AI Radar

11 madde 27/31 kaynak 194 tarandı
düşen kaynak: import-ai, reddit-localllama, reddit-aivideo, reddit-gamedev

Üretim4

MiniMax H3 + Fizgig Nodes: As Stylized Illustrations GEN (Txt2Img) True Image Generation Model, Native 3072x1536 in 1 Minute, WF + Prompt for Every Example.

görselmodel·reddit-stablediffusion·1 saat önce

WF + Prompts - https://civitai.red/posts/31658448 Nodes - https://github.com/shootthesound/ComfyUI-Fizgig-H3-Still/tree/main - 3072x1536, 16 Steps in 1Min (5060 Ti 16gb). - Close to Krea 2 for Prompt Understanding. - Needs LoRAs trained on static hi-res style images. - Highly uncensored out of the box. - Great concepts / world knowledge. submitted by /u/-Ellary- [link] [comments]

7.66
corroboration+0.00
kaynak+1.50
eksen+4.24
sinyal+0.00
tazelik+1.33
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.59

Sorry, I‘m Wrong. Minimax H3 BFS character swap so good

3D·reddit-stablediffusion·4 saat önce

The new BFS character swap works great. https://reddit.com/link/1x6g3fw/video/qddya9inblvh1/player https://preview.redd.it/ysw622ggclvh1.png?width=768&format=png&auto=webp&s=73ac36c51cf40266bb76e0f0b2c80fd5ed68448b The new BFS character swap works great. Character submitted by /u/Mysterious_Pride_858 [link] [comments]

6.63
corroboration+0.00
kaynak+1.50
eksen+3.30
sinyal+0.00
tazelik+1.24
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.59

VITAL: From Visual Motion to Precise Action

görselvideo·arxiv-cv·18 saat önce

World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined independently of visual dynamics. This motivates deriving action targets directly from visual motion. We first examine a simpler setting in which trajectories are reconstructed from recorded future images. Even without the need for future prediction, reconstruction captures the dominant maneuver but leaves centimeter-scale errors. We therefore introduce VITAL (Vision-Induced Tokens for Action Learning), an action tokenizer that couples a visually…

6.11
corroboration+0.00
kaynak+1.00
eksen+4.34
sinyal+0.00
tazelik+0.77
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.00

CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation

görselaraç·arxiv-cv·18 saat önce

We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our framework is organized along a three-stage pipeline of information extraction, semantic rewriting, and multi-condition generation: Prompt-Eval-Refine-Loop Agent (PERL-Agent) distills heterogeneous SKU metadata into concise selling-point text lists through an iterative closed loop of VLM and LLM roles scored by a reference-free rubric. Layout Thinker Optimization (LTO) aligns the rewriter with both caption and rendered-image quality via alternating…

5.78
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kaynak+1.00
eksen+4.01
sinyal+0.00
tazelik+0.77
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.00

Oyun2

AI is mass decompiling games. That's both awesome and scary.

oyun·simonwillison·20 saat önce

AI is mass decompiling games. That's both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and producing a boom in game preservation projects to get old games working on new hardware (often in a browser using WebAssembly). I like how this piece captures both the positives and the negatives of this new development: Most people already realized the scary part by now. If AI can decompile games and software, then the entire tech industry is about to explode. Anybody can…

6.75
corroboration+0.00
kaynak+2.50
eksen+3.52
sinyal+0.00
tazelik+0.73
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.00

Modeller & Araştırma4

Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

↑ yükselişte · yenidenHN'de GPT-6.1 0→8 hikâyemodel·openai·6 gün önce

Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

7.91
corroboration+0.00
kaynak+3.00
eksen+2.86
sinyal+0.00
tazelik+0.06
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+2.00

Gemini 4 Argon: Built to solve your hardest problems

↑ yükselişte · yenidenHN'de Gemini 4 Argon 0→5 hikâyemodelşirket·deepmind·2 gün önce·+1 kaynak

TBD

7.81
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kaynak+3.00
eksen+1.43
sinyal+0.00
tazelik+0.32
capraz_kaynak+1.20
gurultu_cezasi+0.00
hype+1.86

Gemini 4 Argon for Robotics

↑ yükselişte · yenidenHN'de Gemini 4 Argon 0→5 hikâyemodelşirket·deepmind·2 gün önce
6.61
corroboration+0.00
kaynak+3.00
eksen+1.43
sinyal+0.00
tazelik+0.32
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+1.86

Ekosistem1

ActionOPD: Distilling Decisions, Not Tokens

şirket·hf-papers·2 gün önce·46 oy

Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which states and at what granularity should this alignment be performed? Offline imitation provides dense but token-specific supervision and can suffer from state-distribution mismatch. Token-level on-policy distillation (OPD) addresses this mismatch but typically requires teacher token probabilities and compatible tokenization. We argue that semantic actions provide a natural unit of supervision for agent distillation. We introduce…

5.29
corroboration+0.00
kaynak+2.00
eksen+1.76
sinyal+0.68
tazelik+0.37
capraz_kaynak+0.00
gurultu_cezasi+0.00
hype+0.48

Kısa kısa8