Kelvin

kelvin

An Introduction to Building on the Solana Network

Kelvin Umechukwu is an engineer with a plethora of skills that have enabled him to thrive in various fields including management, business development, community building, engineering innovation, and entrepreneurship. He is the co-founder of Bumpa, a startup infrastructure with the main focus of helping African entrepreneurs manage their businesses digitally, and track and increase sales by creating commercial access and driving digital inclusion. Kelvin is passionate about helping people and businesses develop themselves. On his Linkedin, he shares that his biggest goals are to help young Africans source opportunities and become world-class, problem solvers, and also to support businesses in achieving high sustainable growth through technology, data, and collaboration. He also has a wealth of experience in this field, having worked at TechQuest Stem Academy as a business and community manager and ccHub as a Program Manager, before founding Bumpa. In this episode of Founders Connect, Kelvin and I have a conversation a conversation about his career thus far, his background, how community has helped him develop as an individual and a founder, his reasons for focusing on community impact, how his foray into entrepreneurship began and everything in between. Watch the video https://www.youtube.com/watch?v=MJkq9iFb4dU Video timestamps 1:35 What was the first thing you did when you started your career? 2:39 Kelvin talks about the first time he was employed. 4:33 What was your best experience like at CC Hub? 5:23 Kelvin shares a synopsis of what 'Finding Kelvin' is like? 10:06 Kelvin shares top career lessons he has learnt so far? 14:25 How has being a part of a community helped your career? 17:46 If you had to go back in time, what would you do differently in your career? If yes or no, why? 21:35 Kelvin talks about the importance of volunteering

