Entries matching “stocks”

kwindla

@kwindla

Weekend project: play chess with Muse Spark running on a DGX Spark with code Muse Spark wrote. Setup: - projector - HDMI, camera, mic, and display server (Raspberry Pi) - pipecat bot (DGX Spark) - vision pipeline (RTX 5090) - coding agents running on the Pi, the DGX

Setup. We run Qwen3.8-Flash-Next NVFP4 as our main agentic model (SGLang, RTX PRO 6000). Before its output reaches a human or gets merged, a second local model acts as judge: reviews the diff, flags real bugs only. Hosted on a 5090 32GB, so we're limited to ~30B NVFP4/GGUF class models. The metric that matters is NOT detection rate — it's false alarms on correct code. A judge that cries wolf gets ignored within a week, exactly like a flaky CI. We built our own battery: 20 injected bugs + 20 clean-but-suspicious snippets (intentional swallowed exceptions, deliberate mutability, weird-but-correct concurrency, short hashes, float patterns that look wrong). Ground-truth labeled, and a stronger model (GLM-5.2 API) arbitrates the judge's prose so scoring isn't vibes. Two passes minimum — single runs lie. Results (40 cases, temp 0, same baremo for everyone): Qwen3.8-27B NVFP4 (no-thinking) • Bugs found: 17/20 • False alarms: 3/20 • Verdict: only pass Nemotron Lightning 30B • Bugs found: 17/20 • False alarms: 0→9 across runs • Verdict: non-reproducible as judge Muse-Glimmer 30B GGUF • Bugs found: 19/20 • False alarms: 12/20 • Verdict: hypercritical Granite 4.1 30B (no-thinki

Sina

@SinaHartung

small musecase but obsessed with muse tracking things for me it also helps me reorder my skincare, household items, etc across all platforms i just have a master file of things i love and it’ll keep it all stocked

Tyler

@tjshibata

Muse Spark 1.1 can use browsers and it refilled our office fridge - Took 2 pics of our fridge stocked / not stocked - Told Muse to analyze the pics and restock what we were low on - It analyzed each shelf and which brand / items that were missing - Used the @juliusai built in

I'm like you guys and am constantly experimenting with new models, seeing what they're all good at, how I can make use of them for certain projects and goals. I've been using Qwen 3.8 27b for minor coding work and it has been impressive. But with just regular chatting I have been impressed with Muse Glimmer. It seems to be able to have the ability to follow and hold good, deep and meaningful conversations without coming off as a typical chatbot. No repeated statements like "I hear what you're saying", "that sounds really deep..." none of what sounds generic or like it's blowing smoke up your ass. I was impressed with how natural it comes across just in natural conversation. I think it's one of the best "chat" models you could get right now as it's one of the only local models that doesn't feel like you're chatting with an AI when having a conversation. I'm thinking of finding a way to run both Qwen3.8 and Muse at the same time. It's fun to play with these things.

Michael Blau

@blauyourmind

Muse @Meta 🤝 @trydrip Ask your Muse to buy the latest stock picks from top analysts on dripstack.com

@Srimi1

@Srimi1

An experimental macOS gateway that keeps the stock Muse Code CLI in charge of the UI, sessions and tools but swaps the model connection for OpenAI or Z.ai models via a loopback gateway.

Rich Rose tests Muse on real business tasks for a week: building Excel order forms from color charts, scanning photo order forms, updating pricing and cross-checking out-of-stock items.

D I G I

@digicodermon

Connected @muse with my @NotionHQ meal planner. Now I can take a picture of my grocery receipt have each purchase registered to an ingredient in my pantry, Updating my pantry stock, tracking per item cost, and suggests meals based on what I actually have in stock vs my recipes.

Benchmarked Muse Glimmer 30B on my RTX 5090 (32GB), 262k context, UD-Q5_K_M + dflash-kquant + mmproj. Workload Stock master + DFlash ngram-simple PR #26842 + DFlash Code patch 78 t/s 57 t/s 220-253 t/s Mixed agent turn 77 t/s 68 t/s 188-213 t/s Tool-call JSON 71 t/s 75 t/s 155-181 t/s Heavy reasoning 52 t/s 58 t/s 120-130 t/s PR #26842 moves the DFlash draft argmax from CPU to GPU, which was the bottleneck. I cherry-picked it onto master (it branched before the Muse merge, one conflict to resolve manually) and it builds clean. Code generation now matches Meta's published 233 t/s, which I could not reproduce on stock master. Notes: • ngram-simple loses to DFlash on every coding workload. • Server caps context at the model's metadata context_length, use --override-kv for 262k. • The reasoning budget flags do not work with this template. This is verified: with the budget set to 64, the model still burned 2000+ chars thinking and the budget message never appeared. Leave max_tokens headroom for the reasoning block. Flags: llama-server ^ --model Muse-Glimmer-30B-UD-Q5_K_M.gguf ^ --mmproj mmproj-kquant.gguf ^ -c 262144 --parallel 1 ^ --override-kv "muse-glimmer.context_le