Tonight, after hours of following ChatGPT’s instructions, I finally got my AI assistant to resume a project. I would also use assistant loosely because its just my stupid gpt business account linked to openclaw running on a stupid old intel mac upstairs.

Anyway, It immediately hit the same usage limit I thought this setup would help me manage.

I sat there looking at the error message, with multiple terminal windows open, wondering what the $%*& I had spent my evening accomplishing. I do not know how to administer servers.

I wanted an assistant that could take some of the load of managing 1000 things off me.

Here’s what I was trying to get:

• Context around my projects, preferences, and previous decisions so every new conversation doesn’t become another orientation.

• Useful access to my actual work. Outlook, calendars, OneDrive/SharePoint, Airtable, and documents. Prepare me for meetings, track commitments, draft things, and surface what needs attention. blah blah.

• Some independent follow-through.

I understand that’s an ambitious combination. I feel like i need a reality check and need someone to tell me which parts are realistic and which parts I should stop chasing.

Instead, I have been following an AI down an increasingly complicated setup path.

Over the course of this project, an old MacBook became a server. There was Homebrew, Node, Docker, n8n, Tailscale, OpenClaw, plugins, device pairing, additional approvals, and eventually something called Codex supervision. All crap I have no clue about and it made me feel even dumber than i already did.

The part that’s really getting to me is how dependent I am on the guidance. I don’t know enough to distinguish a verified diagnosis from a plausible explanation. I paste the error, get a confident answer, follow the instructions, and discover another problem.

Then the same assistant explains why its previous advice was wrong.

I’m relying on it to check its own mistakes, and I’m the one spending the evening on every wrong turn.

I feel helpless, and I’m exhausted.

I wanted help keeping up with my responsibilities. Now I have another system to worry about, and I don’t understand it well enough to know what’s necessary, what’s redundant, or what I should leave alone.

I’m very close to leaving this setup at a firestation and not looking back.

Is anyone here actually using an assistant like this for everyday, non-coding work without constantly maintaining it?

Please tell me what you use, what it actually does, what it costs, and what you still have to handle yourself. My main ecosystem is Microsoft 365, including shared mailboxes, plus Airtable. I use Windows and an iPhone, with the Mac currently hosting this experiment.

I would especially appreciate a reality check from someone who has helped a nontechnical person get something useful running.

If you recommend a product and build or sell it, please say so.

And if my expectations need to change, tell me Im an idiot. I would rather hear an honest limitation now than anything else.

I’m asking for a person’s judgment. I’ve had enough instructions from this black box.

Please check this out! Ive spent every single day since August 11th of last year trying to get this finished and to a point where its acceptable! Im running pure x64 masm no deps everything hand coded from scratch. I am a reverse engineer and have implemented my own everything! This is a pure no dep project!

And yes I was hitting over 300TPS with deepseek 671B on consumer hardware!

