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Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.

Last year, Image Line introduced Gopher for FL Studio, an AI chatbot that was basically a glorified instruction manual. You asked it how to do something, and it would serve up the relevant instructions. It's the kind of thing I actually use AI for on a semi-regular basis. But in the new release, Gopher can […]

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One of the things I do when approaching a new codebase is to find the entry points and start exploring down the call paths. This gives a good overview of the different components in the code and how they're connected. I wanted to translate that to a visual experience, similar to how you would use call graphs, but there's a couple of problems with classical call graphs. One, call graphs represent flow at the function level, so the architectural context is lost. And call graphs tend to get very la

OpenSuperWhisper 评测：macOS 上最被低估的开源语音转文字工具？ 30秒结论 ：OpenSuperWhisper 是一个基于 OpenAI Whisper 模型的 macOS 原生听写（dictation）应用。如果你受够了 macOS 自带听写的间歇性抽风，或者不想每月交钱给 Otter.ai，这个免费开源项目值得一试。 但别期待开箱即用 ——你需要自己配置模型、处理依赖，而且目前只支持 macOS。 适合人群：macOS 重度用户、需要离线语音转文字、对隐私敏感、愿意折腾配置的开发者。 不适合：Windows/Linux 用户、不想碰终端的人、需要实时流式转写（目前不支持）。 核心功能：代码实操 1. 安装部署 # 克隆仓库 git clone https://github.com/Starmel/OpenSuperWhisper.git cd OpenSuperWhisper # 安装依赖（需要 Python 3.10+） pip install -r requirements.txt # 直接运行 python app.py 坑点1 ： requirements.txt 里没写版本号，我踩了 numpy 版本冲突的坑。建议手动指定： pip install numpy == 1.26.0 torch == 2.1.0 whisper == 20231117 坑点2 ：macOS 14 Sonoma 上需要手动授权麦克风权限。第一次运行会 crash，因为没处理 PermissionError 。workaround：在 System Settings > Privacy & Security > Microphone 里手动勾上终端或 Python 的权限。 2. 基本使用 启动后会在菜单栏出现一个小图标（类似 macOS 原生听写）。快捷键是 Option + Space （可自定义）。 核心逻辑：按下快捷键 → 录音 → 松开 → 调用 Whisper 转写 → 结果写入当前光标位置。 代码层面 ，核心函数在 whisper_handler.py 里： # 简化版核心逻辑 import whisper import sounddevice as sd import numpy as np class WhisperHandler : def __init__ ( self , model_size = " base " ): self . model = whisper . load_model ( model_size ) self . sample_rate = 16000 def transcribe_from_mic ( self , duration = 5 ): # 录音 recording = sd . rec ( int ( duration * self . sample_rate ), samplerate = self . sample_rate , channels = 1 ) sd . wait () audio = recording . flatten (). astype ( np . float32 ) # 转写 result = self . model . transcribe ( audio , language = " zh " ) return result [ " text " ] 实测 ：默认 model_size="base" 时，中文准确率约 85%。换成 "large-v3" 能到 92%，但首次加载要 2GB 内存，转写一条 10 秒语音需要 8-12 秒（M1 Pro 芯片）。 3. 自定义快捷键 config.yaml 里可以改： hotkey : modifier : " option" key : " space" model : size : " base" # 可选: tiny, base, small, medium, large-v3 device : " cpu" # 或 "mps" (Apple Silicon) output : paste_delay : 0.3 # 转写后粘贴延迟，防止焦点丢失 注意 ： device: "mps" 在 macOS 14.2 上会报 MPS backend not available 。需要安装 PyTorch 的 MPS 版本： pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu 性能测试 测试环境：MacBook Pro M1 Pro (

