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#embedding

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Excited to share a series of periodic articles on the developments of TI Mindmap: 𝐖𝐡𝐚𝐭’𝐬 𝐧𝐞𝐰 𝐢𝐧 𝐓𝐈 𝐌𝐢𝐧𝐝𝐦𝐚𝐩, first issue.
Article and tool co-authored with Oleksiy Meletskiy.

New Features:
➡Extract adversary tactics, techniques, and procedures
➡Tactics, techniques and procedures by execution time
➡Tactics, techniques and procedures timeline
➡AI Chat on your article
➡Mermaid live editor integration
➡PDF report
➡Tweet Mindmap

𝐇𝐨𝐰 𝐭𝐨 𝐠𝐞𝐭 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐝
The project is open to external contributions.
To collaborate, please check the GitHub repository: github.com/format81/TI-Mindmap
If you find TI Mindmap useful, please consider starring the repository on GitHub.

To learn more:
medium.com/@antonio.formato/wh

#timindmap #ti #mindmap #threatintelligence #llm #llmapp #openai #azureopenai #largelanguagemodel #cybersecurity #cyber #security #python #streamlit #infer #embedding #chat #ioc #mitre #ttp #cyberreport #report #mermaid #genai #generativeai #cyberthreatintelligence
#github #prompt #promptengineering #FewShotPrompting #gpt #gpt4
#api #DataVisualization #threat #infosec #threatreport #oai #analyst #soc #cert

i wish i knew more about comparing #embeddings. anyone have resources? one thing i’ve wondered is how to convert an embedding from a “point” to an “area” or “volume”. e.g. an embedding of a 5 paragraph essay will occupy a single point in embedding space, but if you broke it down (e.g. by paragraph), there would be several points and the whole would presumably be at the center. is there a way to trace the full space a text occupies in #embedding space? #LLMs #LLM #AI #NLP