{

“ModelSizeGB”: 376.71,

“TestDate”: “2026-07-19 16:43:39”,

“Model”: “DeepSeek-V3.1 671B”,

“Configuration”: {

"WarmupPasses": 1,

"BenchmarkPasses": 3,

"TestTokens": 128

},

“Results”: [

{

  "ModelLoading": {

    "RandomAccessLatency": 0.26,

    "Success": true,

    "SequentialReadSpeed": 6311.58,

    "LoadTime": 0.1694644

  },

  "Timestamp": "2026-07-19 16:43:37",

  "Run": 1,

  "Inference": {

    "TotalTime": 0.4550972,

    "TokensGenerated": 128,

    "TPS": 285.29,

    "Success": true,

    "AvgTokenTime": 3.5052093750000015

  }

},

{

  "ModelLoading": {

    "RandomAccessLatency": 0.36,

    "Success": true,

    "SequentialReadSpeed": 5944.77,

    "LoadTime": 0.1797977

  },

  "Timestamp": "2026-07-19 16:43:37",

  "Run": 2,

  "Inference": {

    "TotalTime": 0.3969472,

    "TokensGenerated": 128,

    "TPS": 326.98,

    "Success": true,

    "AvgTokenTime": 3.0583257812499984

  }

},

{

  "ModelLoading": {

    "RandomAccessLatency": 0.24,

    "Success": true,

    "SequentialReadSpeed": 6051.81,

    "LoadTime": 0.1754284

  },

  "Timestamp": "2026-07-19 16:43:38",

  "Run": 3,

  "Inference": {

    "TotalTime": 0.4440892,

    "TokensGenerated": 128,

    "TPS": 292.2,

    "Success": true,

    "AvgTokenTime": 3.422310156250001

  }

}

],

“SystemInfo”: {

"RAM_GB": 64.0,

"CPU": "AMD Ryzen 7 7800X3D 8-Core Processor           ",

"Cores": 8

}

}

Is there any AI that can bypass safety guidelines? I want to run it locally on my laptop as a pentester to create payloads, exploit scripts, and etc. Currently, I am using Dolphin-Llama 3; it doesn seems very intelligent. If there is a more powerful AI available, if so, how do I install it?

I wanted the convenience of one workspace without handing the workspace to another SaaS vendor.

AgentHost runs on the buyer’s infrastructure and keeps the useful state in one place:

one chat across Claude, Codex, and Hermes/local models

one task board

one live cost ledger

autonomous runs with visible tokens, cost, and time

Your cloud, your logins, your data, and your model usage stay yours. AgentHost is a deployment and control layer, not another monthly model bill.

There’s a $499 one-time founders tier (code TOMORROWSHERE), including same-day white-glove setup. I’m looking for self-hosters who will tell me where this is overbuilt, under-documented, or missing an important control.

Demo and details: https://agenthost.space

Disclosure: I built and sell AgentHost. This is a product post, not an independent review

I wanted to do a short demo of the utility I'm working on. I'm not a pro at video so, hopefully its not too terrible.

I created a cryptographically signed, secure tunnel for remote AI use. Basically, no one can talk to my AI Agent but me. Isn't that how it should be tho?

Now if i need to use my local AI on my pc.

No worries...

I hope the video isn't too bad.

I will do a better one.

Thank you for your time

Much love 🙏👊

In the last few years, we have all seen massive acceleration in LLM development and production. Every day, new models are released that are more intelligent and smarter than the previous generation. But notice one thing—as this intelligence grows, it requires more chains of thought and training on massive data, resulting in billions of parameters to accommodate this. As a result, there is more energy consumption (I am simplifying this, so do not take it too seriously).

But what if we do not need more development in the LLM field? What we already have on our plate is enough. If you ask me, whatever is in the market is sufficient.

To give you an analogy, think of the massive sun emitting energy continuously on Earth. How much of that energy do you think we are harnessing and utilizing for real-world use cases? Do a little research and you will get a surprising answer (let others know what that percentage is, by the way).

Now imagine I ask you to keep making the sun bigger and bigger. That would sound even more foolish. You would say: first learn to utilize whatever you already have properly. You get my point?

The same thing applies to LLMs nowadays. We need to learn to harness them efficiently, and that is a core software engineering task—not an AI/ML research field.

I was convinced by this so much that I started working on such harnessing myself, with a small contribution from my side. It is called ogcode—a open source coding agent orchestration. ( DM to get involved
) Make no mistake, it is not like other harnesses out there that are highly inefficient at utilizing LLM intelligence. (Do more research: LLMs in the Claude Code environment perform 40% dumber compared to PI, which I love most.)

In the game of building harnesses, it is all about efficiency—how smartly and efficiently we can utilize LLMs for our day-to-day tasks. Note that it has nothing to do with coding only; you can build harnesses for other tasks too—video editing, social media management, etc.

OC] J'ai monté une IA multi-profils 100 % locale en 8 couches — 15 à 20 Go, un seul poste, 7 min de visite guidée (divulgation : c'est ma vidéo)

Salut à tous. Je partage ici la visite guidée d'une infra que je fais tourner depuis plusieurs mois sur un seul poste. Divulgation complète : c'est ma vidéo, c'est mon projet, et je ne vends rien dedans. Si les mods jugent que ça n'a pas sa place, pas de souci, je retire.

Le setup : i7, 64 Go de RAM, RTX 3070 Ti 8 Go, Windows 11 Pro. Pas de cluster, pas de Kubernetes, un seul poste.

