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  • Deal Mind : AI Sales Assistant

    I Built a Sales Assistant That Actually Remembers Customers What if your sales assistant could write the perfect email… …but had no idea who the customer was? That bothered me while building AI sales assistants. Today, an LLM can write a convincing email in seconds. It can summarize a meeting.

    Dev.to AI6 min read
  • What Happened When DealMind Could Recall Past Objections

    What Happened When DealMind Could Recall Past Objections Building persistent deal memory with Hindsight changed how I thought about context, retrieval, and the boundary between application state and agent memory. The first version of DealMind could answer questions about a deal.

    Dev.to AI10 min read
  • Only Gemini Failed My False-Premise Benchmark — 7 Models Tested

    Only Gemini Failed My False-Premise Benchmark — 7 Models Tested The itch I kept noticing a specific failure mode: when a question embeds a false assumption, most models correct the user cleanly — but some play along and confabulate detailed answers that fit the wrong premise.

    Dev.to AI4 min read
  • The Observer Effect in Chain-of-Thought Monitoring

    Every instrumentation layer is an interrogation. You add a trace, and the trace changes the testimony. When we wrap an agent's reasoning chain with logging, we are not passive observers. We are inserting variables into the system's decision surface.

    Dev.to AI3 min read
  • Deal Mind : AI Sales Assistant

    I Built a Sales Assistant That Actually Remembers Customers What if your sales assistant could write the perfect email… …but had no idea who the customer was? That bothered me while building AI sales assistants. Today, an LLM can write a convincing email in seconds. It can summarize a meeting.

    Dev.to AI6 min read
  • Building a Deal Intelligence Agent with Persistent Memory

    Sales conversations rarely happen in isolation. A customer might discuss pricing in one call, raise an objection in another, introduce a new stakeholder later, and finally ask for a specific next step several conversations after the first meeting. The problem is that these details are easy to lose.

    Dev.to AI7 min read
  • My AI Sales Rep Learned From Hang-Ups With Hindsight

    The worst call my voice agent ever made went like this: "Hi Imran, Aisha from TableTap. Our billing app starts at just nine hundred ninety nine rupees a month." Imran runs a cloud kitchen.

    Dev.to AI8 min read
  • How Hindsight helped my agents remember architectural constraints

    Building Stateful AI Workflows: Moving Past Raw Logs with Long-Term Agent Memory If you've ever watched an LLM-powered assistant forget a critical workflow decision three turns after you made it, you already know the wall that stateless agent architectures hit.

    Dev.to AI4 min read
  • Building an AI-Powered Sales Deal Intelligence Agent with Persistent Memory

    In modern B2B sales cycles, account executives juggle multiple conversations with a single prospect over weeks or months. From initial discovery calls to deep-dive technical reviews, every interaction builds a complex web of specific requirements, budget constraints, security concerns, and…

    Dev.to AI3 min read
  • DIAS — Individual Technical Submission Package

    What Should an AI Coding Agent Remember? Building Persistent Domain Memory An AI coding agent can write code today, solve a debugging problem, and understand an architecture decision — and then appear to forget all of it when the next session begins. That creates a practical problem.

    Dev.to AI14 min read
  • What is Jev and How to Use It with an LLM? (Free Lab)

    Jev is a new AI that is making news and it doesn't chat. You can use it do small classification tasks. In this video, we take one real support ticket from a customer named Alan and follow it all the way through: what we send Jev, what comes back, and how the program decides what to do with the…

    KodeKloud
  • How to Build a Reliable AI Assistant with the Claude API

    Large language models can answer questions, summarise documents, write code, and interact with external systems. But building a reliable AI application requires more than sending a prompt and displayi

    freeCodeCamp — AI12 min read
  • Nvidia boosts share buyback program by record $150B

    Nvidia Corp. today announced plans to spend an additional $150 billion on share buybacks through January 2028. The move represents the largest-ever expansion of a stock repurchase program. Furthermore, Nvidia plans to boost its current dividend of 25 cent per share.

    SiliconANGLE3 min read

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