KRAFTON Launches ‘Raon,’ Its First Open-Source AI Model Family - Tribune India
KRAFTON Launches ‘Raon,’ Its First Open-Source AI Model Family Tribune India
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AI offensive cyber capabilities are doubling every six months, safety researchers find
AI models are rapidly improving at exploiting security vulnerabilities. According to a new study, their offensive cyber capability has been doubling every 5.7 months since 2024, with Opus 4.6 and GPT-5.3 Codex now solving tasks that take human experts about three hours. The article AI offensive cyber capabilities are doubling every six months, safety researchers find appeared first on The Decoder .
Error While using langchain with huggingface models
from langchain_core.prompts import PromptTemplate from langchain_community.llms import HuggingFaceEndpoint import os os.environ[“HUGGINGFACEHUB_API_TOKEN”] = “hf_your_new_token_here” prompt = PromptTemplate( input_variables=[“product”], template=“What is a good name for a company that makes {product}?” ) llm = HuggingFaceEndpoint( repo_id=“mistralai/Mistral-7B-Instruct-v0.3”, temperature=0.7, timeout=300 ) chains = prompt | llm print(“LLM Initialized with Token!”) try: response = chains.invoke({“product”: “camera”}) print(“AI Suggestion:”, response) except Exception as e: print(f"Error details: {e}") when i run this i get Value error can anyone help me out? Its a basic prompt template and text gen code but still it doesnt work i used various models from Huggingface and its not working well
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trunk/02711cea997665055814b95264c4dcd8fb605a0a: [torchtitan hash update] update the pinned torchtitan hash (#178727)
This PR is auto-generated nightly by this action . Update the pinned torchtitan hash. Pull Request resolved: #178727 Approved by: https://github.com/pytorchbot
Docling Studio — open-source visual inspection tool for Docling pipelines
Hey everyone I built Docling Studio , an open-source visual inspection layer for Docling. The problem: if you’ve used Docling, you know the extraction engine is powerful — but validating outputs means digging through JSON and mentally mapping bounding box coordinates back to the original pages. No visual feedback loop. What Docling Studio does: Upload a PDF, configure your pipeline (OCR engine, table extraction, enrichment) Run the conversion Visually inspect every detected element — bounding boxes overlaid on original pages, element types, content preview on click Two modes: local (embedded Docling) or remote (Docling Serve) Stack: Vue 3 / TypeScript + FastAPI / Python, fully Dockerized (multi-arch), 180+ tests. Why it matters for RAG workflows: without seeing what Docling extracts, it’s
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