Code Is Cheap. Engineering Judgement Is Now the Scarce Resource
underlying society is changing. That was one of the ideas from Max Buckley’s talk at AI Engineer Singapore, and it has stuck with me ever since. For decades, software engineering was organised around scarcity. Code was expensive to write, engineers were scarce and features took time. This assumption shaped how teams worked. We prioritised carefully […]
From Local App to Public Website in Minutes
, I’ll show you 3 completely free ways to quickly deploy a simple web app so it’s publicly available on the internet. By “simple,” I mean a static web app, i.e., one that has no database back end, user accounts, or server-side logic. While this might seem a little bit limiting, there’s still a lot […]
From Regex to Vision Models: Which RAG Technique Fits Which Problem
Ms don’t deserve the classic playbook. Article 3 said there is no THE RAG technique. You still have to pick one. This article is the diagnostic that tells you which. Most teams building RAG systems reach for the same playbook: parse the document into chunks, embed every chunk, drop them in a vector store, embed […]
Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn
earnings and income, we tend to credit success to hard work and intelligence. Other times, we just assume that certain people got lucky, and despite their sub-standard education levels or lack of expertise, they were able to succeed in their profession and earn comfortably. The truth, however, lies somewhat in between these two extremes. Yes, […]
Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?
In this article, you will learn how to benchmark three text classification approaches — from a classical TF-IDF pipeline to a zero-shot large language model — to understand when each is most appropriate. Topics we will cover include: How to implement and evaluate a classical TF-IDF and logistic regression text classification pipeline. How to apply […]
GitHub Copilot users see token-based price hikes
Whether or not an average user will end up paying more depends very much on the individual user, and to a certain extent on who you ask. The Comments section of the GitHub Community Discussions page that announced the changes back in April 2026 has many reports of users finding that their credits are being […]
Alibaba’s Qwen Team Launches Qwen3.7-Plus, Adding Vision, Deep Reasoning, Tool Invocation, and Autonomous Iteration on the Bailian Platform
Alibaba’s Qwen team has released Qwen3.7-Plus. The model is now available through Alibaba Cloud’s Bailian platform. Bailian is the console international users access as Model Studio. It offers API services to external developers. The release follows Alibaba’s May unveiling of the Qwen3.7 generation. Qwen3.7-Plus Qwen3.7-Plus is a multimodal large language model. The model understands images […]
JetBrains Releases Mellum2: A 12B MoE Model for Fast, Specialized Tasks in Multi-Model AI Pipelines
JetBrains released Mellum2, open-sourcing the weights under the Apache 2.0 license. The first version of Mellum was a completion-focused 4B dense model. Mellum2 is its successor: a general-purpose model specialized in software engineering. It covers code generation and editing, debugging, multi-step reasoning, tool use and function calling, agentic coding, and conversational programming assistance. JetBrains team […]
Advancing youth safety and opportunity through global leadership
AI can be a powerful tool for young people. It can help students understand a difficult concept, practice a new language, prepare for a job interview, explore creative ideas, or learn a new skill. Just as the literacy movement spread across the globe in the middle of the 20th century required mass distribution of printed […]
How to Speed Up Transformer Training Using NVIDIA Apex (FusedAdam, FusedLayerNorm) and Native torch.amp
print(“\n### SECTION D: end-to-end Transformer (vanilla fp32 vs Apex fused + AMP) ###”) VOCAB, D, NHEAD, LAYERS, SEQ, BATCH, STEPS = 2000, 256, 4, 4, 128, 32, 60 class Block(torch.nn.Module): def __init__(self, d, nhead, norm_cls): super().__init__() self.attn = torch.nn.MultiheadAttention(d, nhead, batch_first=True) self.ff = torch.nn.Sequential(torch.nn.Linear(d, 4 * d), torch.nn.GELU(), torch.nn.Linear(4 * d, d)) self.n1, self.n2 = […]