Top AI News of the Week (June 17 – 24) 1. Anthropic introduces Claude Tag
2. Cursor announces three new features, including a model trained with SpaceX 3. Nous Research ships blank slate mode 4. Midjourney launches a new medical imaging division 5. OpenAI launches
CREATIVE AI
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Top AI news of the week: Claude, Cursor, Nous, Midjourney, OpenAI
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Seedance 2.0 Mini: 5 cents per second or 365 days free
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5 👏 CENTS 👏 PER 👏 SECOND 👏
— Charly Wargnier (@DataChaz) 24 juin 2026
That's what @TopviewAIhq is now charging for Seedance 2.0 Mini in Credit Mode with the Ultra Annual pack 🤯
Or, if you’re not in a hurry, you can also generate credit-free for 365 days.
The cost of shipping AI video has never been lower. https://t.co/sNvCed3Hmv pic.twitter.com/vCh40kDbc25 CENTS PER SECOND That's what @TopviewAIhq is now charging for Seedance 2.0 Mini in Credit Mode with the Ultra Annual pack Or, if you’re not in a hurry, you can also generate credit-free for 365 days. The cost of shipping AI video has never been lower.
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Street View Grounding Now Available for Google’s Offline Video Models
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street view grounding now available for google’s offline video models. can’t wait till you can do “spatial RAG” to load in the right panoramas to reference – suddenly large scale real world locations become movie sets! https://t.co/AR3gLfj0p6
— Bilawal Sidhu (@bilawalsidhu) 23 juin 2026street view grounding now available for google’s offline video models. can’t wait till you can do “spatial RAG” to load in the right panoramas to reference – suddenly large scale real world locations become movie sets!
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Cursor and MagicPath for Interactive Component Variations
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The best way to design and build with your repository is Cursor/Codex + MagicPath.
— Pietro Schirano (@skirano) 23 juin 2026
Watch me import components from my repo into the MagicPath canvas inside Cursor, then explore completely different variations.
All while they stay interactive and faithful to the source code 👇 pic.twitter.com/ZNMZoaA8AvThe best way to design and build with your repository is Cursor/Codex + MagicPath. Watch me import components from my repo into the MagicPath canvas inside Cursor, then explore completely different variations. All while they stay interactive and faithful to the source code
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Modify a brand asset with AI: prompt and pray
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Modifying a brand asset with an AI image model is often a matter of 'prompt and pray'. Each result seems almost done, so you regenerate and hope it will get it right next time. Goodeye checks each modification and locks your
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ArtiFixer: Generative 3D Scene Reconstruction from Text or Few Views
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ArtiFixer doesn't just clean up 3D meshes—it is a robust generative engine. Even if you entirely drop the initial 3D rendering conditions, the model can rely on text prompts or just a few reference views to reconstruct the high-level structure of the scene and synthesize
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ArtiFixer uses DMD to make bidirectional video models 70x faster
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Bidirectional video models provide great coherence but are computationally heavy. ArtiFixer solves this via Self-Forcing-style Distribution Matching Distillation (DMD). By distilling the bidirectional model into a causal auto-regressive one, ArtiFixer achieves up to a 70x
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ArtiFixer delivers sharp results, outperforming baselines by 1-3 dB PSNR
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The results are incredibly sharp. When benchmarked on challenging datasets with sparse views like Mip-NeRF 360 and DL3DV, ArtiFixer handles highly degraded initial renderings effortlessly. It outperforms existing baselines (like GenFusion and 3DGUT) by a massive 1–3 dB PSNR
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Opacity Mixing: Balancing Consistency and Hallucination in Video
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How do you keep the generated video consistent with existing views without losing the ability to hallucinate new content? The answer is Opacity Mixing. Instead of starting from pure noise, ArtiFixer:
> Downscales the rendering's opacity map.
> Mixes Gaussian noise specifically -
ArtiFixer combines 3D reconstruction and video generation in two phases
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Instead of treating 3D reconstruction and video generation as standalone alternatives, ArtiFixer combines their strengths. > Phase I: Trains a powerful bidirectional generative video model to transport degraded renderings into clean frames.
> Phase II: Distills the teacher model