AI Student to Software Engineer Transition

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

A clear path into AI engineering using 10 GitHub repos Step-by-step plan you can follow and show as proof of work Foundations 1. Learn the basics of machine learning and deep learning • ML for Beginners, AI for Beginners Output: 3 small projects with short READMEs that explain the goal, data, and result. Go deeper 2) Build neural nets from scratch • Neural Networks: Zero to Hero Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why. Read papers in code 3) Study real architectures by walking through annotated implementations • DL Paper Implementations Output: pick one model and re-implement a minimal version. Write what you simplified. Ship real software 4) Move from notebooks to apps and services • Made With ML Output: refactor one project with a simple API, tests, and a one-click run script. Work with LLMs 5) Learn the core pieces end to end • Hands-on LLMs Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base. Make RAG better 6) Compare advanced techniques • Advanced RAG Techniques Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table. Learn agents 7) Build simple agents that take steps toward a goal • AI Agents for Beginners Output: an agent that checks a site, writes a summary, and files a ticket. Take agents toward production 8) Add memory, orchestration, and basic security • Agents Towards Production Output: logging, retry logic, and input checks. Note what fails and how you fixed it. Round out your portfolio 9) Adapt working examples • AI Engineering Hub Output: 2 more apps that solve real tasks, each with a clear demo and setup guide. How to pace this • One repo per week is a good rhythm. • Keep a single repo called “ai-engineering-journey” with subfolders per step. • After each step, post a short write-up with a 30-second screen recording. What hiring managers look for • Working code that runs on first try. • Clear README, data source, and limits. • Small tests and a simple eval, even if manual. • Changelog that shows steady progress. Save this and start with step 1 today. Repos and links 1. ML for Beginners — https://lnkd.in/dQ6nAJRC 2. AI for Beginners — https://lnkd.in/dXwJJjMm 3. Neural Networks: Zero to Hero — https://lnkd.in/dagQ3kmA 4. DL Paper Implementations — https://lnkd.in/dyw54m73 5. Made With ML — https://lnkd.in/duHjr2CY 6. Hands-On Large Language Models — https://lnkd.in/dxEGzsgc 7. Advanced RAG Techniques — https://lnkd.in/dd2TKA5P 8. AI Agents for Beginners — https://lnkd.in/deznrHdf 9. Agents Towards Production — https://lnkd.in/dz-WgU-3 10. AI Engineering Hub — https://lnkd.in/d9cNqy7c

How to become an AI Engineer in 2026 (without getting lost) Everyone wants to become an AI Engineer in 2026. But most people are learning the wrong way. They spend months: -watching tutorials -collecting certificates -trying 25 tools -copying notebooks from GitHub And still can’t build even one production-ready AI system. Because AI Engineering in 2026 is not about “knowing AI tools”. It’s about building AI systems like an engineer. Here’s the real roadmap. Step 1: Learn Engineering first (AI comes later) Before LLMs, agents, and fine-tuning… You need fundamentals: Python (clean code, OOP, async basics) Data structures & algorithms (not LeetCode obsession, just core thinking) APIs (FastAPI), JSON, auth, retries Git + testing If you can’t ship an API with tests, you’re not ready for production AI. Step 2: Understand ML like a system, not a subject You don’t need PhD math. But you must understand: supervised vs unsupervised learning overfitting, bias/variance evaluation metrics feature engineering mindset Because the biggest AI failure is not model quality. It’s wrong evaluation. Step 3: Become strong in data + retrieval (this is where jobs are) In 2026, most AI Engineering work will still be: pipelines ingestion chunking embedding strategy retrieval + ranking RAG is not “upload PDFs and chat”. Real RAG requires: metadata strategy hybrid search reranking caching observability If you learn this properly, you become employable fast. Step 4: Learn LLM application architecture (the real differentiator) Stop thinking “prompt”. Start thinking: prompt templates and versioning function calling/tool use structured outputs guardrails cost control (token budgets) failure handling In production, your AI system must survive: bad queries empty context hallucinations rate limits partial tool failures That’s engineering. Step 5: Master Agentic AI (but only after Step 4) Agents are the future. But agents without fundamentals are just demos. Learn: planning vs execution memory patterns multi-agent orchestration human-in-the-loop workflows tool reliability Your goal is not “cool agents”. Your goal is predictable automation. Step 6: Build a portfolio like a real AI engineer Not 20 small projects. Build 3 serious systems: Project 1: Production-grade RAG ingestion embeddings hybrid retrieval + reranking eval harness Project 2: AI Workflow Agent multi-step tool execution retries and guardrails structured output Project 3: AI Observability + Cost Dashboard tracing latency and token metrics evaluation and quality reports This portfolio beats certificates every time. The truth: In 2026, “AI Engineer” doesn’t mean model researcher. It means someone who can: ship AI into real products make it reliable make it measurable make it cost-efficient If you can do that, you won’t just get hired. You’ll become hard to replace.

