Developers are increasingly relying on AI coding assistants to accelerate our daily workflows. These tools can autocomplete functions, suggest bug fixes, and even generate entire modules or MVPs. Yet, as many of us have learned, the quality of the AI’s output depends largely on the quality of the prompt you provide. In other words, prompt engineering has become an essential skill. A poorly phrased
GitHub - modelcontextprotocol/servers: Model Context Protocol Servers
Official integrations are maintained by companies building production ready MCP servers for their platforms. 21st.dev Magic - Create crafted UI components inspired by the best 21st.dev design engineers. 2slides - An MCP server that provides tools to convert content into slides/PPT/presentation or generate slides/PPT/presentation with user intention. ActionKit by Paragon - Connect to 130+ SaaS inte
January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
Building a recommendation engine inside Postgres with Python and Pandas | Crunchy Data Blog
Building a recommendation engine inside Postgres with Python and Pandas I'm a big fan of data in general. Data can tell you a lot about what users are doing and can help you gain all sorts of insights. One such aspect is in making recommendations based on past history or others that have made similar choices. In fact, years ago I wrote a small app to see if I could recommend wines based on how oth
Llama 3.1 の新機能と使い方｜npaka
以下の記事が面白かったので、簡単にまとめました。 ・Llama 3.1 - 405B, 70B & 8B with multilinguality and long context 1. Llama 3.1 の新機能「Llama 3.1」の新機能は、次のとおりです。 ・128Kトークンの大きなコンテキスト長 (元は8K) ・多言語 ・ツールの使用 ・4,050億パラメータの非常に大きな高密度モデル ・より寛容なライセンス 8B、70B、405Bの3つのサイズがあり、それぞれにベースモデルと指示モデルがあります。128Kトークンのコンテキスト長と、英語、ドイツ語、フランス語、イタリア語、ポルトガル語、ヒンディー語、スペイン語、タイ語を含む8つの言語をサポートしています。「Llama 3.1」は、より長いコンテキストに役立つ効率的な表現である「Grouped-Query Attention」(
Processing large JSON files in Python without running out of memory
Processing large JSON files in Python without running out of memory by Itamar Turner-Trauring Last updated 06 Jan 2023, originally created 14 Mar 2022 If you need to process a large JSON file in Python, it’s very easy to run out of memory. Even if the raw data fits in memory, the Python representation can increase memory usage even more. And that means either slow processing, as your program swaps
Tier 4 Support § Support for these targets is entirely experimental. If this target is provided by LLVM, LLVM may have the target as an experimental target, which means that you need to use Zig-provided binaries for the target to be available, or build LLVM from source with special configure flags. zig targets will display the target if it is available. This target may be considered deprecated by
Announcing DuckDB 1.3.0
To install the new version, please visit the installation guide. Note that it can take a few hours to days to release some client libraries (e.g., Go, R, Java) and extensions (e.g., the UI) due to the extra changes and review rounds required. We are proud to release DuckDB 1.3.0. This release of DuckDB is named “Ossivalis” after Bucephala Ossivalis, an ancestor of the Goldeneye duck that lived mil
Try Mastering Emacs for free! Are you struggling with the basics? Have you mastered movement and editing yet? When you have read Mastering Emacs you will understand Emacs. It’s that time again: there’s a new major version of Emacs and, with it, a treasure trove of new features and changes. Notable features include the formal inclusion of native compilation, a technique that will greatly speed up y
Python behind the scenes #11: how the Python import system works
If you ask me to name the most misunderstood aspect of Python, I will answer without a second thought: the Python import system. Just remember how many times you used relative imports and got something like ImportError: attempted relative import with no known parent package; or tried to figure out how to structure a project so that all the imports work correctly; or hacked sys.path when you couldn
An AI-native software engineer is one who deeply integrates AI into their daily workflow, treating it as a partner to amplify their abilities. This requires a fundamental mindset shift. Instead of thinking “AI might replace me” an AI-native engineer asks for every task: “Could AI help me do this faster, better, or differently?”. The mindset is optimistic and proactive - you see AI as a multiplier
After studying how companies deploy generative AI applications, I noticed many similarities in their platforms. This post outlines the common components of a generative AI platform, what they do, and how they are implemented. I try my best to keep the architecture general, but certain applications might deviate. This is what the overall architecture looks like. This is a pretty complex system. Thi
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
Django for Startup Founders: A better software architecture for SaaS startups and consumer apps
In an ideal world, startups would be easy. We'd run our idea by some potential customers, build the product, and then immediately ride that sweet exponential growth curve off into early retirement. Of course it doesn't actually work like that. Not even a little. In real life, even startups that go on to become billion-dollar companies typically go through phases like: Having little or no growth fo
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The Prompt Engineering Playbook for Programmers
GPT in 60 Lines of NumPy | Jay Mody
0.10.0 Release Notes ⚡ The Zig Programming Language
What's New in Emacs 28.1?
The AI-Native Software Engineer
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