Automation-friendly framework for Continuous Testing by
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Updated
Sep 1, 2026 - Python
Automation-friendly framework for Continuous Testing by
Multi-agent demo platform for Titans (arXiv:2501.00663) — neural networks that learn to memorize at test time. 7 AI agents, native desktop UI.
Unofficial Implementation of Titans: Learning to Memorize at Test Time
Complete PyTorch reproduction of Google's TITANS, MIRAS, and NL neural memory papers. 52 tests, 87% coverage, Docker support.
This repository contains an experimental implementation of the Titans Transformer architecture for sequence modeling tasks. The code is a personal exploration and may include errors or inefficiencies as I am currently in the learning stage. It is inspired by the ideas presented in the original
Titans: Learning to Memorize at Test Time
Visual animated walkthroughs of the DeepMind "Titans: Learning to Memorize at Test Time" paper using Manim, aimed at making complex ML concepts accessible.
Educational demo of Google's Titans surprise-based memory mechanism with PyTorch, interactive notebooks, and visualizations
Python based MCP server for BlazeMeter API Monitoring and Testing tool
Memory-centric inference system: frozen RWKV/Mamba backbone + Titans associative memory, test-time training, vector DB — learns at inference time
Self-hostable memory for AI agents. A neural surprise gate decides what to keep for zero LLM tokens, and nothing leaves your machine. Memory and a Gemma 4 model run on one AMD MI300X, ROCm end to end. Free, open source for devs, sovereign for enterprise.
Durable background execution for AI agents - jobs, DAG workflows, scheduling, recovery and MCP. Signed releases, one-line install. Free tier.
Titans-style neural long-term memory for Qwen transformers
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