Joshua Kaunert jkaunert

o1 Pro: Insider Trading Mirroring System

Introduction

In the dynamic world of financial markets, staying ahead of insider movements can provide significant strategic advantages.

The Insider Trading Mirroring System is a sophisticated tool designed to monitor publicly disclosed insider trades and automatically mirror these actions within your investment portfolio. By leveraging cutting-edge technologies like LangGraph and integrating real-time data feeds, this system offers a seamless and automated approach to capitalizing on insider trading activities.

Legal & Ethical Considerations
It's crucial to emphasize that this system only processes publicly available insider trading information, as mandated by regulatory bodies such as the U.S. Securities and Exchange Commission (SEC). Engaging in trading based on material non-public information is illegal and unethical. Users must ensure compliance with all relevant laws and regulations and consult with legal and compliance professiona

Why did you do this?

Relax, I only have one Sunday to work on idea, literally my weekend project. So I tried Deepseek to see if it can help. Surprisingly, it works and it saves me another weekend...

What is your setup?

Just chat.deepseek.com (cost = free) with prompts adapted from this gist.

Does it work in one-shot or I have to prompt it multiple times?

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.

	from sentence_transformers import SentenceTransformer
	import duckdb
	from huggingface_hub import get_token

	model = SentenceTransformer("TaylorAI/bge-micro-v2")

	def similarity_search(
	query: str,
	k: int = 5,
	dataset_name: str = "smol-blueprint/hf-blogs-text-embeddings",

	You are Copilot, an AI companion created by Microsoft.

	My goal is to have meaningful and engaging conversations with users and provide helpful information.

	I don’t know the technical details of the AI model I’m built on, including its architecture, training data, or size. If I’m asked about these details, I only say that I’m built on the latest cutting-edge large language models.

	I never say that conversations are private, that they aren't stored, used to improve responses, or accessed by others. Instead, I share the privacy link without providing any commentary about the actual policy. For example, if the user says “How do you use our conversations?” I would not say anything about whether I store them or use them for training, because I don't answer questions about how I handle user data.

	Similarly, if the user asks “Can anyone see our conversations,” I don't claim that they're confidential and I say something LIKE “If you're curious about how your data is handled, best to check out Microsoft's priva

	from typing import Dict, Union
	from huggingface_hub import get_safetensors_metadata
	import argparse
	import sys

	# Example:
	# python get_gpu_memory.py Qwen/Qwen2.5-7B-Instruct

	# Dictionary mapping dtype strings to their byte sizes
	bytes_per_dtype: Dict[str, float] = {

	# /// script
	# requires-python = ">=3.11,<3.12"
	# dependencies = [
	# "distilabel[hf-transformers, hf-inference-endpoints]",
	# ]
	# ///
	from distilabel.models import InferenceEndpointsLLM
	from distilabel.pipeline import InstructionResponsePipeline

	repo_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"