FoundersConnect: Interview with Kelvin Umechukwu, CEO and co-Founder of Bumpa

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

HONG KONG, Hong Kong, November 4th, 2024/Chainwire/--Legend of Arcadia(LoA) , a new card-based RPG set in an expansive universe inspired by Kingdom Hearts, is bringing a community-driven operational model to Web3 gaming. With a focus on sustainable growth, LoA aims to establish itself as a leading platform by integrating blockchain technology, unique NFT mechanics, and community-centric features that foster long-term player engagement. As the Web3 gaming industry is forecasted to grow to $305 billion by 2030, LoA stands out by addressing key sustainability issues that have previously limited the success of Web3 gaming ventures. Many blockchain-based games have struggled with player retention and engagement, often due to unsustainable reward systems. LoA’s approach, built on years of gaming experience from its team’s work with industry leaders like Tencent, Hero Entertainment, and Seasun Studios, centers on creating an inclusive, feedback-driven community. Community-Driven Growth And Operations Legend of Arcadia (LoA), a card-based RPG game built on a vast story universe inspired by Kingdom Hearts, has a strong vision for reimagining live operations in Web3, called community-driven operations. The formula combines building a sticky and vocal community driven by a strong feedback loop and nimble implementation of the ideas. This has helped LoA stand out from others. The team has spent the past two years perfecting the formula based on their decade-long experience in driving high DAU and retention at leading game companies such as Tencent, Hero Entertainment, and Seasun Studios. At the same time, building a 60k+ strong community with users that actively interact with the team and provide them with feedback. The right feedback loop between the community and the developer has led to innovative tactics for driving community engagement. For example, rather than hosting traditional alpha tests, the team mixed the idea of esports and competition and invited people to form their own teams to compete during the test. This led to a brand new Guild called Alter Ego Guild being formed in the community, and they remain some of the game’s biggest supporters, helping new players onboard and get deep into LoA. During its three-month campaign, LoA had 100,000+ registered users with minimal marketing spend. It also has very high next-day and 7-day retention of 75% and 56%, respectively, higher than many traditional games' 50% and 20%. As it moves towards its TGE and official game launch, the community continues to provide feedback and support the team. With backing from 10+ investors, including Animoca Brands and OKX Ventures, LoA can further develop the game's vastness, ensuring the community is constantly engaged with new content and utilities of its native token, $ARCA. This ranges from the endless possibilities of different NFT heroes to the countless actions and ways to earn $ARCA tokens. Innovative NFT Mechanics and Extensive Gameplay In LoA, each NFT hero is unique, with randomized genetic traits such as appearance and abilities drawn from a large gene pool of over five trillion combinations, including over 48 types of basic attributes, 40,000+ skill combinations, and more than 28 million gene combinations. This ensures that no two NFT heroes are the same. The game team, supported by AI, has also created countless words and storylines, shaping different toy characters and enriching the stories of LoA. It also enhances player retention by implementing seasonal demands created by the in-game mechanics, which will eventually boost the trading volume. $ARCA Token Distribution and Roadmap In LoA, players can earn tokens through a variety of activities and strategies involving their NFT heroes. Inspired by a system commonly used in web2 games, by combining White Ores from mining with Black Ores earned in battles, they can produce $ARCA tokens. Heroes can also be customized through special events and features, which not only alter their appearance but can also provide gameplay advantages, such as enhanced mining power when using event-specific skins. Players can also participate in PvE content by clearing dungeons and defeating bosses for valuable loot or diving into PvP battles to compete against others for rewards and climb seasonal leaderboards for even greater gains. To further boost their earnings, players can stake $ARCA tokens, unlocking additional rewards like free NFTs or a share of profits from NFT sales, adding another layer of value to their in-game investments. In addition to earning tokens through in-game monetization, LoA further enhances sustainability by distinguishing between in-game and external economic factors. This structure allows players to monetize external elements, such as staking $ARCA tokens for special privileges and rewards or sharing in proceeds from NFT assets. This approach aims to foster long-term value creation over short-term speculative gains. LoA will have a total supply of 40,000,000 $ARCA tokens that are distributed among seven pools where each pool will be further subdivided into different rank tiers based on the amount of Gacha Points earned. With countless opportunities and activities in the world of LoA, the game constantly rejuvenates the excitement of its community and players, which will ensure its sustainability in the long run. The game also has plans for extensive single-player content (PVE mode) and multiplayer modes where players can form teams and challenge others in real-time battles. Legend of Arcadia is currently set to launch in November 2024. About Legend of Arcadia Legend of Arcadia (LOA) is a card-based RPG game built on a vast story universe inspired by Kingdom Hearts. LOA is built with the latest technology for the modern gamer, powered by blockchain. Players can enjoy the game for free, on the go, on a mobile device, and socialize in real-time with gamers from across the globe through immersive PvP game modes and by trading highly composable NFTs. Website | X | Discord Contact PR Manager Kelvin Yeo Everest Ventures Group kelvin.yeo@evg.