Image 1 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 2 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 3 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 4 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 5 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 6 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 7 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 8 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 9 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents
Image 10 — AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents

AI Agent Infographics - how to make a self-evolving agent with sandboxing and autonomous subagents

This is a series of infographics I made as part of designing my personal AI Agent system. It shows the fundamentals of how I made a microkernel architecture that allows the agent to write their own code and self-evolve. Agent code is written using a custom SDK, validated, and then executed within a sub-processed sandbox to ensure security. The agent cannot do anything without going through a security policy first. This makes it better than Pi, which has no security like this. It's also better than Hermes and OpenClaw because the kernel is 25,000 lines of code as opposed to over a hundred thousand. It's a truly lightweight and powerful system, but that's enough self-promotion! The point here is that I hope these infographics can give you some ideas for how to improve your own agent systems.

Thoughts? Feedback is appreciated!

u/donotfire — 1 day ago
▲ 11 r/agenticAI+1 crossposts

Infographics I made for designing a safe and practical AI Agent system

I apologize if this isn't the right place to post this

u/donotfire — 1 day ago
▲ 2 r/agenticAI+1 crossposts

Demo of Second Brain completing a 20-minute task

I asked my agent to make an ASCII art generator, then schedule a subagent to deliver me the daily news using the ASCII art as the headline. This demo shows off the sandboxed code system and permissions policy. All scripts and plugins the agent writes are validated and sandboxed, so it’s completely safe. It also shows some of the UI, which lets you categorize conversations and peek in on subagent conversations. You can load and interact with subagent conversations like any other. The UI is a PWA with Tailscale. Works on iOS like any other app.

u/donotfire — 1 day ago

How to Build a Memory

Supports AgentSkills.io 

Skill = Instruction Document + Metadata + Resources
Note = General-Purpose Document + Metadata
Memories = Skills + Notes

Two Parts:

  1. Curation: note/skill creation, updating, and deletion (CRUD).
  2. Retrieval: of skills and notes.

Curation techniques:

  1. Mid-conversation, by the agent: Give the agent the curation tool that they can use mid-conversation, as needed at opportune times. Relies on agent intelligence.
    1. Pros: intentional.
    2. Cons: the agent may forget to do so.
  2. After conversation, by a subagent: Whenever a conversation ends, a subagent is prompted to investigate and read it, and then use the curation tool to perform skill/note CRUD.
    1. Pros: happens automatically.
    2. Cons: the subagent may do too much or too little.

The curation tool stores skills/notes in a programmatic fashion, with args for the operation (create, update, delete), name, type, description, and body — the timestamp is supplied automatically. 

Retrieval techniques:

  1. Mid-conversation, by the agent: Give the agent a retrieval tool which receives the name of a skill or note and loads its information into context, on demand. (Accuracy improves with a better system prompt.)
    1. Pros: intentional.
    2. Cons: the agent may forget to do so.
  2. In the system-prompt: A service to display the names of relevant skills and notes in the system prompt, along with their descriptions. Use a search algorithm to find the most relevant skills and notes to the current situation, and display those.
    1. Pros: More relevant results, highly scalable. 
    2. Cons: may leave out important files.

The retrieval tool allows the agent to read memories easily. When the conversation ends, the subagent looks at every time the tool was used to determine whether the retrieval was actually useful, and what could be done better. Pruning occurs when a memory hasn’t been retrieved for a long time, despite being suggested in the system prompt. This provides a signal that the memory should be deleted.

In sum:

  1. Two plugins for curation: curation tool and the after-conversation subagent task.
  2. Two plugins for retrieval: retrieval tool and the system-prompt injecting service.

Clear winners:

  • Build a dedicated memory curation tool (tool_curate_memory) and then make both the main agent and subagent use the same tool. All memory curation thus occurs with the same tool. 
  • Embed the text from skills and notes using the data pipeline. Develop a search algorithm that finds relevant memories to the current conversation context. Display the top results, but show that other results exist — like “20 of 67 results”.
u/donotfire — 14 days ago

How to Build a Memory

Supports AgentSkills.io-style skills

Skill = Instruction Document + Metadata + Resources
Note = General-Purpose Document + Metadata
Memories = Skills + Notes

Two Parts:

  1. Curation: note/skill creation, updating, and deletion (CRUD).
  2. Retrieval: of skills and notes.

Curation techniques:

  1. Mid-conversation, by the agent: Give the agent the curation tool that they can use mid-conversation, as needed at opportune times. Relies on agent intelligence.
    1. Pros: intentional.
    2. Cons: the agent may forget to do so.
  2. After conversation, by a subagent: Whenever a conversation ends, a subagent is prompted to investigate and read it, and then use the curation tool to perform skill/note CRUD.
    1. Pros: happens automatically.
    2. Cons: the subagent may do too much or too little.

The curation tool stores skills/notes in a programmatic fashion, with args for the operation (create, update, delete), name, type, description, and body — the timestamp is supplied automatically. 

Retrieval techniques:

  1. Mid-conversation, by the agent: Give the agent a retrieval tool which receives the name of a skill or note and loads its information into context, on demand. (Accuracy improves with a better system prompt.)
    1. Pros: intentional.
    2. Cons: the agent may forget to do so.
  2. In the system-prompt: A service to display the names of relevant skills and notes in the system prompt, along with their descriptions. Use a search algorithm to find the most relevant skills and notes to the current situation, and display those.
    1. Pros: More relevant results, highly scalable. 
    2. Cons: may leave out important files.

The retrieval tool allows the agent to read memories easily. When the conversation ends, the subagent looks at every time the tool was used to determine whether the retrieval was actually useful, and what could be done better. Pruning occurs when a memory hasn’t been retrieved for a long time, despite being suggested in the system prompt. This provides a signal that the memory should be deleted.

