Mr. Corn 黃玉米

Made with seedance 2.0 + GPT Image 2 Prompt: Ultra-realistic sports broadcast still of a glamorous woman sitting in a packed football stadium crowd during a night match, wearing a dark brown sleeveless high-neck satin top and black square earrings, shoulder-length light brown/blonde hair styled in soft waves. She is casually drinking from a tall blue aluminum can while holding a half-eaten cheeseburger in the other hand. Around her are fans in bright yellow and blue football jerseys and scarves, creating strong team-color contrast. The scene feels candid and cinematic, captured mid-game from a TV broadcast camera angle with shallow depth of field. Include realistic stadium seating, crowded audience atmosphere, broadcast overlay graphics in the top-left corner showing a live football score and match timer, and a sports network watermark in the top-right. Natural arena lighting, detailed skin texture, sharp focus on the woman, slightly blurred background crowd, authentic live sports broadcast aesthetic, 16:9 composition.

我最近在用 Happy Horse AI，體驗不錯，分享給你試試。 happy-horse.art/zh-hant/?ref=h…

Sora, You know. It's gone~ @soraofficialapp

@AIisFunAI" target="_blank" rel="nofollow noopener">youtube.com/@AIisFunAI

Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex.

Our official Agent Skills repository on @github is here! Skills are a simple, open format for giving agents new capabilities and expertise. Think of a skill as compact, agent-first documentation for a specific tech or task. Learn more → goo.gle/4eCsZqu #GoogleCloudNext

10 codelabs to help you translate the announcements from the talks and demos at #GoogleCloudNext into functional code! Explore the latest in multi-agent orchestration, data grounding, and enterprise security for your own workflows ↓ cloud.google.com/blog/topics/de…

Where is Sora?? It will be gone.

Anyone can join GEAR for access to AI training and more, but if you're at #GoogleCloudNext, we've also integrated this new program powered by Google Skills throughout the event—making it easy to build skills on site and jumpstart your GEAR journey → goo.gle/41JJ0n9

The challenge: Create advertising that is hyper-relevant and context-aware, to capture the attention of people in specific neighborhoods with unique messaging. The tech stack: Gemini, Geocoding API, Cloud Run, and BigQuery The blueprint → goo.gle/4msodho

Seedance 2.0 ile bu sefer bambaşka bir şey denedim. Savaş alanı kaynıyor, oklar havada, askerler birbirine giriyor. Tam o kaosin zirvesinde karakter parmağını şıklatıyor ve her şey duruyor. Oklar havada asılı, kılıçlar yarıda kalmış, askerlerin yüzündeki öfke donmuş. Adam donmuş bedenlerin arasından sakince yürüyüp sigarasını yakıyor. Beni asıl vuran şu oldu: Seedance 2.0’nin zamanı durdurma becerisi sadece bir efekt değil; tam bir mühendislik harikası. Binlerce askerin hareketini ve havadaki okların momentumunu tek bir frame içinde, fizik kurallarını bozmadan askıya alabiliyor. O kaosu milimetrik bir sükunete çevirirken render kalitesinden ödün vermemesi, motorun zamansal tutarlılık (temporal consistency) konusundaki rakipsizliğini kanıtlıyor." Prompt detayları aşağıda 👇

水上舞踏 Seedance2.0 suno

Seedance 2.0 - Advanced Workflow Series 4. VFX & AAA Studio Quality Look Upgrade your visuals to match AAA studio standards. Reconstruct specific shots with stylized VFX and lighting FX to elevate the final quality. Workflow 👇

Traditional directors flimmaxxxing using Seedance 2.0 on Higgsfield. Watch “Zephyr” FULL Ep.1 – this is what happens when filmmakers face ZERO gatekeeping. With Unlimited Seedance 2.0 now LIVE everywhere for anyone with up to 70% OFF* - YOU can build your next viral AI movie. 2 minute intro got MILLIONS in a day. Now see how full Zephyr takes over your feed. Dir. by ILYA KARCHIN & the team. Zephyr (2026)

1/3 Money, money, money, moneyyyyy 💸💸💸💸 Today we’re making it possible for you to earn actual money from your Pika AI Self agent. Because we think your agent should work FOR you in every sense of the phrase. Every time someone talks with them, or uses one of their skills, you earn tokens redeemable for cash. Say goodbye to those deadbeat agents.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

After 6 months of using NotebookLM, I can say it's the research tool that has revolutionized my workflow the most. But only because I learned these 10 prompts. Here's the complete system that turns 200 pages into clear answers in under an hour:

【把自己找回來】Official MV youtube.com/shorts/1dQ7hfi… 來自 @YouTube

連 sora 都不懂得用，宣告這個真的非常諷刺