29+ min ago (321+ words) Most people think running AI coding models locally is confusing, slow, and not worth it'here's the simple playbook for private, fast dev that works " You don't need the cloud to ship faster. You need control, privacy, and zero surprise bills. I learned this after testing seven open models on a normal laptop. Local wins when latency, security, and cost matter. Your code never leaves your machine, so risk drops fast. Tokens are free after setup, so usage can scale without panic. Modern 15B70B models handle code assist, tests, and docs well. Example. On a 16GB RAM laptop with a 15B model, code completions arrived in 0.9 seconds on average. Unit tests generated in eight seconds per file. We cut review time by 32 percent and saved 400 dollars a month in API fees. Setup took 45 minutes using a container and a GPU driver. " A simple way…...

37+ min ago (81+ words) [jaideepparashar] Why the Next Unicorns Will Be Built With 5 People and AI Jaideep Parashar " Nov 25 #ai #productivity #performance #devops Templates let you quickly answer FAQs or store snippets for re-use. - Work Director ReThynk AI Innovation & Research Pvt Ltd "The Hidden Cost of AI Hype in Developer Communities." Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well...

37+ min ago (196+ words) This post breaks down how we deploy TensorFlow Lite Micro (TFLM) on ESP32-S3 to run real-time wake word detection and other edge-AI workloads. If you're exploring embedded ML on MCUs, this is a practical reference. ESP32-S3 brings a useful combination of: It's powerful enough to run quantized CNNs for audio, IMU, and multimodal workloads while staying power-efficient. ESP-DSP supports optimized FFT, DCT, and filtering primitives. 2. Feature extraction (MFCC) MFCC remains the standard for low-power speech workloads: On ESP32-S3, MFCC extraction typically takes 23 ms per frame. 3. Compact CNN model Model size after int8 quantization: 100300 KB. Convert & quantize: 4. Deployment to MCU Convert .tflite " C array: Load + run with TensorFlow Lite Micro: Because the workflow is generalizable, simply swapping the model unlocks new tasks: Environmental sound classification Glass break, alarm, pet sound detection (812 FPS depending on model) Vibration & anomaly detection Predictive maintenance for pumps, motors, or fans....

38+ min ago (450+ words) We're entering a new era of company building, one that doesn't look anything like the venture-funded, 200-person startup model of the past decade. The next generation of billion-dollar companies won't be built in glass offices, with huge teams, layers of management, and complex org charts. They will be built by 5 people and AI. This isn't a slogan. It's the new economic reality shaped by automation, intelligence, and leverage. Let me explain why this shift is happening, and why it's inevitable. 1. AI Has Collapsed the Cost of Building Products Ten years ago you needed: Today, a 5-person team with AI can: A small, smart team is now more powerful than a 200-person team from 2015. It's not headcount anymore. It's intelligence per person. 2. AI Gives Founders Leverage That Was Impossible Before The most important word in the next decade is leverage. AI…...

45+ min ago (930+ words) AWS CDK has newly introduced Mixins as a major feature that will become the core of future CDK development, currently available as a Developer Preview. This feature is expected to significantly change how CDK will be used in the future. As of November 2025, Mixins is in Developer Preview. Please be aware that the specification and behavior may change significantly in the future. Mixins is a mechanism for applying composable abstractions to any Construct, regardless of whether it's an L1 or L2 Construct. It was introduced in CDK v2.229.0, but as of November 2025, it is still in Developer Preview. Not all features are fully available yet, and more functionality and improvements are planned to be added in the future. This article explains the overview and usage of Mixins in a more accessible way, based on the content from the Mixins README and RFC. Mixins…...

52+ min ago (679+ words) Machine Learning (ML) has become one of the most influential technologies of our time. Whether it's understanding customer behavior, automating tasks, or creating intelligent systems, ML is everywhere. And at the heart of this revolution lies Python, the most preferred programming language for machine learning worldwide. In this blog, you'll learn why Python dominates ML, how the workflow looks, and why learning Machine Learning With Python can transform your career'especially in 2025 and beyond. Why Machine Learning and Python Go Hand-in-Hand Python has been the backbone of ML for years because of its simplicity, flexibility, and powerful libraries. For beginners, Python makes it easy to understand machine learning logic. For professionals, Python provides the efficiency needed to build and deploy powerful ML models quickly. The Benefits of Learning Machine Learning With Python Beginner-Friendly and Clean Syntax Python doesn't overwhelm learners with complex…...

53+ min ago (540+ words) If you have built even a single React project, you have seen this line more times than you can count: Most developers copy this snippet from a tutorial, paste it into their project, and" move on. But sooner or later, a question comes up: If you have ever wondered about this, you are not alone. This article breaks down the real story behind React and ReactDOM; how they interact, why they were separated from the beginning, and how this architecture powers everything from web apps to mobile apps to VR. React is often described as a view library'but that description does not do it justice. React is the architect of your UI. It understands: React is extremely smart. It reasons about your UI with near-perfect efficiency. But here is the twist: React does not know how to talk to the…...

1+ hour, 4+ min ago (1616+ words) Have you noticed a subtle yet profound shift in how we interact with technology? Gone are the days of clunky keyword typing; instead, we're engaging in natural, conversational exchanges with our digital assistants. We no longer punch in "Paris weather"; we simply ask, "What's the weather like in Paris this afternoon?" This isn't merely a minor convenience; it's a quiet revolution reshaping the landscape of online visibility. With over 8.4 billion voice assistants active globally and close to one in five individuals (20.5% of worldwide users) embracing this technology, overlooking this trend means losing access to an ever-growing segment of your potential audience. This is where Voice Engine Optimization (VEO) steps in, poised to become the indispensable discipline of 2025. Far from being a mere appendage to traditional SEO, VEO represents a completely reimagined approach to digital communication. Join us as we explore…...

1+ hour, 5+ min ago (559+ words) If you've ever set up a test database, you know the pain: This "data" is useless for realistic testing. You can't demo your app to stakeholders, test search algorithms, validate UI formatting, or catch edge cases. I'm building SynthDB to solve this - a zero-config database seeder that reads your PostgreSQL schema and generates statistically realistic, semantically coherent data. The project is in active development and I'm looking for contributors! The key insight: column names contain semantic information. merchant_name " should be a company name support_email " should be a support email (matching the company) mac_address " should be a valid MAC address birth_date " should be a realistic age By analyzing column names and types together, we can infer context and generate appropriate data. How It Works (Current Implementation) SynthDB works in six stages: Schema Introspection - Read tables, columns, constraints Dependency Analysis - Topological sort for foreign key order…...

1+ hour, 7+ min ago (981+ words) I have been fooling around a lot with ai recently, and I thought I would write something about what I've been doing. There are a few things that I've been doing, and they're all fascinating. This is part one of a small series that I have created to walk through the process I went through to get decent code. I had a crazy idea. I thought to myself, let's write something that will go through my git repos and automagially update my dockerfiles so that the dockerfile uses a fixed but more recent version of the base image. Most Dockerfiles have a fixed base image line that looks something like this: This is painful to trawl through and update. Not least because I don't actually know what the latest version is, and I'm not particularly keen on just using the…...