Whether you're aiming for that dream job, wanting to boost your skills, acing academics, or even achieving fitness goals, Break Into Data is a community that will support and empower you to reach new heights!
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Break Into Data reposted this
Don't start building AI Agents until you've mastered these 3 model components: 👇 - retrieval, to give access to data - tools, to take real-world actions - memory, to learn from past interactions All of these are called model context components. See an example for each in the document below. If you want to learn more, star my repo ⭐️: https://lnkd.in/gxiY-H4m I will be uploading more examples of agentic systems this week! #AIEngineer
If you only had to choose 5 Github repos to learn AI, choose these:
If I only had to choose 5 Github repos to learn AI Engineering, I would choose these: 👇 1. AI Engineer Toolkit by Break Into Data. Build your AI projects with the most popular frameworks and tools in the industry. (400 stars) https://lnkd.in/gxiY-H4m 2. Roadmap to GenAI by Krish Naik. Learn fundamental concepts of deep learning and Python. (3k stars) https://lnkd.in/g9V8bxqC 3. Awesome GenAI Guide by Aishwarya Naresh Reganti. Find research papers and online courses to upskill in GenAI. (11k stars) https://lnkd.in/g4YiJWTG 4. AI Notes by Latent Space Podcast and Swyx. Find the latest tech articles from notable scientists and engineers. (5k stars) https://lnkd.in/g3xf-ZCT 5. AI Agents by Nir Diamant. A comprehensive guide for the development and implementation of agents. (8k stars) - https://lnkd.in/gdqRF82x If you find these helpful... 👍 React ♻️ Share 💬 Comment So more people can learn. #AIEngineer
Break Into Data reposted this
I just built an AI Assistant to help write the Best Cover Letters in 3 seconds! 🔥📝 Simply, upload your resume in PDF and a link to the job listing. I used: - Firecrawl (for web scraping the job url) - PyPDF2 (for resume parsing) - GPT-4o (for AI model) It will write the best cover letter highlighting your skills to match the job description. No need to waste hours, when AI can do it better and faster :) You can find the code here: https://lnkd.in/g4QhvuTC ... 🚀 If you want to build AI applications like these, but in a production environment and with Typescript, join our AI engineering Bootcamp that starts on March 10th. Sign Up here: https://lnkd.in/gz3GWT6q
Break Into Data reposted this
Breaking into AI engineering should be 100% project-based. Here is a step-by-step guide: 1. Figure out what project you want to build that fits your industry. 2. Decide whether or not you want to code in TypeScript (Next.js + AI SDK) or Python. 3. Create a dataset to retrieve from and pass it to LLM 4. Then make it more fun with custom workflows (i.e., integrate some 3rd party APIs for tool calling) 5. Then upgrade it to an Agent using LangGraph 6. Don’t forget to create custom evals and guardrails for your app! 7. Deploy it as an API if you are building for developers, or as a web app if you are building for users. 8. Track your logs on Langsmith or Pydantic Logfire. And iterate. That's it. If you want to get real-time feedback and build with the community of AI engineers join our next cohort in March! Link in the comments. #AIengineering
Break Into Data reposted this
I know everything about AI engineering. False. But I do know the fundamentals. And that's enough to figure out the rest. There's a limiting belief that keeps many stuck: "You need to know everything" When in reality, you just have to: - know foundation models. - know AI system design. - and have empathy for users... Tomorrow, I will be covering these fundamental building blocks. In 1 hour, you'll build an AI agent with no experience required. If you are ready to shift your mindset about AI engineering and take a bold step forward, register here: 👇 https://lnkd.in/gKt5eBta Seats are limited! Only the first 1,000 people will be able to join on Zoom. If you're late, you won’t get in. See you tomorrow!
