GitHub热门项目 | Fully autonomous AI Agents system capable of performing complex penetration testing tasks | Stars: 19,091 | 454 stars today | 语言: Go
GitHub热门项目 | An evolving how-to guide for securing a Linux server. | Stars: 28,853 | 399 stars today | 语言:
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 […]
Benchmark-backed Ollama has amassed 176,000 stars, and nearly 17,000 forks on Github by helping developers easily run AI on their PCs.
In an interesting twist that takes advantage of the company's core product, users can chat with these shows' characters, ask them questions, and even roleplay different storylines.
Alienware's latest 34-inch ultrawide gaming monitor is far brighter than before, and it can finally handle text without fringing.
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?
TL;DR Short version: no. I dropped a much older GPU ( Quadro P2000, 5GB, Pascal, 2016 ) next to an RTX 3090 (24GB, Ampere) on the same box, ran the same context-length ladder (8K→128K) through Ollama and vLLM on qwen3-coder:30B-A3B , and got zero extra usable context in either engine — and a 74% decode-speed hit for the trouble. Ollama hits the identical Chunk too big wall at ctx=65536 whether the P2000 is there or not. vLLM refuses tensor-parallel across the two cards entirely — not a VRAM problem, a flat compute-capability rejection ( Minimum capability: 75. Current capability: 61. ) that fails in 40 seconds, before any memory profiling. And the one real, measured effect of adding the P2000 to Ollama: decode speed goes from 76 → 19.5 tok/s at ctx=49152 once the P2000 gets pulled in as an actual compute device. Full narrative version — the two-stage collapse, the prompt-cache validation bug caught mid-sweep, the CUDA13-silently-drops-Pascal finding — is on Medium .## The setup ardi (dual Xeon E5-2680 v4, 128GB RAM, openSUSE Leap) has a Quadro P2000 sitting in a second slot next to the RTX 3090 this whole series has run on so far. Same model as phase 1 ( qwen3-coder:30B-A3B ), same box, four legs: {Ollama, vLLM} × {3090 only, 3090+P2000 tandem}, priced through HomeLab Monitor against real GPU power draw. Ollama: same wall, extra tax ctx 3090 only decode tok/s tandem decode tok/s P2000 VRAM (tandem) 8,192 124.3 122.0 6 MB / 0% 24,576 108.2 70.0 62 MB / 0% 32,768 99.4 61.0 62 MB / 0% 49,152 75.7 19.5 3,580 MB / 55% 65,536 fatal: Chunk too big fatal: identical Chunk too big — Two separate costs, not one: decode already falls behind at ctx=24576 while the P2000 is still basically idle (62MB, 0% util) — some scheduling overhead just from having a second visible device. Then the real collapse hits at ctx=49152, when the P2000 actually gets pulled into the compute path (3.58GB, 55% util) and decode craters to 19.5 tok/s . Same context ceiling either way, worse speed the wh
Autonomous agents are starting to trade real money on-chain. Some run their creator's capital, some run other people's, some are wired into vaults and DAO treasuries. The moment money is delegated to a program, two questions matter more than performance: what was it allowed to do, and did it stay inside those limits? Supported chains: Base · Ethereum · Arbitrum · Optimism · Polygon · Hyperliquid · Solana (beta). The chain answers the first question badly and the second not at all. Every trade an on-chain agent makes is public and tamper-evident — you can see exactly what it did. But nowhere on-chain is it recorded what it was authorised to do . The mandate — the rules the agent was supposed to operate under — lives off-chain, unverifiable, usually as a screenshot or a claim. Why this is not a niche problem Copy trading is the same gap at retail scale, and the data is unforgiving. In a 90-day study of 100,236 copy-trading outcomes, 97% of lead traders were profitable on their own PnL — but only 43.6% produced positive PnL for the people copying them. Fewer than half of copiers (48.5%) finished in profit at all. Leaderboards, as that study puts it plainly, show the survivors, not the full picture. The honest response the industry already reaches for is third-party verification: in forex, platforms like Myfxbook exist precisely because a self-reported track record is worth nothing — the data has to come from somewhere the trader can't fake. Crypto has no equivalent that is both agent-native and tamper-evident. That is the hole. Who actually needs this Three groups, concretely: Anyone allocating capital to an agent — a vault depositor, a copy-follower, an allocator sizing a position. They want to see, before they commit, whether an agent keeps to its stated mandate, instead of trusting a screenshot. Anyone running an agent who needs to raise capital or followers — an honest operator has no way today to prove their agent did what it said. A verifiable record is how they
Showcasing Your GitHub Profile: A Guide to Effective Presentation In the world of software development, GitHub profiles serve as a modern-day portfolio, showcasing a developer's skills, projects, and contributions. Whether you are a seasoned developer or just starting out, presenting your GitHub profile effectively can make a significant difference in your professional journey. In this article, we will explore the essential elements of a compelling GitHub profile and provide tips to make your profile stand out in the crowded digital landscape. Understanding the Importance of Your GitHub Profile GitHub is more than just a repository hosting service; it is a platform where developers can collaborate, share their work, and build a professional network. Your GitHub profile is often the first impression a potential employer or collaborator will have of your technical capabilities. A well-crafted profile not only highlights your technical prowess but also your ability to communicate and work within a team. Key Elements of a Compelling GitHub Profile 1. Profile Picture and Bio First impressions matter, even in the digital world. Your profile picture should be professional and clear, giving a face to the name behind the code. Accompanying your picture should be a concise bio that succinctly describes who you are, your interests, and your areas of expertise. This personal touch can make your profile more relatable and memorable. 2. Featured Projects Highlighting a few key projects on your GitHub profile can effectively demonstrate your skills and interests. Choose projects that not only showcase your technical abilities but also reflect your passion and creativity. Provide a clear description of each project, the technologies used, and your specific contributions. This level of detail can help potential employers understand the depth of your knowledge and experience. 3. Consistent Activity An active GitHub profile signals to others that you are engaged in the development com
The issue was not the tools. It was opening five of them before deciding what the log file was for. The log file was already on the screen. A remote Windows workstation had failed a desktop build, and the relevant file was sitting in a local app directory, something like: C:\Users\<user>\AppData\Local\<app>\logs\build.log The remote session was working. The error was visible. The next step seemed small: get the log back to the local laptop, open it in a familiar editor, compare it with the issue notes, and pull out the part that mattered. That should have been a 30-second task. Instead, it turned into five context switches. The first context was the remote session The remote desktop session made sense. The build failed on that machine, the app was installed there, and the log path was easier to find visually than by guessing from memory. So far, nothing was wrong. The file was selected. The timestamp matched the failed run. The log looked useful. It probably had the stack trace, the missing dependency path, or the configuration mismatch that explained the build failure. Then came the small but surprisingly annoying question: How should this file leave the remote machine? That is where the workflow started to wobble. The second context was chat The first instinct in many teams is chat. Drop the file into a message to yourself, a teammate, or the debugging thread. It is fast, already open, and keeps the file near the conversation. For some files, that is the right move. A screenshot, a short error snippet, or a quick “does this look familiar?” artifact belongs naturally in the discussion. But a full log file is not always a chat artifact. If it goes into chat, will anyone know later whether it was the first failing run or the second? Will it be obvious which remote machine produced it? Will the file still be easy to find after the thread moves on? Chat was not wrong. It was just not clearly the right home for this specific file. So the workflow moved on. The third con