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mayafree 
posted an update about 23 hours ago
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4024
Leaderboard of Leaderboards — A Real-Time Meta-Ranking of AI Benchmarks

MAYA-AI/all-leaderboard

Hundreds of AI leaderboards exist on HuggingFace. Knowing which ones the community actually trusts has never been easy — until now.

Leaderboard of Leaderboards (LoL) ranks the leaderboards themselves, using live HuggingFace trending scores and cumulative likes as the signal. No editorial curation. No manual selection. Just what the global AI research community is actually visiting and endorsing, surfaced in real time.

Sort by trending to see what is capturing attention right now, or by likes to see what has built lasting credibility over time. Nine domain filters let you zero in on what matters most to your work, and every entry shows both its rank within this collection and its real-time global rank across all HuggingFace Spaces.

The collection spans well-established standards like Open LLM Leaderboard, Chatbot Arena, MTEB, and BigCodeBench alongside frameworks worth watching. FINAL Bench targets AGI-level evaluation across 100 tasks in 15 domains and recently reached the global top 5 in HuggingFace dataset rankings. Smol AI WorldCup runs tournament-format competitions for sub-8B models scored via FINAL Bench criteria. ALL Bench aggregates results across frameworks into a unified ranking that resists the overfitting risks of any single standard.

The deeper purpose is not convenience. It is transparency. How we measure AI matters as much as the AI we measure.
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SeaWolf-AI 
posted an update 3 days ago
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10978
🏟️ Smol AI WorldCup: A 4B Model Just Beat 8B — Here's the Data

We evaluated 18 small language models from 12 makers on 125 questions across 7 languages. The results challenge the assumption that bigger is always better.

Community Article: https://huggingface.co/blog/FINAL-Bench/smol-worldcup
Live Leaderboard: ginigen-ai/smol-worldcup
Dataset: ginigen-ai/smol-worldcup

What we found:

→ Gemma-3n-E4B (4B, 2GB RAM) outscores Qwen3-8B (8B, 5.5GB). Doubling parameters gained only 0.4 points. RAM cost: 2.75x more.

→ GPT-OSS-20B fits in 1.5GB yet matches Champions-league dense models requiring 8.5GB. MoE architecture is the edge AI game-changer.

→ Thinking models hurt structured output. DeepSeek-R1-7B scores 8.7 points below same-size Qwen3-8B and runs 2.7x slower.

→ A 1.3B model fabricates confident fake content 80% of the time when prompted with nonexistent entities. Qwen3 family hits 100% trap detection across all sizes.

→ Qwen3-1.7B (1.2GB) outscores Mistral-7B, Llama-3.1-8B, and DeepSeek-R1-14B. Latest architecture at 1.7B beats older architecture at 14B.

What makes this benchmark different?

Most benchmarks ask "how smart?" — we measure five axes simultaneously: Size, Honesty, Intelligence, Fast, Thrift (SHIFT). Our ranking metric WCS = sqrt(SHIFT x PIR_norm) rewards models that are both high-quality AND efficient. Smart but massive? Low rank. Tiny but poor? Also low.

Top 5 by WCS:
1. GPT-OSS-20B — WCS 82.6 — 1.5GB — Raspberry Pi tier
2. Gemma-3n-E4B — WCS 81.8 — 2.0GB — Smartphone tier
3. Llama-4-Scout — WCS 79.3 — 240 tok/s — Fastest model
4. Qwen3-4B — WCS 76.6 — 2.8GB — Smartphone tier
5. Qwen3-1.7B — WCS 76.1 — 1.2GB — IoT tier

Built in collaboration with the FINAL Bench research team. Interoperable with ALL Bench Leaderboard for full small-to-large model comparison.

Dataset is open under Apache 2.0 (125 questions, 7 languages). We welcome new model submissions.
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sdiazlor 
posted an update 3 days ago
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2539
More OSS than ever with the latest pruna 0.3.2 release. It extends existing algorithm families, such as compilers, kernels, and pruners, and adds new ones, including decoders, distillers, enhancers, and recoverers. But it's not only a collection of algorithms; instead, you can easily combine them to get the biggest efficiency win.

