imnotkitty (Leco Li)

reactedto their post with 🔥 about 6 hours ago

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631

Just tried tencent/HY-World-2.0 — a multimodal world model that takes in text or a single image and generates editable 3D scenes.

Unlike Google's Genie and HY-World 1.5, v2.0 generates engine-ready 3D content:
🎮 Direct import into Unreal Engine and Unity — no format wrangling
🧊 Supports multiple 3D asset formats: Mesh, 3DGS, point cloud, etc.
✏️ Fully editable — not a baked video, but actual geometry you can modify
🤖 Also usable for embodied simulation environments

Basically: from "AI generates a world you can look at" → "AI generates a world you can ship."

posted an update about 9 hours ago

Post

631

Just tried tencent/HY-World-2.0 — a multimodal world model that takes in text or a single image and generates editable 3D scenes.

Unlike Google's Genie and HY-World 1.5, v2.0 generates engine-ready 3D content:
🎮 Direct import into Unreal Engine and Unity — no format wrangling
🧊 Supports multiple 3D asset formats: Mesh, 3DGS, point cloud, etc.
✏️ Fully editable — not a baked video, but actual geometry you can modify
🤖 Also usable for embodied simulation environments

Basically: from "AI generates a world you can look at" → "AI generates a world you can ship."

liked a model about 10 hours ago

tencent/HY-World-2.0

Image-to-3D • Updated about 3 hours ago • 129

posted an update 14 days ago

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126

Qwen3.6-Plus is now available — near-Claude-level performance

Key highlights:
✅ 1M context
✅ One-shot Vibe Coding (3D scenes, games, full-stack apps)
✅ Outperforms models 2-3x its parameter size
✅ Strong Vibe Coding results: 3D scenes, interactive games, full websites from single prompts
✅ Optimized for Agent workflows (OpenClaw, Qwen Code, Cline, etc.)

API cost is significantly lower than comparable models.

reactedto SeaWolf-AI's post with 🔥 17 days ago

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4677

🌍 World Model Bench — does your world model actually think?

FID measures realism. FVD measures smoothness. But neither tells you whether the model understood the scene.

We just released WM Bench — the first benchmark for cognitive intelligence in world models. The core question: when a beast charges from 3 meters away, does the model know to sprint — not walk? Does it respond differently to a human vs an animal? Does it remember the left corridor was blocked two steps ago?

Those are cognitive questions. No existing benchmark asks them. So we built one.

3 Pillars · 10 Categories · 100 Scenarios · 1,000-point scale

- 👁 P1 Perception (25%) — Can it read the scene?
- 🧠 P2 Cognition (45%) — Does it predict threats, escalate emotions, utilize memory?
- 🔥 P3 Embodiment (30%) — Does the body respond with the right motion?

All evaluation is via simple JSON I/O — no 3D engine, no special hardware. Any model with an API can participate.

We also built PROMETHEUS as a live reference implementation — runs in your browser on a T4, no install needed. Combines FloodDiffusion motion generation with a LLM cognitive brain (Perceive → Predict → Decide → Act). Scored 726/1000 (Grade B) on Track C — the only directly verified model so far. Submissions from other teams very welcome.

---

🗂 Dataset → FINAL-Bench/World-Model
🌍 Demo → FINAL-Bench/World-Model
🏆 Leaderboard → FINAL-Bench/worldmodel-bench
📝 Article → https://huggingface.co/blog/FINAL-Bench/world-model

Part of the FINAL Bench Family — alongside FINAL Bench (Feb 2026). Feedback on rubrics and missing models always welcome!

liked a model 17 days ago

Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

Image-Text-to-Text • 28B • Updated 10 days ago • 585k • 2.67k

posted an update about 1 month ago

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194

🚀 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? 👀

1 reply

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reactedto branikita's post with 🚀 about 1 month ago

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1869

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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reactedto BibbyResearch's post with 👍 about 1 month ago

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2540

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.

reactedto mayafree's post with 🚀 about 1 month ago

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5881

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.

5 replies

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repliedto their post about 1 month ago

Me too!

reactedto SeaWolf-AI's post with 🚀 about 1 month ago

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5016

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.

repliedto their post about 1 month ago

Nuh......

reactedto their post with 🚀 about 1 month ago

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2275

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)

2 replies

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posted an update about 1 month ago

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2275

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)

2 replies

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posted an update about 2 months ago

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298

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?

3 replies

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reactedto etemiz's post with ❤️ about 2 months ago

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6335

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%

6 replies

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reactedto NJX-njx's post with 🔥 about 2 months ago

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7485

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