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Building AI agents is the new gold rush. But every developer knows the biggest bottleneck: getting the AI to actually communicate to your data. Today, travel giant Agoda is tackling this problem head-on. They have officially launched APIAgent, an open-source tool designed to turn any REST or GraphQL API into a Model Context Protocol (MCP) […]
Alibaba Cloud just updated the open-source landscape. Today, the Qwen team released Qwen3.5, the newest generation of their large language model (LLM) family. The most powerful version is Qwen3.5-397B-A17B. This model is a sparse Mixture-of-Experts (MoE) system. It combines massive reasoning power with high efficiency. Qwen3.5 is a native vision-language model. It is designed specifically
The AI industry is currently obsessed with ‘agents’—autonomous programs that do more than just chat. However, most current multi-agent systems rely on brittle, hard-coded heuristics that fail when the environment changes. Google DeepMind researchers have proposed a new solution. The research team argued that for the ‘agentic web’ to scale, agents must move beyond simple
Moonshot AI has officially brought the power of OpenClaw framework directly to the browser. The newly rebranded Kimi Claw is now native to kimi.com, providing developers and data scientists with a persistent, 24/7 AI agent environment. This update moves the project from a local setup to a cloud-native powerhouse. This means the infrastructure for complex
In this tutorial, we build a self-organizing memory system for an agent that goes beyond storing raw conversation history and instead structures interactions into persistent, meaningful knowledge units. We design the system so that reasoning and memory management are clearly separated, allowing a dedicated component to extract, compress, and organize information. At the same time,
How to Build a Self-Organizing Agent Memory System for Long-Term AI Reasoning Read More »
In this tutorial, we build a complete, production-grade synthetic data pipeline using CTGAN and the SDV ecosystem. We start from raw mixed-type tabular data and progressively move toward constrained generation, conditional sampling, statistical validation, and downstream utility testing. Rather than stopping at sample generation, we focus on understanding how well synthetic data preserves structure, distributions,
[In-Depth Guide] The Complete CTGAN + SDV Pipeline for High-Fidelity Synthetic Data Read More »
OpenAI just launched a new research preview called GPT-5.3 Codex-Spark. This model is built for 1 thing: extreme speed. While the standard GPT-5.3 Codex focuses on deep reasoning, Spark is designed for near-instant response times. It is the result of a deep hardware-software integration between OpenAI and Cerebras. The results are game-changing. Spark is 15x
Google announced a major update to Gemini 3 Deep Think today. This update is specifically built to accelerate modern science, research, and engineering. This seems to be more than just another model release. It represents a pivot toward a ‘reasoning mode’ that uses internal verification to solve problems that previously required human expert intervention. The
Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability, the KV cache footprint increases and becomes a major bottleneck for throughput and latency. For modern Transformers, this cache can occupy multiple gigabytes. NVIDIA researchers have introduced KVTC (KV
Google Research is proposing a new way to build accessible software with Natively Adaptive Interfaces (NAI), an agentic framework where a multimodal AI agent becomes the primary user interface and adapts the application in real time to each user’s abilities and context. Instead of shipping a fixed UI and adding accessibility as a separate layer,