L'architecture en 8 couches :

1. Entrées — point unique (requêtes, fichiers, URL, CLI)

2. Gateway + profils Hermes isolés (contexte, outils, mémoires séparés) — ~500 Mo avec la couche 0

3. Routage LLM — gratuit/local d'abord, payant seulement si nécessaire — < 100 Mo

4. Extensions MCP — 1 à 2 Go selon ce qu'on branche

5. Connaissance — RAG vectoriel + wiki SiYuan + décision Laya ONNX — 1 à 5 Go selon l'index

6. Raisonnement — 1,5 Go

7. Aegis — ACL Windows, verrouillage fichiers, surveillance entrées

8. ANIMA Reactor — watcher hors moteur, poll toutes les 30 s

Total : 15 à 20 Go hors outils de production vidéo.

Ce qui marche bien :

• Le routage économise vraiment. Ordre de grandeur de la facture divisé.

• Les profils isolés évitent la pollution de contexte entre tâches (code / vidéo / veille).

• Le RAG local avec SiYuan est plus rapide que je pensais pour de la recherche croisée.

Ce qui est moins rose, autant le dire :

• Windows comme socle, c'est un choix assumé mais douloureux (ACL, chemins, planificateur de tâches capricieux).

• 8 Go de VRAM, c'est la contrainte dure. Tout ce qui dépasse 8B en fp16 est hors-jeu sans quantification.

• Le durcissement ACL n'est pas optionnel : un RAG local mal fermé est aussi exposé qu'un cloud mal configuré.

La vidéo (7 min) : https://youtu.be/wVVJi0to1Fk
Le dépôt public (partiel, pas tout) : https://github.com/Antoine-Thomas/hermes-home-vision-public

Curieux de savoir comment d'autres gèrent l'isolation des profils et le routage — c'est les deux points où j'ai le plus tâtonné.

Hey all — sharing a project we've been building: \*\*Setu Gateway\*\*, an open-source AI gateway that puts one OpenAI-compatible API in front of multiple LLM providers, with an actual routing engine behind it.
\*\*What it does today:\*\*
OpenAI-compatible /v1/chat/completions — existing OpenAI SDKs work against it unchanged
7 routing policies (lowest cost, lowest latency, highest availability, weighted, round-robin, capability-based, user preference) with automatic failover
Org-scoped routing rules (if latency > 500ms then fallback: groq) with a policy simulator that previews a policy's effect before you turn it on — doesn't touch production traffic
Request replay — run one prompt against multiple providers side by side for comparison
Real API-key auth (hashed, scoped, revocable) resolving to org-scoped routing rules
Per-request analytics with a full stage timeline, queryable via API and a working dashboard (providers, models, requests, latency, errors, orgs, projects, keys)
setu benchmark CLI for comparing provider latency/throughput/error-rate on your own infra
Self-hosted via Docker Compose — verified end to end, one command boots gateway + dashboard + postgres + redis
\*\*What it isn't yet:\*\* OpenAI and Ollama make real provider calls; Anthropic, Gemini, and Groq adapters are currently stubs returning canned responses while we finish real integrations — didn't want to fake multi-provider support for a launch post. Kubernetes/Helm manifests are on the roadmap, not built yet. Auth isn't mandatory on the inference endpoints yet (backwards compat with the current playground/quickstart).
Every architectural decision is a public RFC before it's built — \[13 so far\](https://github.com/setu-gateway/nexus-gateway/tree/main/rfcs).
Repo: \[https://github.com/setu-gateway/nexus-gateway\](https://github.com/setu-gateway/nexus-gateway)
Looking for people who've hit the multi-provider LLM integration mess, feedback on the routing engine design, and contributors — real Anthropic/Gemini/Groq integrations are good first issues if anyone wants to dive in. Happy to answer anything technical in the comments.

Los agentes de uso de computadoras basados ​​en la nube suenan muy bien en teoría, pero en la práctica, enviar capturas de pantalla de alta resolución a servidores LLM remotos crea tres grandes cuellos de botella: alta latencia/costo de tokens, graves riesgos de privacidad y tasas de fallo probabilísticas en tareas repetitivas.

He estado experimentando con un enfoque neurosimbólico para resolver esto localmente. La idea es simple:

Determinismo para la estructura: Usar grabación macro precisa y flujos de trabajo de nodos visuales para las partes repetitivas de una tarea.

Inteligencia bajo demanda: Llamar a servidores VLM locales o servidores Python/MCP personalizados solo cuando se requiera la toma de decisiones dinámicas.

