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RIGVEDA 1st Shloka shows Speed of Light as Cubic Volume #VedicScience #Sankhya #Kapilamuni

Want your AI to understand how humans buy, not just guess? 🧠 Meet an AI that understands why people buy. Consumer_Intent_AI — an open-source project that predicts human purchase intent almost like real consumers. 🔗 Repo & paper link: github.com/budprat/Consum… AI that gauges human buying mindset at scale. 🚀 Ideal for: • Real-time consumer insight dashboards • Product validation before launch • Ad campaign optimization • Academic or behavioral research • Marketing teams scaling purchase-intent surveys • Product teams validating new offerings • Research labs replicating human behaviour with LLMs • Automation of consumer-insight workflows 🤖 The problem with most “consumer AI”: • They guess sentiment, not intent • Need tons of labeled data • Forget human nuance • Can’t replicate real survey reliability Consumer_Intent_AI fixes all of that. 📊 Why it’s robust: • Supports demographics conditioning → significant ρ improvement ( 40 pp) • Works for cohort distributions & full survey pipelines • Built for production: REST API, web-app, Docker, full test suite. 💡 The breakthrough: Semantic Similarity Rating (SSR) Instead of asking “Would you buy this?” — the AI compares new prompts against human-rated reference sets and outputs a Likert-scale intent score (1–5). • Uses LLMs to generate “synthetic consumers” • Measures similarity to human responses • Hits ~90% of human test-retest reliability

Ever wish your AI agent could actually remember what it learned — including its screw-ups? Meet ReasoningBank — an open-source take on Google & UIUC’s new “self-evolving agents with reasoning memory” paper. The twist: it learns from failures too. 🧠👇 🔗 GitHub: github.com/budprat/Reason… 📖 Paper: ReasoningBank: Scaling Agent Self-Evolving (Google/UIUC 2025) Most agents today are kinda amnesiac: ❌ They repeat mistakes ❌ Forget what didn’t work ❌ Never improve ❌ Start from zero every time ReasoningBank fixes that with a 5-step closed loop: 1️⃣ Retrieve past experiences 2️⃣ Act (ReAct-style) 3️⃣ Judge success or failure 4️⃣ Extract strategies or lessons 5️⃣ Consolidate into memory Every run → smarter agent. ⸻ 💡 Core idea: Dual-Prompt Memory Extraction ✅ If it works → keep the winning pattern ❌ If it fails → store the “never again” lesson Basically, it asks itself: “What went wrong — and how do I avoid this next time?” ⸻ 🏗️ Architecture Query → Memory-Augmented ReAct → Judge → Extract → Store • Works with Claude, GPT-4, Gemini • JSON-based memory • Embedding retrieval via cosine similarity • 254 tests, full coverage ⸻ 🚀 MaTTS (Memory-Aware Test-Time Scaling): Parallel = sample & select the best Sequential = iterative refinement The agent pulls memories, acts, judges, learns. ⸻ 🎯 Why it’s special • Self-improving over time • Learns from wins and losses • No fine-tuning needed • Works across LLMs • Persistent memory between sessions • Docker-ready ⸻ 🔧 Perfect for • Support bots learning from tickets • Code assistants remembering bugs • Research agents building knowledge • Automation that adapts to edge cases