I’m working as a Software Engineer at Facebook (Meta) with over 20 years of experience. If I were beginning my career again in 2026 and wanted to become an ML Engineer, these are the skills I would master first… [1] Python as the main language - Write Python every day, focus on clean functions, classes and modules. - Automate boring tasks like data cleaning, file handling and API calls. - Pick one backend stack, for example FastAPI or Django, and build simple APIs for your models. [2] Core machine learning fundamentals - Learn supervised and unsupervised learning, loss functions and metrics like accuracy, F1 and AUC. - Implement key algorithms from scratch in Python, such as linear regression, logistic regression, decision trees and k-means. - Take real job descriptions and map each requirement to a concept or method you know. [3] Deep learning and frameworks - Pick PyTorch and learn tensors, modules, optimizers and training loops. - Build small projects in vision and text, for example image classification and sentiment analysis. - Recreate a few public projects end to end, from raw data to a trained model with clear metrics. [4] Software engineering and production thinking - Use git every day, write tests, add logging and handle errors in a clear way. - Design simple services that load a model once and serve fast predictions through an API. - Turn your experiments into repeatable pipelines with configs, scripts and a fixed folder structure. [5] ML lifecycle and MLOps - Track experiments and models with tools like MLflow, or with a clear manual system at the start. - Learn Docker and package a small ML service into a container you can run anywhere. - Schedule training and batch inference jobs with simple tools, then move to managed cloud services as you grow. [6] AI and LLM skills - Learn NLP basics, tokenization, embeddings and how to evaluate text models. - Use LLMs to build small features such as summarization, classification or simple chat flows. - Practice prompt design and learn at least one method like fine tuning or retrieval to adapt models to real tasks. [7] Communication and soft skills - Explain every project with three points: problem, approach, and impact. - Write short docs for your work and treat them as part of your portfolio. - Practice speaking your thinking during mock interviews so your reasoning stays clear while you code. [8] Cloud foundations - Pick AWS and learn core services like S3, EC2, and one database option. - Deploy at least one ML service to the cloud, even if it is simple. - Learn basic cost and reliability trade-offs so your designs stay lean and practical.