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here HONG KONG, Hong Kong, November 4th, 2024/Chainwire/--Legend of Arcadia(LoA) , a new card-based RPG set in an expansive universe inspired by Kingdom Hearts, is bringing a community-driven operational model to Web3 gaming. With a focus on sustainable growth, LoA aims to establish itself as a leading platform by integrating blockchain technology, unique NFT mechanics, and community-centric features that foster long-term player engagement. As the Web3 gaming industry is forecasted to grow to $305 billion by 2030, LoA stands out by addressing key sustainability issues that have previously limited the success of Web3 gaming ventures. Many blockchain-based games have struggled with player retention and engagement, often due to unsustainable reward systems. $305 billion $305 billion LoA’s approach, built on years of gaming experience from its team’s work with industry leaders like Tencent, Hero Entertainment, and Seasun Studios, centers on creating an inclusive, feedback-driven community. Community-Driven Growth And Operations Legend of Arcadia (LoA), a card-based RPG game built on a vast story universe inspired by Kingdom Hearts, has a strong vision for reimagining live operations in Web3, called community-driven operations. Legend of Arcadia Legend of Arcadia The formula combines building a sticky and vocal community driven by a strong feedback loop and nimble implementation of the ideas. This has helped LoA stand out from others. The team has spent the past two years perfecting the formula based on their decade-long experience in driving high DAU and retention at leading game companies such as Tencent, Hero Entertainment, and Seasun Studios. At the same time, building a 60k+ strong community with users that actively interact with the team and provide them with feedback. The right feedback loop between the community and the developer has led to innovative tactics for driving community engagement. For example, rather than hosting traditional alpha tests, the team mixed the idea of esports and competition and invited people to form their own teams to compete during the test. This led to a brand new Guild called Alter Ego Guild being formed in the community, and they remain some of the game’s biggest supporters, helping new players onboard and get deep into LoA. During its three-month campaign, LoA had 100,000+ registered users with minimal marketing spend. It also has very high next-day and 7-day retention of 75% and 56%, respectively, higher than many traditional games' 50% and 20%. As it moves towards its TGE and official game launch, the community continues to provide feedback and support the team. With backing from 10+ investors, including Animoca Brands and OKX Ventures, LoA can further develop the game's vastness, ensuring the community is constantly engaged with new content and utilities of its native token, $ARCA. This ranges from the endless possibilities of different NFT heroes to the countless actions and ways to earn $ARCA tokens. Innovative NFT Mechanics and Extensive Gameplay In LoA, each NFT hero is unique, with randomized genetic traits such as appearance and abilities drawn from a large gene pool of over five trillion combinations, including over 48 types of basic attributes, 40,000+ skill combinations, and more than 28 million gene combinations. This ensures that no two NFT heroes are the same. The game team, supported by AI, has also created countless words and storylines, shaping different toy characters and enriching the stories of LoA. It also enhances player retention by implementing seasonal demands created by the in-game mechanics, which will eventually boost the trading volume. $ARCA Token Distribution and Roadmap In LoA, players can earn tokens through a variety of activities and strategies involving their NFT heroes. Inspired by a system commonly used in web2 games, by combining White Ores from mining with Black Ores earned in battles, they can produce $ARCA tokens. Heroes can also be customized through special events and features, which not only alter their appearance but can also provide gameplay advantages, such as enhanced mining power when using event-specific skins. Players can also participate in PvE content by clearing dungeons and defeating bosses for valuable loot or diving into PvP battles to compete against others for rewards and climb seasonal leaderboards for even greater gains. To further boost their earnings, players can stake $ARCA tokens, unlocking additional rewards like free NFTs or a share of profits from NFT sales, adding another layer of value to their in-game investments. In addition to earning tokens through in-game monetization, LoA further enhances sustainability by distinguishing between in-game and external economic factors. This structure allows players to monetize external elements, such as staking $ARCA tokens for special privileges and rewards or sharing in proceeds from NFT assets. This approach aims to foster long-term value creation over short-term speculative gains. LoA will have a total supply of 40,000,000 $ARCA tokens that are distributed among seven pools where each pool will be further subdivided into different rank tiers based on the amount of Gacha Points earned. With countless opportunities and activities in the world of LoA, the game constantly rejuvenates the excitement of its community and players, which will ensure its sustainability in the long run. The game also has plans for extensive single-player content (PVE mode) and multiplayer modes where players can form teams and challenge others in real-time battles. Legend of Arcadia is currently set to launch in November 2024. About Legend of Arcadia Legend of Arcadia (LOA) is a card-based RPG game built on a vast story universe inspired by Kingdom Hearts. LOA is built with the latest technology for the modern gamer, powered by blockchain. Players can enjoy the game for free, on the go, on a mobile device, and socialize in real-time with gamers from across the globe through immersive PvP game modes and by trading highly composable NFTs. Legend of Arcadia Legend of Arcadia Website | X | Discord Website Website X X Discord Discord Contact PR Manager Kelvin Yeo Everest Ventures Group kelvin.yeo@evg.co 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

