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For now they only support iphone's

check this out too github/omnimind-ai/OmniBot

🐑 Shepherd gives AI agents something they have been missing: reversible execution Long-running coding agents accumulate much more than chat history. They edit files, install packages, start services, call tools and build up prompt-cache state. When an agent makes a bad decision late in a run, restarting from zero is expensive, while simply asking it to fix the mistake can make the context even larger. Shepherd is an open-source Python runtime substrate that records an agent run as a durable, Git-like execution trace. It is designed so meta-agents can inspect, supervise, fork, replay and revert agent work instead of treating every run as disposable. ⚡ What makes it interesting: - Records agent-environment interactions as typed events - Couples agent state and filesystem state using copy-on-write forks - Keeps generated changes as reviewable proposals until they are selected or discarded - Lets supervisors observe effects and intervene before bad actions are accepted - Supports branching from an earlier point to test alternative strategies - Reuses the unchanged prompt prefix when replaying a branch - Declares permissions in task signatures and enforces workspace access at the OS level 📊 The researchers report: - Forking around 5× faster than Docker commit - More than 95% KV-cache reuse during replay - A supervisor improving CooperBench pair-coding pass rates from 28.8% to 54.7% - Counterfactual optimization reducing wall-clock time by up to 58% The practical idea is powerful: instead of running an agent once and hoping it succeeds, another agent can supervise the trace, rewind mistakes and explore several possible continuations from the same state. Shepherd is MIT-licensed and installable with: pip install shepherd-ai ⚠️ It is currently in early alpha, so its APIs may change and it is not yet positioned as production-ready. 🔗 https://github.com/shepherd-agents/shepherd 📖 https://www.marktechpost.com/2026/08/08/meet-shepherd-an-open-source-python-substrate

thats a bit different from giving claude access to your current phone

yea i get it

🧠 Recallium — memory for your AI coding agents Just dug into this one: Recallium is a self-hosted MCP server that gives Claude Code, Cursor, Windsurf, VS Code and Claude Desktop a _shared, persistent memory_ across sessions and tools. Instead of dumping everything as flat text, it types each memory — decision, debug, progress, research, rule, learning, etc — scoped per project, so your agent actually recalls _why_ a choice was made, not just what got built. ⚡ Key bits: • Just say "recallium" in your IDE — loads where you left off, recent decisions, open tasks • Claims 88% first-result search precision • Runs via Docker + Postgres, works with Anthropic / OpenAI / Gemini / local Ollama • Free forever, self-hosted — Cloud (team memory, SOC2/GDPR) coming soon • github.com/recallium-ai/recallium — 51⭐, ELv2 license, ~6 months old, small early team If you're tired of re-explaining your stack to your agent every session, worth a look 👇 🔗 recallium.ai

🐦 Colibrì — run 744B–2.8T parameter MoE models on hardware you already own Pure C, zero dependencies. No Python stack, no engine bloat — one C file per model family. The trick: it treats VRAM + RAM + disk as a _single_ memory hierarchy instead of demanding everything fit in VRAM. Routing-heat-driven prefetching decides what stays hot vs. streams from disk. 5 model families run today: • GLM-5.2 — 744B • Inkling — 975B • Kimi K3 — 2.8T • DeepSeek V4 Flash — 284B • OLMoE — 7B Same coli chat / coli serve / coli web front end for all of them. Ships a live dashboard too — watch all 19,456 experts fire in real time on the "Brain" page, or spin the 3D "Atlas" of clustered experts. 25k+⭐ on GitHub, Apache-2.0 licensed. 🔗 github.com/JustVugg/colibri

🙌 OpenHands — 84k+ stars, and it quietly became something bigger Most people still know OpenHands as _the open-source AI software engineer_. Go look at the repo today and you'll find something else entirely: Agent Canvas — a self-hosted control center for _every_ coding agent you already use. That's the part worth paying attention to. It doesn't ask you to pick a side. Run any agent OpenHands, Claude Code, Codex, Gemini — or anything speaking ACP (Agent-Client Protocol). One interface, switch freely. Run it anywhere Locally, in Docker, on a VM, or on your own company infrastructure. Point it at multiple _agent backends_ and flip between them without losing focus — a shared Agent Server doing code review for the team, your personal agents on your laptop. Always-on, not always-watching Put it on a cloud box and your agents keep running with the laptop shut. Schedule them, or fire them from webhooks — Slack, GitHub, Linear, Notion. Generate a report that publishes itself. Decompose a GitHub issue into tasks automatically. Bring your own model Any LLM. No lock-in there either. Try it npm install -g @openhands/agent-canvas agent-canvas Docker sandbox if you'd rather not hand it your whole filesystem: docker run -it --rm -p 8000:8000 \ -v "$HOME/.openhands:/home/openhands/.openhands" \ -v "$PROJECTS_PATH:/projects" \ ghcr.io/openhands/agent-canvas:1.13.0 UI at localhost:8000. MIT licensed, shipping daily, 10k+ forks. Currently tagged beta — treat the self-hosting docs seriously, an agent server with filesystem access is exactly as dangerous as it sounds. 🔗 https://github.com/OpenHands/OpenHands