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫?  Here is the only roadmap you ever need 𝐏𝐇𝐀𝐒𝐄 𝟎: 𝐓𝐡𝐞 "𝐒𝐡𝐢𝐩 𝐅𝐚𝐬𝐭" 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 ⚡    Learn just enough to be dangerous: → Python basics (loops, functions, APIs-that is it)   → How to call an LLM API (literally 10 lines of code)   → Prompt engineering fundamentals 🚨 Stop here and BUILD something. A chatbot. A summarizer. Anything. Most people skip this. They study for months without shipping. Do not be them. --- 𝐏𝐇𝐀𝐒𝐄 𝟏: 𝐓𝐡𝐞 "𝐌𝐚𝐤𝐞 𝐈𝐭 𝐔𝐬𝐞𝐟𝐮𝐥" 𝐋𝐚𝐲𝐞𝐫 🔧   Now that you've hit the wall of toy projects: → Tool integration (GPT wrappers, function calling, API authentication)   → RAG basics (embeddings, vector stores, retrieval)   → Data pipelines (SQL/NoSQL, scraping, webhooks) Build #1: A RAG system that answers questions about YOUR data. You will immediately see why 80% of "AI products" are just fancy retrieval systems. --- 𝐏𝐇𝐀𝐒𝐄 𝟐: 𝐓𝐡𝐞 "𝐌𝐚𝐤𝐞 𝐈𝐭 𝐒𝐦𝐚𝐫𝐭" 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 🧠    Your AI is working but dumb. Time to level up: → Agent frameworks (BeeAI, LangChain, CrewAI concepts)   → Planning & orchestration (when to call which tool)   → Memory systems (short-term, long-term, episodic) → ML fundamentals (yes, NOW—not at the beginning)   → Transformer architecture (understanding, not implementing)   → Fine-tuning basics (when to do it, when not to) Build #2: An agent that can use 3+ tools autonomously. --- 𝐏𝐇𝐀𝐒𝐄 𝟑: 𝐓𝐡𝐞 "𝐌𝐚𝐤𝐞 𝐈𝐭 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧" 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤 🏗️   Congrats, your agent works on your laptop. Now what? → MLOps essentials (Docker, CI/CD, monitoring)   → Cost optimization (caching, prompt compression, model selection)   → Security (prompt injection defense, PII handling)   → Responsible AI (bias detection, hallucination mitigation) → Cloud deployment (AWS/GCP/Azure—pick one)   → Drift detection (models degrade, catch it early) Build #3: Deploy something users can actually break. Watch it fail. Fix it. Learn 10x faster. --- 𝐏𝐇𝐀𝐒𝐄 𝟒: 𝐓𝐡𝐞 "𝐌𝐚𝐤𝐞 𝐈𝐭 𝐒𝐜𝐚𝐥𝐞" 𝐌𝐚𝐬𝐭𝐞𝐫𝐲 🚀    You are no longer a beginner. Now you are engineering systems: → Multi-agent architectures (when one agent isn't enough)   → Advanced RAG (query rewriting, multi-hop retrieval, re-ranking)   → Custom fine-tuning (LoRA, PEFT, domain adaptation)   → Evaluation frameworks (LLM-as-judge, human-in-the-loop) Build #4: A system that coordinates multiple specialized agents. --- 𝐓𝐡𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐧𝐨𝐭𝐢𝐜𝐞: Build → Hit wall → Learn what you need → Build again   NOT: Study everything → Feel ready → Build Here is the controversial take: You do not need to understand backpropagation to build production AI systems. (You DO need to understand APIs, data pipelines, and prompt engineering.) Learn the math after you know why it matters. 𝐖𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐣𝐨𝐮𝐫𝐧𝐞𝐲? ♻️ Repost this to help your network get started ➕ Follow Jothi Moorthy for more

