Sunday Build with HWS+: Bake a slice of Pi! Create a macOS app that helps you memorize the digits of pi by mapping numbers to letters, then building stories from them. 🥧 #SundayBuild#

OpenClaw v2026.7.1 is out, with 3,063 contributions from 532 contributors! • Web UI and onboarding overhauls • Major iOS, Android and MacOS app work • Telegram, Slack, Discord, and iMessage improvements • GPT-5.6, Muse Spark 1.1, and MORE

I did a mini test on some tier 2 models I have. The test is to generate a macOS app that displays a Siri Remote device according to a product image I provided. Xiaomi MiMo is at a disadvantage in this situation since it doesn't support the image function. Moreover, it was the slowest of the four. It took 10 min just to complete this interface. It is excusable considering it's free($0.01) for now. Gemini's performance among these four models is actually quite impressive. It’s not as bad as others have complained.

so last week i launched a project that i have been working on for about 2 years now - the Mnemos Living Memory MCP. the project as a whole is much more than the mcp, though. it spans a web app, macOS app, ios app, and hermes plugin as well. the mcp was the first installment and first real look into the system for all of you folks. the plugin for @NousResearch (hermes) is installment #2#, and this was the original video i made to go along with it. I decided to go a different creative route for the hackathon entry (i really hope that choice was right/appreciated 😅), but this is the one thats really made to explain Mnemos in a more practical sense. Hermes is actually the native harness for polyphonic agents in the desktop app too. you can use other harnesses as well, but the polyphonic system is specifically designed to run on Hermes. and that wil be coming out soon with mnemos integrated into the app itself. <3

Built my own screen studio/editor to automate support and training videos with AI. Got support/guide videos? Published an UI update? Give a script to your agent, get a video back. • one click mcp setup for motion, auto-zooms, auto-annotations, narration driven by agents. • native macOS app, so it can handle high resolutions and complex workflows with dozens of layers/effects. • hold control option ⌃⌥ to explain complex stuff over a video, paste it in your ai agent

oMLX 0.7.1.dev2 is out! oMLX can now serve Qwen3.8 and Qwen3.6 through Splash as an external engine. Splash runs the model on its own Metal engine with DFlash2, and the model still shows up in oMLX's model list, API, dashboard, cache stats and benchmarks like any other. This release also helps first-time users find a model that fits their Mac without guesswork, reworks model settings and chat in the web UI and the macOS app, and speeds up GLM-5.3 and DFlash. Splash as an external engine - Turn it on per model in Model Settings > Advanced. oMLX finds the Splash launcher and hands it the model and DFlash2 draft from your model directories, so nothing extra is downloaded. - Memory and context window are fitted to your oMLX memory limit, and an MLX model can run alongside it. - Vision checkpoints take images too. Speedups in this release - GLM-5.3-Flash decode on M5 Ultra: 85.5 -> 95.3 tok/s (+11%) at 64K context. - GLM-5.3-Flash, 4 streams on a shared 32K prompt on M5 Ultra: 81 -> 91 tok/s (+12%). - DFlash with 4 concurrent requests on M3 Ultra: +82% to +157% (MiMo V2.6 Flash, Qwen3-4B, Muse Glimmer 30B). What's new - Find the right model for your Mac. New users no longer have to guess which model to start with. The new Recommended tab shows only oQ models that will actually load on your Mac, with Lightning MTP on by default. - Reworked model settings. The web UI groups them into sections with a side menu, and the macOS app splits them into pages that save as you edit. - A new chat composer. Pick the model, profile and reasoning level right where you type, with a reasoning slider that follows the levels each model accepts. - DFlash now runs inside the batched engines, so it gets continuous batching and the prefix cache. DSpark drafters are accepted 61-85% of the time instead of 16-21%. - Requests survive memory pressure. They are paused and resumed instead of aborted, and cancelled prompts stay cached. - /v1/decisions for any chat model, plus the Decision 3.0 models with image and video input. - YaRN for Qwen3.5-family models past the native 262K context. - An "Allow inference without API key" toggle for LAN setups, and a Mistral Vibe launcher. - oQ for DeepSeek V4.1 at oQ2 to oQ2.7 and for EmbeddingGemma 2, plus MiniCPM-V 4.7 support. - A log table in the macOS app, copy buttons for model names, and Downloaded badges in the model browser. Most of the speedups came from contributors again. Thanks to everyone who sent PRs and tested the dev builds!