64K VS 1MIntroduction: The Context Friction in Vibe CodingIn the current AI agent landscape, the industry is heavily focused on memory management and context compression to mitigate token costs. Take Claude Code, for example: even with a massive 1M con…
Nous Research has published Lighthouse Attention, a selection-based hierarchical attention mechanism that wraps around standard scaled dot-product attention during pretraining and is removed afterward. Unlike prior methods such as NSA and HISA that pool only keys and values, Lighthouse pools Q, K, and V symmetrically across a multi-resolution pyramid, reducing the attention call from O(N·S·d) to O(S²·d) and running stock FlashAttention on a small dense sub-sequence. Tested on a 530M Llama-3-style model at 98K context, it achieves a 1.40–1.69× end-to-end wall-clock speedup against a cuDNN SDPA baseline with matching or lower final training loss.
The post Nous Research Proposes Lighthouse Attention: A Training-Only Selection-Based Hierarchical Attention That Delivers 1.4–1.7× Pretraining Speedup at Long Context appeared first on MarkTechPost.
In this tutorial, we implement how Memori serves as an agent-native memory infrastructure layer for building more persistent, context-aware LLM applications. We start by setting up Memori in a Google Colab environment and connecting it to both synchronous and asynchronous OpenAI clients, so that every model call can automatically pass through the memory layer. We […]
The post A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications appeared first on MarkTechPost.
The Engineer’s Guide to Building Autonomous Systems That Actually WorkContinue reading on Towards AI »
For the model itself, “compressing” context is essentially a matter of “erasing” details from its own past experience.Continue reading on Towards AI »
Boris Cherny called Claude Code “the thinnest possible wrapper over the model.” Then Anthropic shipped it with eight distinct compaction…Continue reading on Medium »
Building an AI agent has never been easier. But getting one into production that’s reliable is still hard. Most teams can ship a working demo in a day. The agent…
The post Beyond models: How context and evals make agents work in production appeared first on Arize AI.
Previous Articles:Continue reading on Towards AI »
How Smart Prompting, Chat History, and Personalization Signals Shape Every Response You GetContinue reading on Medium »
Moonshot AI, the Chinese AI lab behind the Kimi assistant, today open-sourced Kimi K2.6 — a native multimodal agentic model that pushes the boundaries of what an AI system can do when left to run autonomously on hard software engineering problems. The release targets practical deployment scenarios: long-running coding agents, front-end generation from natural language, […]
The post Moonshot AI Releases Kimi K2.6 with Long-Horizon Coding, Agent Swarm Scaling to 300 Sub-Agents and 4,000 Coordinated Steps appeared first on MarkTechPost.