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Friday

  1. Shao Meng

    Matt Palmer demonstrates using Cursor Cloud Agent to generate a UI motion video against real Slack

    Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of a Cursor Cloud Agent, having the Agent generate a Slack UI motion video with code, and then iterating and revising after checking easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes) and an outer loop (a human provides references, preferences, and permissions and accepts the output), and proposed the Do→Codify→Generalize ladder, packaging one success into a Skill and then generalizing it. He emphasized that Slack was not decompiled; he merely gave the Agent a reference and asked it to extract visual patterns by comparison.

    Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of a Cursor Cloud Agent, having the Agent generate a Slack UI motion video with code, and then iterating and revising after checking easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes) and an outer loop (a human provides references, preferences, and permissions and accepts the output), and proposed t

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.
  2. Story1 reports · 1 facts

    Matt Palmer demonstrated using the Cursor cloud Agent to generate an interface motion animation video against real Slack

    On October 30, 2026, Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of the Cursor cloud Agent, having the Agent use code to generate a Slack interface motion animation video, and then iterating and revising after checking the easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes it) and an outer loop (a human provides references, preferences, and permissions and accepts the output), and proposed the ladder of Do→Codify→Generalize, turning one success into a Skill and then generalizing it. He emphasized that Slack was not decompiled; he merely gave the Agent a reference and asked it to extract visual patterns by comparison.

    On October 30, 2026, Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of the Cursor cloud Agent, having the Agent use code to generate a Slack interface motion animation video, and then iterating and revising after checking the easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes it) and an outer loop (a human provides references, preferences, and pe

    Matched fieldsTitle

  3. FactRelated story

    Matt demonstrates using Cursor Cloud Agent to generate UI motion videos by referencing real Slack

    On October 30, 2026, Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of the Cursor cloud Agent, having the Agent use code to generate a Slack interface motion animation video, and then iterating and revising after checking the easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes it) and an outer loop (a human provides references, preferences, and permissions and accepts the output), and proposed the ladder of Do→Codify→Generalize, turning one success into a Skill and then generalizing it. He emphasized that Slack was not decompiled; he merely gave the Agent a reference and asked it to extract visual patterns by comparison.

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    View story:Matt Palmer demonstrated using the Cursor cloud Agent to generate an interface motion animation video against real Slack

  4. Story1 reports · 1 facts

    Artificial Analysis launches image-to-video benchmark AA-Video-I2V v2.1

    Artificial Analysis launches the image-to-video evaluation benchmarks AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, ranking models at up to 1080p resolution across 10 use-case categories, 10 capabilities, and multiple visual styles. The two versions are based on more than 58,000 human preference votes on over 1,000 prompts and more than 44,000 votes on 500 prompts, respectively, with only 5% of prompts made public in the public arena and rotated monthly. In the first version of the leaderboard, Dreamina Seedance 2.5 ranks first overall, statistically tied with the Seedance 2.0 and MiniMax H3 series, at a price of $34.12/min; the open-weight MiniMax H3 (768p) ranks third, at a price of $4.80/min.

    Artificial Analysis launches the image-to-video evaluation benchmarks AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, ranking models at up to 1080p resolution across 10 use-case categories, 10 capabilities, and multiple visual styles. The two versions are based on more than 58,000 human preference votes on over 1,000 prompts and more than 44,000 votes on 500 prompts, respectively, with only 5% of prompts made public in the public arena and rotated monthly. In the first version of the leaderboard

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  5. FactRelated story

    Artificial Analysis launches image-to-video benchmark AA-Video-I2V v2.1

    Artificial Analysis launches the image-to-video evaluation benchmarks AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, ranking models at up to 1080p resolution across 10 use-case categories, 10 capabilities, and multiple visual styles. The two versions are based on more than 58,000 human preference votes on over 1,000 prompts and more than 44,000 votes on 500 prompts, respectively, with only 5% of prompts made public in the public arena and rotated monthly. In the first version of the leaderboard, Dreamina Seedance 2.5 ranks first overall, statistically tied with the Seedance 2.0 and MiniMax H3 series, at a price of $34.12/min; the open-weight MiniMax H3 (768p) ranks third, at a price of $4.80/min.

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    View story:Artificial Analysis launches image-to-video benchmark AA-Video-I2V v2.1

Thursday

  1. Artificial Analysis

    Artificial Analysis releases the image-to-video benchmark AA-Video-I2V v2.1

    Artificial Analysis launches the image-to-video evaluation benchmarks AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, ranking models at up to 1080p resolution across 10 use-case categories, 10 capabilities, and multiple visual styles. The two versions are based on more than 58,000 human preference votes across over 1,000 prompts and more than 44,000 votes across 500 prompts, respectively, with only 5% of prompts made public in the public arena and rotated monthly. In the first version of the leaderboard, Dreamina Seedance 2.5 ranks first overall, statistically tied with the Seedance 2.0 and MiniMax H3 series, at a price of $34.12/min; the open-weight MiniMax H3 (768p) ranks third, at a price of $4.80/min.

    Artificial Analysis launches the image-to-video evaluation benchmarks AA-Video-I2V v2.1 and AA-Video-I2V-Silent v2.1, ranking models at up to 1080p resolution across 10 use-case categories, 10 capabilities, and multiple visual styles. The two versions are based on more than 58,000 human preference votes across over 1,000 prompts and more than 44,000 votes across 500 prompts, respectively, with only 5% of prompts made public in the public arena and rotated monthly. In the first version of the lea

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    #editorial#Evaluations / benchmarks#Video#Image generation

Saturday

  1. Ahead of AI

    Sebastian Raschka releases an 11-hour video course on building a reasoning model from scratch

    Sebastian Raschka has released 8 programming videos on building a reasoning model from scratch, totaling 11 hours, using Python and PyTorch to implement evaluation, inference-time scaling, reinforcement learning, and distillation starting from a pretrained Qwen3 0.6B model. The course first builds a math answer verifier and evaluates on MATH-500, then implements temperature sampling, top-p filtering, and self-consistency, followed by RLVR training with a simplified version of GRPO, and finally distills a small model using answers generated by DeepSeek R1. The content accompanies his book "Build a Reasoning Model (From Scratch)"; the early text generation and evaluation examples can run on a CPU, while later training experiments are recommended to use a GPU.

