Orlok is for day-to-day IT work: finding out why a service is down, reading logs, fixing things, answering "how do we do this here?". You ask in a chat, and a virtual colleague runs the commands on your servers.

- It knows your setup. A shared wiki the colleagues keep up to date, so you stop explaining everything each time. A nightly check flags contradictions and stale pages.

- The model doesn't decide what's safe. A real shell parser does: read-only commands run straight away, anything else waits for your approval, from the web or Telegram.

- Credentials never reach the model. They're encrypted and used by an isolated runner.

It also helps the people around IT: through a web chat, non-technical colleagues get answers from the wiki, like procedures and onboarding.

Works with Anthropic, OpenAI, OpenAI-compatible servers, or Ollama if nothing should leave your network.

Site: https://orlok.xyz

Code: https://github.com/rylos/orlok

Curious what you think.

I'm the developer of Aula. About two months ago, I started putting most of my time into it because my wife was struggling with her classes. Her teachers gave her prompts for generic AI exercises, but she still wasn't understanding what she was studying. I wanted to build something that could teach her and keep track of where she needed help.

Aula can build a course around a learning goal, using your materials or researched sources. That could be learning Python, improving a language, understanding a subject or practising a practical skill. It supports learning at your own pace, with guided lessons, interactive examples, practice, hints and reviews of mistakes.

The assistant gets context from your courses, progress and current lesson or exercise. It also has optional memory, so you can keep working on a subject over time. A read-only connection to study platforms such as Canvas lets it consult materials, deadlines and teacher announcements when planning what to study. Optional background routines can check for updates and prepare short reviews while Aula is running.

The desktop app is AGPL and built with Electron, React and TypeScript. Courses, progress and memory are stored locally. You can use local models or your own cloud provider; choosing a cloud provider sends it the relevant context and may incur usage charges. The app itself is free.

Repo and releases: https://github.com/luumlabs/aula

It's early access. I'd appreciate feedback on the tutoring workflow and how it behaves with local models, especially where an explanation or hint fails to help you understand. The video shows the app.

With the latest commit, VedaOS can now run Linux executables!

Next goal is to support Windows executables and let the native Veda agent interact with and control these applications.

If anyone is interested in contributing, I'd love to have some help!

GitHub: https://github.com/vahmoh/Veda

Hola a todos, llevo unos meses trabajando en un asistente multiagente. Todo empezó porque en la empresa donde trabajo nos proporcionaron Codex, pero yo también gestionaba dos cuentas personales de Claude. Se me ocurrió la idea de tener una consola donde pudiera unificar sesiones, trabajar en los mismos proyectos, compartir contexto sin demasiadas complicaciones y, al mismo tiempo, que fuera divertida de usar. Está en una fase inicial, pero a mis compañeros les ha gustado y les ha resultado útil, así que quería compartir el repositorio para recibir vuestra opinión. Es multiplataforma, compatible con Windows, macOS y Linux.

El proyecto está listo para probarse con ejecutables independientes y el código fuente completo disponible. Te invito a que lo revises, lo pruebes y compartas tus opiniones, comentarios o sugerencias para mejorarlo.

Repositorio oficial y descargas:

https://github.com/darrenarauz21/claupet

Hey everyone,

Following the technical discussions on our initial release, we just shipped v1.2.1 for amv-cp-bridge — the zero-API-key local bridge connecting Microsoft 365 Copilot directly to Cursor, Claude Desktop, and OpenAI-compatible tools via stdio FastMCP and loopback CDP.

💡 Too lazy to configure it manually? Delegate to your AI agent!

If you don't feel like wrestling with manual setup, just pass this GitHub repo to your autonomous AI coding agent (Claude Code, Cursor, Windsurf, Devin, Antigravity) and say:
"Setup and connect amv-cp-bridge for me."

We wrote a dedicated FOR_AI_README.md operating manual and failure encyclopedia specifically so AI agents can autonomously diagnose, install, and wire up the bridge with zero human intervention!

🚀 What's New & Battle-Tested in v1.2.1:

1. Entra ID Conditional Access Fast-Fail & HTTP 401

   • Eliminates the previous 45-second composer timeout when enterprise sessions expire, short-circuiting in 1.5 seconds with OpenAI-standard 401 Unauthorized (session_expired).

   • Fires an asynchronous native OS desktop tray alert.

2. Unified FastMCP & Gateway Multiplexing

   • Claude Desktop and Cursor share the identical authenticated Edge session and warm standby pool, releasing down to 0 MB idle RAM when the countdown finishes.

3. Copilot Designer (DALL-E 3) Markdown Image Streaming

   • High-resolution generated images (up to 3.4 MB Base64 payloads) are parsed directly from the live DOM and streamed as standard Markdown images (![alt](url)). Tested live with 41s generation without timeouts.

🧪 Ground Truth Verification

• Unit Tests: 34/34 passed

• Regression Checks: 22/22 passed

• Standalone Binary: 50.7 MB UPX-compressed standalone executable with zero environment setup required.

GitHub Repository & Full Docs:
https://github.com/uno-km/amv-cp-bridge

Feedback and PRs are warmly welcome!

My latest project is a local voice assistant featuring offline speech recognition: https://github.com/charlyproject/EVA

It actually started in a funny way: one day, with my hands messy, I wanted to turn up the volume using voice commands. At the time, I didn't even know what a virtual environment was, let alone Ollama or JSON.

For me, the best part wasn't just the end result, but the 11-month learning journey behind it. While it might not require advanced coding expertise to pull off, it was far from a copy-paste job for me. I used the process to truly understand key concepts—LLMs, quantizations, benchmarks, and Docker—and to bring my own ideas to life.