In version 1.11, HashiCorp introduced Terraform Ephemeral resources and write-only attributes to allow for root configs that do not store secrets in the Terraform statefile. But many users ask about how they can adopt ephemerals. This blog attempts to lay out the ways secrets can be stored in state and how you should update your configurations to remove those secrets. Note: For a primer on ephemerals ( see this blog post ). Scenarios to consider: Data sources that fetch a static secret Resources that receive a secret Resources that generate a dynamic a secret Resources that fetch generated secrets to store in another 3rd party system Scenario 1: Data sources with static secrets Ephemeral resources can often be a drop-in replacement for data sources pulling static values: data "vault_kv_secret_v2" "static_kv" { mount = "kvv2" name = "my_password" } ephemeral "vault_kv_secret_v2" "static_kv" { mount = "kvv2" name = "my_password" } However, using these values has 1 specific difference. The attributes on a ephemeral resource are considered ephemeral and can only be used as ephemeral arguments. That means 2 places: Provider blocks Provider blocks are considered ephemeral, so ephemeral resources may populate arguments: provider "example" { password = tostring ( ephemeral . vault_kv_secret_v2 . static_kv . data . password ) } Write-only arguments Write-only arguments are special arguments that require the ephemeral taint for values: resource "aws_db_instance" "example" { ... password_wo = tostring ( ephemeral . vault_kv_secret_v2 . static_kv . data . password ) } If the resource you wish to pass a value to does not have an available ephemeral, open an issue with that provider. You can reference: this blog post this agent skill Scenario 2: Resources that receive a static secret Without duplicating to the section above, write-only arguments are a way to get secrets out of state. Above has guidance if the secret value comes from a data source, but what if its from a variable?

AI Governance for Engineering Teams: Guardrails, Budgets, and Audit Logs That Actually Scale Hadil Ben Abdallah Hadil Ben Abdallah Hadil Ben Abdallah Follow Jul 7 AI Governance for Engineering Teams: Guardrails, Budgets, and Audit Logs That Actually Scale # ai # llm # api # backend 23 reactions 5 comments 10 min read

It only took ten days to develop the language that powers the web. This article tells the story of JavaScript and the tools that helped shape it. 1995. The birth of a legend The idea for JavaScript was born at Netscape. At the time, web pages consisted almost entirely of HTML, and Netscape wanted to make them more interactive. The first step in that direction was licensing Java for use in the Netscape browser. However, Java's complexity proved challenging for web designers. Brendan Eich was then tasked with creating a programming language that wasn't too complex and could be embedded directly into HTML pages. Eventually, Marc Andreessen, co-founder of Netscape Communications, and Bill Joy, co-founder of Sun Microsystems, also contributed to the language development. To meet the deadline for the Netscape browser release, the companies agreed to collaborate on the language. During its development, the language changed its name several times. For example, the first version Eich created in just ten days was called Mocha. It was then renamed to LiveScript. The final name was chosen because the word Java was already popular and well-known. JavaScript was first announced shortly before the second beta release of Netscape Navigator. Meanwhile, Netscape announced that 28 leading IT companies planned to incorporate JavaScript into their future products. JavaScript 1.0 was released in 1996 alongside Netscape Navigator 2. 1997-1999. ECMAScript In 1996, Microsoft also released JScript as part of Internet Explorer 3, which was an open-source implementation of JavaScript for Windows. By the way, the name was changed to avoid negotiating trademark rights for Java with Sun Microsystems. To eliminate browser incompatibilities caused by different implementations, Netscape handed the JavaScript specification over to the ECMA international organization. So, the ECMA-262 specification was created. The language got the name ECMAScript because JavaScript was already trademarked. Around the

When people picture "coding," they picture fast typing and features coming to life. Nobody pictures the real majority of the job: staring at a stack trace or lets say a particular project trying to figure out why something that should work, isn't. Here's what nobody tells you starting out — getting good at debugging has almost nothing to do with how well you write code, and everything to do with how well you read. The real difference between beginners and experienced devs isn't complex knowledge — it's that experienced devs read carefully and form a hypothesis before touching anything. Beginners (me included) tend to skip straight to changing code and hoping. It feels faster. It rarely is. One thing i'd like to advise other fellow beginner devs is ....Slow down, read the error properly, and follow the stack trace to where it actually starts — not where it ends up. What's a bug that taught you this the hard way?