Legend Of Arcadia: Redefining Community-Driven Operations

የአርካዲያ አፈ ታሪክ፡ በማህበረሰብ የሚመሩ ስራዎችን እንደገና መወሰን

Legend Of Arcadia: новае азначэнне аперацый, якія кіруюцца супольнасцю

Legenda o Arkadiji: Redefiniranje operacija vođenih zajednicom

Legend Of Arcadia: Omdefinering af fællesskabsdrevne operationer

Leyenda de Arcadia: redefiniendo las operaciones impulsadas por la comunidad

Legend Of Arcadia: ridefinire le operazioni guidate dalla comunità

レジェンド・オブ・アルカディア：コミュニティ主導の運営の再定義

រឿងព្រេងរបស់ Arcadia៖ កំណត់ឡើងវិញនូវប្រតិបត្តិការដែលជំរុញដោយសហគមន៍

ຄວາມຫມາຍຂອງ Arcadia: Redefining ການດໍາເນີນງານທີ່ຂັບເຄື່ອນໂດຍຊຸມຊົນ

Легенда за Аркадија: Редефинирање на операции водени од заедницата

Legjenda e Arcadia: Ripërcaktimi i operacioneve të drejtuara nga komuniteti

Legend Of Arcadia: Переосмислення діяльності, керованої спільнотою

Improving Text Embeddings with
Large Language Models: Conclusion and References

first subscribe to my twitter, i tweet tech stuff One more speedrun in the series, let's goooo I just used reinforcement learning (RL) meme on you: you open the post (do a right thing)
you get the meme (a reward) What a bait it was.. omg.. So, why is it hard for meme-Kelvin to learn what RL is? Because he wants to learn the implementation of RL instead of understanding the Concept, THAT’S WHY meme-KELVIN! When you learn a new thing, tool or tech or anything — you do NOT start with it's implementations, you start with ideas, concepts and problems the tool solves! Wo Kelvin, here you go: To understand Reinforcement Learning (RL) — think about playing a video game where you earn points for making the right moves. Reinforcement learning is like that - a program learns by making decisions and getting rewards or penalties based on its actions That was just an example of applying START-WITH-IDEAS principle to Reinforcement Learning. But i promised to tell how to learn the whole ML in 52 seconds... THE SPEEDRUN! To learn ML in 52 seconds you learn the ML Concepts, not implementations, then you google (or GPT) implementations in PyTorch or whatever library name you like, anyway it is going to change in the next year and it's fine, ideas will stay the same for much longer — go for ideas! Machine Learning Ideas 1. Supervised Learning In supervised learning, a program is taught using examples with answers (called labeled data). This helps the program learn the connection between the examples and answers, so it can guess the answers for new examples it hasn't seen before. Algorithms and problems to solve: predicting house prices (linear regression), deciding if a customer will buy a product (decision trees) 2. Unsupervised Learning Here, the program looks at examples without answers (unlabeled data) and tries to find patterns or groups within them. This can help with tasks like grouping similar items together or reducing the amount of information needed to describe the data. Algorithms and problems to solve: grouping people with similar music tastes (k-means clustering), compressing images without losing too much information (principal component analysis) 3. Reinforcement Learning In reinforcement learning, the program learns to make decisions by trying things out and getting feedback in the form of rewards or penalties. The goal is to make better decisions over time and solve problems more effectively. Algorithms and problems to solve: teaching a robot to walk (Q-learning), training a program to play chess (policy gradient methods) 4. Feature Engineering This is the process of choosing important information (features) from raw data to help the program learn better. Sometimes, this includes creating new features using expert knowledge and creativity. Example: using the length and width of a leaf to help identify a plant species 5. Model Evaluation Checking how well a machine learning model is doing is important to see if it's working well. Measurements like accuracy, precision, recall, F1-score, and mean squared error are often used to check a model's performance. ⏲️ 24 seconds left, you go good! Let’s also cover Deep Learning! Deep Learning Ideas 1. Neural Networks (NN) This is really abstract. You may imagine an NN as brain-neurons ordered in columns and pinging each other from left to right with different force (via connections) — the force with which a neuron (in each layer) is pinged defines the next ping and so the end result itself. Algorithms and problems to solve: recognizing objects in images (feedforward neural networks), translating languages (radial basis function networks) 2. Backpropagation When you make a mistake, you learn from it and try not to repeat it. Backpropagation is a way for a program to do the same thing. It helps the program understand where it went wrong and get better at finding the right answers. 