Browsing: Business & Startups

Data Engineering is not just about moving data from point A to point B. In 2026, data engineers are expected to design scalable, reliable, cost-efficient, and analytics-ready data systems that support real-time decision making, AI workloads, and business intelligence. Modern data engineers work at the intersection of distributed systems, cloud platforms, big data processing, and analytics and reporting. They collaborate closely with data scientists, analysts, ML engineers, and business stakeholders to ensure that data is trusted, timely, and usable. This article covers 30+ commonly asked interview questions for a data engineer, with explanations that interviewers actually expect, and not just the…

Creating Ad copies and blog content, enabling data collection, optimizing campaigns, processing customer data to build detailed personas, and even automating your entire marketing workflow from lead nurturing to conversion tracking. AI is growing so fast that it can heavy-lift the majority of your marketing tasks. However, non-compliant use of AI for marketing, like pasting sensitive customer data into public LLMs without consent or not informing your audience of how you process their data, can result in fines, lawsuits, and reputational damage. In this article, we’ll share some key regulations that guard-rail AI use for marketing, ethical frameworks to consider,…

Anthropic has been buzzing as of late. It recently caused a stock market meltdown with its release of the Claude Cowork tool that tanked the stocks of major SaaS providers across the world. And now they’re about to revolutionize reasoning models with their latest release, Claude Opus 4.6, which they’re claiming as their best coding model yet.  Whether it is up to the claims or not we’ll find out in this article where we put it to the test to see how well it fares across coding and reasoning tasks.  Claude Opus 4.6! The Opus line is the top tier…

Virtual assistants in business are changing fast. Massive enterprise systems like OpenClaw pack hundreds of thousands of lines of code, but nanobot challenges the idea that bigger automatically means better. With just 4000 lines of Python, it delivers core AI assistant capabilities in a lightweight, focused package while cutting codebase size by about 99% without sacrificing essential functionality. Whether nanobot can replace enterprise tools depends on what users actually need. In this article, we explore how nanobot achieves this balance and what it means for practical AI development. What is Nanobot? The AI assistant Nanobot functions as a personal assistant…

AI has evolved far beyond basic LLMs that rely on carefully crafted prompts. We are now entering the era of autonomous systems that can plan, decide, and act with minimal human input. This shift has given rise to Agentic AI: systems designed to pursue goals, adapt to changing conditions, and execute complex tasks on their own. As organizations race to adopt these capabilities, understanding Agentic AI is becoming a key skill. To assist you in this race, here are 30 interview questions to test and strengthen your knowledge in this rapidly growing field. The questions range from fundamentals to more…

Image by Author | Diagram from Chronos-2: From Univariate to Universal Forecasting   # Introduction  Foundation models did not begin with ChatGPT. Long before large language models became popular, pretrained models were already driving progress in computer vision and natural language processing, including image segmentation, classification, and text understanding. The same approach is now reshaping time series forecasting. Instead of building and tuning a separate model for each dataset, time series foundation models are pretrained on large and diverse collections of temporal data. They can deliver strong zero-shot forecasting performance across domains, frequencies, and horizons, often matching deep learning models that…

GPT-5.3-Codex represents a new generation of the Codex model built to handle real, end-to-end work. Instead of focusing only on writing code, it combines strong coding ability with planning, reasoning, and execution. The model runs faster than earlier versions and handles long, multi-step tasks involving tools and decisions more effectively. Rather than producing isolated answers, GPT-5.3-Codex behaves more like a working agent. It can stay on task for long periods, adjust its approach mid-way, and respond to feedback without losing context. Codex 5.3 Benchmarks OpenAI’s GPT-5.3 Codex sets new performance standards on real-world coding and agentic benchmarks, outperforming prior models…

Image by Author   # Introduction  Artificial intelligence (AI) engineering is one of the most exciting career paths right now. AI engineers build practical applications using existing models. They build chatbots, retrieval-augmented generation (RAG) pipelines, autonomous agents, and intelligent workflows that solve real problems. If you’re looking to break into this field, this article will walk you through everything from programming basics to building production-ready AI systems.   # What AI Engineers Actually Build  Before we look at the learning path, let’s take a closer look at what AI engineers work on. Broadly speaking, they work on large language model (LLM) applications,…

The rivalry between Anthropic and OpenAI has intensified, from competing Super Bowl ads to launching new coding models on the same day. Anthropic’s Claude Opus 4.6 and OpenAI’s Codex 5.3 are now live. Both show strong benchmarks, but which one truly stands out? I’ll put them to the test and compare their performance on the same task. Let’s see which one comes out on top. OpenAI Codex 5.3 vs Claude Opus 4.6: Benchmarks Claude 4.6 Opus scores for SWE-Bench and Cybersecurity are described as “industry-leading” or “top of the chart” in their release notes, with specific high-tier performance indicated in…

Image by Editor   # The Fragile Pipeline  The gravitational pull of state of the art in modern machine learning is immense. Research teams and engineering departments alike obsess over model architecture, from tweaking hyperparameters to experimenting with novel attention mechanisms, all in the pursuit of chasing the latest benchmarks. But while building a slightly more accurate model is a noble pursuit, many teams are ignoring a much larger lever for innovation: the efficiency of the pipeline that supports it. Pipeline efficiency is the silent engine of machine learning productivity. It isn’t just a cost-saving measure for your cloud bill, though…