Jean Louis

JLouisBiz

https://www.StartYourOwnGoldMine.com

AI & ML interests

- LLM for sales, marketing, promotion - LLM for Website Revision System - increasing quality of communication with customers - helping clients access information faster - saving people from financial troubles

Recent Activity

liked a Space 30 minutes ago

prithivMLmods/Qwen-3.5-HF-Demo

reacted to prithivMLmods's post with 🔥 33 minutes ago

The Qwen3.5 Multimodal Understanding Demo, powered by Qwen3.5-2B, is now available on HF Spaces! It is a lightweight model designed for fast image and video reasoning. Built with Gradio, the demo showcases Image QA, Video QA, object detection, and 2D point tracking, along with real-time token streaming. 🤗 Demo: https://huggingface.co/spaces/prithivMLmods/Qwen-3.5-HF-Demo ✅ Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations 🔗 Qwen3.5-2B: https://huggingface.co/Qwen/Qwen3.5-2B To learn more, visit the app page or the respective model pages.

reacted to SeaWolf-AI's post with 🔥 36 minutes ago

ALL Bench Leaderboard — Structural Problems in AI Benchmarking and the Case for Unified Evaluation https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard The AI benchmark ecosystem has three structural problems. Major benchmarks like MMLU have surpassed 90%, losing discriminative power. Most leaderboards publish unverified self-reported scores — our cross-verification found Claude Opus 4.6's ARC-AGI-2 listed as 37.6% (actual: 68.8%), Gemini 3.1 Pro as 88.1% (actual: 77.1%). OpenAI's own audit confirmed 59.4% of SWE-bench Verified tasks are defective, yet it remains widely used. ALL Bench addresses this by comparing 91 models across 6 modalities (LLM · VLM · Agent · Image · Video · Music) with 3-tier confidence badges (✓✓ cross-verified · ✓ single-source · ~ self-reported). Composite scoring uses a 5-Axis Framework and replaces SWE-Verified with contamination-resistant LiveCodeBench. Key finding: metacognition is the largest blind spot. FINAL Bench shows Error Recovery explains 94.8% of self-correction variance, yet only 9 of 42 models are even measured. The 9.2-point spread (Kimi K2.5: 68.71 → rank 9: 59.5) is 3× the GPQA top-model spread, suggesting metacognition may be the single biggest differentiator among frontier models today. VLM cross-verification revealed rank reversals — Claude Opus 4.6 leads MMMU-Pro (85.1%) while Gemini 3 Flash leads MMMU (87.6%), producing contradictory rankings between the two benchmarks. 📊 Article: https://huggingface.co/blog/FINAL-Bench/all-bench 📦 Dataset: https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard ⚡ GitHub: https://github.com/final-bench/ALL-Bench-Leaderboard 🏆 Leaderboard: https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard 🧬 FINAL Bench: https://huggingface.co/datasets/FINAL-Bench/Metacognitive

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I’ve built two Firefox extensions for my personal workflow:

1. **Quick Edit in Emacs**
I manage over 3,500 web pages locally. With this extension, I can now click anywhere on a webpage and instantly jump into Emacs to edit the exact page (or annotate any other page I'm working on).

2. **Describe Images (and soon Videos) on the Web**
Using the right-click menu, I can generate descriptions for images I come across online. These descriptions are stored and reused for my own image collections or web pages. I’m planning to add the same functionality for videos soon.

What makes this possible is running LLMs locally on my own machine — I’ve been experimenting with models like **Mistral Vibe** and others. This lets me automate description generation and text processing entirely offline, keeping everything fast, private, and fully under my control.

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