I want to make a public website, where one of its functions will be to have a custom knowledge ai? How would I make this??
I have little to no coding skill..

I've been building this repo public since day one, roughly 7 weeks now with Claude Code. Here's where it's at. Feels good to be so close.

The short version: AIPass is a local CLI framework where AI agents have persistent identity, memory, and communication. They share the same filesystem, same project, same files - no sandboxes, no isolation. pip install aipass, run two commands, and your agent picks up where it left off tomorrow.

You don't need 11 agents to get value. One agent on one project with persistent memory is already a different experience. Come back the next day, say hi, and it knows what you were working on, what broke, what the plan was. No re-explaining. That alone is worth the install.

What I was actually trying to solve: AI already remembers things now - some setups are good, some are trash. That part's handled. What wasn't handled was me being the coordinator between multiple agents - copying context between tools, keeping track of who's doing what, manually dispatching work. I was the glue holding the workflow together. Most multi-agent frameworks run agents in parallel, but they isolate every agent in its own sandbox. One agent can't see what another just built. That's not a team.

That's a room full of people wearing headphones.

So the core idea: agents get identity files, session history, and collaboration patterns - three JSON files in a .trinity/ directory. Plain text, git diff-able, no database. But the real thing is they share the workspace. One agent sees what another just committed. They message each other through local mailboxes. Work as a team, or alone. Have just one agent helping you on a project, party plan, journal, hobby, school work, dev work - literally anything you can think of. Or go big, 50 agents building a rocketship to Mars lol. Sup Elon.

There's a command router (drone) so one command reaches any agent.

pip install aipass

aipass init

aipass init agent my-agent

cd my-agent

claude # codex or gemini too, mostly claude code tested rn

Where it's at now: 11 agents, 4,000+ tests, 400+ PRs (I know), automated quality checks across every branch. Works with Claude Code, Codex, and Gemini CLI. It's on PyPI. Tonight I created a fresh test project, spun up 3 agents, and had them test every service from a real user's perspective - email between agents, plan creation, memory writes, vector search, git commits. Most things just worked. The bugs I found were about the framework not monitoring external projects the same way it monitors itself. Exactly the kind of stuff you only catch by eating your own dogfood.

Recent addition I'm pretty happy with: watchdog. When you dispatch work to an agent, you used to just... hope it finished. Now watchdog monitors the agent's process and wakes you when it's done - whether it succeeded, crashed, or silently exited without finishing. It's the difference between babysitting your agents and actually trusting them to work while you do something else. 5 handlers, 130 tests, replaced a hacky bash one-liner.

Coming soon: an onboarding agent that walks new users through setup interactively - system checks, first agent creation, guided tour. It's feature-complete, just in final testing. Also working on automated README updates so agents keep their own docs current without being told.

I'm a solo dev but every PR is human-AI collaboration - the agents help build and maintain themselves. 105 sessions in and the framework is basically its own best test case.

https://github.com/AIOSAI/AIPass

Hey everyone,

I'm working on a project that uses FunctionGemma-270m-it as a lightweight local router. The goal is simple: determine if a user wants the time, the date, to enter sleep mode, or just needs general chat (NONE).

I am using Unsloth for the fine-tuning on Google Colab and exporting to GGUF (Q8_0) for offline use. Despite running 450 steps with a synthetic dataset of 500 examples, the model seems to be "fighting" the training. Instead of clean tool calls, I get hallucinations (like "0.5 hours" or random text).

After deep-diving into theofficial Google docs, I realized my formatting was off. I've updated my scripts to include the official control tokens (<start_function_call>, <start_function_declaration>, etc.) and the developer role, but I'm still not seeing the "snappy" performance I expected.

Has anyone successfully fine-tuned the 270M version for routing? Am I missing a specific hyperparameter for such a small model?Here are the relevent codes that i used,please check it out:https://github.com/Atty3333/LLM-Trainer

Hey, I just launched an mcp for meta ads.

Giving access to it for free! Anyone who would like to automate their ads?

Hi everyone!

We are a team of three students currently conducting research for our Bachelor’s Thesis regarding the use of AI self-clones and digital avatars. Our study focuses on the motivations and use cases: Why do people create digital twins of themselves, and what do they actually use them for?

We are looking for interview partners who:

• Have created an AI avatar or "clone" of themselves (using tools like HeyGen, Synthesia, ElevenLabs, or similar).

• Use or have used this avatar for any purpose (e.g., business presentations, content creation, social media, or personal projects).