3. Convolutional Neural Networks (CNNs) CNNs are special neural networks that can understand grid-like data, like pictures. They have layers that help them learn patterns and recognize parts of the picture, such as lines and shapes. Algorithms and problems to solve: detecting faces in photos (LeNet-5), identifying different types of animals in images (AlexNet, VGG) 4. Recurrent Neural Networks (RNNs) RNNs are designed to work with data that comes in a sequence, like a series of numbers or words. They can remember previous inputs and use that information to make better decisions. Algorithms and problems to solve: predicting stock prices (Long Short-Term Memory, LSTM), generating text based on a given style (Gated Recurrent Units, GRU) 5. Transfer Learning This is when a program model that has already learned a lot is fine-tuned to work on a new task with limited data. It helps the program learn faster and perform better because it already knows useful things from its previous learning. Example: using a model trained on many dog breeds (like ResNet) to recognize specific types of cats 6. Regularization Techniques These techniques help the program avoid learning too much from the data, which can cause problems like overfitting STOP 🏁 finished in: 0 min, 52 sec This is basically it, now go and train your agents Kelvin googling implementations for algorithms and problems you want to solve! To start using a tool, you start with a problem the tool solves and learn what Concept(s) are used to solve the problem. Don’t keep implementation in your head like "how to do something with a tool", it's complicated and everything won't fit in your head anyway. Learn ideas, google implementations. After 5~10 times of googling an implementation you will memorize it, then the technology becomes obsolete and you forget it, its ok, it happens all the time Ideas are hard to forget, they fit in your memory for a long time Bye and see you in the next drag race Wait akshually! Think about following the twit bird if you want to see education systems to be practice-first and beneficial <3 Anyway you may follow twitter if you just liked the text and want more or you are addictive to fun on social networks or don’t follow anybody and don’t listen to anyone! make your own way! I actually want you to follow my twits, it was just a sale. Check my "Learn REACT in 43 seconds" first subscribe to my twitter, i tweet tech stuff first subscribe to my twitter , i tweet tech stuff my twitter One more speedrun in the series , let's goooo the series I just used reinforcement learning (RL) meme on you: you open the post (do a right thing) you get the meme (a reward) you open the post (do a right thing) you get the meme (a reward) What a bait it was.. omg.. So, why is it hard for meme-Kelvin to learn what RL is? Because he wants to learn the implementation of RL instead of understanding the Concept , THAT’S WHY meme-KELVIN! learn the implementation instead of understanding the Concept When you learn a new thing, tool or tech or anything — you do NOT start with it's implementations, you start with ideas, concepts and problems the tool solves! When you learn a new thing, tool or tech or anything — you do NOT start with it's implementations, you start with ideas, concepts and problems the tool solves! Wo Kelvin, here you go: To understand Reinforcement Learning (RL) — think about playing a video game where you earn points for making the right moves. Reinforcement learning is like that - a program learns by making decisions and getting rewards or penalties based on its actions That was just an example of applying START-WITH-IDEAS principle to Reinforcement Learning. But i promised to tell how to learn the whole ML in 52 seconds... THE SPEEDRUN! To learn ML in 52 seconds you learn the ML Concepts, not implementations , then you google (or GPT) implementations in PyTorch or whatever library name you like, anyway it is going to change in the next year and it's fine, ideas will stay the same for much longer — go for ideas! learn the ML Concepts, not implementations go for ideas! Machine Learning Ideas 1. Supervised Learning In supervised learning, a program is taught using examples with answers (called labeled data). This helps the program learn the connection between the examples and answers, so it can guess the answers for new examples it hasn't seen before. Algorithms and problems to solve: predicting house prices (linear regression), deciding if a customer will buy a product (decision trees) Algorithms and problems to solve: 2. Unsupervised Learning Here, the program looks at examples without answers (unlabeled data) and tries to find patterns or groups within them. This can help with tasks like grouping similar items together or reducing the amount of information