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  • The Prompt Engineering Playbook for Programmers

    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

      The Prompt Engineering Playbook for Programmers
    • The Scary Thing About Automating Deploys - Engineering at Slack

      Most of Slack runs on a monolithic service simply called “The Webapp”. It’s big – hundreds of developers create hundreds of changes every week. Deploying at this scale is a unique challenge. When people talk about continuous deployment, they’re often thinking about deploying to systems as soon as changes are ready. They talk about microservices and 2-pizza teams (~8 people). But what does continuo

      • Changing std::sort at Google’s Scale and Beyond

        TL;DR; We are changing std::sort in LLVM’s libcxx. That’s a long story of what it took us to get there and all possible consequences, bugs you might encounter with examples from open source. We provide some benchmarks, perspective, why we did this in the first place and what it cost us with exciting ideas from Hyrum’s Law to reinforcement learning. All changes went into open source and thus I can

          Changing std::sort at Google’s Scale and Beyond
        • Better Fbx Importer & Exporter

          About Virus WarningThe Bitdefender Enterprise Support Team has verified that it is a false positive, here is the reply: Hello, Thank you for contacting the Bitdefender Enterprise Support Team. We have received an update from our laboratories. The files are clean and detection should be removed in the next couple of updates. Please let us know if there is anything else we can assist you with or if

            Better Fbx Importer & Exporter
          • A Walk with LuaJIT

            The following is a chronicle of implementing a general purpose zero-instrumentation BPF based profiler for LuaJIT. Some assumptions are made about what this entails and it may be helpful to read some of our other work in this area. One major change from prior efforts is that instead of working with the original Parca unwinder we are now working with the OpenTelemetry eBPF profiler. If you missed t

              A Walk with LuaJIT
            • 0.10.0 Release Notes ⚡ The Zig Programming Language

              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 Masonry 0.1, and my vision for Rust UI

                When I see the landscape of native GUI in 2022, I feel like something is missing. I don’t just mean Rust UI. My frustrations with UI frameworks started long before I’d even heard of Rust. The origin story: Qt and fear The Qt framework is a C++ toolkit for writing GUI apps. In 2019, I spent a year working on a Qt project for an energy company, a diagram editor meant to be used by electrical enginee

                • The Alkyne GC · mcyoung

                  Alkyne is a scripting language I built a couple of years ago for generating configuration blobs. Its interpreter is a naive AST walker1 that uses ARC2 for memory management, so it’s pretty slow, and I’ve been gradually writing a new evaluation engine for it. This post isn’t about Alkyne itself, that’s for another day. For now, I’d like to write down some notes for the GC I wrote3 for it, and more

                    The Alkyne GC · mcyoung
                  • Node.js — Node.js v23.0.0 (Current)

                    2024-10-16, Version 23.0.0 (Current), @RafaelGSS We’re excited to announce the release of Node.js 23! Key highlights include: Enabling require(esm) by default for Node.js applications Removing support for Windows 32-bit systems Stabilizing the node --run command Enhancements to the test runner, including glob pattern support for coverage files Node.js 23 will replace Node.js 22 as the ‘Current’ re

                      Node.js — Node.js v23.0.0 (Current)
                    • April 2021 (version 1.56)

                      Join a VS Code Dev Days event near you to learn about AI-assisted development in VS Code. Update 1.56.1: The update addresses these security issues. Update 1.56.2: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the April 2021 release of Visual Studio Code. The VS Code team has been busy this month working

                        April 2021 (version 1.56)
                      • Practical SQL for Data Analysis

                        Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such

                          Practical SQL for Data Analysis
                        • 地面を見下ろす少年の足蹴にされる私

                          Contracts提案(P2900R14)がC++26に向けて採択され、C++26では契約プログラミング機能を言語サポートの下で実践できるようになります。この記事は、その契約プログラミング機能の一部として導入されている違反ハンドラというものについてのお話です。 契約プログラミング機能における違反ハンドラの概要 ユーザー定義違反ハンドラ std::contracts::contract_violation より一般的な利用 外部ツールの共通コールバック機構として 実行時エラーハンドリングのコールバック機能として(P3290R2) プロファイル機能の実行時検査におけるコールバックとして(P3081R0) 未定義動作の実行時ハンドリング機能として(P3100R0, P3229R1, P3205R0) 参考文献 契約プログラミング機能における違反ハンドラの概要 C++26の契約プログラミング機能は

                            地面を見下ろす少年の足蹴にされる私
                          • 大規模言語モデル RWKV-worldで学習で巨大なデータ(学会の予稿集のpdf)をファインチューニング(LoRA)する|Kan Hatakeyama

                            概要学会の予稿集のような、大規模な文章(pdf)データを大規模言語モデルに学習させてみます 1.5 M tokenほどあります モデルは、学習と出力が高速なRWKVにしました 他のタスクでGPUリソースを使っているので、0.1B/ 1.5Bモデルでのお試しです 1.5Bは学習時にVRAM 7GBほど使います 執筆時、日本語最強のオープンLLMと謳われるRWKV-4-World-JPNtunedが本命ですが、7bを動かせるGPUが空いていなかったので、小さいモデルで試しています 前提: pdfデータの処理LLMとは直接関係がありませんが、一般論として、テキスト学習にはデータの前処理が必要です。 今回は、数百MBのpdfデータとして存在する学会の予稿集をきれいなテキストに変換しました。 まずは、巨大すぎるpdfを分割しておきます。 #pdfの分割 import os from PyPDF2 i

                              大規模言語モデル RWKV-worldで学習で巨大なデータ(学会の予稿集のpdf)をファインチューニング(LoRA)する|Kan Hatakeyama
                            • Version 1.0

                              Version 1.0# For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs Major Feature something big that you couldn’t do before. Feature something that you couldn’t do before. Efficiency an existing feature now may not require as much computation or memory. Enhancement a miscellaneous minor improvement. Fix somethin

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