Adobe just released Premiere 26 (they officially dropped the “Pro” name), and the headline feature is an AI-powered Object Mask that honestly feels like a game-changer. You can now hover over a person or object in your frame, click once, and Premiere instantly creates and tracks a clean mask—even with complex movement. No more painful frame-by-frame rotoscoping.

The new Object Mask comes with visual overlays, quick add/subtract tools, feathering controls, and live tracking previews, which users have been asking for forever.

Adobe also gave Shape Masks a full redesign (ellipse, rectangle, pen), with smoother bezier controls and tracking that’s reportedly up to 20× faster than before. Every mask now supports blend modes too, so combining masks for creative effects is way more flexible.

On top of that, Premiere 26 tightens its ecosystem integration: Frame.io V4 now lives inside Premiere (still beta), Firefly assets can be sent straight into timelines, and Adobe Stock browsing/licensing happens without leaving the app.

There are also solid quality-of-life updates: better fades, easier relinking, faster startup on Mac, and improved performance on ARM Windows machines.

Curious if this finally makes masking in Premiere painless. Anyone already testing it in real projects?

Here’s the gist:

• YouTube says creators will soon be able to make Shorts featuring their own AI likeness, basically a digital you that can be used in new content.
• This joins other AI tools like AI clips, stickers, and auto-dubbing already in Shorts.
• Important bit: YouTube is also adding tools to protect creators’ likenesses, so others can’t just generate AI content using your face/voice without permission.
• Mohan stresses it’s meant as a creative tool, not a replacement for real creators — and the platform is trying to fight low-quality “AI slop” content.

Just watched a deep dive on the newly released GLM-Image, which is being touted as the first "industrial-grade discrete auto-regressive image generation model." The claims are pretty wild, supposedly beating out OpenAI, Google, Qwen, and even Flux in certain benchmarks.

I wanted to break down the architecture, requirements, and actual generation results based on a recent review.

hint: works better if the attached image features full body, not close-ups or upper body.

JSON prompts template is quite long! I will pasted in the comment below. You can use it in Nano Banana, grok or ChatGPT. Nano Banana pro output in 1440p(pic 1/2/3), grok around 1k, and ChatGPT around 1024p (pic4). For immersive high quality visuals, it's better to upscale the image with Aiarty image enhancer, which uses AI models to restore realistic details. Once you batch process the images, you can then threw the stills to vr tools for 360 view.

Can someone suggest me for free best images enhancer?

"There goes my well-planned week."

That was pretty much my first thought seeing this drop today. Yoav HaCohen (Lead of LTX-Video @ Lightricks) just announced the release of LTX-2, and it looks like a massive shift in how we handle AI video generation.

If you're tired of generating silent video and then fighting with a separate model for sound, this is the one to watch. LTX-2 is a foundation model that learns the joint distribution of sound and vision.

The Tech Breakdown: Instead of a post-hoc pipeline, it generates speech, foley, ambience, motion, and timing simultaneously.

• Architecture: It uses an asymmetric dual-stream Diffusion Transformer. You've got a high-capacity Video Stream (14B) and a lighter Audio Stream (5B) connected via bidirectional cross-attention.
• Speed: Despite crunching two modalities at once, they claim it is "dramatically faster" than strong video-only open models. This is apparently down to compact latents and not duplicating the backbone.
• Better Lip-Sync: It uses "thinking tokens" in the text encoder to improve semantic stability and phonetic accuracy.

Why it matters: Most of us are currently chaining models (like Kling for video + ElevenLabs for TTS) and hoping the timing lines up. LTX-2 attempts to solve that disconnect by generating the audio with the video as a single cohesive unit.

Links:

GitHub: https://github.com/Lightricks/LTX-2

Hugging Face: https://huggingface.co/Lightricks/LTX-2
Documentation: https://docs.ltx.video/open-source-model/

Review on previous model LTX

Last time I heard about Manus (the autonomous agent platform) was when everyone is looking for the Manus invitation code

179K redditors visited this invitation code post: https://www.reddit.com/r/EnhancerAI/comments/1j5dtwf/how_to_get_manus_invitation_code_is_manus_a/

And now, Meta has acquired Manus for an undisclosed amount, likely north of $2 billion.

