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Claude Sonnet 5, Grok 4.5, GPT-5.6 Sol/Terra/Luna, and Kimi K3 all shipped within three weeks. Here's the full timeline — what launched, what it costs, how it benchmarks, and what the wave means.
Moonshot AI's Kimi K3 is the largest open-weight model released yet — 2.8T parameters, a 1M-token context, and $3/$15 pricing. Here's how it benchmarks against Opus 4.8, GPT-5.6, and Grok 4.5, plus how to use it and when open weights land.
Explore the essential factors in choosing an LLM API, ensuring portability and avoiding vendor lock-in while maintaining performance and cost-effectiveness.
Explore the Model Context Protocol (MCP), its significance for LLMs, and practical guidance for adoption in your projects.
Explore AI safety and alignment fundamentals, including capabilities, threat models, and key distinctions to understand current challenges.
Explore the real implications of 1M-token models, examining long context tradeoffs in performance, recall, latency, and costs.
Explore prompt caching, its mechanisms, design strategies for efficiency, and why it’s crucial for optimizing LLM performance.
Explore crucial LLM cost optimization strategies, including prompt caching and model routing, to manage and reduce operational expenses effectively.
Unpack the importance of on-device LLMs for privacy, explore current capabilities, and understand the tech behind it.
Learn the essentials of self-hosting LLMs, focusing on serving engines, batching techniques, and cost considerations for open-weights production.
Explore how latency impacts real-time voice agents, from ASR to TTS, and learn about efficiency in speech-to-speech LLM systems.
Explore how LLMs integrate with browser agents for web automation, including DOM parsing and action space intricacies.
Explore code completion LLMs, examining context, predictions, and what distinguishes top-performing tools like GitHub Copilot.
Explore video generation AI, its challenges, leading models, and costs. Delve into text to video and video diffusion innovations.
Explore the state of image generation in 2026, comparing diffusion vs autoregressive methods in quality, speed, and application.
Explore how multimodal LLMs unify text, images, and audio, detailing architecture and future implications in AI.
Explore how constitutional AI, RLHF, and DPO models teach LLMs to be helpful and honest, ensuring better chatbot behavior.
Explore how to create effective llm evals and benchmarks tailored to your use case for accurate model evaluation.
Explore model distillation, a method enabling small language models to harness the power of larger counterparts for improved performance.
Explore GPU vs TPU vs custom AI silicon for deep learning tasks. Learn which is best for training and inference workloads.
Explore LLM quantization methods like GPTQ and FP8 to optimize model inference while managing quality and performance concerns.
Explore the key differences between open weights and proprietary LLMs, including cost, quality, and privacy for informed decision-making.
Explore the top vector databases in terms of performance, cost, and use cases. Learn when to choose pgvector, Qdrant, Pinecone, and beyond.
Explore embeddings explained, vector search methodologies, and essential indexing tradeoffs for optimal production performance.
Learn when to use fine-tuning, RAG, or effective prompting for optimal results in AI. Understand the decision framework for each approach.
Explore agentic AI patterns like ReAct and Plan-and-Execute, including their strengths, weaknesses, and optimal use cases.
Explore function calling and tool use in LLMs, their mechanics, and designing effective tools for AI agents.
Explore prompt engineering fundamentals that still work for today's instruction-tuned models and understand what changed in AI interactions.
Explore what long context LLMs provide, their quadratic costs, and effective usage strategies for optimal performance.
Explore how the mixture of experts architecture efficiently scales parameters while minimizing cost per token in AI models.
Explore how self-attention and transformer architecture drive the performance of LLMs, including insights on scaling and efficiency.
Explore retrieval augmented generation, its mechanics, and advantages over fine-tuning in natural language processing.