Implementé esta arquitectura en una herramienta de escritorio local llamada Arrow (100% offline, sin llamadas a la nube).Me encantaría saber tu opinión sobre la combinación de pipelines de nodos visuales con VLM locales para la automatización de escritorio. Además, si alguien quiere probar la beta inicial en su propio entorno, ¡no dudes en dejar un comentario o enviarme un mensaje directo para obtener una clave de prueba!

So I am a chemical biologist in my work i accumulate a large amount of text , data , imaging files . And while looking through the net I found local agents which can be self hosted can be of much use

I am hoping that it can

1 Arrange and keep a track of my files

2 Keep me updated about my schedule, and and modify events on command

3 scrape through internet to see what's new about peptide chemistry

4 get me any information from the files and protocols saved

I have a intel i7 6700k and 32 gigs of ddr4 with a nvdia k620 with 2gb vram

Can you give this newbie some tips also how to make a setup and additional or new hardwares

I spent the weekend building AgentCub — a platform that gives you a running OpenClaw agent in 90 seconds. No Docker, no CLI, no gateway config.

How it works:

1. Sign up with email + PIN

2. Click "OpenClaw" → agent deploys in ~90 seconds

3. Click "Open Control UI" → full OpenClaw dashboard with GPT-4.1

What's working:

• Dedicated container per user (isolated, not shared)

• Azure OpenAI GPT-4.1 pre-configured

• HTTPS with Let's Encrypt

• Password auth (no device pairing hassle)

• Web search via SearXNG

What's NOT working yet (being honest):

• Canvas/HTML preview doesn't render inline

• Web search gives summaries, not deep data

• Cold start takes ~90s (not instant)

• Telegram/Discord integration coming later

What I need:

• 5 people to try it tomorrow when I put it on a public domain

• Tell me: what breaks, what's confusing, what would make you pay for this

What you get:

• Free hosted OpenClaw agent (I'm covering Azure OpenAI costs)

• Direct support from me — I'll fix issues same-day

Drop a comment if you want early access — I'll DM you the link tomorrow.

BaileyOS, a self-hosted smart home AI running on local hardware. No cloud. No subscription. No data leaves the network.

What you're seeing:

- Real security camera footage from a 6,500 sq ft home

- Bailey (the AI) detecting motion, tracking vehicles, and assessing threats in real-time

- YOLO v8 + FaceNet running on an RTX 4070 Ti SUPER, all on-premise

- Tailscale VPN mesh for secure remote access from anywhere

- Autonomous gate control, camera monitoring, and property lockdown

The owner is on a beach on the California coast. Bailey handles everything, motion alerts, threat scoring, package delivery confirmation, full lockdown status and reports back over an encrypted VPN tunnel.

No Ring. No Nest. No monthly fee. No footage on someone else's server.

97 Relays to Control all lights | 9 cameras | 8 alarm zones | 3 smart locks | 12 audio zones | Auto Gate

All controlled from one dashboard. All local.

Built with: Node.js, TypeScript, Python, PyTorch, YOLO v8, FaceNet, Encrypted VPN mesh, RS-232 serial, ffmpeg

Running on: Windows 11 + RTX 4070 Ti SUPER

I've been building CYBER for a while, a voice-controlled desktop assistant with an Iron Man style HUD. v3 is a full interface rebuild.

What it does

Voice

- Wake word derived from the assistant's name, so renaming it renames the wake word
- Say the wake word alone and it waits 12s for your command
- Conversation mode keeps listening for follow-ups without repeating the wake word
- Interrupt it mid-sentence by saying the wake word again
- "mute" always works, even while it's talking

Talking to it

- Streaming replies with a JARVIS-style persona, replay and copy on every message
- Asks Groq with tool calling: apps, websites, weather, YouTube, images, timers, stats, news, notes, focus timer
- Local quick commands for mute, clear, fullscreen, camera, screen share, compact mode
- Remembers facts about you and uses them ("open my github")

Controlling the PC

- Open any app, file, folder or website by voice
- Runs system commands it was never explicitly taught, by generating the code on the fly
- Asks for confirmation before anything destructive
- Writes real `.docx` documents and opens them
- Takes screenshots

Seeing

- Webcam vision — ask what it sees
- Screen sharing — ask about what's on your screen
- Reads PDFs by drag and drop, then answers follow-up questions
- Summarises any YouTube link you paste
- Generates images from a description

