Clicks tell you what happened. Hotjar shows you why it happened. With: ✔ Heatmaps ✔ Session recordings ✔ Real user feedback ✔ Quick implementation What tool do you use to understand user behavior? #UserBehavior# #Hotjar# #UXResearch# #ProductGrowth# #CRO# #Nimblechapps#

# Useful but Little-Known Features of ADK 2.0 🌍 What if you could replay realistic user conversations to evaluate your agent — without needing actual users? ADK 2.0's user simulation feature dynamically generates conversations with virtual users of varying personas, enabling systematic evaluation of your agent's response quality. 📌 Title: User Simulation 🔗 URL: 🧩 Overview User simulation automatically generates dynamic conversational prompts for agent evaluation. You define a `ConversationScenario` with a `starting_prompt` (the opening message), a `conversation_plan` (how the conversation should progress), and a `user_persona` (the simulated user's behavior). Three built-in personas are provided: EXPERT (detail-oriented, corrects the agent's mistakes), NOVICE (passive, waits for prompts and guidance), and EVALUATOR (detail-oriented but does not correct errors). You can also create custom personas with `UserPersona`. 🛠 How to use it Use the CLI command `adk eval_set generate_eval_cases` to generate evaluation cases. For custom personas, use the `UserPersona` class. ```python from google.adk.evaluation.conversation_scenarios import ConversationScenario from google.adk.evaluation.simulation.user_simulator_personas import ( UserBehavior, UserPersona, ) # Define a custom persona impatient_user = UserPersona( id="IMPATIENT", description="Prefers short answers and shows frustration with lengthy explanations", behaviors=[ UserBehavior( name="Short responses", description="The user provides very short, sometimes incomplete responses", behavior_instructions=["Keep your responses under 10 words.", "Omit polite phrases."], violation_rubrics=["The user response is over 10 words."], ) ], ) # Define a scenario scenario = ConversationScenario( starting_prompt="Help me book a trip", conversation_plan="Confirm destination → dates → budget, then complete the booking", user_persona=impatient_user ) ``` 🏗 Building it into production ・Integrate evaluation scenarios into CI/CD pipelines to run regression tests automatically on agent updates ・Use all three built-in personas (EXPERT, NOVICE, EVALUATOR) to cover different user segments ・Design `conversation_plan` around real business workflows to match actual usage patterns ・Create custom personas that mirror your product's specific user behavior patterns 💡 Use cases 🧪 Automatically detect conversation quality regressions after prompt or model changes 👥 Verify that different response strategies work for beginners vs. power users 📊 Run batch evaluations across multiple scenarios to systematically identify agent weaknesses 🔄 Test how well the agent handles topic shifts mid-conversation or ambiguous questions ⚠️ Watch out Since simulations are LLM-generated, the same scenario may produce slightly different conversation flows on each run. For reproducible evaluations, run multiple iterations and assess results statistically. Also, each built-in persona behaves quite differently — make sure you select the one that matches your evaluation goals. ✨ User simulation lets you continuously improve your agent's conversational quality without involving real users. Make multi-persona evaluation a regular part of your development workflow. #ADK# #AIAgent#

Generative recommender systems reformulate personalization as sequence modeling over user behavior, and NVIDIA recsys-examples now provides an end-to-end HSTU inference workflow with Dynamo-Triton. PyTorch AOTI compiles HSTU ranking models into native C++ artifacts, while FlexKV-backed KV caching stores reusable attention state to avoid recomputing long user histories. On an @nvidia RTX PRO 6000 Blackwell Workstation Edition GPU, the eight-layer HSTU model achieved up to 5.93x lower latency at batch size 8 with a 100% GPU KV-cache hit rate compared to the same AOTI configuration without caching. Read the full blog post:

Putting a fund onchain is just the starting point. The value comes from letting people earn yield from it while using it as collateral. @thomasxchen explains the user behavior BNB Chain wants to support at scale at @rwasummit.

Managing 26 million logged-in users requires a centralized data ecosystem to target audiences, promote content, and automate marketing. 🛠️ Using Snowflake CoWork, @TF1 unified user behavior into a 360-degree view, eliminating data access complexity while maintaining total conformity and compliance. Learn more 👉🏻

Tip for building in public: If making content feels like extra work, show the work already happening inside your product. A tiny screen recording, the first version, or the user behavior that changed your design. The reasoning matters more than production value.

🌈 New #listing# 🌟 $XYZ (Web3 Growth Agent) will be listed on LBank! @W3growthagent WGA is a Web3 infrastructure project that leverages AI and Soft DePIN technology to transform user behavior data into trusted, verifiable digital assets. ❤️ Details:

South Carolina passed a law that aims to protect kids from addictive social media use. The law requires large social media companies to estimate a user’s age using information they already collect, such as behavior, activity, account details, or device data. >If someone uses the app for more than 25 hours within six months, the company must be at least 80% confident the person is older than 15. >If the person reaches 50 hours of use, the company must be 90% confident. >If the company cannot reach that confidence level, it must assume the user is a child. The company must also recheck the age estimate every additional 100 hours of use and whenever it uses recommendation systems or analyzes user behavior. If the company wrongly treats a child as an adult, it can face fines up to $10,000 per violation. When a user is treated as a child, the platform must add protections such as parental permission tools, fewer addictive features like infinite scrolling or highly personalized feeds, stronger privacy settings, less data collection, more parental controls, and yearly safety reviews focused on risks to minors. The law applies to major online platforms that operate in South Carolina and are likely to be used by people under 18. Sc:

It’s been just one year since we launched AI Mode in Google Search and the momentum has been incredible: 1B+ monthly active users and queries doubling every quarter! But what’s even more incredible is seeing how user behavior is changing: + Beyond simple questions → 3X avg search query length compared to a traditional query + Beyond keywords → Planning queries (like weekend plans) are growing 80% faster + Beyond text → 1 in 6 searches are now multimodal, with image queries growing 40% MoM + Beyond quick q’s → Top first words in AI Mode queries: 1) what, 2) how, 3) I, 4) is, 5) can More insights here: