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As generative AI models grow their context windows, a hidden problem emerges: more information often leads to worse answers. Known as context rot, this phenomenon reveals a U-shaped performance curve where accuracy peaks at moderate context sizes, then degrades as signal is buried in noise. Bigger memory doesn’t guarantee better reasoning—effective context does.

raditional JSON tool calling is fragile. "Code Mode" changes the game: convert MCP tools to TypeScript APIs and let AI agents write executable code. It’s faster, handles complex logic, and uses secure sandboxes. Get the full code demo here.

Is JSON bloating your LLM costs? Discover TOON (Token-Oriented Object Notation), the high-efficiency alternative designed specifically for Generative AI. By decoupling schema from data, TOON can slash token usage by nearly 50% compared to standard JSON. Perfect for RAG pipelines, lowering API latency, and maximizing context windows. Check out our head-to-head code benchmark to see exactly how much syntax overhead you can eliminate today.

Nano Banana brings precision, speed, and coherence to AI image editing like never before. Its power lies in natural-language edits—one-shot, context-aware, and visually consistent. Though still experimental, platforms like LMArena and FluxProWeb offer rare glimpses. For now, creators and AI watchers alike are eagerly following every update and sharing discoveries as they emerge.

GenAI transforms travel planning into a smart, seamless experience—crafting personalized, adaptive itineraries in seconds.

Letta's new .af format standardizes AI agent portability, memory, and sharing—powering the next-gen agent ecosystem.

Meta's Llama 4 unleashes Scout, Maverick & Behemoth—redefining open-source AI with power, efficiency & scale.

Explore the rise of language models from GPT-2 to GPT-4 and their game-changing impact on AI and industry.

Google launches Gemini 2.5, its most advanced AI yet, outperforming rivals in reasoning and multimodal tasks.

AI is transforming education through personalized learning, adaptive tutoring, and digital platforms, making learning more interactive.

QLoRA revolutionizes LLM fine-tuning with efficient quantization and low-rank adaptation, reducing resource needs without loss.

LLM inferencing uses pre-trained models to process inputs and generate outputs, leveraging AI's power for real-time text and data analysis.

LoRA: A game-changing technique for fine-tuning LLMs like GPT-4, using low-rank matrices for efficient, scalable, and cost-effective AI.