Launch Directory/AI Agents/Hassan El Mghari

Hassan El Mghari · Launch Video Breakdown: Hook, Pacing & Motion Design

The top 1,000 research papers of the last year, summarized and visualized. All 1,000 summaries cost $4 with DeepSeek V4 Flash.

AI AgentsLaunchAugust 19, 2026@nutlope

Website View on X

9.2 /10

Hook score

Product UI Walkthrough

Cuts / min

0:19

Runtime

112K

Views

Likes

Reposts

Replies

Reusable motion template

Interactive Research Atlas Walkthrough

Interactive Product UI & Data Visualization

Adapt it.

Replace 'AI Papers' with 'Quantum Computing Patents' in all UI text.
Update company logos to quantum tech leaders (e.g., IBM, Google, D-Wave).
Modify content cards to display patent titles, inventors, and filing dates.
Adjust search queries and results to reflect quantum computing terms.
Integrate subtle, abstract quantum-inspired background animations.
Ensure all UI elements maintain the original video's clean, minimalist design language.

Source: X / Twitter launch · View original post

Launch overview & key findings

A 19-second interactive product ui & data visualization launch video by Hassan El Mghari, unveiled on August 19, 2026 with over 112K views on X. Evaluated at a 9.2/10 hook score with an editing tempo of 3 cuts per minute, the production demonstrates This video showcases Hassan El Mghari's 'The Year in AI Papers' platform, an AI agent that summarizes and visualizes the top 1,000 research papers annually. The core technological breakthrough is the efficient summarization and visualization of complex AI research, making it accessible. The motion style is characterized by clean, minimalist UI animations, smooth camera movements, and a deliberate pacing that emphasizes user interaction within the application.