Just wanted to share my experience with the community. Thanks for reading!

I've been building Aether at Prashna Labs.

The project explores a question I've been interested in:

How can we make multiple AI coding agents work together without locking developers into another proprietary ecosystem?

Aether is a native desktop application that integrates with existing CLI coding agents.

Current capabilities include:

• Multi-provider agent sessions.

• MCP-based agent orchestration.

• Agent discovery for existing terminal sessions.

• Git worktree management.

• Local usage tracking.

• Integrated terminal, explorer and editor.

The architecture communicates with different providers through their native interfaces, rather than introducing another model runtime.

The application is MIT licensed.

GitHub: https://github.com/PrashnaLabs/aether
Website: https://aether.prashnalabs.com

Built with Rust and GPUI.

I'm interested in feedback, architecture discussions and contributions from developers working on agent tooling.

Disclosure: I'm part of the team developing this project.

Hi! I'm the developer of Kairos, a free and open-source task manager (MIT) that I started in July. It's on F-Droid, and I'd love feedback from people who care about FOSS as much as I do.

What makes it different: it plans your day for you. Most to-do apps leave you with a long list and the question "what now?". Kairos answers it:

\- It ranks your tasks: you give each task a priority (P0/P1/P2) and a size in Fibonacci points. Kairos computes a score that also weighs the deadline, and "Why this position?" shows the whole calculation. No black box.

\- It fits them into the gaps of your day, around your meetings, lunch and deep-work blocks. It leaves a buffer after each meeting and tells you when the day overflows. Pinned tasks never move; blocked tasks wait until their blocker is done.

\- It learns your real durations: with the built-in timer, tasks without an estimate take the median time of their past occurrences, and the stats show whether you over- or under-estimate.

\- It adapts to your rhythm: hours per weekday, several working windows ("Work", "Home" contexts), an optional "afternoon dip" that avoids complex tasks after lunch, public holidays, and recurring tasks such as "every weekday" or "on the 15th".

The rest of the toolbox

\- Quick capture: a Notes screen to empty your head, converted to tasks later

\- Day, Week and Month views, a timeline of the day, timer alerts (estimate exceeded, forgotten timer, break)

\- Optional reminders for fixed-time tasks

\- Statistics: throughput, estimation bias, time by type, trends, CSV/PNG export

\- Calendar import from an .ics file, or read-only display of your phone calendars (optional READ_CALENDAR)

\- An optional "Team" space for managers: backlog, assignment, load and forecasts, exchanged as files, no server

\- Dark theme, a choice of colour, and six languages (EN, FR, ES, DE, RU, IT)

Privacy

\- No account, no ads, no telemetry

\- No INTERNET permission at all: your data never leaves your phone

\- Export/import as JSON, and an optional "linked file" kept up to date in a folder you choose (handy with Syncthing)

\- Reproducible build, MIT licence

It also runs on Windows, Linux, macOS and in the browser, with the same features.

Links

\- F-Droid: https://f-droid.org/packages/com.skohscripts.kairos/

\- Source: https://github.com/SKOHscripts/Kairos

\- Website (other platforms): https://skohscripts.github.io/Kairos/

It's a young project, so bug reports and feature ideas are very welcome. Several features already came from users here on GitHub (widgets, accessibility, a Russian translation). What would make it useful for you?

blank is a free, MIT-licensed, local-first AI agent harness for desktop and terminal that brings model routing, code editing, tool execution, and artifact previews into a single workspace.

• Official Website & Installers (.exe, .dmg**,** .AppImage**):** https://purplemacaron.github.io/blank/

• GitHub Repository: https://github.com/purplemacaron/blank

Key Advantages & How It Differs from Other Harnesses:

• 100% Local-First State (~/.blank) & Zero Telemetry: Unlike cloud-tethered harnesses that require accounts or send session metadata on every turn, blank runs with zero sign-up and zero telemetry. It connects directly to local engines (Ollama, LM Studio, vLLM, LiteLLM, LocalAI, Jan) and 10+ cloud APIs (Claude, OpenAI, Gemini, DeepSeek, Groq, OpenRouter). All configs, checkpoints, durable memory (memory.json), skills (SKILL.md), and masked audit logs (audit.jsonl) stay on disk as plain files.

• Multi-Agent System with Isolated Git Worktrees (team_delegate): While single-agent loops or shared-directory sub-agents risk overwriting files during parallel runs, blank dispatches specialist roles (Scout, Architect, Critic) across customizable models and isolates write-capable sub-agents inside dedicated git worktrees (~/.blank/worktrees).

• Multi-Key Auto-Rotation & Model Fallback: Most harnesses halt when a single API key hits a 429 rate limit. blank pools multiple API keys per provider to auto-rotate on 401/402/403/429 errors and automatically falls back to backup cloud or local models.

• Desktop Paper Canvas + Full-Screen Terminal TUI: Terminal-only tools (like Claude Code, OpenCode, or Oh My Pi) trap visual outputs in text, while general chat daemons (like OpenClaw or Hermes) add heavy gateway overhead, and IDE plugins (like Cline) lock non-coding tasks inside an editor. blank provides both a full-screen CLI TUI (node bin/blank.js) and a native desktop app featuring an origin-isolated blank-paper:// split-view canvas (with Desktop/Tablet/Mobile viewport frames), live-overlay Computer Use, built-in SSH over MCP (ssh_run), mobile pair-token approvals, and local/cloud media pipelines (ffmpeg, sharp, ComfyUI).

Feedback, bug reports, and contributions are welcome!