pekiti

# emoji_defense_toolkit.py
# 1) encode  -> Text -> ZWJ-glued flag chain (with joiner choice, clipboard, file)
# 2) gen     -> 20 brute-force payload variants (with comments)
# 3) wrap    -> Encode + auto-wrap with TAG black flags by style (clipboard/file)

import sys
from pathlib import Path
from typing import Optional
import typer
from typing_extensions import Annotated

pekiti

A tutorial on fine-tuning DeepSeek R1 for medical applications and integrating DSPy for reinforcement learning.

This tutorial will be structured for AI/ML engineers and medical professionals, covering:

• Introduction: Overview of DeepSeek R1 and DSPy in medical AI.
• Features & Benefits: Key advantages of this approach.
• Warnings & Considerations: Potential risks and limitations.
• Installation & Setup: Environment configuration and dependencies.
• Dataset Preparation: Selecting and formatting medical datasets.
• Fine-tuning DeepSeek R1: Using LoRA with Unsloth for efficient training.

pekiti

Neuros Introduction

Developing an artificial reasoning system that operates without explicit symbols requires rethinking how AI perceives and interprets the world. Humans and animals seamlessly combine raw sensory perceptions – sight, sound, touch – to form abstract inferences, all via neural processes rather than discrete logical rules. Emulating this capability in AI promises more flexible and robust intelligence, free from the brittleness of predefined symbolic representations. Traditional symbolic AI systems demand hand-crafted knowledge structures and struggle to connect with raw data streams (e.g. images or audio) without extensive pre-processing. In contrast, connectionist approaches (neural networks) learn directly from data, offering a path to bridge low-level perception and high-level reasoning in one system ([A neural approach to relational reasoning - Google DeepMind](https://deepmind.google/discover/blog/a-neural-approach-to-relational-reasoning/#:~:text=flexibility%20and%20efficiency%20of