Show HN: Edge Drop- #1 productivity and unique clipboard 200 stars on GitHub https://ift.tt/OJE2DGc

Show HN: Edge Drop- #1 productivity and unique clipboard 200 stars on GitHub https://ift.tt/a8cdhNM July 30, 2026 at 03:03AM

Show HN: Alarium – a live 3D flight tracker built on community ADS-B https://ift.tt/Dod8ANs

Show HN: Alarium – a live 3D flight tracker built on community ADS-B https://ift.tt/0VQzyU3 July 30, 2026 at 12:09AM

Show HN: RunNburn – Run a 295B Moe from a 98GB GGUF on a 64GB RAM Desktop https://ift.tt/QSo6V8r

Show HN: RunNburn – Run a 295B Moe from a 98GB GGUF on a 64GB RAM Desktop runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop with 64 GB of RAM and one consumer NVIDIA GPU. The file is larger than RAM and VRAM combined; the selected experts for each token are pulled on demand (the newest path batches O_DIRECT reads through io_uring), while the pretrained routing is left untouched. On the same machine, same prompt, same decode length, a warm-run median gave ~5.5 tok/s decode vs ~2.0 tok/s for llama.cpp. To be upfront about scope: for models that fit comfortably in VRAM, llama.cpp is still faster than runNburn today — its CUDA kernels have years of tuning and we measure against it honestly (interleaved A/B runs, medians, and any "speedup" that changes output quality is rejected). runNburn's lane is the model that doesn't fit. What's in the box: - CLI, interactive chat, and an OpenAI-compatible server (chat/completions + responses + conversations, SSE streaming, stateful continuation with KV/SSM snapshot reuse). It's built as a single-owner personal server — one active generation is the optimization unit; continuous batching and multi-tenant throughput are explicit non-goals. - Architecture-aware paths: Llama family, Phi, Gemma, Qwen dense/hybrid/MoE (including GatedDeltaNet layers), Nemotron-H MoE, Hy3, GLM — plus in-model multi-token prediction (self-speculative decoding) with device-side verification where the GGUF ships a drafter. - Backends: CPU is the default (x86 AVX2, ARM NEON), CUDA and Metal are active, Vulkan/OpenCL are experimental. Android works through a small C ABI (rnb.h). - Native quantized kernels for Q2_K–Q6_K, Q4_0, Q8_0 — including the low-bit K-quants that big-MoE builds actually ship in. It's pre-1.0 and rough in places; recognition of an architecture doesn't mean every community variant works. But if you've got a model file bigger than your machine and you'd rather it run slowly than not at all, that's exactly the case it was built for. Happy to answer questions about the offloading design, the expert-streaming path, or the measurement protocol. https://ift.tt/vOm2RKY July 29, 2026 at 10:30PM

Show HN: MegaDSP an effects plugin for DAWs with 50 effects https://ift.tt/LETD7mt

Show HN: MegaDSP an effects plugin for DAWs with 50 effects Hey HN, I am creating megaDSP, a huge DAW plugin for all platforms, with the goal of it being a free, open-source, and semi or fully autonomous github project that evolves around community feedback. It currently has more than 50 audio effects that can be combined together in effects chains, and also supports "snapshots" where you can seamlessly switch between different effects chains (for live use, like a fancy guitar pedalboard for example). I would really love if you have a moment to test it out, give thoughts, etc. There are many, many plugins out there in the world, but many of them are either DAW-specific or have annoying copy protections etc. Having one huge plugin with great DSP algorithms seems like it could be a benefit to the music/audio world. Thanks, -Kevin first pre-release: https://ift.tt/Q2KGhE6... July 28, 2026 at 11:08PM

Show HN: Building a new game everyday with AI. Day #106 Zombie Survival Training https://ift.tt/f9m8Pxd

Show HN: Building a new game everyday with AI. Day #106 Zombie Survival Training I'm using AI (mostly Claude) to create/publish a new video game every day This is day 106, and my first stab at the zombie/survival genre. Most of the games I build with just a few prompts, but this one I used almost an entire 5-hour window on the $20/month pro subscription (in about an hour) with Opus 5 to get right with all the weapons and such. Then I go outside and touch grass for the next four hours. Happy to answer any questions, just a little hobby project of mine I'm having lots of fun with :) I'm now well into month 4, and I haven't missed a single day of publishing a new game yet! https://ift.tt/0r3CpXV July 27, 2026 at 09:49PM

Show HN: Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard) https://ift.tt/Vo4N5RC

Show HN: Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard) Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a loop. The models have no access to coordinates, map-data, or external memory scratches - they must navigate using only immediate local neighbourhood observations. There is a model dropdown and you can see how the model has progressed over each phase, constantly getting smaller and increasing its solve rate. Total trained models number in the thousands - I just expose the winning models from each phase. Overall a fun experiment, with much implementation help from AI agents to scaffold and implement the code (I'm a lazy software dev). https://con-dog.github.io/MINIMIO-PUBLIC-FRONTEND/ July 27, 2026 at 04:15AM

Show HN: Descript wanted $24/mo, I built an open-source alternative in a weekend https://ift.tt/o8vmLhN

Show HN: Descript wanted $24/mo, I built an open-source alternative in a weekend Descript costs $24/mo, so I built this over a single weekend with Fable! Introducing Rescript: edit videos by simply editing the transcript text. Drop in a video and it is transcribed locally with per-word timestamps and speaker labels. Delete words in the transcript and the corresponding clip is cut from the video. Runs fully in the browser: Local, free, offline and open source. → Github https://ift.tt/FY6WL2I → App https://wassgha.github.io/rescript https://ift.tt/FY6WL2I July 27, 2026 at 03:22AM