Plain definitions for AI, data systems, software architecture, operations, and security. Hypler uses SI as company terminology for its intelligence infrastructure direction; AI remains the standard technical term. Examples are hypothetical.
88 of 88 terms
Hypler’s company terminology for intelligence infrastructure and increasingly capable AI systems.
Public terminology should distinguish company language from standard technical terms and from demonstrated system behavior.
Hypothetical: a company describes an evaluation workflow for an advanced model without claiming a deployed capability.
A field of computer science that builds systems able to perform tasks associated with learning, reasoning, perception, language, or decision-making.
Hypler uses SI as company terminology; AI remains the standard term for the broader technical field and historical references.
Hypothetical: a classifier groups documents by topic using learned patterns.
Model Context Protocol, an open protocol for connecting AI applications to tools, data sources, and prompts through defined interfaces.
Public engineering discussions can use MCP to explain typed interfaces, authorization boundaries, and tool discovery.
Hypothetical: an AI client reads a tool schema before requesting a permitted operation.
A service boundary that routes MCP traffic and can apply discovery, authentication, authorization, and policy controls.
Gateway design is a public engineering concern because shared tool access needs clear boundaries.
Hypothetical: one gateway routes clients to separate read-only and write-capable tool sets.
Machine-readable metadata that tells a client how to request authorization for a protected resource.
Authorization metadata helps public interface designs describe expected audience and scope without exposing protected systems.
Hypothetical: a client reads metadata to find required authorization scopes after a 401 response.
The intended recipient or resource for an access token; a token for one resource should not authorize another.
Audience checks are a general control against accepting credentials outside their intended boundary.
Hypothetical: a token issued for an inventory API is rejected by a separate workflow API.
A named permission that limits which resources or actions an authenticated client may use.
Scope design separates reading, proposing, and executing so each action can receive appropriate review.
Hypothetical: a read scope permits listing records but not changing them.
An HTTP response header that explains an authentication challenge and may identify authorization details a client needs.
Clear challenge responses help clients recover safely from missing or insufficient authorization.
Hypothetical: a 401 response directs a client to the expected authorization metadata.
A catalog of tools with their interfaces, permissions, owners, and operational expectations.
A registry makes tool authority inspectable before an AI system is allowed to use a tool.
Hypothetical: a registry labels a tool read-only and lists its JSON input schema.
A workflow that requires review and approval before a consequential state-changing action runs.
Approval gates are a general design pattern for work that needs human judgment, rollback planning, or accountability.
Hypothetical: an automation presents a change preview for approval before applying it.
A structured, no-write representation of a proposed future change.
Previews let reviewers inspect expected effects and rollback information before a change is approved.
Hypothetical: a preview lists candidate file edits without editing any files.
Information that can be traced to a source record, citation, event, response, or other verifiable origin.
Evidence provenance helps readers judge whether a claim has enough support for its intended use.
Hypothetical: a summary links each reported fact to its source record.
A structured collection of facts, sources, context, and status prepared for review.
Packet-shaped evidence can make complex review inputs easier to inspect and retain.
Hypothetical: a review packet combines citations, validation results, and unresolved questions.
A structured packet prepared for an AI system to summarize, classify, prioritize, or review within defined limits.
Curated context is safer and more auditable than unrestricted access to unrelated systems.
Hypothetical: an AI reviews a bounded incident summary and returns a labeled risk assessment.
Selecting a model or provider for a request according to capability, latency, cost, privacy, or context needs.
Routing criteria should be explicit so reviewers can evaluate the tradeoffs for a workload.
Hypothetical: a system sends short classification work to a smaller model and complex review to another model.
Privately controlled computing resources used to run services, store data, or support operations.
Public descriptions can explain the concept without disclosing host names, network paths, or credentials.
Hypothetical: a private environment hosts an internal dashboard behind authenticated access.
A visual or structured representation of systems, dependencies, data stores, routes, and job flows.
