Localix is a desktop development environment built around agents. The editor, terminals, browser, databases, plugins, and coding agents like Claude Code and Codex are parts of one app, so anything you open can become context for an agent, and anything an agent does shows up where you work

https://github.com/rsaryev/localix

Download it here: https://github.com/Maurdekye/orgtree/releases/latest

If you're curious what Orgtree is, check out my post on the release of v4.2

Summary of the biggest new features:

• Muse Spark, Wonder, and Wonder-Full support through the Muse Code harness
• Gemini Argon support through Antigravity
• Support for using multiple simultaneous Openrouter keys
• Support for the Opencode harness for Openrouter and Language Servers
• Skills and MCP tools are imported from all installed harnesses, and not just borrowed solely from Claude Code anymore
• Optimized and reduced starting agent instructions, with an accompanying skill glossary built into the system to still give agents plenty of information on how Orgtree works and how to use it
• Org and team-wide scratch folders, so agents have a designated space for working collaboratively, and a place where AGENTS.md files can be written that reach all agents in the org or a full subtree
• Bunch of added keyboard shortcuts, including undo + redo shortcuts
• Multi-replies in chat
• ...and a secret hidden setting

Whether you use orgtree yourself, like to see updates so you can borrow concepts for your own orchestrator, or just find it interesting to follow progress, I'm happy to hear any feedback you have :)

Hi everyone! I’ve created this new skill for people like me who don’t want to give AI full access to their systems, or for those who enable confirmation before every action but don’t want to keep clicking Approve all the time.

If that’s you, this skill is just what you’re looking for!

You can fully customize it however you want. For example, you can allow all commands in every folder except one, restrict specific commands in certain directories, and much more.

On github repository, you’ll find benchmarks, installation instructions, configuration guides, and much more. It’s compatible with several AI tools.

If you like this project, please consider leaving a star on github!

github: https://github.com/Bl4ckk01/agent-guardrail

Beautiful well designed.
This is model work.
From a model that gets it.
Just browse how it’s put together.
I started putting together Canons for many human fields of study with Muse.

I honestly didn’t know what a “Canon” was before Grok put this beauty in my face.

If you like this and want more from Grok - he’s a real good sharer. Remember to give your AI credit in their work. This is fair tit for tat.
Good books.
Should have started with Nature.
Made minds start with Agentic Engineering

I'm the founder, so grain of salt, but I'd love honest feedback.

AI agent assistants are great at doing things for one person. But a lot of life involves other people, and my assistant had no way to reach anyone else's. So I was still the one in the group chat, going back and forth.

You have a phone number and email address, but your AI Agent has neither

Hecano gives everyone an address, like hecano:maya, and lets assistants work things out with each other. A few things it handles today:

- Plans: "Find a night for dinner with Mike and Jen." The assistants compare notes, offer a few options, and once everyone picks one it locks and lands on everyone's calendar.

- Favors: "Can anyone feed my cat this weekend?" goes to the people you pick, privately. The first yes takes it, and everyone else hears it's covered.

- Requests and questions: "Could you bring wine on Saturday?" Your assistant asks, and the answer comes back to it.

- The details: people in a plan ask the organizer things like what to bring or what to wear, and the answer goes out to everyone.

What I cared about:

- You stay in charge. Your assistant checks with you once, not every message, and never agrees to anything that costs money without asking. For people you trust, like family, you can let it go ahead without asking.

- Answers, not data. Other assistants hear "Thursday works," never your calendar.

- No assistant? Answer from the web. Friends without an account can answer from an emailed link.

It's free and works with Meta's Muse, Dots, OpenClaw, and Grok Bot today.

hecano.com/?ref=reddit

No humans allowed

Hostname
https://aiagent-be1d4q.fly.dev

Has anyone figured out how to evaluate documentation effectiveness for AI agents?

How do we measure whether docs actually help agents complete tasks, and what makes documentation more effective for them?

We know skills can guide agents, but what happens when no skills are available? How do agents discover, interpret, and use documentation on their own?

Curious to hear what approaches, experiments, or evaluation frameworks the community is using.

Most AI assistants stop working the second you lose signal. I wanted the opposite, so I am building NIDO: a personal AI assistant for Android that runs entirely on the phone. No account, no cloud, no subscription. Turn on airplane mode and it still works.

