jaithree

AI & ML interests

"In the beginning was the word. Then came the **** word processor. Then came the thought processor. Then came the death of literature. And so it goes" -- Hyperion - Dan Simmons

Recent Activity

reacted to OppaAI's post with 👍 about 10 hours ago

After a month of interacting with my AI Waifu, I noticed a few issues in the system; so I decided to spend this week revisiting the systems implemented in Phase 1.0, 1.5 and 2.0, and try to make them to be more like production-grade as much as possible: 1) Memory Degradation - recalled memories are not as good as in the beginning, causing AI Waifu to be more chaotic as she hallucinates over contaminated memories like a bad vicious cycle. So I transformed the original stateless sqlite-vec vector store to be a simple entity co-mention graph. And even make a studio to visualize the memories stored inside the vector db. Just by looking at the graph, I saw a couple issues: a) After 1.5 months of interactions, there should be only one month of pinned memory (in green) over 1.5 months of active memory (in purple). How come pinned memory is in majority over active ones? I suppose the forgetting curve I had set too aggressive and memory half-life and shelf life too short, active memory got decayed way before monthly consolidation and got lost forever. b) I saw she memorized me into 3 different entities: my username, my nickname and my Github user ID (leaked into pinned memory, presumbly during nightly dreaming process). 3B small param LLM has hard time to correlation 3 different entities into single person, I may have to harden into one. 2) RAM burst during voice input - for some reason the tensor calculation of SileroVAD of the voice input uses PyTorch, and that's the only place in the whole codebase using torch after removing it from TTS synthesization. By switching to SileroVAD-onnx integrated in the ASR sherpa-onnx, the RAM usage drops at least 0.5GB (after shaving off ~1GB from TTS) by completely remove PyTorch dependencies. 3) Introduced a better Wake Word system using Livekit-Wake word instead of using ASR to do the wake word activation to save computation. Optional features like Speak Verification, Barge-in sensitivity, etc, need to find the optimum settings.

liked a model about 11 hours ago

TheDrummer/Rocinante-XL-16B-v1

reacted to Abhisek987's post with 👍 about 13 hours ago

New dataset: 24,922 real Python dependency compatibility experiments🧪 Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test. The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable. What's inside: - 21,490 development + 3,432 sealed validation experiments - 41 unique packages, Python 3.8–3.14, Linux x86_64 - The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages - Full per-stage logs: exact commands, exit codes, stdout/stderr - Normalized error text + Python exception types for every failure - Wheel metadata with Python/ABI/platform tag compatibility decisions - PEP 751 lockfile hashes - 142 engineered features per experiment, ready for training Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure. I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures. CC BY 4.0. Load it directly: ```python from datasets import load_dataset ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments") ``` Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility Zenodo DOI: https://doi.org/10.5281/zenodo.21729353 Code: https://github.com/Abhisek12378/DepLab If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.

View all activity

Organizations

None yet

models 5

datasets 0

None public yet