A lot of people ask me, what’s the roadmap to become an AI engineer? It’s not just about finishing a few ML courses, it’s about mastering the fundamentals, applying them in real projects, and building the skills to ship AI systems into production. Here’s a step-by-step path that works in 2025. 📓Step 0 – Build a Strong Technical Foundation → Learn Python deeply (AI’s primary language) plus working knowledge of C++, Java, or R. → Master data structures, algorithms, and software engineering principles. → Strengthen math for AI: linear algebra, calculus, probability, and statistics. 📌 Tip: Code along with tutorials and build small utilities like dataset parsers, algorithm implementations, or math simulations. 📗Step 1 – Learn ML & AI Fundamentals → Understand core ML concepts: supervised vs. unsupervised learning, regression, classification, clustering. → Explore deep learning: CNNs, RNNs, Transformers, NLP, generative AI. → Work hands-on with PyTorch, TensorFlow, and scikit-learn. 📌 Tip: Build a neural network from scratch using only NumPy to really understand how backpropagation works. 📘Step 2 – Apply Knowledge Through Projects → Start with small, scoped projects: image classification, NLP chatbots, recommendation engines. → Use public datasets from UCI, Kaggle, or Hugging Face Datasets. → Contribute to open-source AI projects or Kaggle competitions. 📌 Starter Ideas: → Sentiment analysis with real-time inference → Retrieval-Augmented Generation (RAG) PDF summarizer → Price prediction model with explainable AI dashboards 📕Step 3 – Learn Data Engineering, MLOps & Deployment → Practice data preprocessing and visualization with pandas, NumPy, matplotlib. → Learn to deploy models on AWS Sagemaker, GCP Vertex AI, or Azure ML. → Master MLOps: CI/CD pipelines, retraining workflows, monitoring, model version control. 📌 Tip: Build an end-to-end pipeline - from raw data ingestion to a deployed model API - using Docker + FastAPI. 📙 Step 4 – Specialize in an AI Domain → Choose a focus: Computer Vision, NLP, Generative AI, Reinforcement Learning, or Robotics. → Read research papers, replicate results, and keep up with top AI conferences (NeurIPS, ICML). 📌 Tip: Maintain a “replication repo” where you implement recent papers in your chosen field. 📔Step 5 – Build a Portfolio & Network → Showcase your projects on GitHub with clear READMEs and deployment instructions. → Write technical blogs, create short videos, or speak at meetups about your projects. → Join AI communities (Slack, Discord, LinkedIn), attend hackathons, and network with peers. If you’re starting today, pick one problem you care about, find a dataset, and commit to shipping a working MVP in 3 weeks. That’s how you start building momentum ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

You don't need to go deep on ML theory to become an AI FDE.  Go wide on understanding the AI fundamentals and shipping AI. Becoming an AI FDE is part technical and part consulting. If you are a developer or solutions architect, you already have the skills that transfer directly like discovery, system design, debugging production issues, cost optimization, stakeholder management and shipping under pressure. These skills are primary differentiators for AI FDE. And you already have them. You just need to map them to the AI domain. To get ramped up on technical part, here is a roadmap! You just need these 4 stages: Stage 1: AI Foundations + First Shipped Demo - Spend 3-4 days on LLM fundamentals (tokenization, APIs, prompt patterns) - Build a working chatbot (Streamlit + OpenAI) in a weekend - Deploy it somewhere (Vercel, Render) and add monitoring - Outcome: Your first “production AI” app (even if it’s simple) Stage 2: RAG Deep Dive + Enterprise Architecture - Learn embeddings, vector databases, and retrieval patterns - Build a RAG system on real documents (PDFs, Notion, Slack exports) - Deploy with FastAPI + authentication + cost tracking - Outcome: You understand why RAG is the workhorse of enterprise AI Stage 3: Agents + Customer Handoff - Learn agent frameworks (LangChain agents, ReAct) - Build an agent that integrates with a real API (Salesforce mock, Stripe, etc.) - Write a complete handoff package (runbooks, troubleshooting, training) - Outcome: You can build and hand off complex AI systems Stage 4: Portfolio + Interview Prep - Package your 3 projects into a case study portfolio - Practice technical interviews (system design + field scenarios) - Tailor your resume: emphasize discovery, production debugging, cross-team communication - Outcome: Interview-ready for AI FDE roles! Getting into AI is a lateral career move using your existing strengths in a new domain. Your discovery skills, production debugging skills, and stakeholder management will get you there in 3-6 months. NOTE: Timelines for each stage might vary depending on your specific situation.  Each Stage can be a week or month or more. But it’s definitely doable with consistent efforts. ——— ♻ Repost to help others find this post ➕ Follow Prasad Rao to excel in your cloud and AI career

