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Day 27 of my 50 days of GenAI challenge. Ask an agent a question. Get a great answer. Ask a follow-up — and it has no idea what you just talked about. By default, LangChain agents are stateless. Ev
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Day 26 of my 50 days of GenAI challenge. LLMs generate text. But they can't look things up. Ask for today's weather and they'll guess. Or refuse. That changes with agents. An agent is an LLM that
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Day 25 of my 50 days of GenAI challenge. We’re halfway through. So far, the series has covered the core building blocks of modern AI systems: • LLM fundamentals • Tokens and cost • Memory and conte
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Day 24 of my 50 days of GenAI challenge. Not all LLMs perform the same. Even within the same family, models differ in: • Speed • Cost • Reasoning ability • Output quality In this example, we compa
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Day 23 of my 50 days of GenAI challenge. LLMs don’t always generate the same response. That behavior is controlled by sampling parameters. These parameters decide how the model chooses the next tok
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Day 22 of my 50 days of GenAI challenge. Embeddings used to work within a single modality. Text was compared with text. Images with images. Now everything can live in the same embedding space. Wit
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Day 21 of my 50 days of GenAI challenge. Multimodal models don’t just analyze images. They follow instructions. The same image can produce completely different responses depending on the prompt. Y
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Day 20 of my 50 days of GenAI challenge. So far, we’ve been sending text to models and receiving text back. But modern models are multimodal — they can process images too. In this example, we send
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Day 19 of my 50 days of GenAI challenge. We’ve spent the last few days learning how agents work: • Tool calling • Multi-tool routing • Agent loops (think → act → observe) Today we combine them into
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So far, we’ve seen: • Single tool calls • Multiple tool routing Today, we move to the agent loop. An agent doesn’t just call one tool. It can: Think Decide which tool to use Execute the tool Observ
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Yesterday, we gave the LLM one tool. Today, we gave it three. The moment you introduce multiple tools, the LLM stops being just a caller — it becomes a router. Each tool is defined with: • A name
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LLMs can generate text. But they cannot: • Access real-time data • Query databases • Call APIs • Execute code Tool calling changes that. Instead of answering directly, the model can request to cal
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About @average.yash

Yashwanth S is a Instagram micro influencer. With 12.3K followers and 44 posts, average.yash's account shows a follower-to-following ratio of 323.4:1. This profile demonstrates strong community engagement within its niche. View all content anonymously on Pictame - no login required.

average.yash's engagement rate sits at 2.08%. For accounts with 10K-100K followers, the average is around 2.5%, so there's some room to grow — though every community is unique.

Out of the last 12 posts, 100% are videos and 0% are photos. New posts appear roughly every 3 days — a steady, reliable rhythm. Captions lean detailed, averaging 987 characters.

2.08% engagement rate — room to grow compared to average for 10K-100K follower accounts.
New content every 3 days — a steady rhythm.
323.4:1 follower-to-following ratio.

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average.yash
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@average.yash

Yashwanth S

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44
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Top Hashtags
#GenAI#LLMEngineering#AIDevelopment#AIAgents#MachineLearning#MultimodalAI#ToolCalling#LangChain

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#igers#cats#nofilter#workout#foodporn#hair#instapic#trending#viral#explore#reels#explorepage
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Engagement
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Documenting life in progress Body • Health • Tech • Growth Showing up, even on hard days

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