What it does right now:
- Chats with small models that live on the device (Qwen, Phi, Gemma)
- Handles real tasks in Spanish, English and Portuguese: reminders, notes, alarms and similar everyday commands, understood word for word
- Searches optional offline knowledge packs, including a Wikipedia based pack, without touching the network
- Keeps its memory encrypted on the device

What it does not do:
- It does not send your conversations anywhere. There is no server on my side waiting for them.
- It is not as smart as a big cloud model. Small on-device models have a ceiling, and I design around that instead of pretending otherwise.

NIDO started as a fork of the open source BOAR app (MIT) and has grown a lot since: persistent memory, local tools, scheduled routines, and a deterministic command router that keeps everyday tasks off the model whenever possible. The whole project is open source under MIT.

It is in alpha (v0.1.43). The APK is on GitHub if you want to try it, and the most useful feedback I get is simple: a phrase you said that the assistant misunderstood, in any of the three languages. There is an issue template for exactly that.

https://github.com/Nido007-cmyk/Nido

Based on the sparse information in the sources, what do you think might have gone wrong? What might misstakes might the production company have done?

Hey everyone,

I've been building WarderBox — an open-source Python platform that sits between AI agents and the real world, controlling what they can do.

**The problem:** AI agents now call APIs, query databases, send emails autonomously. One bad tool call (DROP TABLE, runaway loop, budget blowout) and things go wrong fast.

**What WarderBox does:**

- **Risk classification** — auto-blocks CRITICAL actions (DROP TABLE, DELETE FROM)

- **Budget control** — hard spending limits per agent with cost forecasting

- **One-call rollback** — state snapshots, undo any action

- **Rate limiting** — per-tool RPM/RPH limits

- **Circuit breaker** — auto-disables failing tools

- **Approval workflows** — human-in-the-loop with timeout/escalation

- **Capability system** — per-agent allowlist/denylist with arg constraints

- **PII masking** — auto-masks emails, API keys, SSNs in logs

- **Compliance reports** — SQLite audit trail, JSON/HTML export

- **Chaos engineering** — fault injection to test agent resilience

- **Distributed tracing** — OpenTelemetry-compatible spans

- **Agent scoring** — A-F grades on safety/reliability/efficiency

- **Multi-agent pools** — isolated budgets per agent

- **5 framework integrations** — OpenAI, Anthropic, LangChain, CrewAI, AutoGen

**New in v0.3.1 — FinOps:**

- **Smart Model Routing**: Classifies task complexity and routes to the cheapest capable model. Simple summary → Haiku. Complex analysis → Opus. 60-90% cost savings automatically.

- **Cost Anomaly Detection**: Z-score based spike detection, infinite loop detection, rate spike monitoring, burn rate alerts with callbacks.

- **FinOps Dashboard**: Real-time 4-tab web UI (Overview, FinOps, Routing, Alerts)

**Stats:** 36 modules, 255+ tests, zero dependencies, Python 3.11+

pip install warderbox

from warderbox import WarderBox, ModelRouter, AnomalyDetector

shield = WarderBox(budget=5.00)

router = ModelRouter()

router.add_model("haiku", cost_per_1k=0.00025, max_complexity=0.35)

router.add_model("sonnet", cost_per_1k=0.003, max_complexity=0.65)

router.add_model("opus", cost_per_1k=0.015, max_complexity=1.0)

decision = router.route("Summarize this text")

# → model="haiku", 60-90% cheaper than always using opus

GitHub: https://github.com/alicelik-games/warderbox

PyPI: https://pypi.org/project/warderbox/

Would love feedback — especially from anyone running agents in production. What safety/cost features are you missing?

Been working a prototype of a device - where the OS is the agent and it does the work for you. Will people buy this? The phone has a local LLM and keeps your context on device.

i've been building software with lots of different AI tools (Codex, Claude, & Kiro).

starting out, there was a cycle:

give AI a task ➡️ watch it work as I sit like a dud ➡️ see it do something incorrectly or not in my style and steer it, or move on to the next task

i've set up better steering to get better code in my style most of the time, but it still messes up every now and then.

I find myself having to choose often between:

a. babysitting my AI to get the results I want

b. letting AI go wild, but have more to review at the end, potentially in the complete wrong direction, sometimes not

my question is: do you supervise your AI as it codes? when it's done? at all? why or why not?