0:00 · The Hook · Overview of AI Research Landscape

0:00 / 0:00

The Hook
Overview of AI Research Landscape
“(No spoken dialogue — ambient visual with mouse clicks and scrolls)”
On screen
THE YEAR IN AI PAPERS 1,000+ papers mapping AI research from 2025-2026. EXPLORE BY RESEARCH LAB VIEW ALL LABS → OpenAI Anthropic Moonshot AI DeepSeek MiniMax ZAI / GLM BROWSE BY TOPICS 01 Reasoning 177 02 Agents 279 03 Multimodal 144 04 Video 164 05 Systems 143 06 Robotics 59 VIEW ALL TOPICS → 01 TRENDING THIS YEAR The papers that moved AI forward Selected for citation impact, official-code-adoption, recency, and field-wide significance. VIEW ALL TOP PAPERS → DINOv3 SAM 3 GLM 5 MOST CITED 01 Qwen3-VL Technical Report Nov 2025 1802 02 DINOv3 Aug 2025 1216 03 InternVL3.5: Advancing Open-Source Multimodal Models in Complex Reasoning, and... Aug 2025 1212 View top 100 → EXPLORE THE YEAR VIEW ALL MONTHS → JUL 2025 AUG 2025 SEP 2025 OCT 2025 NOV 2025 DEC 2025 JAN 2025 FEB 2025 MAR 2025 APR 2025 MAY 2025 JUN 2025 JUL 2025 AUG 2025 SEP 2025 OCT 2025 1,000+ papers well summarized THIS PAPER WAS PICKED JUST FOR YOU META AI / AUG 2025 DINOv3 DINOv3 is a self-supervised vision foundation model that scales to 7B parameters, trained on a curated dataset of 1.6B billion images (LVD-16B9M) from Instagram, combined with ImageNet-1k. The model uses a ViT architecture with RoPE embeddings and constant... READ THE PAPER →
Camera
Static overhead shot of a web application interface. The camera is fixed, showing the full browser window.
Motion
Subtle mouse cursor movements and scroll animations within the web UI, highlighting interactive elements and content flow.
Product Reveal
Deep Dive into a Research Paper
“(No spoken dialogue — ambient visual with mouse clicks and scrolls)”
On screen
THE YEAR / Z.AI / GLM PAPER 2082.15763 GLM-5: from Vibe Coding to Agentic Engineering Z.AI / GLM GLM-5 from Vibe Coding to Agentic Engineering GLM-5-Team, J. Anhan Zeng, Xin Lu, Zhengyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin, Chendi Ge, Chenghua Huang, Chengxing Xie, et al. PUBLISHED Feb 2026 RESEARCH LAB Z.ai / GLM → CITATIONS 295 GITHUB 6.9K stars TOPICS Coding agents Agent training and self-evolution RL for reasoning 01 IN BRIEF Summary GLM-5, developed by Zhipu AI and Tsinghua University, is a next-generation foundation model that shifts from vibe coding to agentic engineering. It builds on the ARC (agentic, reasoning, coding) capabilities of its predecessor, GLM-4.7, and introduces DeepSeek Sparse Attention (DSA) to reduce training and inference costs while maintaining long-context fidelity. The model scales to 744B total parameters (40B active) and is trained on 28.5T tokens. A new asynchronous reinforcement learning infrastructure decouples generation from training, improving post-training efficiency, and novel asynchronous agent RL algorithms enhance learning from long-horizon interactions. GLM-5 achieves state-of-the-art performance on benchmarks like SWE-bench Verified (77.8), BrowseComp (75.9 with context management), and scores 50 on the Artificial Analysis Intelligence Index v4.0, the first open-weights model to do so. It also demonstrates strong long-horizon task performance, e.g., $4,432 on Vending-Bench 2. The model is full-stack adapted to Chinese GPU ecosystems, including Huawei Ascend and Moore Threads. GLM-5 is released open-source, with code and models available at https://github.com/zai- org/GLM-5. 02 FROM THE PAPER Abstract Large language models have made significant progress in mathematical reasoning, which PUBLICATION VENUE arXiv.org PAGES 40 DOI 10.48550/arXiv.2602.15763 LICENSE Creative Commons Attribution 4.0 International Read original View code Project page
Camera
Smooth, controlled zoom-in and pan-down on the web page, focusing on specific sections of the research paper summary.
Motion
Zoom and pan with easing, simulating a user's focused interaction. Content scrolls into view as the camera pans.
Feature Teaser
Exploring Trending Papers and Categories
“(No spoken dialogue — ambient visual with mouse clicks and scrolls)”
On screen
THE YEAR IN AI PAPERS 1,000+ papers mapping AI research from 2025-2026. EXPLORE BY RESEARCH LAB VIEW ALL LABS → OpenAI Anthropic Moonshot AI DeepSeek MiniMax ZAI / GLM BROWSE BY TOPICS 01 Reasoning 177 02 Agents 279 03 Multimodal 144 04 Video 164 05 Systems 143 06 Robotics 59 VIEW ALL TOPICS → 01 TRENDING THIS YEAR The papers that moved AI forward. Selected for citation impact, official-code-adoption, recency, and field-wide significance. VIEW ALL TOP PAPERS → DeepSeek-V3.2 Kimi K2.5 Qwen3-VL DINOv3 SAM 3 GLM-5 DeepSeek-OCR Kimi K3 Why Language Models Hallucinate SELECTED PAPERS 9 #VERSION One year METHOD Editorial DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models PUBLISHED Dec 2025 CITATIONS 671 CODE Not linked Read summary MOONSHOT AI Kimi K2.5: Visual Agentic Intelligence PUBLISHED Feb 2026 CITATIONS 313 CODE 2.3K stars Read summary ALIBABA / QWEN Qwen3-VL Technical Report PUBLISHED Nov 2025 CITATIONS 1.8K CODE 20K stars Read summary META AI DINOv3 PUBLISHED Aug 2025 CITATIONS 1.2K CODE 11K stars Read summary META AI SAM 3: Segment Anything with Concepts PUBLISHED Nov 2025 CITATIONS 711 CODE 11K stars Read summary Z.AI / GLM GLM-5: from Vibe Coding to Agentic Engineering PUBLISHED Feb 2026 CITATIONS 295 CODE 6.9K stars Read summary DEEPSEEK DeepSeek-OCR: Contexts Optical Compression MOONSHOT AI Kimi K3: Open OPENAI Why Language Models Hallucinate
Camera
Rapid horizontal pan across a grid of research paper cards, simulating a quick browse through trending content.
Motion