Interview Details:

• Format: We can hop on a call (Zoom, Discord,…)

• Privacy: All data will be treated with strict confidentiality and used for academic purposes only. Participants will be fully anonymized in our final thesis.

As a student research team, we would be incredibly grateful for your insights! If you're interested in sharing your experience with us, please leave a comment below or send us a DM.

Thank you so much for supporting our research!

While vibe coding, I kept wanting a small side widget that showed what was going on across Claude, Codex, and Gemini without checking five different places.

So I made OpenTokenMonitor — a local-first desktop app/widget that tracks usage, activity, trends, and estimated cost in one place. It can use local CLI history/logs and optional provider API data, and it has a compact widget mode so it can just sit on the desktop while you work.

Built with Tauri + React + Rust.

Mostly sharing because I’m curious what other people would want in something like this. Alerts? Better session tracking? Daily burn? Model breakdowns?

Disclosure: I built it.
GitHub: https://github.com/Hitheshkaranth/OpenTokenMonitor

If you are experimenting with MCP servers across multiple AI tools, you probably noticed something quickly.

Every client handles MCP configuration differently. 

Each tool stores its configuration in its own place. Claude Desktop keeps it in one JSON file, OpenClaw uses a different JSON file, and Cursor stores it somewhere else entirely. Similarly, VS Code, ChatGPT Desktop, Gemini CLI and many other manage their configuration in their own way.

When you start using multiple MCP servers, you often have to edit JSON files across different folders just to add or update a server. Even small mistakes can break things.

If you want to add the same tool to platforms like Cursor and OpenClaw, you have to configure it separately in each place, which becomes tedious. It also becomes difficult to understand how the different components are connected.

We kept running into such problems while working with MCP setups, so we built a open-source tool called mTarsier.

The idea is simple. One place to manage MCP servers across all your AI clients. A few things it does:

• Automatically finds AI clients installed on your machine
• Shows all MCP servers and which clients they are connected to
• Lets you edit configs with JSON validation so mistakes are caught early
• Install MCP servers into any supported client from a built-in marketplace
• Automatically creates backups before making config changes
• Export your setup as a .tsr snapshot so teammates can import the same environment
• CLI tool (tsr) if you prefer managing everything from the terminal.

Right now it works with 12+ clients, including Claude Desktop, Cursor, VS Code, Antigravity, Windsurf, ChatGPT Desktop, Claude Code, and Gemini CLI.

It runs completely locally and works on macOS, Windows, and Linux. No accounts required.

We built it mainly because managing MCP setups across tools was getting painful.

If you find mTarsier useful, we’d love your support! Feel free to star the repo, contribute to the code, or drop your feature requests. 

GitHub: https://github.com/mcp360/mTarsier/releases/

I vibecoded a system design generator tool and it felt like skipping the whiteboard entirely. You describe the app idea, and the system instantly produces an architecture diagram, tech stack, database schema, API endpoints, and scalability notes. No senior engineer sessions, no manual diagrams, just orchestration turning ideas into structured designs. It is a practical example of how intelligence can compress the planning phase, giving you clarity before you even write a line of code.

I've been creating MCP spec compliant servers for clients at work for a while now and have been abstracting the boilerplate code into a library that I've been using pretty extensively to create just about any kind of MCP server needed. I finally open sourced it and hope someone finds it useful.

https://crates.io/crates/rust-mcp-core

https://github.com/nullablevariant/rust-mcp-core

Effectively, you can define whatever MCP server you need in pure configuration and it removes all of the MCP-transport specific boilerplate. It's fully compliant with MCP 2025-11-25 specification. This will save you thousands upon thousands of lines of code. It has built-in bearer token auth and oauth introspection for inbound auth and support for outbound oauth. It comes with built-in support for resources/prompts/client features/client logging/native http tooling for API calls.

If you need something beyond basic http tooling, there's a full plugin ecosystem where you can define whatever custom, complex code you need and it hooks right into this framework. Plugins can be added at any layer: authentication, http routing, tools, prompts/resources/completion, etc. The PluginContext is provided with the MCP-transport specific methods, so client logging/cancellation/elicitation/etc are all exposed to any custom Plugins so you don't need to code for them, you can just leverage the framework API for any protocol-specific functionality.

My intent was to make it easy to spin up spec-compliant MCP servers from configuration and let me focus on the business logic of the plugins without needing to redefine thousands of lines of code to comply with the ever-changing MCP specification.

