1046 posts · Page 73 of 88. ← Blog
How the Foundry provider snaps a project endpoint, a credential, and a model deployment name into a runnable agent.
The Foundry provider is three inputs: a project endpoint, a credential, and a model deployment name. See how foundryprovider.NewAgent snaps them into an agent.
A cache is a small space pretending to be a big one, and the pretense only works if it's clever about what to keep. When a bounded cache fills up, every new item forces out an old one — and which one you evict determines your hit rate, which determines whether the cache is worth having at all. Eviction policies are the algorithms that make this choice, and understanding them (especially the workhorse, LRU) is essential to building caches that actually stay effective.
A cache is a small space pretending to be a big one, and the pretense only works if it's clever about what to keep. When a bounded cache fills up, every new item forces out an old one — and which one you evict determines your hit rate. Eviction policies are the algorithms that make this choice.
Back the agent with Azure OpenAI's Responses API, and toggle whether conversation state lives on the server or locally.
Back an agent with Azure OpenAI Responses via NewResponsesAgent, and use DisableStoreOutput to keep chat history local instead of stored server-side.
Google's GKE AI infrastructure docs list ~40 integrations. Here's a field map of which ones actually matter when the workload is a HIPAA-aware multi-agent medical AI, and where the gaps sit.
Build a GDPR Article 22 compliant explanation endpoint in Go that turns audit logs and eval stores into regulator-friendly answers for AI decisions.
Request → N-eyes approve → window-of-time → automatic expiry, with every transition written to a hash-chained audit log. The package that closes Gap #1 from the PCSE map.
A cache lives or dies by one number: its hit rate. Every cache access is a small bet — that the data will be there (a hit, served fast) rather than missing (a miss, served slow, plus the overhead of caching it). Whether caching helps at all comes down to how often that bet pays off, and understanding hits, misses, and hit rate — and what you should and shouldn't cache — is the foundation of using caches effectively. Get these fundamentals right, and the rest of caching makes sense.
A cache lives or dies by one number: its hit rate. Every cache access is a small bet — that the data will be there (a hit, served fast) rather than missing (a miss, served slow). Whether caching helps at all comes down to how often that bet pays off.
The same agent primitive, wired to Azure OpenAI's Chat Completions API where the model name is your deployment name.
Back an agent with Azure OpenAI Chat Completions via NewChatCompletionsAgent, where the Model field is your Azure deployment name, not a catalog model ID.
Why HL7 v2 — a 50-year-old pipe-delimited protocol — still drives most US hospital ADT integrations in 2026, and what a clean Go parser looks like in ~300 lines.
Studying for the IAPP AI Governance Professional credential? Here's an open-source Go codebase that demonstrates ~70% of the body of knowledge in working code.
There's an old joke that there are only two hard things in computer science: cache invalidation and naming things. It's a joke because caching is everywhere and sounds simple — just keep a copy of stuff you'll need again — and it's true because getting caching right is genuinely, surprisingly hard. Caching is one of the most universal and powerful ideas in computing, appearing at every layer from CPU to CDN, and understanding why it exists and when it helps is foundational to building fast systems.
There's an old joke that there are only two hard things in computer science: cache invalidation and naming things. It's a joke because caching sounds simple, and true because getting it right is genuinely hard. Caching is one of the most universal ideas in computing, appearing at every layer from CPU to CDN.
Reach a Foundry-hosted model through the OpenAI-compatible Responses API by configuring a plain openai.Client with three request options.
Reach a Foundry model through the OpenAI-compatible API by configuring an openai.Client with base URL, an Azure token credential, and the ai.azure.com scope.