Fast horizontal scroll with a slight bounce, creating a dynamic and engaging content preview.
Feature Teaser
Interactive Search and Filtering
“(No spoken dialogue — ambient visual with mouse clicks and scrolls)”
On screen
HK SEARCH THE YEAR IN AI ESC q deep 01 RESEARCH PAPER GrandCode: Achieving Grandmaster Level in Competitive Programming vi... 02 DEEPSEEK FIPO: Eliciting Deep Reasoning with Future-KL Influence Policy Optimiza... 03 mHC: Manifold-Constrained Hyper-Connections 04 DEEPSEEK DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models 05 GOOGLE DEEPMIND PanerBanana: Automating Academic Illustration for AI Scientists Navigate Open Exit | Close K q deepseek 01 DEEPSEEK mHC: Manifold-Constrained Hyper-Connections 02 DEEPSEEK DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models 03 DEEPSEEK DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning 04 DEEPSEEK DeepSeek-OCR: Contexts Optical Compression 05 RESEARCH PAPER SLAI T-Res: Full-Parameter Post-training of the DeepSeek-V4 Family on As... Navigate Open Exit | Close DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning Zhihong Shan, Yuanxiang Luo, Chengba Lu, Z. Z. Ren, Jianwen Ma, Tian Ye, Zhubin Gou, Shirong Ma, Xiaokang Zhang PUBLISHED Nov 2025 RESEARCH LAB DeepSeek → CITATIONS 60 GITHUB 1.6K stars TOPICS RL for reasoning Reasoning methods Safety and alignment 01 IN BRIEF Summary DeepSeekMath-V2 is a large language model for natural-language theorem proving, built on DeepSeek-V3.2-Exp-Base, that achieves self-verifiable mathematical reasoning. The authors argue that final-answer rewards are insufficient because correct answers do not guarantee correct reasoning and are inapplicable to theorem proving. They train a verifier using reinforcement learning with expert-annotated proof scores and introduce meta-verification to reduce hallucinated issues. A proof generator is then trained using the verifier as a reward model, incentivizing it to identify and resolve issues in its own proofs. The verifier and generator are iteratively improved in a synergistic cycle, with scaled verification compute enabling automated labeling of new proofs. DeepSeekMath-V2 outperforms GPT-5-Thinking-High and Gemini 2.5-Pro on CNML-level problems, and with scaled test-time compute achieves gold-level scores on IMO 2025 and CMO 2024, and 118/120 on Putnam 2024, surpassing the highest human score of 90. On IMO-ProofBench, it outperforms DeepMind's DeepThink (IMO Gold) on the basic set and remains competitive on the advanced set. 02 FROM THE PAPER Abstract Large language models have made significant progress in mathematical reasoning, which PUBLICATION VENUE arXiv.org PAGES 19 DOI 10.48550/arXiv.2511.22570 LICENSE DeepSeek Perpetual, non-exclusive license Read original View code Project page
Camera
Focus on a search bar interaction, with a simulated keyboard input and dropdown results, followed by a smooth transition to a detailed paper view.
Motion
Typing animation, dropdown menu reveal, and a quick page transition with a subtle zoom-out/zoom-in effect.
Call to Action
Browse by Topic Areas
“(No spoken dialogue — ambient visual with mouse clicks and scrolls)”
On screen
THE YEAR IN AI Papers THE RESEARCH ATLAS Browse the ideas that shaped the year. 24 collections across 8 topic areas, covering 1,018 papers from reasoning systems to scientific discovery. 01 TOPIC AREA Reasoning 2 collections PAPERS 177 LABS 7 CODE 113 Explore topic → 02 TOPIC AREA Agents 5 collections PAPERS 275 LABS 10 CODE 214 Explore topic → 03 TOPIC AREA Multimodal 4 collections PAPERS 144 LABS 9 CODE 109 Explore topic → 04 TOPIC AREA Video and spatial AI 3 collections PAPERS 164 LABS 4 CODE 135 Explore topic → 05 TOPIC AREA Systems and efficiency 5 collections PAPERS 143 LABS 10 CODE 80 Explore topic → 06 TOPIC AREA Robotics and embodied AI 1 collection PAPERS 59 LABS 2 CODE 45 Explore topic → 07 TOPIC AREA Science and medicine 2 collections PAPERS 21 LABS 2 CODE 19 Explore topic → 08 TOPIC AREA Safety and analysis 2 collections PAPERS 35 LABS 3 CODE 15 Explore topic →
Camera
Vertical scroll through a grid of topic cards, revealing more content as the user navigates down the page.
Motion
Smooth vertical scroll with a slight ease-out, mimicking natural web browsing behavior.

Synthesizable in Remotion · key components of the quantum-patent-atlas template.

UIZoomPanFocus

A smooth camera movement that zooms into a specific UI element (e.g., a text block or image) and then pans to reveal more content within that focused area.

+ Code construction recipe

Use Remotion's `interpolate` and `spring` for camera `transform` properties (scale, translateX, translateY). Define keyframes for zoom level and pan coordinates. Apply `ease-in-out` for smooth transitions.

HorizontalCardCarousel

A rapid horizontal scrolling animation that showcases multiple content cards (e.g., research paper summaries) in quick succession, often with a slight overshoot and bounce effect.

+ Code construction recipe

Implement a `translateX` animation on a container holding the cards. Use `spring` for the `translateX` value to achieve the bounce effect. Control the speed and damping for a snappy feel.

TypingSearchReveal

Simulates a user typing into a search bar, with characters appearing sequentially, followed by a dropdown menu revealing search results, and then a transition to the selected item's detail page.

+ Code construction recipe

For typing, use `sequence` and `delay` to reveal characters in a text input. Animate the `opacity` and `scale` of the dropdown menu for a reveal effect. Transition to the detail page using a `fade` and `zoom` effect on the new content.