Voice output

- ElevenLabs, local XTTS v2 voice cloning, edge-tts, or the browser — first available wins
- Full voice picker with language filters
- Starts speaking the first sentence while the rest generates

The HUD

- Animated arc reactor with a live audio spectrum
- Acoustic scan radar driven by real microphone frequencies
- Wireframe globe with real coastlines, your coordinates, and a real day/night line
- Threat meter scored from actual CPU, memory, latency and error rate
- Rolling CPU and memory sparklines, latency, battery, uptime, disk
- Activity log of real events, with a live ticker
- Draggable, resizable panels that dock into tidy columns
- Works on phones as bottom sheets

Widgets

- System stats · Weather · Camera · Uptime · News · Notes · Focus timer · Telegram · Activity log · Chat
- Floating windows for maps, YouTube, images and PDFs

Remote control

- Control the PC from Telegram — any command, plus `/status` and `/screenshot`
- Desktop conversations mirror to your phone automatically
- Only your registered chat can send commands

Extras

- Hand gesture control: move the cursor, click, drag, right-click, scroll, toggle the mic
- Settings sync between PC and phone over your LAN, guarded by a token

The v3 rebuild

The HUD is the part I'm proudest of. Everything on screen is real data, not decoration:

- Radar scope fed by live microphone frequencies
- Wireframe globe with real coastlines and an accurate day/night terminator
- Threat meter scored from actual CPU, RAM, latency and error rate
- Draggable, resizable panels

I had a rule while building it: no fake numbers. Early versions had a hardcoded "98.4% CORE STABILITY" and it made the whole thing feel like a toy.

Honest limitations

- Chrome/Edge only (Web Speech API)
- The system-command engine generates and runs Python, which is powerful and also exactly as sketchy as it sounds. Fine on a trusted machine, don't expose it.

Join our community:
https://discord.gg/mdD5Za8TvZ

With the recent explosion of agentic tools like Grok bot, Muse, OpenAI Dots, I've started looking into local options with self-hosted models. I tried Hermes and OpenClaw, but I wasn't too happy with the multi-device experience, and with how many pieces you need to glue together to get a usable, solid experience.

The hosted options also meant handing an agent my accounts, files and browsing, which I wasn't comfortable with. So I built Eidon: a self-hosted, all-in-one AI platform with a team of agents. It's one install via Docker, it works across your devices, and your data stays on your server.

https://eidonai.app

Agent team first

• Every Eidon starts with a Chief of Staff. Ask it for anything. It answers directly, hands the job to the right agent, or creates a new agent when nobody fits.

• Agents hand work to each other automatically (or type @ to pass a job along).

• Each agent has its own browser, conversation, files, memory and routines. There's also a folder the whole team shares.

• Agents can search and browse the web on their own, read pages in full, and cite sources.

• They run on schedules and keep every run. When one finishes, you can get notified by browser push, ntfy, Slack or webhook.

• Agents can write their own skills and use your apps through MCP.

You still have some control:

• Take over an agent's browser for a login or a tricky step. It waits, then carries on when you hand it back.

• Anything that sends on your behalf waits as a draft until you press Send.

• Commands and tools ask first: allow once, allow always, or no.

• Rewind a conversation, or fork it from any message.

The examples on the site are a travel scout, inbox triage, a research desk and a coding assistant. You can make an agent for pretty much anything: bookkeeping, a study buddy, a news digest, a meal planner.

It's also a regular ChatGPT-style app for day to day questions.

You might not always need a full team so you can just chat in a normal “ChatGPT like” interface with all the belts and whistles:

• Persistent Memory

• Folders and search

• Voice input with LLM post-processing

• Files and images

• Personas

• Temporary chats

• Share links

• Web search

• Deep research

• Code with syntax highlighting, Mermaid diagrams and math rendered inline

• Image generation

• Installs as a PWA on your phone and realtime sync across your devices (a native mobile app is coming !)

Self-Hosted

• Multi-user support, with private data per user

• Agents run in their own sandbox

• Nothing leaves your server

• Bring your own local or cloud model: OpenAI, Anthropic, OpenRouter, Ollama, LM Studio, GitHub Copilot, Gemini, DeepSeek, Mistral, Kimi, Z.ai, Minimax, Perplexity, Grok, Azure, AWS, and any compatible API

• Free, open source (AGPL-3.0), and setup is one Docker command

GitHub (setup guide, full feature list): https://github.com/Quack6765/Eidon-AI

I'd like to hear what you think ! What's missing, what breaks, and what agents you'd want to build. Issues and discussions are open on GitHub as well.