needed to describe the data. Algorithms and problems to solve: grouping people with similar music tastes (k-means clustering), compressing images without losing too much information (principal component analysis) Algorithms and problems to solve: 3. Reinforcement Learning In reinforcement learning, the program learns to make decisions by trying things out and getting feedback in the form of rewards or penalties. The goal is to make better decisions over time and solve problems more effectively. Algorithms and problems to solve: teaching a robot to walk (Q-learning), training a program to play chess (policy gradient methods) Algorithms and problems to solve: 4. Feature Engineering This is the process of choosing important information (features) from raw data to help the program learn better. Sometimes, this includes creating new features using expert knowledge and creativity. Example: using the length and width of a leaf to help identify a plant species Example: 5. Model Evaluation Checking how well a machine learning model is doing is important to see if it's working well. Measurements like accuracy, precision, recall, F1-score, and mean squared error are often used to check a model's performance. ⏲️ 24 seconds left, you go good! Let’s also cover Deep Learning! Deep Learning Ideas 1. Neural Networks (NN) This is really abstract. You may imagine an NN as brain-neurons ordered in columns and pinging each other from left to right with different force (via connections) — the force with which a neuron (in each layer) is pinged defines the next ping and so the end result itself. Algorithms and problems to solve: recognizing objects in images (feedforward neural networks), translating languages (radial basis function networks) Algorithms and problems to solve: 2. Backpropagation When you make a mistake, you learn from it and try not to repeat it. Backpropagation is a way for a program to do the same thing. It helps the program understand where it went wrong and get better at finding the right answers. 3. Convolutional Neural Networks (CNNs) CNNs are special neural networks that can understand grid-like data, like pictures. They have layers that help them learn patterns and recognize parts of the picture, such as lines and shapes. Algorithms and problems to solve: detecting faces in photos (LeNet-5), identifying different types of animals in images (AlexNet, VGG) Algorithms and problems to solve: 4. Recurrent Neural Networks (RNNs) RNNs are designed to work with data that comes in a sequence, like a series of numbers or words. They can remember previous inputs and use that information to make better decisions. Algorithms and problems to solve: predicting stock prices (Long Short-Term Memory, LSTM), generating text based on a given style (Gated Recurrent Units, GRU) Algorithms and problems to solve: 5. Transfer Learning This is when a program model that has already learned a lot is fine-tuned to work on a new task with limited data. It helps the program learn faster and perform better because it already knows useful things from its previous learning. Example: using a model trained on many dog breeds (like ResNet) to recognize specific types of cats Example: 6. Regularization Techniques These techniques help the program avoid learning too much from the data, which can cause problems like overfitting STOP 🏁 finished in: 0 min, 52 sec finished in: This is basically it, now go and train your agents Kelvin googling implementations for algorithms and problems you want to solve! This is basically it, now go and train your agents Kelvin googling implementations for algorithms and problems you want to solve! To start using a tool, you start with a problem the tool solves and learn what Concept(s) are used to solve the problem. Don’t keep implementation in your head like "how to do something with a tool", it's complicated and everything won't fit in your head anyway. Learn ideas, google implementations. Learn ideas, google implementations. After 5~10 times of googling an implementation you will memorize it, then the technology becomes obsolete and you forget it, its ok, it happens all the time Ideas are hard to forget, they fit in your memory for a long time Bye and see you in the next drag race Wait akshually! Wait akshually! Think about following the twit bird if you want to see education systems to be practice-first and beneficial <3 following the twit bird Anyway you may follow twitter if you just liked the text and want more or you are addictive to fun on social networks want more or don’t follow anybody and don’t listen to anyone! make your own way! I actually want you to follow my twits, it was just a sale. I actually want you to follow my twits, it was just a sale. Check my "Learn REACT in 43 seconds" "Learn REACT in 43 seconds"

