I am a Web3 developer and currently the shinny object in the blockchain space is AI, previously it was ZK(zeroKnowledge Proofs) and its application in web3. To keep up with the trend, I attend hackathons to learn and collaborate with others. Next up is the Agentic Ethereum Hackathon (https://ethglobal.com/events/agents/info) and its focus will be on building AI agents as regards blockchain applications. This post is to openly ask everyone what application of AI agents in blockchain can you think of outside trading bots?

The goal of this group is to help you navigate the complex and rapidly evolving world of data science and artificial intelligence. This is your hub to stay up-to-date on the latest trends, learn specialized skills to turn raw data into valuable insights, connect with a community of like-minded individuals, and ultimately, become a Data Alchemist. Together, let's decode the language of data and shape a future where knowledge and community illuminate our way. Rules - Don't sell anything here or use Data Alchemy as any kind of funnel - We delete low effort community posts, and posts with poor English. Proofread your post first. - Help us make the posts high quality. If you see a low quality post, then click on the 3 dots on the post and "Report To Admins". Start by checking out these links - Classroom - Introduction - Roadmap - Contribution Be Aware of Scammers - Please be aware that this is a public group. Unfortunately, some people abuse the Skool platform to send DMs or post comments to trick people. This is the internet, so always do your own due diligence. Never automatically trust someone here on the Skool platform other than @Dave Ebbelaar's official account. To kick things off, please comment below, introducing yourself. Let us know: 1. Your name and where you're from 2. What project(s) you're currently focused on See you in the comments!

Hi all, I'm guessing that most of you here want to start freelancing or their bussines in AI/ML/DS. At least I am, and one problem we all face is the chicken and egg problem. How to get that first client without having a references or previous client's success stories. I've been thinking about this and I want to share my take on how to navigate that first stage. This dilemma is common and other industries have attempted different strategies to overcome it. So I studied them and proposed some ideas that could work in freelancing: 1. Ride-sharing industry: Uver and Lyfr initially faced this problem, drivers wouldn't sign up without passengers, and passengers wouldn't sign up without drivers. They solved this by offering finnacial incentives to both parties. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗼𝗳𝗳𝗲𝗿 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝗲𝗱 𝗼𝗿 𝗲𝘃𝗲𝗻 𝗳𝗿𝗲𝗲 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗼 𝗮 𝗳𝗲𝘄 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗶𝗻 𝗲𝘅𝗰𝗵𝗮𝗻𝗴𝗲 𝗳𝗼𝗿 𝘁𝗲𝘀𝘁𝗶𝗺𝗼𝗻𝗶𝗮𝗹𝘀. 𝗜 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗶𝘀 𝘄𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗶𝗻 𝗺𝘆 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. 2. Social media platforms: Facebook and Twitter had to get users to join without much existing content. They focused on small, tightly-knit communities to generate initial activity and then scaled up. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝘀𝘁𝗮𝗿𝘁 𝗯𝘆 𝘁𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴 𝗮 𝗻𝗶𝗰𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗼𝗿 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝘄𝗵𝗲𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗺𝗮𝗱𝗲. 𝗢𝗻𝗰𝗲 𝘀𝘁𝗿𝗼𝗻𝗴 𝗿𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁, 𝗲𝘅𝗽𝗮𝗻𝗱 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗼 𝗮 𝗯𝗿𝗼𝗮𝗱𝗲𝗿 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲. 3. Online marketplaces: eBay and Etsy initial had to attract both buyers and sellers. They often started with a focus on unique or high-demand items to draw in users. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗶𝗻 𝗮 𝗵𝗶𝗴𝗵-𝗱𝗲𝗺𝗮𝗻𝗱 𝗼𝗿 𝘂𝗻𝗶𝗾𝘂𝗲 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝘁𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗲 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻. 𝗣𝗶𝗰𝗸 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗻𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝗲 𝗶𝗻 𝘁𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗔𝗜/𝗠𝗟. 4. Payment Platforms: Paypal and Venmo faced the challenge of getting users to trust a new way of handling money. They built trust through partnerships. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗼𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂𝗿 𝗻𝗮𝗺𝗲 𝗼𝘂𝘁 𝘁𝗵𝗲𝗿𝗲. 𝗝𝗼𝗶𝗻 𝗳𝗼𝗿𝗰𝗲𝘀 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿𝘀 𝗼𝗻 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗴𝗮𝗶𝗻 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝗲𝘅𝗽𝗼𝘀𝘂𝗿𝗲. 5. Subscription services: Netflix and Spotify offered free trials to get users hooked on their services before requiring payment. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗼𝗳𝗳𝗲𝗿 𝗮 𝗱𝗲𝗺𝗼 𝗼𝗿 𝗣𝗢𝗖 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀/𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝘆𝗼𝘂 𝘂𝗽 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀.

Interesting to see how the share market is taking the news from DeepSeek. The news highlights the possibility of cheaper AI infrastructure altering global competition and slowing U.S. capital expenditure on AI, raising concerns among investors about potential disruption to the AI chip market, leading to a significant sell-off in stocks like Nvidia, Broadcom, and AMD. Found this article on this topic: https://www.cbsnews.com/news/what-is-deepseek-ai-china-stock-nvidia-nvda-asml/

An open source model that compares to Open AI's o1 reasoning model on several benchmarks. The research paper: DeepSeek_R1.pdf. https://github.com/deepseek-ai/DeepSeek-R1 """ We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. """