How to Land an AI Engineer job in 2026! It's not just "learn LangChain" ↓ ① 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝟭𝟬𝟭 Python is the bare minimum. You’re expected to understand software architecture and design patterns. ② 𝗦𝗪𝗘 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗮𝗿𝗲 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 AI Engineering is ~10% AI, ~90% software. Version control, testing, Docker, CI/CD aren’t optional. ③ 𝗔𝗜 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 (𝗮𝗳𝘁𝗲𝗿 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀) Build agent components (e.g. tool calling) from scratch first. Then use abstractions like LangGraph, Pydantic AI, LlamaIndex ④ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 = 𝗻𝗲𝘄 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Context engineering is the new feature engineering. Memory, retrieval, grounding, and failure handling decide quality. ⑤ 𝗔𝗴𝗲𝗻𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝗹𝗹𝘀 Learn common agent patterns like ReACT, RAG, Self-Reflection ⑥ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 Most AI Engineer roles aren’t entry-level. Build an API. Deploy it to AWS. Handle latency, cost, and reliability. ⑦ 𝗟𝗟𝗠/𝗔𝗴𝗲𝗻𝘁 𝗲𝘃𝗮𝗹𝘀 𝗵𝗮𝗽𝗽𝗲𝗻 𝗮𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗹𝗮𝘆𝗲𝗿𝘀 You don't just apply "AI Judge" and call it a day. You write tests for individual tools and logic. And use Human Loop or AI Judge to eval output quality. ⑧ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝘁𝗲𝘀𝘁 In AI Engineer interviews, it's not just textbook questions like "What is RAG?". It's also AI system design questions are open-ended. Can you frame it? Choose trade-offs? Propose a technical direction? ⤷ Found it insightful? ♻️ Repost 💬 Comment 👉 Follow Daniel Lee for AI/ML tips! --- ✓ Ace data/ML interviews on datainterview.com ✓ Learn AI on joinaischool.com

Google, Meta and NVIDIA are hiring AI Engineers: This is the exact 6-month roadmap I would follow to become an AI Engineer in 2026: Month 1: Programming + Math Foundations Goal: Write clean Python and understand data. Learn: • Python (core syntax, OOP, virtual environments) • NumPy, Pandas • Basic linear algebra, probability, statistics • Git & GitHub Free resources: • Python by freeCodeCamp: https://lnkd.in/gRJncmfr Month 2: Machine Learning Fundamentals Goal: Train and evaluate ML models confidently. Learn: • Supervised vs unsupervised learning • Regression, classification, clustering • Model evaluation, overfitting, bias/variance • Scikit-learn workflows Free resources: Google ML Crash Course - https://lnkd.in/gNnbbRSs Month 3: Deep Learning + Neural Networks Goal: Build end-to-end deep learning models. Learn: • Neural networks, backpropagation • CNNs (vision), RNNs/Transformers (text) • PyTorch or TensorFlow • Training pipelines & tuning Free resources: Learn Deeplearning by Codebasics: https://lnkd.in/gerSDa78 Month 4: Generative AI + LLM Engineering Goal: Build real LLM applications. Learn: • Transformers, embeddings, vector databases • Prompt engineering & evaluation • RAG pipelines • OpenAI / Claude / Gemini APIs Free resources: LearnPrompting - https://lnkd.in/gUZvgwDe Month 5: MLOps + Deployment Goal: Deploy models to production. Learn: • FastAPI / Flask model serving • Docker basics • CI/CD for ML • Monitoring & evaluation Free resources: MLOps course by freeCodeCamp - https://lnkd.in/gBj7EpUY Month 6: Projects + Portfolio Goal: Build 3–4 strong projects: • RAG chatbot with memory • Image classifier web app • Recommendation system • AI SaaS mini-tool Focus on: Problem → Data → Model → Deployment → Impact Most people never start because the path seems unclear. If you're staring at AI Engineer job postings wondering if you're qualified, this roadmap is for you. If you found this useful, then please: ♻️ Repost to help others in your network 💭 Tag someone you know or Comment “CFBR” below I hope this helps, All the best! P.S: Join 2,500+ subscribers as I post FREE job search tips and resources daily: https://lnkd.in/g95XcGMP