emre
321 posts

emre
@aegucer
i like making videos @lattedcom

Prompting is so 2025. If you ever got stuck prompting again and again trying to get the right shot, we have a new solution: Advanced Camera Control. Step into the scene. Capture the perfect shot. This is completely FREE. Comment CAMERA and I'll DM you $10 video-gen credits.





@Lattedcom turn this into anime. One shot.

you’re pitching garry tan “so what do you guys do” you start explaining he’s furiously typing . two keyboards. one hand on each. you’ve never seen this before “who are your top customers” you explain. he types. his apple watch is a strobe light of notifications “who’s your competition and why should i invest” you explain that there’s no competition and you are the best and only product in the space “false!” garry jumps out of his seat “i am the competition!” you are speechless “in this meeting, i vibe coded your entire company. and my gstack has already closed your top customers.” you check your phone. your stripe graph shows 100% churn “and look at this” garry shows you his imessage. there’s a text from 35 seconds ago. your top enterprise prospect that you’re trying to close? garry’s AI is trading baking recipes with the CEO’s mom “thank you for playing!” you have no moat. you are not admitted to the YC spring 26 batch.




We just solved creating infinitely long AI videos from a single prompt. We built Latted as the unified workspace for AI video projects. Then, we made the world's first AI generated feature film with it!




We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️






