOs: A straightforward count to track debt size. Total Number of TODO s: A straightforward count to track debt size. Total Number of TODO Average Age of TODOs: A snapshot of how long tasks have been pending. Average Age of TODO s: A snapshot of how long tasks have been pending. Average Age of TODO Top Contributors: A list showing which team members authored the most TODOs, allowing teams to follow up on specific tasks. Top Contributors : A list showing which team members authored the most TODO s, allowing teams to follow up on specific tasks. Top Contributors TODO Using this data, Todoctor generates a comprehensive HTML report that can be added to a codebase or shared with the team. To start using Todoctor, simply install it and run the following command in the root of your project: npx todoctor npx todoctor Within seconds, Todoctor will analyze your codebase and generate a report, giving you a full view of your technical debt and actionable insights. Why Visualizing Technical Debt Matters Technical debt may seem like a small issue until it begins to slow down development and affect code quality. TODO comments, while useful in the short term, can easily become forgotten liabilities that ultimately need to be addressed. By making these reminders visible and measurable, Todoctor empowers teams to reclaim control over their codebase, ensuring it remains maintainable and ready for future growth. TODO Try Todoctor : Try Todoctor https://github.com/azat-io/todoctor?embedable=true https://github.com/azat-io/todoctor?embedable=true

How Accumulating TODOs Turns Into Technical Debt (and My Open Source Tool to Manage It)

Как натрупването на TODO се превръща в технически дълг (и моят инструмент с отворен код за управлението му)

Cómo la acumulación de tareas pendientes se convierte en deuda técnica (y mi herramienta de código abierto para gestionarla)

Kuinka kertyvät TODOt muuttuvat tekniseksi velaksi (ja avoimen lähdekoodin työkaluni sen hallintaan)

Ki jan akimile TODO yo vin tounen dèt teknik (ak zouti sous louvri mwen an pou jere li)

TODO の蓄積が技術的負債に変わる仕組み (およびそれを管理するためのオープンソース ツール)

របៀបដែលការប្រមូល TODOs ប្រែទៅជាបំណុលបច្ចេកទេស (និងឧបករណ៍ប្រភពបើកចំហរបស់ខ្ញុំដើម្បីគ្រប់គ្រងវា)

Kaip kaupiami TODO virsta technine skola (ir mano atvirojo kodo įrankiu jai valdyti)

Kā TODO uzkrāšana pārvēršas par tehnisko parādu (un mans atvērtā pirmkoda rīks tā pārvaldībai)

څنګه د TODOs راټولول په تخنیکي پور بدلیږي (او زما د خلاصې سرچینې وسیله د دې اداره کولو لپاره)

Jinsi Ukusanyaji wa TODO unabadilika kuwa Deni la Kiufundi (na Zana Yangu Huria ya Kuisimamia)

TODO-lary nädip toplamak tehniki bergä öwrülýär (we ony dolandyrmak üçin açyk çeşme guralym)

Як накопичення TODO перетворюється на технічний борг (і мій інструмент із відкритим вихідним кодом для керування ним)

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