imnotkitty (Leco Li)

posted an update 1 day ago

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🚀 GLM-5-Turbo just dropped: The "OpenClaw Native" Model

Z.ai just shipped a 744B MoE beast that's 2-3x faster than GLM-5, with 200K context + 128K max output.

What's different:
👉 Tool call stability (no more random failures mid-chain)
👉 Complex instruction decomposition (breaks down messy prompts)
👉 Time-aware execution (understands scheduled/persistent tasks)
👉 High-throughput long-chain efficiency (doesn't choke on 50-step workflows)

📊 ZClawBench: Leads mainstream models in OpenClaw scenarios
💰 Trade-off: +20% price vs GLM-5

Anyone trying it yet? 👀

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reacted to branikita's post with 🚀 1 day ago

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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/

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reacted to BibbyResearch's post with 👍 1 day ago

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We are working on the largest Dataset and Pre-trained model for Text to Speech and Speech to text for the low-resourced language called Marwari in India.

reacted to mayafree's post with 🚀 4 days ago

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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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replied to their post 11 days ago

Me too!

reacted to SeaWolf-AI's post with 🚀 12 days ago

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ALL Bench — Global AI Model Unified Leaderboard

FINAL-Bench/all-bench-leaderboard

If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and — uniquely — all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale.
Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots.
The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench — the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind.
Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores.
All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.

replied to their post 12 days ago

Nuh......

reacted to their post with 🚀 13 days ago

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The most popular OpenClaw tool has been released!

🥇 Claw for All: The ultimate all-rounder. Simplifies deployment for both devs & pros with a seamless web/mobile experience.
🥈 OpenClaw Launch: Speed is king. Deploy your apps in under 30 seconds with a single click.
🥉 ClawTeam: Skip the setup. Get pre-configured AI agent blueprints built specifically for OpenClaw.
4️⃣ vibeclaw: Local-first. Run OpenClaw in your browser sandbox in literally 1 second.
5️⃣ Tinkerclaw: The startup favorite. Zero-code platform to deploy, manage, and scale AI assistants.
6️⃣ ClawWrapper: The "last mile" tool. Simplifies the entire packaging and launch process.

Which one are you adding to your stack? 🛠️
(Source: OpenClaw Directory)

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reacted to danielhanchen's post with 🔥 13 days ago

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Qwen releases 4 new Qwen3.5 Small models: 0.8B • 2B • 4B • 9B!

Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.

The vision reasoning LLMs perform better than models 4x their size.

GGUFs to run: https://huggingface.co/collections/unsloth/qwen35

Guide: https://unsloth.ai/docs/models/qwen3.5

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posted an update 14 days ago

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The most popular OpenClaw tool has been released!

🥇 Claw for All: The ultimate all-rounder. Simplifies deployment for both devs & pros with a seamless web/mobile experience.
🥈 OpenClaw Launch: Speed is king. Deploy your apps in under 30 seconds with a single click.
🥉 ClawTeam: Skip the setup. Get pre-configured AI agent blueprints built specifically for OpenClaw.
4️⃣ vibeclaw: Local-first. Run OpenClaw in your browser sandbox in literally 1 second.
5️⃣ Tinkerclaw: The startup favorite. Zero-code platform to deploy, manage, and scale AI assistants.
6️⃣ ClawWrapper: The "last mile" tool. Simplifies the entire packaging and launch process.

Which one are you adding to your stack? 🛠️
(Source: OpenClaw Directory)

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posted an update 15 days ago

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One week until DeepSeek V4 drops.

Reports say it's a full multimodal model—native image, video, and text generation. And it puts serious pressure on OpenAI and Google.

What are you hoping to see from this release?

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reacted to etemiz's post with ❤️ 15 days ago

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AHA 2026 scores of Qwen3.5

27B
Huihui abliteration 65%
Heretic abliteration 55%
Normal 50%

35B
Huihui abliteration 64%
@jiaojjjjje abliteration 57%
@LeadFootThrottleCock abliteration 56%
Normal 49%

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reacted to NJX-njx's post with 🔥 15 days ago

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Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.

On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.

On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.

Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.

This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it

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replied to their post 15 days ago

Yuppppp😩

replied to their post 15 days ago

Do u have any pics can show me🧐

reacted to their post with 🚀 18 days ago

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In the Text-to-Video arena, Seedance 2.0 has first secured a spot in the LMArena Top 10, while Kling 3.0 has topped the Artificial Analysis leaderboard, with the Kling family claiming 7 spots in the top 15.

Which one do you prefer?

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posted an update 18 days ago

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In the Text-to-Video arena, Seedance 2.0 has first secured a spot in the LMArena Top 10, while Kling 3.0 has topped the Artificial Analysis leaderboard, with the Kling family claiming 7 spots in the top 15.

Which one do you prefer?

4 replies

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reacted to robtacconelli's post with 🚀 19 days ago

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🏆 Nacrith: a 135M model that out-compresses everything on natural language

What if a tiny LM could compress english text better than _every_ compressor out there — classical or neural, small or large?

Nacrith pairs SmolLM2-135M with an ensemble of online predictors and high-precision arithmetic coding.

What's inside

The standard LLM+arithmetic coding approach wastes ~75% of CDF precision on large vocabularies. Our CDF-24 fix alone recovers 0.5 bpb. On top: a token N-gram that skips the GPU on predictable tokens, an adaptive bias head, llama.cpp backend (7× faster than PyTorch), multi-GPU parallel compression, and a binary file format (NC06) — the first LLM-based binary compressor we know of.

Runs on a GTX 1050 Ti. ~500 MB weights, ~1.2 GB VRAM per worker.

💻 Code: https://github.com/robtacconelli/Nacrith-GPU
⭐ Space: robtacconelli/Nacrith-GPU
📄 Paper: Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding (2602.19626)

Try it, break it, share your results — all feedback welcome. ⭐ on the repo appreciated!

Results across all systems we tested:
- alice29.txt → 0.918 bpb (−44% vs CMIX, −20% vs ts_zip) — below the 2nd-order Shannon entropy bound
- enwik8 (100 MB) → 0.9389 bpb (−8% vs FineZip/LLMZip's 8B model, −15% vs ts_zip)
- Unseen text → 0.723 bpb on a doc published after training cutoff — no memorization, 26% better than FineZip/LLMZip on the same model

SmolLM2-135M by

HuggingFaceTB

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reacted to prithivMLmods's post with 🚀 19 days ago

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FireRed-Image-Edit-1.0 (Rapid) Fast Experimental Demo is Out! 🚀🤗

Demo: prithivMLmods/FireRed-Image-Edit-1.0-Fast

-> Paired the EditPlusPipeline with the Diffusers-compatible transformer weights of Rapid AIO from Qwen-Image-Edit. (experimental)
-> This fusion delivers more accurate instruction following, higher image quality, and consistent visual coherence @ 4-step fast inference.
-> Better maintains text styles with high fidelity, along with high-quality old photo restoration, enhancement, and best-in-class virtual try-on.

reacted to MikeDoes's post with 🔥 20 days ago

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At Ai4Privacy, our goal is to empower researchers to build a safer AI ecosystem. Today, we're highlighting crucial research that does just that by exposing a new vulnerability.

The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.

We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:

Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.

Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.

This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.

🔗 Read the full paper: https://arxiv.org/html/2408.17354v1

🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset

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