Maps help operators understand dependencies while allowing sensitive implementation details to remain restricted.
Hypothetical: a map shows a public application, its API, and an asynchronous job queue.
An inventory of services and their ownership, interfaces, dependencies, health information, and documentation.
A registry supports clear accountability and faster incident investigation.
Hypothetical: a registry entry identifies a service owner and links to a health-check runbook.
Status and freshness information for an external source, import, feed, crawl, or data pipeline.
Freshness and failure state make evidence quality visible to users and reviewers.
Hypothetical: a dashboard marks a feed stale after its expected update window passes.
An interface that groups operational, business, or source signals for scanning and investigation.
Signal grouping helps operators notice status changes without treating a summary as proof by itself.
Hypothetical: a map groups source freshness, error rate, and queue depth by system.
The server-side interface that exposes defined data and actions to clients or integrations.
API contracts keep business rules and data access consistent across multiple interfaces.
Hypothetical: a client requests product data through a documented endpoint and response schema.
A fast storage layer for reused results that reduces repeated computation or upstream requests.
Cache design must include freshness, invalidation, and fallback behavior.
Hypothetical: an application caches a public lookup result for a short, documented interval.
A design approach that uses data models, indexes, caches, and workload boundaries to keep common reads predictable.
Performance goals should be measured against real workload conditions and freshness requirements.
Hypothetical: an indexed query serves a filtered list without waiting for a slow import.
A separate runtime path for resource-intensive work such as batch processing, model inference, or imports.
Separating heavy work from request handling can protect interactive response time.
Hypothetical: batch embedding generation runs separately from a public search request.
A managed backlog for asynchronous work with status, retries, priority, and failure handling.
Queue states make delayed or retried work inspectable instead of hiding it behind a request timeout.
Hypothetical: an import job moves from pending to processing, then completed or failed.
An external data feed supplied by a partner that needs validation and normalization before use.
Feed contracts should separate source claims, validation results, and downstream product decisions.
Hypothetical: a feed update is rejected when required inventory fields are missing.
Search that uses meaning or similarity signals in addition to exact keyword matching.
Semantic results should retain source and retrieval context so users can assess their relevance.
Hypothetical: a query for payment risk finds related records with different wording.
A numeric vector representation of content that can be compared for similarity.
Embedding systems need model versioning, source boundaries, and regeneration rules.
Hypothetical: related documents have nearby vectors despite using different words.
A logical partition of vector records, often organized by model, source, tenant, or use case.
Namespaces reduce accidental mixing of vectors that have different sources or model versions.
Hypothetical: separate namespaces isolate public documentation embeddings from internal test data.
Structuring content so AI systems and semantic search tools can identify topics, sources, and relationships.
Clear public content improves retrieval without implying that an AI system has verified every claim.
Hypothetical: an article states its scope, definitions, and source links in machine-readable form.
Machine-readable metadata that describes a page, entity, product, service, or article.
Structured data supports consistent discovery and clear relationships between public terms.
Hypothetical: a glossary page publishes a DefinedTermSet for its visible definitions.
Rules that define which systems may communicate over public, private, local, brokered, or blocked paths.
Route policy makes intended connectivity reviewable before systems are connected.
Hypothetical: a policy permits a public application to call one API but blocks direct database access.
A network route intended for authenticated operators or trusted systems rather than public visitors.
Private routes need appropriate authentication and should not be described with sensitive connection details in public content.
Hypothetical: an internal dashboard is reachable only after identity verification.
A record of an action, actor, time, resource, outcome, and relevant context.
Audit events support investigation and review when a system performs consequential work.
Hypothetical: a change request records its approver, target, and resulting status.
A property where repeating the same request has the same intended effect as running it once.
Idempotency controls reduce duplicate effects when clients retry a request after a timeout or network failure.
Hypothetical: a retry uses the same idempotency key and does not create a second order.
A no-write description of the records and ordered steps a proposed transaction would change.
