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  • 2025: The year in LLMs

    31st December 2025 This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about AI in 2023 and Things we learned about LLMs in 2024. It’s been a year filled with a lot of different trends. The year of “reasoning” The year of agents The year of coding agents and Claude Code The year of LLMs on th

      2025: The year in LLMs
    • Everything a developer needs to know about Generative AI for SaaS

      Everything a developer needs to know about Generative AI for SaaS Few months ago, I knew almost nothing about AI. I used ChatGPT and Co-Pilot (I'm civilized, after all), but a lot of the content around AI was Greek to me. Terms like models, transformers, training, inference, RAG, attention, and agents were unfamiliar. Last week, I have completed my first end-to-end AI-based product: AI Code Assist

        Everything a developer needs to know about Generative AI for SaaS
      • Annotated history of modern AI and deep neural networks

        For a while, DanNet enjoyed a monopoly. From 2011 to 2012 it won every contest it entered, winning four of them in a row (15 May 2011, 6 Aug 2011, 1 Mar 2012, 10 Sep 2012).[GPUCNN5] In particular, at IJCNN 2011 in Silicon Valley, DanNet blew away the competition and achieved the first superhuman visual pattern recognition[DAN1] in an international contest. DanNet was also the first deep CNN to win

          Annotated history of modern AI and deep neural networks
        • 🪆 Introduction to Matryoshka Embedding Models

          In this blogpost, we will introduce you to the concept of Matryoshka Embeddings and explain why they are useful. We will discuss how these models are theoretically trained and how you can train them using Sentence Transformers. Additionally, we will provide practical guidance on how to use Matryoshka Embedding models and share a comparison between a Matryoshka embedding model and a regular embeddi

            🪆 Introduction to Matryoshka Embedding Models
          • Compiling a subset of JavaScript to ARM assembly in Haskell - Micah Cantor

            A toy compiler for a subset of JavaScript to ARM assembly, using Haskell. Published: May 29, 2022 I recently got a copy of the book Compiling to Assembly from Scratch by Vladamir Keleshev, which details how to write a compiler for a subset of JavaScript to 32-bit ARM assembly code. The choice to use ARM assembly is mainly for its simplicity in comparison to x86. Keleshev elects to use TypeScript t

              Compiling a subset of JavaScript to ARM assembly in Haskell - Micah Cantor
            • Lit Launch Day: Lit 3.0, Labs graduations, a compiler and more!

              Lit Launch Day: Lit 3.0, Labs graduations, a compiler and more! It's launch day for the Lit project, and we have a bunch of exciting releases to share with the Lit and web components communities! After several months of development, the Lit team is happy to announce the final release of Lit 3.0 – our first major version since Lit 2.0 in early 2021, the first graduating class of Lit Labs packages @

                Lit Launch Day: Lit 3.0, Labs graduations, a compiler and more!
              • GitHub - taishi-i/awesome-ChatGPT-repositories: A curated list of resources dedicated to open source GitHub repositories related to ChatGPT and OpenAI API

                awesome-chatgpt-api - Curated list of apps and tools that not only use the new ChatGPT API, but also allow users to configure their own API keys, enabling free and on-demand usage of their own quota. awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better. awesome-chatgpt - Curated list of awesome tools, demos, docs for ChatGPT and GPT-3 awesome-totally-open-chat

                  GitHub - taishi-i/awesome-ChatGPT-repositories: A curated list of resources dedicated to open source GitHub repositories related to ChatGPT and OpenAI API
                • openai/privacy-filter · Hugging Face

                  OpenAI Privacy Filter OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable. OpenAI Privacy Filter is pretrained autoregressively to arrive at a check

                    openai/privacy-filter · Hugging Face
                  • GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI

                    ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu

                      GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
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