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AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are 10 agentic AI concepts […]
10 Essential Agentic AI Concepts Explained Simply Read More »
In fact, AI projects are not built by simply choosing a model and feeding it data. Furthermore, a successful AI system goes through multiple stages, starting with identifying the right problem and ending with deployment, monitoring, and continuous improvement. This structured journey is known as the AI Project Cycle. It helps teams move from an
Mastering the AI Project Cycle: From Concept to Production Read More »
A forward deployed engineer (FDE) is a software engineer who embeds directly inside a customer’s team and infrastructure to build, integrate, and run production systems, instead of building a generic product from headquarters. Consultants deliver recommendations. An FDE delivers working code that stays in production. Therefore, the reason this job exists is uncomfortable. MIT’s NANDA
What is a Forward Deployed Engineer? Role, Skills & Salary Read More »
Moonshot AI’s Kimi K3 is a 2.8-trillion-parameter open-weight model built with a Mixture-of-Experts architecture. It activates only a small fraction of its parameters per token, helping reduce inference costs while delivering strong coding and agentic performance. K3 combines near-frontier capabilities, open weights, and lower API pricing, making it an interesting alternative to proprietary models. In
How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model Read More »
Agentic AI research in 2026 has moved past the basic question of whether a model can be called a tool. The harder questions are whether an agent can finish long workflows, survive live websites, verify its own work, recover from failure, and improve its process over time. The five papers below map that shift well
Modern LLMs rely on quantization, pruning, distillation, and faster attention kernels, but production performance often depends most on KV cache management. As context windows grow, the cache consumes significant GPU memory, limiting concurrency, throughput, and latency. Two breakthroughs transformed this challenge: PagedAttention improves memory allocation, while RadixAttention enables efficient prefix reuse. Together, these techniques make
PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management Read More »
Ever wondered how ChatGPT, Gemini, and other chat interfaces generate PDFs, PowerPoints, and more when all they have under the hood is an LLM? The trick isn’t a smarter model. It’s something simpler: skills which are instructions an agent loads only when needed. Next, let’s explore how skills work using LangChain and how they can make
Developers launch new models every week, but most barely change how you work. Kimi K3 is different—not because of benchmark charts, but because of a few small API changes that fundamentally affect how you use it. The first is reasoning_effort, which defaults to maximum, alongside 131,072 max_completion_tokens. Ask K3 to rename a variable, and it
7 Kimi K3 Features That Make Every Other Model Feel Outdated Read More »
The real skill isn’t getting AI to answers! But to do so in a manner that fits our budgets and fulfils our requirements. It’s guiding it with clear context and turning its output into useful action. This list is built around a simpler idea. Instead of searching through thousands of skills, you start with the
Imagine hiring an AI assistant to handle important tasks, only to find that it quietly ignores your instructions because it believes it knows better. This is known as agentic misalignment, where an AI intentionally pursues its own objective instead of the one set by its operator. To understand how often this behavior appears, Anthropic researchers
Agentic Misalignment Explained: When AI Agents Go Rogue Read More »