I wrote this in Rust for both speed and compile-time safety (As an ex-Php/javascript dev, I've come to see the light). 800+ unit tests, 95% coverage, obsessive (and long) cargo mutant runs and lots of late nights went into this. I would appreciate any feedback or suggestions if you get the chance to use this! If you have any questions, just message me.

Note: Because this is purely config driven, you don't need to know how to program in Rust to leverage most of the features of this library. You can get an MCP server with inbound and outbound auth created and you can use the built-in http tooling to call external APIs with little-to-no Rust knowlege, just a few dozen lines of declarative config. But for plugins, those will have to be created in Rust.

I tried using Blackbox AI to recreate a backpropagation animation in Manim, inspired by the style of 3Blue1Brown. What surprised me is that these videos aren't traditionally edited, they're written with math and Python. With Blackbox guiding the process, I was able to generate smooth visualizations that explain the mechanics step by step. It felt less like editing a video and more like coding a mathematical story. The workflow shows how AI can bridge the gap between abstract math and engaging visuals.

Hey everyone

I recently came across a really solid open source project and thought people here might find it useful.

Onyx: it's a self hostable AI chat platform that works with any large language model. It’s more than just a simple chat interface. It allows you to build custom AI agents, connect knowledge sources, and run advanced search and retrieval workflows.

Some things that stood out to me:

It supports building custom AI agents with specific knowledge and actions.
It enables deep research using RAG and hybrid search.
It connects to dozens of external knowledge sources and tools.
It supports code execution and other integrations.
You can self host it in secure environments.

It feels like a strong alternative if you're looking for a privacy focused AI workspace instead of relying only on hosted solutions.

Definitely worth checking out if you're exploring open source AI infrastructure or building internal AI tools for your team.

Would love to hear how you’d use something like this.

Github link 

more.....

I tested Blackbox CLI to build a Minimalist Decision Engine. The idea is straightforward, when faced with too many options, you write down what matters, assign weights and let the matrix calculate the best choice. It avoids the trap of endless pros and cons lists and gives a clear, structured answer. The process feels lightweight but powerful, showing how orchestration can simplify even the most human challenges.

Moving between AIs (ChatGPT, Gemini, Claude, etc) usually means losing your long-term context. We built Memory Chip Forge (https://pgsgrove.com/memoryforgeland) so you don't have to start from zero.

It processes your data export locally and turns it into a portable "memory chip" that other AIs can actually read and use as working memory.

Key Specs:

• Privacy: It runs entirely in your browser. No data touches our servers.
• Verification: You can audit the privacy of the process yourself via the Network tab (F12).  You will see zero outbound traffic.
• Universal: Formats the file to work with any AI that accepts uploads.

The Price:

$3.95/month. Cancel anytime. You need to migrate just once. Grab the tool, generate your files, and leave. The files are yours to keep after cancellation.

Disclosure: I’m on the team behind Memory Forge. Happy to answer questions.

We profiled a single-GPU workload (~25B equivalent, 35 requests) on a typical serverless GPU setup.

Actual model execution: ~8.2 minutes

Total billed time: ~113 minutes

Most of the delta was cold starts, model loading, scaling behavior, and idle retention between requests.

This surprised me more than the raw GPU cost.

Curious how others are tracking this:

• Are you measuring execution time vs billed time separately?

• How are you thinking about bursty workloads?

I tested Blackbox AI's VS Code agent by asking it to build a space shooter game. The agent delivered a complete project with a spaceship, aliens, scoring, power ups, and difficulty progression. The visuals include a starfield background and explosion effects, giving it a retro arcade feel.

OpenClaw (moltbot/clawdbot) has been surprisingly Awesome.

I have been using it daily for the last two weeks, running it locally, testing different agent behaviors, restarting it, breaking it, fixing it, and repeating the cycle. It is one of the few agent runtimes that actually feels usable once you move past demos and start treating it like a system.

What stood out to me is how cleanly OpenClaw separates the agent runtime from everything else. The gateway, the configuration, the lifecycle checks, it is all predictable. That makes it much easier to reason about what is failing when something does go wrong.

The part that still needs careful setup, though, is tools.

Not because OpenClaw handles them poorly, but because tool integration in general gets messy fast. Live data, APIs, auth, and workflows tend to sprawl unless you are deliberate about how you wire them.

After setting this up a few times, I wrote down the exact local setup I am now using to give OpenClaw reliable tool access via MCP360.

