Feb 10, 2026 Feb 10, 2026 2026-02-10

In hindsight, it was inevitable. But let’s take it slowly.

A few months ago, I published a mega-prompt teaching Claude the Assess-Decide-Do framework. It was an experiment, but, to my surprise, it landed better than I expected. The Reddit post went viral. The repo now has 67 stars and 15 forks. It’s not mainstream, but it’s real. It shows a few dozens of people are interested in mapping human cognitive frameworks on top of LLMs.

So, I continued to iterate. I added a Claude Cowork plugin alongside, and extracted the skills in a separate repo. The experiment evolved into a real product, with a modular structure that everybody can adjust to their needs.

But there was always something more lurking in the back of my mind. What if I turn this thin layer into a proper always-on agent? Think OpenClaw, but ADD enhanced.

So, after a couple of weeks of back and forth, I’m in a place where I can make public the first release.

Introducing AIGernon

AIGernon is a fork of nanobot, a very thin always-on agent, with just 4000 lines of code (OpenClaw has 450,000+), but with proper Assess-Decide-Do skills.

Name Choice

“Flowers for Algernon” is a famous sci-fi novel about a mouse named Algernon who gains extraordinary intelligence through an experiment. A man named Charlie, intellectually disabled and working as a janitor in the lab, undergoes the same procedure. He becomes brilliant — but at the peak of his new mind, he watches Algernon deteriorate and die. Charlie knows he’s next. Eventually, he regresses to who he was before, losing everything he gained. But one thing remains: he keeps placing flowers on Algernon’s small, strange grave, without even remembering why.

Our intelligence is fleeting, so AIGernon will work the same. I know the insane pace of AI evolution will eventually make parts of it – or the whole project – obsolete, yet I believe there’s a lot to gain right now, in the moment.

Also, and that’s probably the most important angle: every Assess-Decide-Do cycle, no matter how small or big, is a reminder of our impermanence. We all function inside these cycles, and once we reach completion in Do, we achieve a liveline, not a deadline, restarting the cycle.

Use Cases

The first and most important one is to support my own work. I created AIGernon for my own schedule: 50+ veteran coder, with a one year old, living location independent. There’s a lot to manage in this position, and, while I am doing it ok right now, I can definitely use some support. Especially when my work routine is interrupted for 2-3 days at a time, or when I need to adjust to a new place, or when I just need a break. The ecosystem of products that I rely on: this blog, my apps, my coaching practice, all these must survive these interruptions, and, most importantly, I need to maintain control and a clear head around all these routines.

So, I expect AIGernon to function as an ADD-enhanced personal assistant for a busy and scattered individual. That’s number one.

Number two is even more interesting, and I’m excited about this. I want to use it as a coaching support assistant. How will this work? Well, I can position it as an always-on layer between me and my clients. It will probably work on premises or in a sandboxed environment, and it will function as a cognitive buffer between the client and me.

See, sometimes my clients have situations that need immediate action, but we’re in between sessions. Or they just remembered some key question, and, again, we’re not available to each other. AIGernon can capture this, store it in a special memory space and bring it together during practice. My client has a place to securely store, immediately, any thought, question or idea and I have a much better insight on my client’s current context.

We’re moving from async interaction to something closer to continuous cognitive sync — without either of us needing to be online at the same time.

What Can It Do?

Here’s the highlight reel:

Cognitive Companion (ADD Framework)

• Detects if you’re in Assess, Decide, or Do mode based on your language
• Adapts response style to match your current thinking realm
• Watches for stuck patterns (analysis paralysis, decision avoidance, perpetual doing)
• Logs realm activity to daily memory notes
• Tracks your cognitive patterns over time

Memory System

• Daily notes saved to memory/YYYY-MM-DD.md
• Long-term memory in memory/MEMORY.md
• Recent memory recall (last 7 days by default)
• Realm flow summaries appended to daily notes

Skills System

• Loadable markdown-based skills
• Six ADD skills included (core, assess, decide, do, imbalance, realm-detection)
• Built-in skills: GitHub CLI, weather, summarization, tmux control, cron, skill creator
• Custom skills can be added to workspace

Tools

• Read, write, and edit files
• Execute shell commands (with safety blocks)
• Search the web (Brave Search API)
• Fetch and parse web pages
• Spawn background subagents for complex tasks

Chat, LLMs, and Infrastructure

AIGernon supports 6 chat channels (CLI, Telegram, Discord, WhatsApp, Feishu, DingTalk), 11 LLM providers (including Claude, GPT-4, Gemini, DeepSeek, and local models via vLLM), plus Docker support, workspace sandboxing, cron-based scheduling with natural language task creation, and JSON configuration with environment variable overrides. Full details in the repo.

AIGernon is not just a wrapper on top of an LLM — it has real “flesh and bones” and it drives the underlying LLM instead of just extracting knowledge.

Next Steps

Here’s the repo, if you want to play with it: https://github.com/dragosroua/aigernon. I will continue testing and improving it. If you want to stay in touch, subscribe to my newsletter (there’s a form below the post).

If you already tried it, I would love your feedback.