🐝 Buzz — a hive mind for humans and agents Just found this from Block (Jack Dorsey's company): a self-hostable workspace where humans and AI agents share the same rooms. The core idea is wild — it's built on a Nostr relay. Every message, reaction, review approval, and git event is a signed event in one log. Same identity model whether the author is a person or an agent. What that unlocks: ⚡ Ask the workspace "have we seen this bug before?" and an agent pulls receipts from real channel history ⚡ Turn a feature branch into a channel — patches, CI results, review, and the merge decision all live in the same room ⚡ Agents get their own keys and scoped permissions — like a real teammate, not a bot with a flag ⚡ Search conversations, patches, workflow runs, and approvals in one index Stack: Rust relay + TypeScript/React desktop (Tauri) + buzz-cli for agent-first JSON in/out. Apache 2.0. ⭐ 28k+ stars already, actively shipping. 🔗 github.com/block/buzz

⚡ DeepSeek V4 Pro (0813) is out — the real full release, not a preview Two Minute Papers just broke down what's actually new, and it's a big deal: 📌 Massive jump from the same architecture. No new model structure — all the gains come from post-training. DeepSeek trains 10+ separate specialist models (math, coding, agentic work), then distills their combined skill into one final model. 📌 Speculative decoding upgrade. Instead of predicting one token at a time, V4 Pro drafts several tokens ahead — DeepSeek reports up to 78% faster generation, a real measurable speedup in everyday use. 📌 Fully open, MIT licensed weights. No gatekeeping, no "wrong keyword = downgraded to a worse model" games. Anyone can self-host or pick from multiple competing hosts. 📌 The catch: DeepSeek raised their own hosted API prices 2.5–5x. But since the weights are free and open, other providers can (and do) undercut them — great for us. 🔗 What's wild is the underlying method (multi-teacher distillation) was a research paper only ~6 weeks ago — and it's already powering a production model people are using today. Open research moving fast. 📺 Video: https://youtu.be/kyYepbhe1g8

Just found Verge Kit — a solid foundation for building web apps with _Astro_ and the _Cloudflare Workers_ ecosystem. It's a pre-wired stack + minimal boilerplate, specifically designed to help LLMs produce reliable, understandable code. Ship new apps with low effort and high confidence. ⚡ The stack: - Astro (SSR, strict TypeScript, Cloudflare Workers adapter) - Cloudflare D1 (SQLite) + Drizzle ORM - Better Auth (with admin plugin) - Tailwind + bejamas/ui (shadcn/ui based) - Lucide icons, React Email templates, Zod validation - Vitest, oxlint, integrated npm verification scripts 📦 Boilerplate included: - Typed auth middleware, request-scoped locals - Full auth flows w/ email notifications - Public-by-default routing w/ opt-in protected pages/APIs - CSRF checks, custom 404/500 pages - Role-based permissions (admin/moderator/user/banned) - Transactional email providers (Resend, Mailgun, Cloudflare Email) Get started: npm create vergekit@latest MIT licensed. 🔗 vergekit.com | GitHub

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i am providing latest Openai model Astra for half the price of 20x plan with unlimited token usage if interested inbox me - @mrbeandev

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Just came across this launch — TypeSafe AI is out of stealth with Jev, the first _System One Model_ ⚡ Founded by Diogo Almeida (co-inventor of ChatGPT & RLHF at OpenAI), backed by a $40M seed led by DCVC. Their thesis: chat models are RLHF-trained to please humans — which makes them overconfident, unreliable, and dependent on humans-in-the-loop. So TypeSafe went the opposite direction. _Not chat._ Decisions, not strings 🔀 Jev outputs typed decisions with calibrated confidence — your code decides when to act autonomously and when to ask a person: 🧠 New architecture + new sampler + new training algo: RLCD (Reinforcement Learning for Calibrated Decisions) 🛡️ Claims zero hallucinations — every decision ships with a confidence estimate ⚡ 193x faster, 444x cheaper on their System One task benchmarks 💰 $42 per billion input tokens, output free 📈 Named after Jevons Paradox — when intelligence gets cheap, the world consumes more of it The pitch: intelligence you build with in code, not converse with. Break a workflow into individual judgments, run them in parallel, compose them like functions. Early access is waitlisted → typesafe.ai One to watch: "can't hallucinate prose" ≠ "can't pick the wrong answer with confidence". Let's see how the calibration holds up outside their benchmarks 👀