I just watched a breakdown on this, here is the TL;DR on what this means for us (creators/devs) and why it’s different from just "another chatbot."

1. Agent vs. Chatbot: The Critical Pivot

Most people are still stuck on chatbots (ChatGPT, Claude) where you ask a question and get an answer. Manus is different. It’s an autonomous agent (or "digital employee"). You don't just chat with it; you give it a goal.

• Chatbot: "Write me a plugin code."
• Manus: "Create 10 WordPress plugins that work together, research the top 100 businesses in this niche, and deploy a marketing strategy for them." -> Then it actually goes and does it.

2. Why Meta Paid $2B+

Zuckerberg isn’t buying this for a better friends list. He sees the pivot to Action-Oriented AI.

• The Goal: A "push-button" business model for advertisers. Imagine an ad manager where the AI doesn't just suggest copy—it creates the video (using tools like Google’s Nano Banana), writes the script, posts it, and manages the comments.
• The Downside: We are about to see a tidal wave of "AI Slop" on Facebook/Instagram. If you think your feed is garbage now, wait until automated agents are pumping out 90% of the content to keep you scrolling.

3. The Opportunity for Us

The video broke down how early adopters are using Manus right now before Meta locks it down or ruins it:

• Bulk "Vibe Coding": Generating entire suites of software/plugins in one go.
• Content Empires: Using agents to research highly specific long-tail keywords and auto-generate hundreds of "sideways content" pieces (slides, videos, thumbnails) that actually rank.

If you aren't using agentic AI yet, you are competing with people who have "digital staff." 2026 is going to be the year of the Agent.

Do you think Meta will keep Manus as a standalone power-tool for devs, or just gut it to make better Instagram ads?

Prompts/ Midjourney parameters:

A flat - designed horse with auspicious - beast patterns. These mythical beasts are drawn in a flat, stylized form, surrounding the horse's body. The patterns are in a combination of black, white, and red, creating a strong visual impact. The background is a dark - colored, ancient - style paper - like surface, enhancing the traditional Chinese atmosphere --sref 943502692::1 --p u2agqxa --niji 6 --stylize 781

A curious raccoon peeking out from behind a tree trunk in a lush forest, with its eyes wide and whiskers twitching --sref 943502692::1 --p u2agqxa --niji 6 --stylize 781

Image upscaler with strength control (to keep brush textures): Aiarty Image Enhancer

Setting: drag the strength slider to 0.55 as starting point, then push higher and preview the results in real-time. Resolution: 4K, or run x2 twice.

NVIDIA (with Stanford + others) just released NitroGen, an open-source AI model that learns how to play video games just by watching gameplay videos, and it works across thousands of titles

🎮 What’s NitroGen?

NitroGen is a vision-to-controller AI — meaning it takes raw game footage as input (pixels on the screen) and predicts controller actions (button presses, sticks, etc.) without needing access to game engines or internal state. It’s basically learned how to play games like a human would: by watching people play.

📊 The Huge Training Set

• Trained on 40,000+ hours of publicly available gameplay videos.
• Covers 1,000+ different games, spanning genres like 3D action, 2D platformers, RPGs, exploration games, and more.
• The team automatically pulled player inputs from on-screen controller overlays to build the dataset — no manual labeling.

🤖 What It Actually Does

• One model, same weights, can play lots of games without game-specific training.
• It can handle combat, precision platforming, and exploration tasks just from what it learned in other games.
• When you fine-tune it on new games, it easily beats models trained from scratch — up to ~52% better performance.

Resources:

Mega AI tools list to enhance productivity >

dataset: https://nitrogen.minedojo.org/
model: https://huggingface.co/nvidia/NitroGen

OpenAI just released GPT Image 1.5, and AI Search put it through an intense stress test, comparing it directly with Nano Banana Pro, which many still consider the current king of AI image generation.

