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Join as a member to access AI Projects & Member only videos. https://www.youtube.com/channel/UCAHa1-TnXAJrkDVMHbcP1Sw/join Do you really need a GPU to build and deploy AI applications? Many beginners think that every AI application needs a GPU. But that is not true.
Short illustration of how Claude's text watermarking is supposed to work based on Anthropic's released materials.
Becoming Human AI Is Expanding — Here’s What’s Changing For years, this has been where you’ve found us — through Medium, whenever we published something worth your time. That’s changing a …
Short note on the Amazon availability of Build a Reasoning Model From Scratch and a warning about counterfeit black-and-white copies sold through Amazon India.
What is RAFT? RAG + Fine-Tuning In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance …
Overview GPT-5 mini launched on August 7, 2025, alongside full GPT-5 and a third, smaller GPT-5 nano, giving developers three sizes of the same generation to trade off against cost and latency.
Short architecture note on Meta Muse Glimmer 30B, including gated local and global GQA, KV-cache efficiency, and release-time benchmark comparisons.
Every AI agent tool call is just text, and the program reading it is unforgiving — one bracket out of place and the whole response fails to parse. Prompting your way around that gets you "most of the time," which isn't the same thing as "every time." This deep dive explains constrained decoding:…
Short note celebrating the LLMs-from-scratch repository passing 100,000 GitHub stars and summarizing its learning materials.
Follow along in Colab: Building applications with Large Language Models doesn’t have to be complicated. A lot has been going on recently to simplify the development, so you can utilize already pre-trained models and support even complex pipelines with a few lines of code.
Short architecture note on Kimi K3, including LatentMoE, Kimi Delta Attention, Attention Residuals, NoPE, multimodality, and inference-efficiency choices.
Short note on the architectures of six new open-weight models, including Nanbeige 4.2, Laguna S 2.1, Motif-3-Beta, Solar Open 2, Antares 1B, and BTL-3.
Short correction note for the random seed in Listing 6.5 on page 198 of Build a Reasoning Model From Scratch.
Architecture and benchmark notes on Thinking Machines Lab's 975B Inkling MoE, including short convolutions, relative-position bias, training, and effort control.
Short note celebrating Ahead of AI reaching 200,000 subscribers.
Ollama runs language models on your own hardware with almost one command. The catch is picking one your machine can hold. How to read a model listing, do the RAM math, and run it.
Prompt injection turns untrusted data into instructions a model obeys. The categories of prompt injection examples, why they work, and how to defend without pretending it is solved.
The best local LLM is the smallest one that does your task and fits your memory. A buyer's guide organized by hardware tier and task, with honest evaluation advice.
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