In sum:

  1. Two plugins for curation: curation tool and the after-conversation subagent task.
  2. Two plugins for retrieval: retrieval tool and the system-prompt injecting service.

Clear winners:

  • Build a dedicated memory curation tool (tool_curate_memory) and then make both the main agent and subagent use the same tool. All memory curation thus occurs with the same tool. 
  • Embed the text from skills and notes using the data pipeline. Develop a search algorithm that finds relevant memories to the current conversation context. Display the top results, but show that other results exist — like “20 of 67 results”.
reddit.com
u/donotfire — 14 days ago

Moved a Mac mini, KVM switch, and sound controls to hang from the bottom of my desk.

Mac Mini, KVM switch, and sound control system mounted to metal pegboard using Velcro straps, then attached to underside of desk using special tape. I designed it this way so I could have it portable for when I move cities.

Pics:

  1. Front of pegboard
  2. Back of pegboard showing Velcro straps
  3. Top of desk
  4. Underside of desk. Previously, all that stuff was on top.

Question: how do you guys clean up loose wires? I am using Velcro ties to loop the long bits together and shorten it, but it’s not ideal.

u/donotfire — 1 month ago

It’s a curse being on these projects because you see AI everywhere you go

I’ve been trained to recognize even the smallest hint of AI and now I see it everywhere. Ay caramba.

Ignorance is bliss.

reddit.com
u/donotfire — 2 months ago

Project V

Did you guys remember when they said “Just finish the screener and you’ve got the bonus!” Wish I took a darn screenshot. They made it sound like a guaranteed thing. Meanwhile the number of people they actually allowed in was far less than the total. Anybody got the actual number? How many did they let in?

False advertising…

reddit.com
u/donotfire — 2 months ago
▲ 2 r/AgentsOfAI+1 crossposts

I built an alternative to OpenClaw and Hermes: Second Brain

Apologies for the self-promotion. If my post doesn't fit the rules, just let me know.

I built an agent called Second Brain. It's designed with a microkernel architecture and the core is pure Python. It's about 15,000 lines of code — compact. Everything else exists as an installable plugin: tools for the agent to use, autonomous database tasks, backend services like the LLM and OCR, even commands and frontends like Telegram and Discord are totally modular. (It can use standard skills as well.)

The conversations are persisted in SQL and the runtime is a state machine, so everything is fully recoverable on crash, even mid-turn commands can be revived. I have a sponsor but they are optional and add no bloat. This project started as a simple RAG agent but now it has expanded to become much more, with fully self-expandable plugins. You can use it to build a website, a robot, a gadget, an arbitrary coding project. It's like a miniature OS for LLM agents.

Why you would want Second Brain instead of another agent:

  1. It's much smaller and much, much less bloated than Hermes or OpenClaw. The base Second Brain 'kernel' is tiny and boots almost instantly. You can even run it on a phone.
  2. It works on Windows, Mac, and Linux.
  3. There's a SQL backend which agents can use to explore data, including conversations and errors. Everything is fully diagnoseable, because everything is written down and logged.
  4. The application has security: it quarantines plugins that use too much memory, and has timeouts for tasks, tools, and service loads. It can't protect against a plugin calling os._exit(), but it detects crashes and stalls (using a Linux-like heartbeat), and revives automatically. It's stable.
  5. Agents can create their own tools, tasks, services, commands, and frontends on the fly. You can design HTML webpages as custom frontends for whatever you want to build.

Let me know if you guys have feedback on the design. I'd love to hear your ideas. Thanks!

reddit.com
u/donotfire — 2 months ago

Worried about upcoming intake appointment

So I have an upcoming appointment with a ketamine provider and I am somewhat worried because I have a history of drug use and don’t want to come off as someone who is just trying to get high. I’ve learned that it’s generally best to be as transparent as possible with doctors but I don’t know if past drug use will immediately disqualify me.

I am interested in trying ketamine because I have basically tried everything else. I don’t want to just have weird trippy experiences, but I want to find actual insights because I have hit a *brick* *fucking* *wall* in my career. It’s driving me insane, and I need answers. I know ketamine isn’t like a crystal ball but if it can bring me a peace and acceptance, then it will be worth it.

I have an actual history of psychiatric illness (bipolar II). Hospitalizations occurred about five years ago but it’s more under control now. I obviously wouldn’t want to spiral out more than I am.

How can I best prepare for my provider meeting? Thank you all.

reddit.com
u/donotfire — 3 months ago

Fractals, attractors, and waves with various filters on top.

I made these using a website I created: https://second-brain.art/

It lets the user mix and match various backgrounds, filters, and objects. It now has more than 100 different "skills," including famous fractals and glitch effects. You can stack up to six layers, each with various controls.

There are two ways to build: Using the AI chatbot, or manually using the search bar in the control panel. It's free to use and open-source.

u/donotfire — 3 months ago

Fractals I rendered on my “AI Math Art” website

The website is https://second-brain.art

In order of appearance:

  1. Mandelbrot with pixel sort filter
  2. Julia Set with dendritic preset, fisheye and chromatic aberration filters
  3. Buddhabrot with bloom glow filter and white border
  4. Julia set zoomed in
  5. Mandelbrot with white border

To be clear: It doesn’t use AI in the traditional sense to generate the image. The AI decides what code to execute in a Python subprocess sandbox, then the results from that are shown. No image models used.

Anybody can use the chat function to render any of these fractals themselves, remix them, and add crazy filters. It’s a fully customizable studio. The AI part is even optional!

u/donotfire — 3 months ago

Tossed aside like a dirty rag

Removed from Project S with no explanation. No chance to say anything. No warning. Feeling replaceable and angry. Fuck these people.

reddit.com
u/donotfire — 3 months ago