Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now!
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Asyntai.com is an AI chatbot for websites that talks to your visitors, helps, explains, never misses a chat and boosts your conversion rates.
All while knowing your website and business, customized just for you.

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I tested one dangerous-looking first-person video idea through four fal.ai models, compared price and realism, and learned why Seedance 2.0 still won my small experiment overall

I used fal.ai because I could test several video models from one service and keep the same queue flow. I wanted a hard starting prompt: one long first-person action shot, clear height, parachute, industrial yard, and a muddy landing.

I picked Seedance 2.0 as the cinematic baseline, Kling v3 Pro for action and smart shot planning, Veo 3.1 because it is the premium Google option, and Wan 2.7 as a cheaper 1080p challenger.

What I got: Seedance 2.0 was best. It kept the helmet-camera feeling, the hands, the parachute lines, and the story rhythm most clearly. My only real complaint was the realism of the fall into the puddle. It cost about $2.43 for 8s at 720p.

Kling v3 Pro gave me good image quality, but the actions were not realistic. With audio on, 8s cost about $1.34.

Wan 2.7 accepted the prompt, but the result was basically an absurd video made from disconnected cuts. At 1080p, 8s cost about $1.20.

Veo 3.1 was the tricky one. The first request with the same prompt hit a content policy check. Then I softened the wording a little, sent it again, and got a video. It lost the first-person view and switched to third-person, but the result still looked cool. That successful 8s 1080p run with audio cost about $3.20.

So my successful clips cost about $8.17 total if I count the second Veo run. My winner was not the cheapest one; it was the one that followed the whole idea with the least confusion.

See the result 👇

֎ New: ChatGPT users can now build real apps directly from chat

ChatGPT's app store just added AppDeploy, and it works with free accounts too.

Describe what you want to build, ChatGPT writes the code, AppDeploy handles deployment automatically inside the same chat, and you get a working link back right away.

AppDeploy includes the infrastructure needed for real apps:
🔐 User login and permissions
🗄 Storage, database, realtime sync and notifications
🤖 Built in AI capabilities
☁️ Full backend, background jobs and scheduled tasks
🧪 Automatic QA for every change and built in versioning
🌐 Custom domains, secrets management and more

Free to use. No subscription or credit card required.

👉 Install AppDeploy in ChatGPT and launch your first app in a few minutes

🐶 ASO Corgi — platform for the App Store developers.
Find the keywords your apps and competitors rank for, and track positions across every country in one place.
🔑 Keyword research: by topic, by your app's languages, from App Store suggestions, by competitors, and with AI analysis.
• Rankings by country — history, charts, demand score (0–100)
• Global search across any App Store storefront
• ASO assistant builds your listing for each locale
• App Store top charts for any country

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How many AI subscriptions are you paying for?
• ChatGPT
• Claude
• GPT Image
• Runway
• Kling
• Seedance
• ElevenLabs
• Nano Banana
• ...

Every new AI tool means another subscription, another tab, and another interface to learn.

What if one workspace brought them all together?
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GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside:

🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.

Results:

🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace