Browsing: Business & Startups

  # Introduction  Large language models (LLMs) have a taste for using “flowery”, sometimes overly verbose language in their responses. Ask a simple question, and chances are you may get flooded with paragraphs of overly detailed, enthusiastic, and complex prose. This usual behavior is rooted in their training, as they are optimized to be as helpful and conversational as possible. Unfortunately, verbosity is a serious aspect to have under the radar, and can be argued to often correlate with an increased odds of a major issue: hallucinations. The more words are generated in a response, the higher the chances of drifting…

Large language models are no longer just about scale. In 2026, the most important LLM research is focused on making models safer, more controllable, and more useful as real-world agents. From persuasion risk and harmful-content mechanisms to tool-calling, temporal reasoning, and agent privacy, these papers show where LLM research is heading next. Here are the top LLM research papers of 2026 that every AI researcher, data scientist, and GenAI builder should know. Top 10 LLM Research Papers The research papers have been obtained from Hugging Face, an online platform for AI-related content. The metric used for selection is the upvotes…

  # Introduction  FastAPI has become one of the most popular Python frameworks for building modern APIs because it is fast, developer-friendly, and production-ready. Whether you want to build a simple backend, a full-stack web app, or an API for machine learning, FastAPI gives you a strong foundation with clean syntax and excellent performance. But one of the best ways to get better at FastAPI is not just by reading the docs — it is by studying real repositories that show how people actually use it in practice. In this article, we will explore 10 GitHub repositories that can help you…

Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs around storage, retention, retrieval, and control. In this article, we’ll explore agent memory patterns, a practical bridge between cognitive science and AI engineering. What Agent Memory Means Agent memory is the ability of an AI agent to store information, recall it later, and use it to improve future responses or actions. It allows…

Using Claude Code in large projects can lead to skyrocketing token costs. A 2025 Stanford study reveals developers waste thousands of tokens daily, draining budgets as unchecked context limits pile up. By setting strict boundaries from the outset, teams can reduce costs without compromising code quality. Optimizing token usage and context window sizes early on ensures efficiency and keeps projects on track. In this article, we’ll break down the key steps to take to save Claude Code tokens and manage your API costs. The Core Concept As your chat context expands, so do token costs. This includes not only file…

  # Introduction  JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: braces, quotes, commas, and repeated field names on every row. TOON, short for Token-Oriented Object Notation, is a newer format designed specifically to keep the same JSON data model while using fewer tokens and giving models clearer structural cues. The official TOON docs describe it as a compact, lossless representation of JSON for LLM input, especially strong on uniform arrays of objects. In this article,…

If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI agents every engineer should build. For each, you’ll find a GitHub link that provides a sample implementation, so you can either reference or extend the code into your own version. 1. Recommendation Agent Recommendation agents help personalize the user experience by suggesting products, content, or services. They are commonly used in e-commerce, media…

  # Introduction  You’ve probably typed a question into a search bar and gotten results that matched your words but completely missed your meaning. Or watched a recommendation engine surface something eerily relevant even though you never searched for it directly. The gap between “finding exact words” and “understanding what someone actually means” is what makes a search feature useful. Vector search closes that gap by representing text as points in high-dimensional space, where geometric proximity encodes semantic similarity. Two sentences can share zero words and still end up neighbors because the model learned that their meanings are close. This article…

AI technology is leapfrogging, yet that doesn’t mean we always want a revolutionary feature out of it. What most users would want more of are simple capabilities within AI that can help with their everyday tasks, whether in the office, at home, or anywhere else. On those lines, OpenAI may have just come up with one of the most promising updates for its GPT-5.5 in ChatGPT: GPT-5.5 can now make and work inside Excel sheets. Exciting, right? But what exactly does that mean? How does it work? And most importantly, how can it help you? Let us answer all of…

  # Introduction  Anyone who has spent a fair amount of time doing data science may sooner or later learn something: the golden rule of downstream machine learning modeling, known as garbage in, garbage out (GIGO). For example, feeding a linear regression model with highly collinear data, or running ANOVA tests on heteroscedastic variances, is the perfect recipe… for ineffective models that won’t learn properly. Exploratory data analysis (EDA) has a lot to say in terms of visualizations like scatter plots and histograms, yet they aren’t sufficient when we need rigorous validation of data against the mathematical assumptions needed in downstream…