GuideAI in production
Hermes Agent guide: what it is, when to use it, and how to get started
Understand Hermes Agent, its origins, models, memory and skills. Choose a setup, try two checkable examples, and learn where its limits matter.
Articles
Guides, news and explanations about AI and reliable systems.
GuideAI in production
What is OpenRouter? A practical guide to choosing and using AI models
Understand the model marketplace and gateway, compare chat and API workflows, and choose a useful route through its models, providers, costs and privacy controls.
GuideAI in production
Model inference: what happens when you ask AI a question?
What a model does when you ask a question, how we got here, and how to choose between local and hosted inference without confusing fast text with useful work.
ExplainerAI in production
KoboldCpp 1.123 makes agent approvals depend on the tool and workdir
The global auto mode is only part of the permission decision. Follow per-tool overrides, directory scope and restored settings through a copied-runbook exercise.
ExplainerAI in production
Anthropic Scion puts source control behind a cache hierarchy
Scion reduces repeated repository work for online agents. Its cache hierarchy explains why warm checkout speed and fleet recovery need different tests.
GuideAI in production
What is OpenShell and how do I use it?
Run an AI agent with useful access to files and services, then test exactly where OpenShell stops it. A hands-on guide to sandboxes, policy, credentials, and SRE workflows.
ExplainerAI in production
AMD Helion separates agent execution from model inference
Helion places CPU agent loops, token routing, GPU inference and supporting services in separate capacity zones that still share one task outcome.
ExplainerAI in production
Muse Wonder's Active Memory needs an operational test
Test selective recall, corrected state, tool-failure recovery, clarification, and the independent destination result before trusting a long-running agent.
GuideAI in production
Measuring retry amplification in AI agent workflows
Connect logical tasks to every model and tool attempt, then measure cost and latency per accepted outcome without hiding failed or unknown work.
ExplainerAI in production
Scheduled AI work needs an execution contract
Bind the schedule to a model, tools, input state, destination and acceptance evidence, then reconcile the actual run before retrying or declaring success.
ExplainerAI in production
Bit-exact decoding turns inference speedups into testable changes
Compare reference and speculative paths for token-level equivalence, then run a separate capacity trial so speed cannot conceal a serving-contract change.
GuideAI in production
Build an inference compatibility suite before changing the runtime
Pin the model artifact, template, runtime and hardware, then exercise fixed protocol tests and preserve raw differences before approving a serving change.