๐ฅ Trending Repository: superset
๐ Description: Apache Superset is a Data Visualization and Data Exploration Platform
๐ Repository URL: https://github.com/apache/superset
๐ Website: https://superset.apache.org/
๐ Readme: https://github.com/apache/superset#readme
๐ Statistics:
๐ Stars: 69.7K stars
๐ Watchers: 1.5k
๐ด Forks: 16.4K forks
๐ป Programming Languages: TypeScript - Python - Jupyter Notebook - JavaScript - HTML - Shell
๐ท๏ธ Related Topics:
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๐ง By: https://t.iss.one/DataScienceM
๐ Description: Apache Superset is a Data Visualization and Data Exploration Platform
๐ Repository URL: https://github.com/apache/superset
๐ Website: https://superset.apache.org/
๐ Readme: https://github.com/apache/superset#readme
๐ Statistics:
๐ Stars: 69.7K stars
๐ Watchers: 1.5k
๐ด Forks: 16.4K forks
๐ป Programming Languages: TypeScript - Python - Jupyter Notebook - JavaScript - HTML - Shell
๐ท๏ธ Related Topics:
#react #python #flask #data_science #bi #analytics #superset #apache #data_visualization #data_engineering #business_intelligence #data_viz #data_analytics #data_analysis #sql_editor #asf #business_analytics #apache_superset
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: memU
๐ Description: Memory infrastructure for LLMs and AI agents
๐ Repository URL: https://github.com/NevaMind-AI/memU
๐ Website: https://memu.pro
๐ Readme: https://github.com/NevaMind-AI/memU#readme
๐ Statistics:
๐ Stars: 3.6K stars
๐ Watchers: 28
๐ด Forks: 241 forks
๐ป Programming Languages: Python
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Memory infrastructure for LLMs and AI agents
๐ Repository URL: https://github.com/NevaMind-AI/memU
๐ Website: https://memu.pro
๐ Readme: https://github.com/NevaMind-AI/memU#readme
๐ Statistics:
๐ Stars: 3.6K stars
๐ Watchers: 28
๐ด Forks: 241 forks
๐ป Programming Languages: Python
๐ท๏ธ Related Topics:
#python #agent #open_source #benchmark #framework #ai #memory #mcp #sandbox #ai_agents #llm #agentic_framework #agentic_workflow #ai_companion #agentic_ai #ai_roleplay
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฉโ๐ป FREE 2026 IT Learning Kits Giveaway
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๐น Video downloader from YouTube and other platforms
tuitube is a text-based interface for downloading videos from YouTube, ๐, Twitch, Instagram, and Bilibili using yt-dlp. A convenient tool for those who prefer the command line.
๐ Key features:
- Support for multiple video platforms
- Uses yt-dlp for downloading
- Simple text interface
- Easily configurable and command-line driven
๐ GitHub: https://github.com/remorses/tuitube
#python #YouTube
https://t.iss.one/DataScienceNโ
tuitube is a text-based interface for downloading videos from YouTube, ๐, Twitch, Instagram, and Bilibili using yt-dlp. A convenient tool for those who prefer the command line.
๐ Key features:
- Support for multiple video platforms
- Uses yt-dlp for downloading
- Simple text interface
- Easily configurable and command-line driven
๐ GitHub: https://github.com/remorses/tuitube
#python #YouTube
https://t.iss.one/DataScienceN
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Do you want an LLM on your computer: to work offline, not leak data, and seamlessly integrate into a bot? Then let's take DeepSeek Coder and get started!
pip install -U transformers accelerate torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "deepseek-ai/deepseek-coder-6.7b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.float16, # if the GPU supports fp16
device_map="auto" # if there's a GPU โ it will use it
)
model.eval()
prompt = "Write a Telegram feedback bot on aiogram"
inputs = tokenizer(prompt, return_tensors="pt")
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.inference_mode():
outputs = model.generate(
**inputs,
max_new_tokens=180,
do_sample=True, # IMPORTANT: otherwise the temperature doesn't affect
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
โ works locally (after downloading the weights);
โ easily integrates into Telegram/Discord/CLI;
โ can be accelerated on the GPU via device_map="auto".
If memory is limited โ there are quantized versions (4bit/8bit) and GGUF.
#python #soft #code
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