Speedrun Your Understanding of Machine Learning.. in 52 seconds 🏎️

52 সেকেন্ডে মেশিন লার্নিং সম্পর্কে আপনার বোঝার গতি বাড়ান 🏎️

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在 52 秒内快速理解机器学习

FaceStudio: Put Your Face Everywhere in Seconds: Conclusion and References

HONG KONG, Hong Kong, July 21st, 2024/Chainwire/--SoMon, a decentralized Web3 forum with a vision to create a space for user-owned content and connections, recently launched its app on Base and has already gained more than 300,000 on-chain transactions in 14 days since its launch, making it the most active and fastest-growing social app in the Web3 space. SoMon, short for Social Monster, allows users to monetize and create communities (Tribes) around their favorite topics, passions, creators, and fandoms. It is the first app built on OpenSocial Protocol, a composable infrastructure layer for building social applications, and also the first Web3 social app that is truly gasless, allowing it to lower the barrier of entry and onboard users beyond the crypto community. Having launched its mobile app on 19 June 2024, around a month ago, it has garnered over 30,000 on-chain registrations in just two weeks. Today, the community is growing rapidly, with over 2 million social engagements among 300+ different Tribes (communities) and its 80,000+ users. “Crypto is all about community, but instead of creating communities for users, we want communities to be built by users. SoMon is a user-curated space where people are able to connect with one another while taking charge and reaping the benefits of their own social achievements at the same time,” said Sean Tao, CEO & Co-Founder of SoMon. SoMon users started minting their own Tribes using various tools and fragmented ownership powered by NFT technology after the new feature was released on 17 July 2024. Users create content, and the platform is monetized through a tool called ‘Megaphone’, allowing users to pay for promotional visibility, with the revenue being redistributed to Tribe owners and stakeholders. SoMon is currently in its first season, ending on 31 July 2024. In this season, users compete to gain points through interactions and earn badges by climbing to different leagues. At the end of the season, SoMon will officially launch its community token, $OwO, which will be airdropped to the community at the end of the first season, with the first airdrop releasing 3% of its total supply this 1 August 2024, and 1% of its total supply for the subsequent seasons. During upcoming seasons, SoMon is focused on activating the community economy via Tribe Summer campaign for users to create and engage with their bot-free community. About SoMon Social Monster (SoMon) is an open topic-based Web3 social app forum powered by OpenSocial Protocol. It serves as a space where users can create vibrant and engaging communities (Tribes) centered around their passions and fandoms. SoMon ensures that Tribes, content, and social connections are wholly owned by users. Contact Public Relations Manager Kelvin Yeo Jun Hao Everest Ventures Group kelvin.yeo@evg.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. HONG KONG, Hong Kong, July 21st, 2024/Chainwire/--SoMon, a decentralized Web3 forum with a vision to create a space for user-owned content and connections, recently launched its app on Base and has already gained more than 300,000 on-chain transactions in 14 days since its launch, making it the most active and fastest-growing social app in the Web3 space. SoMon, short for Social Monster, allows users to monetize and create communities (Tribes) around their favorite topics, passions, creators, and fandoms. It is the first app built on OpenSocial Protocol, a composable infrastructure layer for building social applications, and also the first Web3 social app that is truly gasless, allowing it to lower the barrier of entry and onboard users beyond the crypto community. Having launched its mobile app on 19 June 2024, around a month ago, it has garnered over 30,000 on-chain registrations in just two weeks. Today, the community is growing rapidly, with over 2 million social engagements among 300+ different Tribes (communities) and its 80,000+ users. “Crypto is all about community, but instead of creating communities for users, we want communities to be built by users. SoMon is a user-curated space where people are able to connect with one another while taking charge and reaping the benefits of their own social achievements at the same time,” said Sean Tao, CEO & Co-Founder of SoMon. “Crypto is all about community, but instead of creating communities for users, we want communities to be built by users. SoMon is a user-curated space where people are able to connect with one another while taking charge and reaping the benefits of their own social achievements at the same time,” said Sean Tao, CEO & Co-Founder of SoMon. SoMon users started minting their own Tribes using various tools and fragmented ownership powered by NFT technology after the new feature was released on 17 July 2024. Users create content, and the platform is monetized through a tool called ‘Megaphone’, allowing users to pay for promotional visibility, with the revenue being redistributed to Tribe owners and stakeholders. SoMon is currently in its first season, ending on 31 July 2024. In this season, users compete to gain points through interactions and earn badges by climbing to different leagues. At the end of the season, SoMon will officially launch its community token, $OwO, which will be airdropped to the community at the end of the first season, with the first airdrop releasing 3% of its total supply this 1 August 2024, and 1% of its total supply for the subsequent seasons. During upcoming seasons, SoMon is focused on activating the community economy via Tribe Summer campaign for users to create and engage with their bot-free community. About SoMon Social Monster (SoMon) is an open topic-based Web3 social app forum powered by OpenSocial Protocol. It serves as a space where users can create vibrant and engaging communities (Tribes) centered around their passions and fandoms. SoMon ensures that Tribes, content, and social connections are wholly owned by users. Contact Public Relations Manager Kelvin Yeo Jun Hao Everest Ventures Group kelvin.yeo@evg.co 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

SoMon Becomes Fastest-Growing Web3 Social App With 300,000 Transactions In Two Weeks

SoMon দুই সপ্তাহে 300,000 লেনদেনের সাথে দ্রুততম বর্ধনশীল Web3 সামাজিক অ্যাপ হয়ে উঠেছে

SoMon wird mit 300.000 Transaktionen in zwei Wochen zur am schnellsten wachsenden Web3-Social-App

SoMon se convierte en la aplicación social Web3 de más rápido crecimiento con 300.000 transacciones en dos semanas

SoMon devient l'application sociale Web3 à la croissance la plus rapide avec 300 000 transactions en deux semaines

सोमन दो सप्ताह में 300,000 लेनदेन के साथ सबसे तेजी से बढ़ने वाला वेब3 सोशल ऐप बन गया

SoMon は 2 週間で 30 万件の取引を達成し、最も急成長している Web3 ソーシャル アプリになりました

SoMon은 2주 만에 300,000건의 거래를 기록하며 가장 빠르게 성장하는 Web3 소셜 앱이 되었습니다.

SoMon se torna o aplicativo social Web3 de crescimento mais rápido, com 300.000 transações em duas semanas

SoMon становится самым быстрорастущим социальным приложением Web3 с 300 000 транзакций за две недели

SoMon, İki Haftada 300.000 İşlemle En Hızlı Büyüyen Web3 Sosyal Uygulaması Oldu

SoMon trở thành ứng dụng xã hội Web3 phát triển nhanh nhất với 300.000 giao dịch trong hai tuần

SoMon 成为增长最快的 Web3 社交应用，两周内交易量达 30 万笔

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models: References

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