TryAuraStream @TryAuraStream - Twitter Profile

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5 months ago

Streamers capture epic moments… then fight to recreate them as assets. Canvas Studio keeps it human + fast: 📸 Snap any gameplay moment Map out your vision in the canvas text or sketch! Coach guides you live: “That skin? Same lighting? Victory vibe?” ✨ Pro asset in 3 minutes Real-time creation. Your vision stays yours. No guesswork.

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DadBodGeoff @BuildingByGeoff

4 months ago

By now we’ve all done it, jumped into an IDE and felt the dopamine of ripping through 100,000 lines of code in like 3 hours. You just popped your 2nd red bull at 1:30 in the morning and it's been years since you had this feeling. Then it comes time to turn it on and you're hit with the biggest wave of depression you’ve felt since that crush in high school said they were not interested.  After 6 months of teaching myself how to orchestrate agents to engineer me different codebases and projects ive come to this conclusion: AI can write very good code and it's not an intelligence problem, it's a context limitation.   So what are we going to do about it? My solution is called “Statistical Semantics”   Drift learns your codebase conventions via AST Parsing (With a regex Fallback) detecting 170 patterns across 15 categories. From here it extracts and indexes meta data from your codebase and stores it locally through jsons that can be recalled through any terminal through the CLI or exposed to your agent through a custom-built MCP server.  Think of drift as a translator between your codebase and your AI. Right now when claude or cursor audits your codebase its through grep or bash. This is like finding a needle in a haystack when looking for a custom hook, that hack around you used to get your websocket running or that error handling it can never seem to remember and then synthesizes the results back to you.  With drift it indexes that and is able to recall the meta data automatically after YOU approve it. Once you do your first scan you go through and have your agent or yourself approve the meta data found and either approve / ignore / deny so only the true patterns you want stay.   The results?   Code that fits your codebase on the first try. Almost like a senior engineer in your back pocket, one that truly understands the conventions of your codebase so it doesn’t require audit after audit or refactor after refactor fixing drift found throughout the codebase that would fail in production.   Quick start guides  MCP Server set up here: https://t.co/YZ08x2DyXm  CLI full start guide: https://t.co/TRdrKUJIu6  CI Integration + Quality Gate: https://t.co/l1SvsLWlRi  Call graph analysis guide: https://t.co/Dtrnqrd4lR  Fully open sourced and would love your feedback!

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