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There is also a little command line tool to search jobs from command line. You can also use the web interface at <a href="https://jobswithgpt.com" rel="nofollow">https://jobswithgpt.com</a>
Hi HN,<p>I’m working on an experimental 3D FPS game engine in C++20, aiming to deeply understand engine internals from first principles rather than just using existing frameworks.<p>Currently I'm strictly following LearnOpenGL docs.<p>This project focuses on:
Entity-Component-System (ECS) architecture for high performance.
OpenGL 4.1 rendering with a PBR pipeline, material system, HDR, SSAO, and shadow mapping.
Modular systems: input, physics (Jolt), audio (miniaudio), assets, hot reload.
A sample FPS game & debug editor built into the repo.<p>Repo: <a href="https://github.com/jackthepunished/horizon-engine" rel="nofollow">https://github.com/jackthepunished/horizon-engine</a><p>This isn’t intended to be a commercial rival to any commercial game engines.<p>it’s a learning and exploration project: understanding why certain engine decisions are made, and how to build low-level engine systems from scratch.<p>I’m especially looking for feedback on:
Architecture choices (ECS design, render loop, module separation) Your thoughts on modern C++ engine patterns<p>What you’d build vs stub early in a homemade engine<p>Tips from experienced graphics/engine developers
Criticism and suggestions are very welcome — it’s early days and meant to evolve.
Thanks for checking it out!
Main tools on Kliga.com:<p>1) AI Music Detector<p>- Detects with 99.9% accuracy with smart models attuned to latest AI models.
- You can upload an audio file or paste a Spotify track URL and Kliga analyzes whether the music is likely AI-generated or human-made.
- It uses spectral + temporal analysis and gives a probability breakdown instead of a simple yes/no.<p>2) Audio Mastering<p>- 100% private, your songs never hit our servers
- Preview with 12 Studio-grade presets designed by industry experts.
- Download in HD-WAV 24 bit, MP3 320kbps for FREE.
- No audio engineering required.<p>There are other useful tools like video compressor, audio cutter, etc. Do check them out.<p>Why I built this:<p>- AI-generated music is everywhere now, but it’s hard to quickly verify authenticity accurately.<p>- Most artists want to master their songs privately before releasing. They want one-click processing happening in their own device without having to download an expensive software.<p>Direct Links to main tools:<p>- AI Detector: <a href="https://kliga.com/ai-music-detector" rel="nofollow">https://kliga.com/ai-music-detector</a><p>- Audio Mastering: <a href="https://kliga.com/mastering" rel="nofollow">https://kliga.com/mastering</a><p>Happy to answer any questions. Thanks for checking it out!
Hi all, I am designing an AWS-based unstructured document ingestion platform (PDF/DOCX/PPTX/XLSX) for large-scale enterprise repositories, using vision-language models to normalize pages into layout-aware markdown and then building search/RAG indexes or extract structured data.<p>For those who have built something similar recently, what approach did you use to preserve document structure reliably in the normalized markdown (headings, reading order, nested tables, page boundaries), especially when documents are messy or scanned?
Did you do page-level extraction only, or did you use overlapping windows / multi-page context to handle tables and sections spanning pages?<p>On the indexing side, do you store only chunks + embeddings, or do you also persist richer metadata per chunk (page ranges, heading hierarchy, has_table/contains_image flags, extraction confidence/quality notes, source pointers) and if so, what proved most valuable? How does that help in the agent retrieval process?<p>What prompt patterns worked best for layout-heavy pages (multi-column text, complex tables, footnotes, repeated headers/footers), and what failed in practice?<p>How did you evaluate extraction quality at scale beyond spot checks (golden sets, automatic heuristics, diffing across runs/models, table-structure metrics)?<p>Any lessons learned, anti-patterns, or “if I did it again” recommendations would be very helpful.
I wanted to control the browser from the terminal so I made buse:<p>buse browser-1 # open chrome<p>buse browser-1 navigate "<a href="https://example.com" rel="nofollow">https://example.com</a>"<p>buse browser-2 # open a second browser<p>buse browser-2 search "cat"<p>buse browser-1 observe # returns JSON about the page<p>buse browser-1 click 16 # clicks on the learn more link<p>I've been reading about agentic computer use and I tried to use MCPs and Browserbase, but there was just a lot of friction for me. So, I brought it to the CLI instead.<p><a href="https://github.com/rinvii/buse" rel="nofollow">https://github.com/rinvii/buse</a>
Not only is 3.9 reached end-of-life, the last release was 3.9.25<p>% python3
Python 3.9.6 (default, Dec 2 2025, 07:27:58)
[Clang 17.0.0 (clang-1700.6.3.2)] on darwin
I've been with a startup through two funding rounds, and growth has been very healthy.<p>I recently asked leadership how much I've been diluted, just for financial planning purposes. I assume I've been diluted "a normal amount" and am fine with that-- I just need to know the number. Instead I got a non-answer from leadership, which surprised me. So I'm curious:<p>- How common is this practice in mid-stage startups?
- What is the actual rationale for withholding this information? I get why companies may want to keep the cap table confidential, but an employee's dilution factor seems like the kind of thing that doesn't matter for cap table confidentiality, but matters a lot to the employee.<p>Thanks in advance for any color or perspective on this.
Did you just wake up from a 20 year coma? Did you build a bunch of buzzword compliant web services back in the early 2000s and want all your SOAP and WSDL to be relevant again? Now you can put the smooth sheen of AI on your pile of angle brackets by exposing your SOAP-based web service as a Model Context Protocol (MCP) server.