@diep.nganhanh: Bộ nồi chảo #bonoichao #noichaochongdinh #xuhuong

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Sunday 30 August 2026 05:44:09 GMT

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Hey, I’m Bashi. I worked as a Software Engineer and here’s how I’d got an AI Engineer offer👇 A lot of software engineers think you need a PhD or insane math skills to break into AI. But what actually works are practical and intuitive understanding of what is going on under the hood. 1. Learned ML foundations (this is the edge) Most SWEs try to jump straight into RAG, function calls, agents, and frameworks. That works until something breaks and then they’re stuck guessing. Instead, I focused on: - probability + statistics (how models reason) - linear algebra (how embeddings actually work) - model behavior + evaluation You don’t need to be a mathematician, but if you understand how models work you become valuable beyond a job title. This is what separates short-term AI engineers who will be gone by the next claude code update from long-term valuable engineers. 2. Built AI side projects I didn’t wait for anyone to “allow” me to work on AI. I started small: text classifiers, embeddings-based search, RAG systems, eval pipelines, model routing, fine-tuning. Each project proved: I can use AI to solve real problems, not just call APIs. 3. Shared everything on the internet I documented what I was learning, broke down ai concepts, then posted projects and results. This built my credibility and a portfolio at the same time. 4. Made every application extremely specific I didn’t send generic applications. I wrote: - why this company - why this AI role - what I could contribute (based on my SWE → AI work) This alone drastically increases application to interview conversion. 5. Mapped my SWE experience to AI impact I showed how I already built real Ai experience: - automated workflows with ML - built internal tools with embeddings - reduced manual work using AI - improved systems using data + models I combined: software engineering + ML foundations + real AI projects And if you can do the same, you become way more valuable than most candidates in the market. If you’re a SWE trying to move into AI engineering, this is the path that actually works. #softwareengineer #aiengineer #ai #aiprojects #machinelearning

Hey, I’m Bashi. I worked as a Software Engineer and here’s how I’d got an AI Engineer offer👇 A lot of software engineers think you need a PhD or insane math skills to break into AI. But what actually works are practical and intuitive understanding of what is going on under the hood. 1. Learned ML foundations (this is the edge) Most SWEs try to jump straight into RAG, function calls, agents, and frameworks. That works until something breaks and then they’re stuck guessing. Instead, I focused on: - probability + statistics (how models reason) - linear algebra (how embeddings actually work) - model behavior + evaluation You don’t need to be a mathematician, but if you understand how models work you become valuable beyond a job title. This is what separates short-term AI engineers who will be gone by the next claude code update from long-term valuable engineers. 2. Built AI side projects I didn’t wait for anyone to “allow” me to work on AI. I started small: text classifiers, embeddings-based search, RAG systems, eval pipelines, model routing, fine-tuning. Each project proved: I can use AI to solve real problems, not just call APIs. 3. Shared everything on the internet I documented what I was learning, broke down ai concepts, then posted projects and results. This built my credibility and a portfolio at the same time. 4. Made every application extremely specific I didn’t send generic applications. I wrote: - why this company - why this AI role - what I could contribute (based on my SWE → AI work) This alone drastically increases application to interview conversion. 5. Mapped my SWE experience to AI impact I showed how I already built real Ai experience: - automated workflows with ML - built internal tools with embeddings - reduced manual work using AI - improved systems using data + models I combined: software engineering + ML foundations + real AI projects And if you can do the same, you become way more valuable than most candidates in the market. If you’re a SWE trying to move into AI engineering, this is the path that actually works. #softwareengineer #aiengineer #ai #aiprojects #machinelearning

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