More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
A production-grade embedded system enabling communication across speech, text, Morse, and haptic signals within a single unified pipeline. Official Project Page: https://anandps.in/projects/unified-assistive-communication-system GitHub Repository: https://github.com/anand-ps/unified-assistive-communication-system Problem Assistive communication systems are fragmented. Most tools so
Arduino VENTUNO Q บอร์ด AI ตัวใหม่จาก Arduino ที่ทำให้ AI อยู่ในมือ maker ทุกคน 🤖 Arduino �เพิ่งประกาศเปิดตัว VENTUNO Q บอร์ดใหม่ล่าสุดที่ใช้พลังจาก Qualcomm Dragonwing IQ8 Series ซึ่งถูกออกแบบมาเพื่อ AI, Robotics และ Actuation โดยเฉพาะ NPU Acceleration สูงสุด 40 TOPS — พลังประมวลผล AI ระดับอุตสาหกรรม Dual-Brain Architecture — รวมพลังระหว่าง Qualcomm chip กับ STM32H5 microcontroller สำหรับ low-
En la Parte 2 de esta serie, logramos entrenar exitosamente una red neuronal recurrente (LSTM) en Python, capaz de predecir la falla de una turbina aeroespacial con un error de apenas 10 vuelos. El modelo era un éxito en la nube. Pero en el mundo del mantenimiento industrial y el Edge Computing, no siempre tenemos servidores disponibles. Quería llevar a MAJN (mi red neuronal) al mundo físico. Así
This is part three of a series on display consistency in embedded systems. The first two parts were technical. This one is about why the technical parts worked. The picture: ATtiny85 thermometer. Neural network inference. QUAD7SHIFT display. Built from datasheets. He had datasheets. No Stack Overflow. No libraries to install. No AI to generate boilerplate. No tutorials that abstracted away the in
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about