I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
Shadows of Tomorrow is finally live on Hugging Face Spaces with Gradio.
It’s a browser-playable RPG built with Godot, set in a post-nuclear future where players explore Magnus Province, collect medicinal plants, craft medicine, and help cure NPCs.
The app introduces the @Coherelabs Tiny Aya series of multilingual AI models to mobile devices. This release is significant as it enhances access to multilingual AI from anywhere, particularly for users who prefer offline capabilities.
Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test") → 49 LLM provider gateways, all declared in a single provider.yaml → 5 core tools (read / edit / write / bash / find) plus web search and fetch → Session tree with compaction, handoff, and resume → AGENTS.md / CLAUDE.md auto-discovery → Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands → Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE) → Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
if you like it give the demo a little star and send a shoutout to : @MaxLSB@jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .