<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Bhavya Bandarupalli</title>
    <description>The latest articles on DEV Community by Bhavya Bandarupalli (@bhavya_bandarupalli_d0647).</description>
    <link>https://dev.to/bhavya_bandarupalli_d0647</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4006441%2F7bd46624-f96c-4996-8206-6b025c4bf6eb.png</url>
      <title>DEV Community: Bhavya Bandarupalli</title>
      <link>https://dev.to/bhavya_bandarupalli_d0647</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/bhavya_bandarupalli_d0647"/>
    <language>en</language>
    <item>
      <title>hack with Hyd 2.0</title>
      <dc:creator>Bhavya Bandarupalli</dc:creator>
      <pubDate>Sun, 28 Jun 2026 11:41:17 +0000</pubDate>
      <link>https://dev.to/bhavya_bandarupalli_d0647/hack-with-hyd-20-3bp6</link>
      <guid>https://dev.to/bhavya_bandarupalli_d0647/hack-with-hyd-20-3bp6</guid>
      <description>&lt;p&gt;Support bots that forget every conversation aren't support bots. They're expensive FAQ pages.&lt;br&gt;
I built SupportMind to fix that — a customer support agent that actually remembers.&lt;br&gt;
The architecture is two layers:&lt;br&gt;
Memory (Hindsight): After every interaction, the agent stores structured context in a vector namespace per user. Next session, it recalls semantically — "payment problem" retrieves "Visa charge failing" even if the words don't match.&lt;br&gt;
Routing (cascadeflow): Not every query needs GPT-4. Password resets go to Groq's free tier. Complex billing disputes escalate. Every decision is logged with model, cost, latency, and reason.&lt;br&gt;
The delta that matters:&lt;br&gt;
Session 1: "Can you tell me your card details and the error you're seeing?"&lt;br&gt;
Session 3 (same user, same issue): "I see you've had recurring issues with your Visa ending in 4242. Last time, clearing billing cache fixed it — want to try that first?"&lt;br&gt;
Same infrastructure. Completely different agent.&lt;br&gt;
On a typical support workload: ~80% simple queries handled by the cheap model. Cost per query dropped from ~$0.012 to ~$0.002.&lt;br&gt;
The part I didn't expect: routing and memory compound. When Hindsight shows a user has had the same issue four times, cascadeflow automatically classifies their next message as complex — even without explicit signals. That fell out of the architecture. 👇&lt;br&gt;
&lt;a href="https://lnkd.in/gn8NwP6Z" rel="noopener noreferrer"&gt;https://lnkd.in/gn8NwP6Z&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;hashtag#AIAgents hashtag#AgentMemory hashtag#Hindsight hashtag#cascadeflow hashtag#LLM hashtag#AI&lt;a href="https://dev.tourl"&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>agents</category>
      <category>ai</category>
      <category>rag</category>
      <category>showdev</category>
    </item>
    <item>
      <title>hack with hyd 2.0</title>
      <dc:creator>Bhavya Bandarupalli</dc:creator>
      <pubDate>Sun, 28 Jun 2026 11:39:45 +0000</pubDate>
      <link>https://dev.to/bhavya_bandarupalli_d0647/hack-with-hyd-20-2pj3</link>
      <guid>https://dev.to/bhavya_bandarupalli_d0647/hack-with-hyd-20-2pj3</guid>
      <description>&lt;p&gt;Support bots that forget every conversation aren't support bots. They're expensive FAQ pages.&lt;br&gt;
I built SupportMind to fix that — a customer support agent that actually remembers.&lt;br&gt;
The architecture is two layers:&lt;br&gt;
Memory (Hindsight): After every interaction, the agent stores structured context in a vector namespace per user. Next session, it recalls semantically — "payment problem" retrieves "Visa charge failing" even if the words don't match.&lt;br&gt;
Routing (cascadeflow): Not every query needs GPT-4. Password resets go to Groq's free tier. Complex billing disputes escalate. Every decision is logged with model, cost, latency, and reason.&lt;br&gt;
The delta that matters:&lt;br&gt;
Session 1: "Can you tell me your card details and the error you're seeing?"&lt;br&gt;
Session 3 (same user, same issue): "I see you've had recurring issues with your Visa ending in 4242. Last time, clearing billing cache fixed it — want to try that first?"&lt;br&gt;
Same infrastructure. Completely different agent.&lt;br&gt;
On a typical support workload: ~80% simple queries handled by the cheap model. Cost per query dropped from ~$0.012 to ~$0.002.&lt;br&gt;
The part I didn't expect: routing and memory compound. When Hindsight shows a user has had the same issue four times, cascadeflow automatically classifies their next message as complex — even without explicit signals. That fell out of the architecture. 👇&lt;br&gt;
&lt;a href="https://lnkd.in/gn8NwP6Z" rel="noopener noreferrer"&gt;https://lnkd.in/gn8NwP6Z&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;hashtag#AIAgents hashtag#AgentMemory hashtag#Hindsight hashtag#cascadeflow hashtag#LLM hashtag#AI&lt;/p&gt;

</description>
      <category>agents</category>
      <category>architecture</category>
      <category>rag</category>
      <category>showdev</category>
    </item>
  </channel>
</rss>
