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Hey everyone! 👋 I'm currently looking for my first job as a developer, and honestly, the journey hasn't been as easy as I expected. I've spent time learning web development, working with React, JavaScript, Node.js, MongoDB, and building projects to improve my skills.
Looked into new AI tools available in market. Most seem to be doing sorta fine but other than the big name ones like claude, chatgpt etc, rest seem to still be finding proper grounds. Thought about creating a new project using MongoDB and MERN stack, a coding practice tool.
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.
I've watched LLMs confidently recommend approving an invoice for an amount that doesn't appear in the purchase order, the invoice, or any past decision. They do it with high confidence and well-structured reasoning. The reasoning sounds right. The number is invented.
Deal Oracle:Giving Sales Deals a Memory Sales teams work with a huge amount of information across their deals — customer requirements, stakeholders, competitors, objections, deal stages, risks, notes, and follow-ups.
AI chat assistants sound just as sure of themselves when they're wrong as when they're right. Asking "are you sure?" doesn't really help. You mostly get the same guess, worded more politely.
The useful part of a deployment agent isn't telling me what a PostgreSQL upgrade is. It's remembering what happened the last time I tried one. That became the central idea behind DeployMind, a DevOps pipeline agent I built around 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.
I Built an Invoice Agent That Remembers Why It Said Yes Most invoice-processing agents treat every invoice as a new problem. That becomes a problem when the same vendor keeps coming back with recurring exceptions.
I built an incident-diagnosis agent that remembers every outage we've resolved. The interesting part isn't the diagnosis. It is the persistence boundary I ended up drawing, and how much of the system's value lives on one side of it.
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.
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…
Why Autonomous SRE Needs Episodic Memory: How We Built Opsight with Hindsight Every on-call engineer knows the sinking feeling of getting paged at 3:14 AM for a SEV-1 outage that feels eerily familiar, yet nobody can remember which config flag or database lock script resolved it last time.
For a long time, every invoice my agent saw was the first invoice it had ever seen from that vendor. It read the PDF, pulled out the fields, made a call, and forgot everything the moment the page reloaded.
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.
An AI-coding landing page with enterprise-grade claims and toy-grade evidence — big compliance promises, nothing on the public surface to back them.
A deterministic, MCP-first coordination protocol for multi-agent coding — unusually honest about its own limits, and exactly the plumbing a fleet-of-agents future needs.
A genuinely agent-native idea — a persistent visual workspace where a coding agent builds artifacts wired to your repo — behind a deliberately minimal, key-gated landing that gives Hlido almost nothing to verify.
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