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At 2:17 AM, my monitoring alert yanked me out of sleep: the customer service bot had suddenly lost its memory. Users were asking “Where is my order?” three times in a row, and it kept asking for their phone number as if they were complete strangers. I opened the logs and saw that ConversationBufferMemory was loading empty message lists. The key was still there in Redis, but somehow deserialization
Ever had users sign up with [email protected] or [email protected]? Disposable email addresses are a headache for any app that relies on real user contact. I built burner-bouncer to solve this — a zero-dependency libra
We’ve all been there: you get your blood test results back, see a bunch of numbers in bold with "High" or "Low" next to them, and immediately spiral into a WebMD rabbit hole. 😵💫 What if instead of panic-searching, you had a team of digital experts—a Medical Researcher, a Certified Nutritionist, and a Data Analyst—working together to turn those raw pixels into a personalized health roadmap? In t
Hello Developers! 👋 The tech landscape is shifting faster than ever. What worked in 2023 is already considered legacy, and the way we build software has fundamentally changed. If you want to stay ahead of the curve, build faster, and ship high-converting applications, you need to adapt. Here is a breakdown of the exact trends dominating software engineering in 2026 and how you can implement them
Background I did some research online and found a nice course that teach how to build LLM from scratch. The course is shared public online and all the assignment resources are here: https://cs336.stanford.edu/. In the following series, I will put the summary and notes starting from lession 1. Tokenization is at the very beginning of the LLM. There were many different tokenization algorithm, suc
The problem AI-generated code is everywhere. GitHub Copilot, Claude, ChatGPT — they all write code fast. But they also introduce subtle bugs, SQL injections, and insecure patterns that look totally fine at first glance. I wanted a tool that sits inside my AI agent and reviews code before I ship it. Not a linter. Not a static analyzer. A strict senior engineer who actually explains why something
You count the weeks between today and your on site. Twelve. You pull up a 90 day FAANG prep plan and the structure looks reasonable: easy problems for two weeks, mediums for six, hards for the rest. Six weeks in, you hit binary trees and realise your recursion is shaky. Two weeks later you try a DP problem and can't formulate the recurrence. Suddenly the 12 week plan is a 6 week plan with 6 weeks