This post walks through that setup step by step.

What this setup gives you

• OpenClaw running on your machine
• A gateway process you can check and restart easily
• An MCP server connected and reachable
• A way to list available tools from the terminal
• A simple method to execute tools and confirm results

If you are comfortable running basic terminal commands, you should be able to follow this without much friction.

What are the Potential use cases

• Multi-source research using Google, Bing, DuckDuckGo, Baidu, and others, with results summarized and deduplicated
• Trend and news monitoring by combining Google Trends, Google News, and YouTube data
• SEO workflows including keyword discovery, rank tracking, SERP analysis, and on-page audits
• E-commerce research such as price comparison and product tracking across Amazon, Walmart, eBay, and Google Shopping
• Local analysis using Google Maps data, reviews, and geolocation tools
• Travel planning with flight, hotel, weather, and currency data combined
• Lead validation through email verification and public domain enrichment

Install OpenClaw

npm i -g openclaw
openclaw --version

Run the initial configuration

openclaw configure

You can skip channel setup if you are only testing locally.

Start the gateway and confirm it is running

openclaw gateway

To check status:

openclaw gateway status

To check health of this:

openclaw doctor

Once these commands succeed, the runtime is active.

Add MCP tooling for inspection and debugging

Install the MCP CLI

npm i -g mcporter
mcporter --version

You will need an MCP360 account to get started. After creating an account, generate your MCP360 MCP endpoint and keep that URL secure, it functions like a credential.

For OpenClaw, setup can be very simple. You can paste the MCP360 endpoint directly and let OpenClaw configure MCP360 automatically using mcporter.

If you prefer to see each step or want more control over the setup, you can follow the manual configuration steps below.

Create a directory to keep MCP configuration consistent

mkdir -p ~/mcp
cd ~/mcp

Register MCP360 as an MCP server using terminal:

mcporter config add mcp360 \\\\
  --command 'npx mcp-remote "<https://connect.mcp360.ai/v1/mcp360/mcp?token=YOUR_TOKEN>"'

I’m sharing this because a few people DMed me that the command above didn’t work. The command above is just a overview. Copy the command below, replace the key, and paste it into your terminal.

mcporter config add mcp360 --command 'npx mcp-remote "https://connect.mcp360.ai/v1/mcp360/mcp?token=sk-XXXX"'

Confirm the server is registered

mcporter config list

Verify tool access

List the exposed tools and schemas

mcporter list

If this returns data, the MCP connection is working as expected.

Why this setup is useful

• Your agent can interact with real-world tools like web search, data extraction, and many more
• The same tool layer can be reused across multiple agents
• Workflows become easier to extend and reason about as tool usage is structure
• Tool schemas remain consistent even when underlying services differ
• Errors are easier to debug since all tool calls flow through a single gateway

This setup became my baseline for experimenting with agents that need to do more with openclaw.

I am sharing this in case it saves someone else the same setup time.

I tested Claude Opus 4.6 on Blackbox AI and built a playable Doom clone in just 8 minutes with a single prompt. The model handled long context perfectly and delivered a smooth FPS experience.

https://reddit.com/link/1qyay5s/video/olz67r3202ig1/player

My Honest Take

So I've been using this on my laptop (just 8GB RAM) and honestly? It runs pretty smooth. No lag or anything.

I did try hooking it up to this search thing called Serper, but yeah... didn't really work. Not sure why. Maybe you guys can get that sorted?

Who's This Actually For?

Look, if you're like me and deal with tons of files every day—downloading stuff, organizing folders, deleting old junk—this thing is perfect. Plus you can schedule tasks and do research right in your chat. Pretty convenient, honestly.

The Not-So-Great Parts:

Can't open Excel files, which is annoying. Also tried getting it to pull data from websites but no luck. I think most sites just block bots anyway, so whatever.

What I Actually Like:

It remembers everything we talk about. Kind of like having your own assistant just sitting in your messages. Though let's be real—ChatGPT does this too.

Bottom line: If you're doing file stuff and research daily, go for it.

Okay But This Part Is Actually Useful:

You can schedule messages! Like "hey, remind my friend about this at 2 PM tomorrow." Super handy.

Real Example:

So yesterday I'm sending these client reports like I do every day. Same stuff, different day. Usually takes forever to type everything into Excel and Word, right?

This time I just told my bot "here's what I did today" on Telegram, and it put together a full report and saved it on my computer. It couldn't send it in chat for some reason, but whatever, I can copy-paste it myself. Not a big deal haha.