Read the full blog here: https://huggingface.co/blog/PrunaAI/pruna-0-3-2-open-source-optimization-algorithms
JonnaMat 
posted an update 2 days ago
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5844
🚀 FlashHead: Efficient Drop-In Replacement for the Classification Head in Language Model Inference

🔎 Check out our latest FlashHead-enabled model: embedl/Cosmos-Reason2-2B-W4A16-Edge2-FlashHead

🧩 Seamless integration with vllm: 
docker run --rm -it \
  --network host \
  --shm-size=8g \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  --runtime=nvidia \
  --name=vllm-serve \
  -e HF_TOKEN=hf_*** \
  -e HF_HOME=/root/.cache/huggingface \
  embedl/vllm:latest-jetson-orin-flashhead \
  vllm serve "embedl/Cosmos-Reason2-2B-W4A16-Edge2-FlashHead" \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.75 \
    --max-num-seqs 2 \
    --trust-remote-code


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branikita 
posted an update 3 days ago
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4034
Testing a parallel gripper with a MaixSense-A010 ToF depth camera (100-point sensor) and pressure sensors.

By combining depth data with force feedback, the gripper closes only when the object is in a graspable position. If the object slips or leaves the grasp zone before closing, the system can automatically retry — as shown in the video.

Gripper repository (version without camera and sensors):
https://github.com/roboninecom/SO-ARM100-101-Parallel-Gripper
  • 1 reply
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marksverdhei 
posted an update about 16 hours ago
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84
The hidden gem of open-source embedding models: LCO-Embedding
for text, image AND audio!

I found this model after reading the recent Massive Audio Embedding Benchmark (MAEB) paper, as it blew the other models out of the water on day zero. I've been using it personally for about a week, and searching my files by describing music, sound effects or images is both practical and entertaining. Really underrated model, would highly recommend checking it out: LCO-Embedding/LCO-Embedding-Omni-7B

PS: If you're looking you run this model on llama.cpp, i've gone ahead and quantized them for you here 👉 https://huggingface.co/collections/marksverdhei/lco-embedding-omni-gguf
branikita 
posted an update about 18 hours ago
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83
Robonine just published a new article! Mechanical backlash is a common limitation in servo-driven robotic joints. In this experiment, paired Feetech STS3215 servos are used with a small opposing preload to eliminate gearbox play, significantly improving positional stability and motion precision in robotic manipulators.

https://robonine.com/backlash-compensation-in-sts3215-servo-actuators/
  • 2 replies
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BibbyResearch 
posted an update about 19 hours ago
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74
Used by Researchers at Allen institute, Simons foundation, Yale and other top universities.. 🤗
Researchers are using AI to write their papers.
That AI is Bibby AI.
Not GPT-5. Not Claude Opus. Not whatever wrapper your institution just paid $50k for.
Bibby.
While the AI research community spent 2025 debating whether LLMs can handle scientific writing — actual scientists at actual top-tier institutions quietly started shipping papers with it. No press release. No hype cycle. Just results.
Here's what they figured out that most people haven't:
The bottleneck in research was never the ideas. It was never the experiments. It was the 3am writing sessions where good science goes to die in a Google Doc. Writer's block, LaTex Learning frustration, formatting issues, compiler errors.
Bibby is built specifically for that gap. Citation-aware. Argument-aware. Knows when to hedge, when to assert, and — critically — knows not to hallucinate your methods section.
The institutions adopting it aren't doing it because it's trendy. They're doing it because the researcher-hours it saves are going straight back into actual research.
This is what the adoption curve looks like before the thing becomes obvious.
→ https://trybibby.com/
Who else here is using AI in their research workflow? Drop it below 👇

Best comments will receive a discounted Bibby AI subscription with a chance to win a $100 grant for their research.
DavidAU 
posted an update about 23 hours ago
kanaria007 
posted an update 1 day ago
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86
✅ Article highlight: *Ethics as Institutional Interface* (v0.1)

TL;DR:
Ethics in SI-Core should not behave like a static safety filter or a one-time compliance checklist. It should behave more like an institution: with roles, principals, red lines, appeals, overrides, break-glass procedures, and civic oversight around auditable runtime decisions.

Read:
kanaria007/agi-structural-intelligence-protocols

Why it matters:
• treats ethics as a structural interface: who can do what to whom, under which constraints, with which recourse
• separates ethical governance into red-line zones, review zones, and metric zones
• makes appeals, overrides, and break-glass explicit, traceable, and reviewable
• connects ETH to PoLB experiments, ID / Role / Persona, and civic oversight

What’s inside:
• ETH as: Principal × Role/Persona × Context → ETH-Constraints → ETHDecision
• a portable ETHDecision object shape (ALLOW | DENY | ESCALATE + exported governance verdicts)
• red lines vs review-required cases vs metric-monitored cases
• appeals (policy change), overrides (case-specific human intervention), and break-glass (pre-negotiated emergency procedure)
• ETH × PoLB × experiments: how ethics becomes a design partner for rollout and evaluation
• ETH × ID × Role & Persona: per-principal constraints, role capability gates, and persona-aware explanations
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