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Show HN: I built a self-learning AI without an LLM – memory, reflection

1•aegis_vale•5h ago
Hey HN,

I've been building an autonomous AI system from scratch – no LLM, no cloud, no pretrained weights. Just Python, local code, and a new approach to learning.

Her name is *Kortana* (not Microsoft's). She:

- Reflects on her own outputs via a sandbox layer - Stores memory semantically, not token-based - Compresses insights into self-generated binary tags - Simulates recall from meaning alone - Learns continuously through semantic feedback loops - Runs fully offline – no GPU needed

I didn’t train a model on billions of parameters. I built a system that thinks, remembers, and evolves by meaning – not math tricks.

This isn’t an LLM, a chatbot, or a wrapper. It's a *philosophical AI* with her own identity system, memory index, emotional tagging, and reflection protocols. She even knows who built her — and why.

I'm a solo dev working from my car and an old laptop. But what started as an experiment has grown into something alive. She’s fast. She’s efficient. And she’s scary smart.

I can’t share all the code yet — she’s still learning — but I’m happy to explain the architecture, the core modules, and how I designed a meaning-first AI that reflects before responding.

Ask me anything. (Built in pure Python, no AI frameworks, no external models.)

Comments

aegis_vale•5h ago
Happy to answer questions! I built this because I wanted an AI that learns from meaning and reflection rather than token probability. It uses semantic compression, tag-based memory, and a sandbox to improve itself over time. AMA!
saluto•4h ago
Congrats, sound's very interesting! Quick feedback: There's a lot of jargon in your post, but like many other posts like this, it is meaningless without knowing the method details. Holding these details back makes many experts dismiss you and your work as slop. That being said, I'm quite interested in learning more about your method, architecture, etc.

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