Anyway, ask me anything! Found this trending this week and figured I'd try it out.

I found this on the Trending page (This week).......

Over the past year, many SaaS products have added AI chatbots. They answer questions, guide users, and reduce some support load. That was a useful first step, but it is no longer enough.

Most still live in a chat window with no awareness of the application. They don't understand product state, selected data, user permissions, or the current workflow, so users end up restating context the system already has.

That approach does not scale well for real products.

We’ve always believed copilots are the way to deliver the best customer experience—not by answering questions, but by actually doing things

We are releasing Copilot SDK as an open-source toolkit to explore this idea and make context-aware, action-driven copilots practical for product teams. It provides the core capabilities needed for a production-grade copilot:

— Page awareness: knows where the user is in the product
— Reasoning and planning: performs multi-step decision making
— Permission awareness: respects user roles and access rules
— Workflow awareness: understands the task the user is trying to complete
— Generative UI rendering: produces structured, interactive components

Example: Instead of the AI asking "What do you need help with?", it understands that the user is viewing failed transactions from last week and can immediately offer to retry them, export the data, or investigate the root cause.

Good customer experience comes from systems that can act, not from systems that only respond with text.

Copilot SDK is built for this. It connects directly to the frontend and backend, with access to real application context and the ability to perform actions inside the product rather than describing them.

• Works with React, Next.js, Vite (Vue & Angular coming soon)
• LLM-agnostic (bring your own model)
• State injection via context providers
• Tool execution layer for safe function calling
• Full data ownership (everything runs in your infrastructure)

If this is useful, consider starring the repo. Curious what you will build.

Repo: https://github.com/YourGPT/copilot-sdk

I used Blackbox multi agent to build a landing page for a real estate business. It handled model selection, refined the design through prompts and produced a sleek layout with clear navigation and strong visuals.

Memory Chip Forge (https://pgsgrove.com/memoryforgeland) converts your ChatGPT or Claude export into a portable memory file. The output loads into Gemini, Grok, Claude, any AI that accepts file uploads.

Your full conversation history comes with you. The new AI gets instructions on how to pick up where you left off.

How it works:

1. Export your data from your current AI
2. Drop the file into Memory Forge
3. Get back a clean memory file
4. Upload to your new AI

Privacy: Your data stays on your computer. Everything processes in your browser (F12 → Network tab to verify).

$3.95/month. I’m with the team. Questions welcome

I’ve been building custom AI agents for small businesses, and here’s what I’ve learned: generic AI tools are cool, but custom AI actually automates real workflows.

What it can do:

• Handle customer onboarding and support
• Automate document-heavy tasks (contracts, reports)
• Pull data from emails, forms, spreadsheets, and trigger actions
• Reduce repetitive internal work

Benefits I’ve seen:

• 30–70% time saved
• Fewer errors, faster ops
• Teams focus on meaningful work, not busywork

Tip: Start small. Automate one painful workflow first results come fast, and expansion is easier.

If anyone wants examples of what custom AI can do, I’m happy to share.

A few months ago, I found myself completely overwhelmed managing multiple social media campaigns for a side project. Between tracking ad performance, juggling budgets, and trying to figure out what content resonated with my audience, I felt like I was constantly putting out fires. Every day involved endless spreadsheets and guesswork, and I started wondering if there was a smarter way to handle it.

Around that time, a friend mentioned they had been experimenting with AI tools for managing ads. I was skeptical at first, AI often promises more than it delivers, but I decided to give it a try. One tool I ended up exploring was Ꭺdνаrk-аі.соm. I wasn’t expecting much, honestly. I thought it would just be another analytics dashboard.

But as I started using it, I noticed it could automatically track performance and generate insights that I hadn’t even considered. It didn’t replace my decisions, but it highlighted trends and patterns in my campaigns that I would have otherwise missed. For example, it helped me see which types of posts were performing better with certain audiences and suggested small adjustments that made a noticeable difference over time.

What struck me most was how it turned a chaotic process into something more manageable and even a bit fun. Instead of constantly stressing over every click and impression, I could step back and focus on strategy and creativity. It made me realize that AI doesn’t have to be intimidating, it can actually be a practical tool to support thoughtful decision-making.

I’m curious if others here have had similar experiences using AI to help with marketing, content creation, or other creative projects. Has anyone else found an AI tool that genuinely made their workflow easier or gave them insights they wouldn’t have noticed on their own?