AI moves weekly—your learning should too! Seven months ago, after 5 years of building and sharing educational content for free, we launched our first-ever course: From Beginner to LLM Developer — a full-stack, no-nonsense course to help engineers build real-world LLM
@whats_ai
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Gemini Integrated into Google Search for Faster Responses
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One-shot questions? I’m back on Google. I’m really enjoying how Gemini now lives right inside Search. Open a tab, type a (possibly messy) prompt, hit Enter—done. It still feels faster and more convenient than waiting for a reasoning model to spool out an answer. I still reach
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Best Practices for AI Model Release Strategies
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What do you think is the best approach for releasing models?
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Zero-to-LLM Engineer Bundle: Complete Learning Path
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Since many of you asked, here it is… our "Zero-to-LLM Engineer" bundle! We ( @towards_AI ) recently released our three core offers for any builders out there, together taking you from "zero" (literally, no coding knowledge or anything required) up to an advanced LLM developer
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Are LLMs Plateauing? Scaling Laws Running Out of Gas
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Are LLMs plateauing? Are Scaling Laws Running Out of Gas? Many think so because: Data ceiling – We’ve scraped most of the internet. Synthetic data helps…but models aren’t great at grading their own homework. Compute pain – Transistors are approaching atomic-scale; energy
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ChatGPT Adoption: Why Avoiding AI is Outdated Thinking
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When someone tells me they avoid ChatGPT… Alright, are you also still waiting for your DVD in line at Blockbuster? Look, I’m not saying generative AI will fix everything—most days it barely spells my name (to be fair, most non-French speakers can't either), but it can be
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Deploying DeepSeek-R1 Distill: GCP Infrastructure Trade-offs
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Excited to share insights from our latest guest post on Decoding ML by @iusztinpaul
! We walk through deploying DeepSeek‑R1 Distill on three infrastructure models—each with its own trade‑offs: 1. Google Cloud Platform (GCP) • Self‑managed VM with an NVIDIA L4 GPU
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Building and Improving RAG Pipelines: From PoC to Production
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Over the past 2 years, we’ve (
@towards_AI
) helped teams create, iterate and improve RAG pipelines, slash hallucination rates, build PoCs, and get LLM demos into production. At some point, we got tired of repeating the same advice on calls, Slack threads, and conference stages—so -
Graduate Builds Invoice Intelligence App Using Advanced LLM Development
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This is yet another proof that “we teach by doing.” A graduate from our Advanced LLM Developer course just shipped a fully-working Invoice Intelligence app during the cohort! What he actually built (and you can test yourself) PDF invoice parsing—handles multi-page PDFs
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Realistic Deadlines in Software and AI Development
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As an employee or consultant, please give *realistic deadlines* I know, especially in software development, and worse in AI, it's super hard to estimate time and to provide realistic expectations. But being *too optimistic* does not help. It won't help if you say it should