HandyDash. A Simple Tree View Monitoring Tool
Author here, happy to answer questions about the tool. I've shared my motivations for writing the tool on the ReadMe page in GitHub, and I'll elaborate on this with a blog article in the coming week or two. Some background on the tool: HandyDash is a cross-platform HTTP, TCP, and IP monitoring tool, intended for desktop use. It is agent free, requires no installation, and saves all configuration to a single portable file. Simply unzip the package and run the executable, and within a few minutes you can view and edit nodes within the browser based UI. Thanks.
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- IMI made tool that let's you see everything about any website2024 · v1.web-check.xyz · ▲411
Yes, it's open source: https://github.com/lissy93/web-check :) Why I'm building this? There are a lot of tools out there for discovering meta and security data relating to a website, IP or server. But currently, there isn't anything that does everything, all in one place and without a paywall/ user sign up. It's still a WIP, and I'm working on a new version, with some more comprehensive checks, so any feedback would be much appreciated :)


- WBWe built a ClickHouse-based logging service2023 · github.com · ▲192
Hey hn! I'm one of the co-founders of highlight.io, an open source monitoring tool. Today we're sharing a ClickHouse-based logging solution we've been working on. We wanted to showcase how we built it and share how you could try it out to give feedback. Since we started working on highlight.io, we've been hyper-focused on "cohesion", or ensuring that when you install your monitoring stack, all of the resources in that stack (user interactions, requests, traces, logs, etc.) are connected in a consumable way. We've written up more about our philosophy on this here [1]. We started building…

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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, June 2024
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