Show HN: Configurable Open Source Audio Spectrum Analyzer https://ift.tt/0aqOkyC

Show HN: Configurable Open Source Audio Spectrum Analyzer Hi, I’ve developed an open-source app for practicing basic skills in digital signal processing and computer graphics using OpenGL. It’s written mainly in C++ for data processing and visualization, with Python used for data input and configuration. This makes it easier to run experiments or adjust settings without recompiling the code, lowering the entry barrier for users unfamiliar with C++. By default, the app captures audio from a microphone in real-time and displays its spectrum on the screen. It’s highly customizable — you can change the number of bars, colors, and the overall color theme. The app runs on both Raspberry Pi and standard Ubuntu desktops. In my Raspberry Pi setup, I use a HiFiBerry DAC+ DSP to analyze music in real-time. The signal comes via optical input (TOSLINK) from a CD player, but you can also connect a microphone for live audio visualization. I’ve written instructions and a tutorial to help you get started — feel free to check it out and give it a try! Demo video (Ubuntu): https://www.youtube.com/watch?v=Sjx05eXpgq4 Demo video (raspberry pi with hifiberry dac+dsp): https://www.youtube.com/watch?v=QA2DYmdZ_Gw Simplified spec: https://sylwekkominek.github.io/SpectrumAnalyzer/ Hope someone finds it useful or fun to play with! https://ift.tt/tKuAPUH August 25, 2025 at 01:25AM

Show HN: Komposer, AI image editor where the LLM writes the prompts https://ift.tt/NV3ZetW

Show HN: Komposer, AI image editor where the LLM writes the prompts A Flux Kontext + Mistral experiment. Upload an image, and let the AIs do the rest of the work. https://www.komposer.xyz/ August 25, 2025 at 12:36AM

Show HN: I built aibanner.co to stop spending hours on marketing banners https://ift.tt/EKwF26t

Show HN: I built aibanner.co to stop spending hours on marketing banners https://www.aibanner.co August 24, 2025 at 05:57AM

Show HN: Python library for fetching/storing/streaming crypto market data https://ift.tt/4Ja5fgz

Show HN: Python library for fetching/storing/streaming crypto market data https://ift.tt/EplTi6M August 23, 2025 at 09:51PM

Show HN: JavaScript-free (X)HTML Includes https://ift.tt/KLmDegb

Show HN: CopyMagic – The smartest clipboard manager for macOS https://ift.tt/iNXxKWE

Show HN: CopyMagic – The smartest clipboard manager for macOS It’s been one month since I launched CopyMagic, a smarter clipboard manager for macOS that makes sure you never lose anything you copy. Instead of digging through endless items, you can type things like “URL from Slack”, “flight information”, or “crypto rate” and it instantly finds what you meant. It’s all completely offline and privacy-first (we don’t even track analytics). https://copymagic.app August 23, 2025 at 12:58AM

Show HN: Open-source web browser with GPT-OSS https://ift.tt/z3mQXkJ

Show HN: Open-source web browser with GPT-OSS Hi HN – we're the founders of BrowserOS.com (YC S24), and we're building an open-source agentic web browser. We're a fork of Chromium and our goal is to let non-developers create and run useful agents locally on their browser. --- When we launched a month ago, we thought we had the right approach: a "one-shot" agent where you give it a high-level task like "order toothpaste from Amazon," and it would figure out the plan and execute it. But we quickly ran into a problem that we've been struggling with ever since: the user experience was completely hit-or-miss. Sometimes it worked like magic, but other times the agent would get stuck, generate a wrong plan, or just wander off course. It wasn't reliable enough for anyone to trust it. This forced us to go back to the drawing board and question the UX. We spent the last few weeks experimenting with three different ways a user could build an agent: A) Drag-and-drop workflows: Similar to tools like n8n. This approach creates very reliable agents, but we found that the interface felt complex and intimidating for new users. One tester (my wife) said: "This is more work than just doing the task myself." Building a simple workflow took 20+ minutes of configuration. B) The "one-shot" agents: This was our starting point. You give the agent a high-level goal and it does the rest. It feels magical when it works, but it's brittle, and smaller local models really struggle to create good plans on their own. C) Plan-follower agents: A middle ground where a human provides a simple, high-level plan in natural language, and the LLM executes each step. The LLM doesn't have to plan; it just has to follow instructions, like a junior employee. --- After building and trying all three, we've landed on C) as the best trade-off between reliability and ease of use. Here's the demo https://youtu.be/ulTjRMCGJzQ For example, instead of just saying "order toothpaste," the user provides a simple plan: 1. Navigate to Amazon 2. Search for Sensodyne toothpaste 3. Select 1 pack of Sensodyne toothpaste from the results 4. Add the selected toothpaste to the cart 5. Proceed to checkout 6. Verify that there is only one item in the cart. If there is more than one item, alert me 7. Finally place the order With this guidance, our success rate jumped from 30% to ~80%, even with local models. The trade-off: users spend 30 seconds writing a plan instead of just stating a goal. But they get reliability in return. Note that our agent builder gives a good starting plan, and then the user has to just edit/customize it. --- You can try out our agent builder and let us know what you think. We're big proponents of privacy, so we have first-class support for local LLMs. You can try GPT-OSS via Ollama or LMStudio and it works great! I'll be hanging around here most of the day, happy to answer any questions! https://ift.tt/k1t2wSQ August 22, 2025 at 10:57PM