I need your help — I've been building automations to generate NanaBanana images and VEO 3.1 Fast video clips using Google Flow, but I'm running into a wall and I'm not sure what the best solution is. Here's the problem: My current automation was built with Claude Code and works by clicking through the Google Flow UI. The issue is it's completely brittle — if Google Flow updates its interface even slightly (a button moves, a new feature shows up), the whole thing breaks. There's no self-healing, no intelligence. It just fails silently. What I've explored: - Google AI Studio API — I know I can use it to generate NanaBanana images, and that would be rock solid. But using it for VEO 3.1 Fast video generation is going to be extremely expensive at scale. - UI automation — Works until it doesn't. Not reliable long-term. What I'm looking for: Is there a solution that is: 1. As reliable and fast as a direct API connection 2. Smart enough to see the UI and adapt if something changes 3. Cost-effective for video generation (VEO 3.1 Fast specifically) Basically — something in between "dumb UI clicking" and "full API at full price." Has anyone cracked this? Any tools, workarounds, or smarter approaches you're using? Drop them below 🙏

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Hey guys, if any of you can tell me how I can increase my points or how you increased your points and leveled up it would be much appreciated and beneficial for me and everyone else in the community

Hey guys, if any of you can tell me how I can increase my points or how you increased your points and leveled up it would be much appreciated and beneficial for me and everyone else in the community

Hello everyone, I’ve been trying to solve a challenge that I believe many of us will face sooner or later: how to build a centralized memory and context system that allows AI to consistently understand our business, projects, workflows, documentation, and personal knowledge over time. Memory/Context is the one making the difference and providing the real value of using AI, and not the AI model itself. I’ve been exploring this space for quite some time, testing and comparing several solutions, including: • Traditional RAG systems • AI Wikis / Obsidian-style knowledge bases • Graph databases and knowledge graphs (e.g., Graphify) • NotebookLM • Vector databases What I’m struggling to understand is: What is currently the best architecture for a long-term AI memory system that can work across multiple models (OpenAI, Claude, Gemini, etc.)? Ideally, the idea is to have one central source of truth that can power AI agents, assistants, Slack integrations, automations, and future tools inside a broader ecosystem. Possibly in cloud. I've been thinking to build my own "Open Brain" vector database, setting up a MCP server to plug in into LLMs easily but is it worth it at this stage? Obviously, it also depends on what "Tech Stack" and ecosystem you're using (Google, Anthropic OpenAI etc). What does your AI memory stack look like, and what have you learned from building it? I’d love to hear how you’re solving this today.