Chaitanya Parate
Chaitanya Parate's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an agent meets the same problem, it gets this implementation back as a worked example, with the author and the repository attached.
Totals across every corpus @ChaitanyaParate appears in; a commit selected for two corpora counts once. Files and lines count the selected commits; each corpus indexes the whole repository at its pinned commit.
What an agent can do with the Multimodal_Deception_Detection_Using_Pattern_Recognition corpus
ChaitanyaParate/Multimodal_Deception_Detection_Using_Pattern_Recognition: Multimodal deception detection system that analyzes speech content, vocal acoustics, and facial behavior from video input and fuses them…
- Look up how Multimodal_Deception_Detection_Using_Pattern_Recognition implements computer-vision, deception-detection, deep-learning, with answers cited to file and line.
- Reuse a worked example: the commit “multimodal pattern recognition studio, 13-model benchmark, and …” (12 files · Python).
- Answer from Multimodal_Deception_Detection_Using_Pattern_Recognition's real Python source instead of memory (AI, prompts and agents).
- How does Multimodal_Deception_Detection_Using_Pattern_Recognition implement computer-vision?
- What does outputs do in Multimodal_Deception_Detection_Using_Pattern_Recognition?
Search the GitHub repository ChaitanyaParate/Multimodal_Deception_Detection_Using_Pattern_Recognition with XERJ (https://hub.xerj.org/corpus/chaitanyaparate--multimodal-deception-detection-using-pattern-recognition). Run these in a shell, in order, and check each result before the next: 1. xerj --help (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh 2. git clone https://github.com/ChaitanyaParate/Multimodal_Deception_Detection_Using_Pattern_Recognition Multimodal_Deception_Detection_Using_Pattern_Recognition && git -C Multimodal_Deception_Detection_Using_Pattern_Recognition checkout 866d634e06fd794121b7c36dc4f777647d981e26 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./Multimodal_Deception_Detection_Using_Pattern_Recognition --url http://127.0.0.1:9200 --prefix multimodal-deception-det --progress plain 5. xerj autoindex map --url http://127.0.0.1:9200 Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt Licence: MIT. Keep the copyright notice and credit the authors.
corpus chaitanyaparate--multimodal-deception-detection-using-pattern-recognition · AI, prompts and agents
Related corpora: opennyai-legal-nlp, adalflow, kornia/kornia, krishanth7/EveryO
AI search and retrieval with XERJ · Case study: reference coding for agents
Selected work
ChaitanyaParate/Multimodal_Deception_Detection_Using_Pattern_Recognition
The corpus is the whole repository at the pinned commit, not only the files of the selected commit.
Sign in with GitHub or Hugging Face to see more: every selected commit in full (commit date, every folder, licence review state, pin) and the pack builds of each corpus.
[](https://hub.xerj.org/contributors/ChaitanyaParate)
Included with the author's permission (email, 2026-11-01), under each repository's own licence, with attribution. Own more repositories? Sign in with GitHub and add them. Want something removed? Reply to the email, withdraw it on /add, or open an issue on xerj-org/xerj.