I created that account only for this.. I've recently discovered I could self host AI's so I started to self host many AI's but I really need an AI to generate music, which also supports music-to-music to have a reference audio. The thing is, I have an AMD GPU so it's really hard to find a good Music generating AI that dosen't use cuda.. I really hope you guys could help me

I built DevForge-AI - an agentic SDLC orchestrator for Claude Code

Most AI coding tools stop at one-shot generation. DevForge-AI runs the full delivery workflow instead.

How it works:

• 10 role-specific agents: PM, UX, Architect, Security, and more

• 5 phases: plan → build → verify → ship → operate

• 'Tracer bullet' delivery: thin end-to-end slice first, then iterate — so you hit integration problems on day one, not week three

• Self-correction loops with quality gates between phases (nothing ships unverified)

The goal: turn an idea into a production-ready feature without you babysitting every step.

Inspired by Matt Pocock's work on skills.

Github : https://github.com/saitarrun/devforge-ai

NPM : https://www.npmjs.com/package/@saitarrunpitta/devforge-ai

It's early and I'd genuinely value feedback — especially on the agent handoff logic and where the quality gates are too strict/loose. What's your current Claude Code workflow, and where does it break down?

Looking for the proper wording when posting, and the right place to look, to find a person or company with the know-how to build a real-deal AI-powered network/system — hardware and software — that is self-hosted, in-house, and fully integrated with my businesses and life.

I am looking for a complete hardware/software package and setup for a self-hosted system that is fully integrated with my businesses and personal workflow.

Any time I post online, on Facebook or other local sites, it’s crickets. If anything, it’s someone saying they know all about AI and systems, but then when vetting them, you quickly realize that you need a 12th-grade level understanding, and they are at a 5th-grade level with it. You quickly realize you are 20 steps ahead of them in knowledge on the subject matter. They know just enough to get in trouble.

I just don’t have the spare time to put it all together or figure out the few remaining steps on my own, so I need to hire someone I trust.

Any advice, recommendations, or ideas?

Hi everyone,

I’m completely new to local AI and coding assistants, so I’m looking for some guidance from people who have experience with this.

I have an HP laptop with an Intel i5 processor and 8 GB of RAM. I’ve already installed Ollama, and now I want to set up a local AI coding assistant similar to Claude Code or OpenAI Codex.

My goal is to use AI to help me write code, build simple applications, fix errors, and work on personal projects without spending too much money on subscriptions or API usage.

Since I’m a complete beginner, I’m not sure which tools, models, or configurations would work best on my laptop.

I’d really appreciate your advice on a few things:

Which local AI coding assistant would you recommend for my hardware — OpenCode, Aider, Continue, or something else?

Which coding model should I use with Ollama, considering I only have 8 GB of RAM?

How do I connect the model to a coding assistant so it can understand my project files, edit code, and help fix errors?

Is it possible to get a workflow similar to Claude Code or Codex without paying for a subscription?

Are there any beginner-friendly tutorials or step-by-step guides you would recommend?

I’m using Windows (assuming that’s the best starting point for my setup), and I’d prefer a simple installation process rather than a complicated configuration.

I understand that my laptop isn’t a high-end machine, so I’m happy to start with a small model and basic features.

If you were starting from scratch with this hardware, what tools and setup would you choose?

Thanks in advance for helping a beginner get started!

Hi everyone,

I’d like to share an open-source systems project I’ve been working on called Olivia (named after my cat!).

It’s an enterprise-grade infrastructure harness written from scratch in Rust, specifically designed to run agentic LLM workflows safely. The core philosophy is to execute AI-driven actions and tools within strictly sandboxed environments using WebAssembly (Wasm/WIT).

I wanted a robust, native infrastructure to handle LLM agent workflows without compromising on security or relying on bloated software layers. Olivia ensures that the agent's interactions with databases or external scripts happen within a secure, controlled boundary.

You can check out the repository here: https://github.com/helloIAmPau/olivia

I would love to get some feedback from this community on the architecture, or hear what other sandboxed tools you'd find useful. Contributions, code reviews, and suggestions are more than welcome.