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Swamy Gadila
Founder of Friday AI • 1d
Big News: Friday AI – Adaptive API is Coming! We’re launching Adaptive API, the world’s first real-time context scaling framework for LLMs. Today, AI wastes massive tokens on static context — chat, code, or docs all use the same window. The result? Higher GPU bills, latency, and poor enterprise scalability. Our Solution: Adaptive API dynamically reshapes requests (12K→1M tokens) in <200ms, analyzing activity and semantic density to save compute and cost. Think of it as an optimizer layer between your app and model. Unlike LangChain or Mem0, it’s composable, selective, and brain-inspired. Works with OpenAI, Anthropic, Grok, and any LLM. Benefits: Developers save 30–40% tokens. Enterprises lower infra costs, monitor usage, and deploy securely. Pricing starts at $0.018/1K tokens. Join our beta launch → fridayai.fun
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Swamy Gadila
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Introducing the World's First Adaptive Context Window for Emotional AI Built by the Friday AI Core Technologies Research Team Most LLMs today work with static memory — 8K, 32K, maybe 128K tokens. But human conversations aren't static. Emotions evo
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Dhruv rathee's AI Fiesta claims you “save 90%” on API costs. Here’s the reality 👇 400k tokens ≈ $1.30 worth of API usage. AI Fiesta charges $3.99 for it. That’s a 237% markup. And the “unlimited” plan? Not really. 400k tokens = ~10 days of actua
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hii, i have created an ai android app where i use gemini api to fetch ai response i use gemini 1.5 flash. It provide rate limit of 15 RPM and 1M TPM (tokens per minute) which is obviously less in production. So, i have an idea that i will generate 20
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Hey everyone, First Indian product on Vision RAG, Currently RAG systems often struggle to maintain context, leading to hallucinations and inaccurate response. At https://blackwhole.dev we solving this so you can upload any document and get highly re
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🚀 How to Save 90% on Your AI Costs Here’s exactly how we cut AI costs from $500/month to $5/month: 1. Avoid LLMs When Possible Use rules, regex, or database lookups for simple tasks (60–80% of workflows). Example: A lead qualification bot using rege
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