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The best 20 accounts to follow in AI: @karpathy = LLMs king @steipete = built openclaw @gregisenberg = startup ideas king @rileybrown = vibecode king @corbin_braun = cursor king @jackfriks = solo apps king @levelsio = solo startups king @marclou = solo startups king @EXM7777 = AI ops + systems king @eptwts = AI money twitter king @godofprompt = prompt king @ai_explorer25 = AI queen @AmirMushich= AI ads king @0xROAS = AI UGCs king @egeberkina = AI images king @MengTo= AI landing pages king @rryssf = automations king @kloss_xyz = systems architecture king @emollick = AI science king @Hesamation = AI/ML king follow them all and learn.
How to become AI engineer in next 6 months: By the end, you want to be able to: - build LLM apps end-to-end - use APIs from OpenAI / Anthropic / open-source stacks - design prompts and context properly - add tool calling and structured outputs - deploy real projects So, let’s discuss your roadmap month by month Month 1: Get solid enough in coding and fundamentals What to learn: - Python really well - Git + GitHub - CLI / terminal basics - JSON, APIs, HTTP, async basics - basic SQL - basic data handling with pandas - virtual environments, package management, error handling - FastAPI or Flask Month 2: Master LLM app development What to learn: - prompting fundamentals - system vs user instructions - structured outputs / JSON schemas - function/tool calling - streaming responses - conversation state - cost / latency / token basics - failure handling - prompt injection awareness Month 3: Learn RAG properly What to learn: - embeddings - chunking - vector databases - metadata filtering - reranking - retrieval quality issues - hallucination reduction - citations and grounding Month 4: Agents, tools, workflows, evals - agent loops - tool selection - state management - retries - when NOT to use agents - multi-step workflows - evaluation harnesses - task success metrics Month 5: Deployment, product thinking, and reliability What to learn: - FastAPI production patterns - Docker - background jobs - queues - auth + API key security - logging - observability - prompt/version management - eval dashboards - cost monitoring - rate limits - caching Month 6: Specialize and become hireable these knowledge and skills you gained can be applied in three directions you need to choose one of them and focus on practice although everything mentioned above is also best learned purely through practice Direction 1: AI product engineer Best if you want startup jobs fast Focus on: - LLM apps - RAG - agents - deployment - product UX Direction 2: Applied ML / LLM engineer Focus on: - fine-tuning - when to fine-tune vs prompt - evaluation - inference optimization - open-source models - training pipelines Direction 3: AI automation engineer Focus on: - workflow orchestration - business process automation - multi-tool systems - CRM, docs, email, support, ops use cases This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work By month six, you will already have several built products or examples of completed tasks And it will be much easier to get a job as an AI engineer Save it so you don't lose it and can return to study later
Anthropic just released a 4-hour course to getting a $500k AI engineering job: 00:15 - The right way to prompt Claude 33:21 - What makes Claude act dumber on your code 01:33:39 - How Anthropic use Claude every day 02:50:56 - The fix that makes Claude way smarter This 4-hour Anthropic free course replaces about 10 paid engineering courses. Watch it today, then read the step-by-step guide on building loops below.
He leads engineering on Gemini at Google. instead of keeping his Claude setup private, he open-sourced it. Addy Osmani. That Google: Chrome DevTools lead, "Learning JavaScript Design Patterns" author. 'agent-skills' - his personal loadout. Drop-in for any project. 68,925 stars. MIT. → github.com/addyosmani/age… bookmark it. This is how your Claude setup goes pro.
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