subquadratic models
Adolfo Usier2026-05-30T05:37:43+00:00A clear guide to subquadratic models, why they matter, and how to test and build systems that handle very long AI inputs.
30
05, 2026
28
05, 2026
Agentic Workflows Guide
Adolfo Usier2026-05-28T05:35:59+00:00A clear guide to Agentic Workflows covering design, tools, safety, and scaling. Includes a checklist and examples to get started.
26
05, 2026
Qwen 3.7-Max-Preview: 1M Token Context Window Explained
Adolfo Usier2026-05-26T05:36:33+00:00Qwen 3.7‑Max‑Preview introduces a 1M token context window, enabling large‑scale text processing and smarter AI workflows.
25
05, 2026
1M context AI models
Adolfo Usier2026-05-25T05:34:45+00:00A practical guide to using 1M context AI models for long running agents and deep document work. Includes patterns, architecture, and a step by step build plan.
23
05, 2026
Fast‑Slow Training Explained
Adolfo Usier2026-05-23T05:34:46+00:00Fast‑Slow Training is a new training method that keeps AI models from forgetting old tasks while learning new ones. It uses slow weights for core knowledge and fast weights for quick adaptation.
22
05, 2026
Context Pinning Explained
Adolfo Usier2026-05-22T05:34:03+00:00A clear guide to context pinning that explains how to store long prompts once, reuse them safely, and cut token cost in AI applications.
21
05, 2026
Agent Orchestration Platform
Adolfo Usier2026-05-21T05:35:14+00:00A practical guide to agent orchestration platforms. Learn core features, safety checks, and how to build repeatable multi agent workflows.
20
05, 2026
Agent First IDE
Adolfo Usier2026-05-20T05:36:08+00:00A clear, practical guide to Agent First IDEs with steps to start small, stay safe, and use modern tools for agent-driven workflows.
19
05, 2026
Open Source AI Agents
Adolfo Usier2026-05-19T05:35:06+00:00A clear guide to open source ai agents, covering what they are, security, architecture, and a simple plan to start today.
18
05, 2026
Hermes Agent 0.13.0
Adolfo Usier2026-05-18T05:35:28+00:00Hermes Agent 0.13.0 introduces a Reflective Phase that lets the agent learn from its own actions, a significant token volume increase, and cleaner reasoning markers. These updates make the agent faster, smarter, and easier to integrate into real‑world projects.