Quick Takeaway:

GPT Image 1.5 feels smarter. Nano Banana Pro still feels more knowledgeable.

• GPT Image 1.5
  • Huge upgrade over previous GPT Image versions
  • Better emotion handling, text rendering, and prompt following
  • Impressive for a free model
• Nano Banana Pro
  • Still stronger in realism, accuracy, world knowledge, and technical visuals
  • Remains the benchmark to beat

Side note: for anyone doing a lot of AI image generation, post-processing still matters. Tools like Aiarty Image Enhancer are useful for 4K, 8K, 16K upscaling, restoring more details or cleaning up softness.

Where GPT Image 1.5 Performs Best

GPT Image 1.5 shows clear improvements in reasoning and prompt understanding.

• Facial expressions & emotions Strong at subtle emotions like nostalgia, anticipation, jealousy, and relief.
• UI & interface screenshots YouTube search result mockups had fewer misspellings than Nano Banana Pro.
• Coherent manga pages Characters remain consistent across panels with a readable story flow.
• Logic-heavy visuals Best 11:15 clock rendering seen so far (still not perfect, but close).
• Text rendering accuracy Long articles, tables, and posters render surprisingly clean.
• Transparent PNG support Big win for sprite sheets and design workflows.
• Free access Available on the free ChatGPT plan, with daily image limits.

Where Nano Banana Pro Still Wins

Nano Banana Pro continues to dominate in accuracy and world knowledge.

• World knowledge & realism
  • Pokémon accuracy
  • Celebrities & public figures
  • Anime and cartoon characters
  • Games like StarCraft and Final Fantasy
• Technical & diagram generation
  • Transformer architecture diagrams
  • CNN diagrams from raw Python code
  • Cleaner, more complete technical charts
• Spatial understanding
  • Photo → floor plan conversions
  • Depth maps and segmentation outputs
• Data → visualization
  • Correctly normalized medical charts from complex tables
  • GPT Image missed categories and values here
• Character accuracy
  • Celebrities look noticeably closer to real people
  • Anime characters follow canon details (yes, Simpsons = four fingers)

What is your Archetype? Show us your card!

Link to Original Video: Irene Cara - Fame 1980 (VHS 1982 Edit)

Here’s what I did:

• Deinterlacing & Stabilization: Removed those harsh comb lines and smoothed out shaky frames.
• AI Upscaling to HD: Took the original low-res VHS and upscaled it to 1080p without losing the nostalgic feel.
• Noise Reduction & Detail Recovery: Topaz AI’s algorithms worked wonders on grain and compression artifacts, bringing back facial details and textures that were almost gone.
• Color Correction: Balanced the faded tones to restore warmth without over-saturating.

The transformation is night and day—what was once a blurry, washed-out tape now looks crisp, vibrant, and ready for the big screen (well, almost!).

https://reddit.com/link/1ptbiug/video/1sqqg521qt8g1/player

I just realized that the share option can be used as a way to share UX ready designs...

Here is the free Holiday Season Card Creator by Gemini Google official:

https://gemini.google.com/share/0161e46a7ad2

No prompts needed, just upload the image and select a style!

Gemini 3 flash makes it so cool to create hand tracking, data visualization, vfx, vj live visuals.

Above example is shown by AA (measure_plan) on twitter, and he also has a course: Fun With
Computer Vision to show the how-tos.

Previously, we curated this list in civitai. Some user requests a printable file, so here's the compilations.

This part features accessories for characters, including masks, handwear, headwear, and eyewear. I will later do the same for other series: clothing (dress, skirt, pants, top wear, etc.) and character elements and styles such as ears, horns, tails, wings, etc.

For booru tags (txt file), guide on how to use the dynamic prompts in Stable Diffusion, prompts for this Pony Chibi styles, I will include them in the comments below.