    Sebastian Raschka has released 8 programming videos on building a reasoning model from scratch, totaling 11 hours, using Python and PyTorch to implement evaluation, inference-time scaling, reinforcement learning, and distillation starting from a pretrained Qwen3 0.6B model. The course first builds a math answer verifier and evaluates on MATH-500, then implements temperature sampling, top-p filtering, and self-consistency, followed by RLVR training with a simplified version of GRPO, and finally d

    Matched fieldsTitle

    #newsletter#Tutorials / practice#Reasoning#English text is being prepared.

Thursday

  1. AYi AI Notes

    The author used Grok Bot to generate the MemT-1.0 popular-science animation video purely with code

    The author says they gave Grok Bot only one sentence and one avatar image and wrote no storyboard prompts at all, yet in the background it used pure front-end code to compile the newly open-sourced memory large model MemT-1.0 into a one-minute illustrated popular-science animation, with the animation, voiceover, and storyboard all written as code in one go and rendered on the spot. The author believes that video-as-code is easy to extend: to change the avatar you only need to change one image path line, and to switch to an English narration you just re-run the compilation and the timing will automatically realign.

    The author says they gave Grok Bot only one sentence and one avatar image and wrote no storyboard prompts at all, yet in the background it used pure front-end code to compile the newly open-sourced memory large model MemT-1.0 into a one-minute illustrated popular-science animation, with the animation, voiceover, and storyboard all written as code in one go and rendered on the spot. The author believes that video-as-code is easy to extend: to change the avatar you only need to change one image pa

    Matched fieldsTitle

    #hot-signal#English text is being prepared.#Agents#Video
  2. FactRelated story

    User says Grok Bot generates an educational animation video using pure front-end code

    On October 22, 2026, a user claimed that Grok Bot generated an illustrated educational animation video. According to an article published by A Yi AI Notes that day, the author stated that they only provided Grok Bot with one sentence and one avatar image, without writing any storyboard prompts, and the backend then used pure front-end code to compile the newly open-sourced memory large model MemT-1.0 into a one-minute illustrated educational animation, with the animation, voiceover, and storyboard all written as code in one go and rendered on the spot. The author also claimed that because the video is code, it is easy to extend: to change the avatar, only one image path line needs to be modified, and to switch to English narration, re-running the compilation automatically aligns the timing. Currently, this claim comes only from this one author's description, and no other sources have cross-verified it.

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    View story:User Says Grok Bot Generated an Educational Animation Video Using Pure Front-End Code

Tuesday

  2. FactRelated story

    Stanford CME 295 Lecture 4 public video and lecture notes

    Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, with both the video and lecture notes now publicly available. The topic of this lecture is Reinforcement learning with LLMs, covering everything from reinforcement learning and reward design to PPO, GRPO, RLVR, and DAPO, explaining each of the notations one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering the Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention inference optimization, MCP and AI Agents, and diffusion language models. Currently, Lecture 4 is public, and no evidence of updates for subsequent lectures has been seen yet.

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    View story:Stanford releases public video and lecture notes for CME 295 Lecture 4

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  1. Story1 reports · 1 facts

    User Says Grok Bot Generated an Educational Animation Video Using Pure Front-End Code

    On October 22, 2026, a user claimed that Grok Bot generated an illustrated educational animation video. According to an article published by A Yi AI Notes that day, the author stated that they only provided Grok Bot with one sentence and one avatar image, without writing any storyboard prompts, and the backend then used pure front-end code to compile the newly open-sourced memory large model MemT-1.0 into a one-minute illustrated educational animation, with the animation, voiceover, and storyboard all written as code in one go and rendered on the spot. The author also claimed that because the video is code, it is easy to extend: to change the avatar, only one image path line needs to be modified, and to switch to English narration, re-running the compilation automatically aligns the timing. Currently, this claim comes only from this one author's description, and no other sources have cross-verified it.

    On October 22, 2026, a user claimed that Grok Bot generated an illustrated educational animation video. According to an article published by A Yi AI Notes that day, the author stated that they only provided Grok Bot with one sentence and one avatar image, without writing any storyboard prompts, and the backend then used pure front-end code to compile the newly open-sourced memory large model MemT-1.0 into a one-minute illustrated educational animation, with the animation, voiceover, and storyboa

    Matched fieldsTitle

  2. Story1 reports · 1 facts

    Stanford releases public video and lecture notes for CME 295 Lecture 4

    Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, with both the video and lecture notes now publicly available. The topic of this lecture is Reinforcement learning with LLMs, covering everything from reinforcement learning and reward design to PPO, GRPO, RLVR, and DAPO, explaining each of the notations one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering the Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention inference optimization, MCP and AI Agents, and diffusion language models. Currently, Lecture 4 is public, and no evidence of updates for subsequent lectures has been seen yet.

    Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, with both the video and lecture notes now publicly available. The topic of this lecture is Reinforcement learning with LLMs, covering everything from reinforcement learning and reward design to PPO, GRPO, RLVR, and DAPO, explaining each of the notations one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering the Transformer architecture, attention variants, RoPE,

    Matched fieldsTitle