Break Into Data reposted this
Everyone thinks AI agents are stuck in POC... It's far from the truth. Here are Top 5 AI agents deployed at scale 👇 1. Uber. (https://lnkd.in/gyj4xayf) To tackle large-scale code migration Uber is using a swarm of AI agents to automate testing. 2. Replit (https://lnkd.in/gseC7rqx) Their multi-agent system helps devs generate code in a flash with a human-in-the-loop feedback. 3. Elastic (https://lnkd.in/gsyCAKCD) Their orchestrated AI agents are reducing labor-intensive SecOPs at scale by detecting high-threat scenarios. 4. Linkedin (https://lnkd.in/gtQrxWAj) LinkedIn's AI agent recruiters are using a hierarchical agent system to automate hiring with conversational search and candidate matching. 5. Johnson & Johnson(https://lnkd.in/gD_vKbiN) AI agents are helping scientists figure out the best way to conduct experiments when considering hundreds of variables that go into testing. ... Interested in AI Agents but not sure where to start? 💡 We are hosting a 5-week AI engineering bootcamp this March, where you will learn to build scalable AI systems with a group and guided support from experts. Apply to join here! https://lnkd.in/gf_s68ia ... Have you already built stuff with AI agents? If so, what tools are you using? #AIagents
Join us for a hands-on webinar on building AI agents!
If you want to learn how to build AI agents, don't start with the hottest frameworks like Langchain, CrewAI or Llamaindex. Sure, you’ve probably heard everyone talking about these latest AI frameworks like they're the secret sauce to success… but here’s the truth nobody tells you. Tools will be only as good as your understanding of the core components. Tech will come and go, but design patterns underneath these tools will stay for much longer! So, focus on these 2 first: - essential components At their heart, AI agents are augmented LLMs that combine retrieval, tool use and memory. Instead of relying on libraries to abstract these away, start by working directly with LLM APIs to implement these feature's yourself. - workflows vs agents: Many people confuse workflows with agents. Workflows follow a fixed set of steps, that are often used alongside agents to support predictability and consistency. Some examples of workflows include: routing, parallelization, and orchestration. Tools and frameworks will continue to evolve, but if if you focus on first principles thinking, you will be more confident navigating it all. ... Join us this Valentine’s weekend and build AI agents in 1 hour using these components! And possibly reignite your love for AI! 💡💻 💙 Together with Hai N., we’ll guide you through a gentle introduction to building state-of-the-art (SOTA) AI agents. 📅 Date & Time: February 16th, 4 pm PT. 🔗 Seats are limited because its on Zoom! Hurry and sign up! https://lnkd.in/gKt5eBta
Break Into Data reposted this
You don't need to solve 100 Leetcode hard questions to get a job in Machine Learning. Based on hundreds of coding interviews (thanks to Karun Thankachan's mentoring experience), here's what actually matters 👇: - companies focus on LeetCode medium-level questions - most common data structures asked are Arrays and Strings - and yes, Dynamic Programming comes up a lot Here are 5 tips to nail your DSA rounds: 1. Quality > Quantity: the total problem count doesn't matter! 2. Be structured: follow a planned approach instead of solving random problems 3. Learn patterns: focus on time & space complexity 4. Learn from others: focus on highly upvoted problems 5. Stay consistent: 1-2 hours daily is better than 7 hours on weekends If you need more structured guidance, join our free DSA workshop with Karun Thankachan and Shivam Shrivastava, where we'll tackle the most commonly asked interview questions for Applied Science and Machine Learning roles. Link: https://lnkd.in/g_DDkAfp ❗️Important: Zoom capacity is limited to 300 participants. Spots will be filled on a first-come, first-served basis! #interview #MachineLearning
AI Engineering Roadmap for 2025.
Most people fail to break into AI because they don’t know where to start. This roadmap will change that. 🚀 I couldn't help but notice how overwhelming it can get for someone to break into AI engineering on their own. That's why I simplified this learning process into 5 main steps: 1. Fundamental AI concepts 2. Prompt Engineering 3. Evaluations (Evals) 4. Retrieval-Augmented Generation (RAG) 5. AI Agents Each node in this map will be continuously populated with: - free and paid learning resources - industry-validated frameworks - insights from AI practitioners Perfect For: Visual learners seeking structured guidance Aspiring AI engineers ready to break into the field Professionals looking to stay current with AI trends Access it here 👇 https://lnkd.in/gaiWg_F7 You will be prompted to share your email for full access.
Break Into Data reposted this
😎 . . . . . Original seen on Memes for Programmers on Reddit