How AI Tools Generate Technical Debt in IoT Systems — and What to Do About It
AI tools speed up IoT development — but closer to the hardware, the same code that looks correct can silently break thousands of devices at once.
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ML Intern in Practice: From Prompt to a Shipped Hugging Face Model
Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is ...
Projects are the bridge between understanding AI and actually building with it. While the last couple of years were dominated by generative models, the shift now is toward systems that can think in steps, use tools, and act with a clear objective. This guide brings together over 15 solved agentic AI...
Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations
A practitioner's decision framework for Ridge, Lasso, and ElasticNet based on three quantities you can compute before fitting a model
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AI chatbots are the new norm. What earlier was “ask Google” has now largely become “ask Claude”. And that is not just a change of platforms. The new form of conversational guidance goes a whole lot deeper than trying to find the best car for you or looking for an upskilling course. It now spills […]...
How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor
One scale parameter determines accuracy in rotation-based vector quantization.
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The “Robust” Data Scientist: Winning with Messy Data and Pingouin
This article uncovers the craftsmanship of using robust statistics in data science processes: illustrating what to do when data fail tests due to not meeting standard assumptions.
MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG
Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. Me...
Learn how the Voxtral TTS model works, what makes its voice cloning and low‑latency performance special, and how to start generating speech with just a few lines of Python code.
Grok Voice Think Fast 1.0: Build Voice AI Agents That Actually Think
Voice assistants that engage in back-and-forth communication are something you’ve likely experienced. But a voice assistant that provides rational, uninterrupted exchanges via spoken dialogue? That’s what xAI delivered with their Grok Voice Think Fast 1.0 in April 2026 and instantly, it became the t...
5 Powerful Python Decorators to Build Clean AI Code
This article outlines five particularly useful Python decorators that, based on developers' experience, haven proven themselves effective to make AI code cleaner.
TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.
4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers
How we replaced Python pipelines with dlt, dbt, and Trino — and cut delivery time from weeks to one day.
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Compressing LSTM Models for Retail Edge Deployment: A Practical Comparison
There can be some practical constraints when it comes to deploying the AI models for retail environments. Retail environments can include store-level systems, edge devices, and budget conscious setup, especially for small to medium-sized retail companies. One such major use case is demand forecastin...
Self-Hosted LLMs in the Real World: Limits, Workarounds, and Hard Lessons
This article is about what actually happens when you take self-hosted LLMs seriously: not the benchmarks, not the hype, but the real operational friction most tutorials skip entirely.
System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine
A deep dive into how Apache Flink works, why it exists, and learning it while building a real-time recommendation engine
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The Next Frontier of AI in Production Is Chaos Engineering
Blast-radius control tells you how much to break. Intent tells you what breaking it will teach. Only one of these has mature tooling.
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PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer
NaNs don’t crash your training — they quietly destroy it.
After losing hours to a silent failure in a ResNet training run, I built a lightweight detector that pinpoints the exact layer and batch where things break. Using forward hooks and gradient checks, it catches issues early with minimal overhea...
How Spreadsheets Quietly Cost Supply Chains Millions
A simulation of how a single forecast change moves through five planning teams, and why most retailers lose money in the gap between Sales and Stores.
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