Transaction previews support review of expected effects before a state-changing workflow runs.
Hypothetical: a preview lists proposed record updates and rollback conditions.
A proposed code or configuration change shown for review before it is applied.
Patch previews make scope and rollback considerations visible before changes are approved.
Hypothetical: a preview identifies target files and expected validation commands.
Operational visibility into repository state, changes, validation, dependencies, and protected paths.
Code operations can make release evidence and unresolved risk easier for operators to inspect.
Hypothetical: a dashboard reports a branch, changed files, and validator status.
Making progress, evidence, blocked work, risks, and next actions visible to responsible people.
Visible state supports informed review instead of relying on hidden logs or memory.
Hypothetical: a review screen shows pending approvals and the evidence behind each one.
An emergency access path for exceptional recovery actions that requires stronger controls and review.
Emergency access should have narrow scope, time limits, logging, and post-action review.
Hypothetical: an operator receives temporary emergency access after an incident authorization.
Large language model: a machine-learning model trained to predict and generate sequences of tokens.
LLM discussions should separate model capabilities from the tools, data, and controls around a model.
Hypothetical: an LLM drafts a summary from a supplied document set.
The process of adjusting model parameters using data and an optimization objective.
Training claims should identify the relevant evidence and avoid implying model behavior from branding alone.
Hypothetical: a model trains on labeled examples to predict a category.
Additional training of an existing model on a narrower dataset or task objective.
Fine-tuning requires evaluation for task quality, unwanted behavior, and data-handling constraints.
Hypothetical: a base model is fine-tuned to classify a fixed set of support topics.
A unit of text or data processed by many language models; a token may be a word, part of a word, or punctuation.
Token counts affect model context limits, latency, and cost estimates.
Hypothetical: a long prompt is shortened to fit a model’s token limit.
The maximum amount of input and generated content a model can consider in one request.
Context limits should shape retrieval, summarization, and evaluation design.
Hypothetical: a system retrieves only the most relevant passages before calling a model.
An attack where untrusted content tries to alter an AI system’s instructions or cause unsafe tool use.
Untrusted content needs clear boundaries from system instructions and tool authorization decisions.
Hypothetical: a retrieved document tells a model to reveal secrets, but the system treats it only as data.
Giving an AI system more authority, tools, autonomy, or access than its task requires.
Authority should stay proportionate to the task and require review for consequential actions.
Hypothetical: a summarization task has no access to deployment or payment tools.
A security principle that grants only the minimum access needed for a specific task.
Least privilege constrains credentials, routes, tools, and actions to reduce accidental or malicious impact.
Hypothetical: a report generator can read one dataset but cannot modify it.
A pattern that retrieves relevant external content and supplies it to a model while generating an answer.
Retrieval design should retain source identifiers, freshness information, and clear limits on what retrieved text proves.
Hypothetical: a model answers from cited policy passages retrieved for a user question.
A data store optimized to index and search vector embeddings by similarity.
Vector storage requires source boundaries, model metadata, and relevance evaluation.
Hypothetical: a search service returns the nearest document vectors for a query embedding.
A model interface pattern where the model requests a structured operation from an external tool.
Tool calls need schema validation, authorization checks, and evidence of actual results.
Hypothetical: a model requests a weather lookup using a typed location argument.
Model outputs constrained or validated against a defined data shape such as JSON Schema.
Structured outputs help downstream systems distinguish a valid machine-readable response from free-form text.
Hypothetical: a model returns a JSON object with fixed summary and risk fields.
A repeatable method for measuring a system against defined quality, safety, or task criteria.
Evaluation plans should state what was measured, against which data, and what the result does not establish.
Hypothetical: a test set measures whether a model extracts required fields accurately.
A standardized evaluation used to compare model performance on a defined task or dataset.
Benchmarks are useful context but may not predict behavior on a specific production workload.
Hypothetical: two models are compared on the same public reasoning benchmark.
Graphics processing unit: a processor designed for highly parallel computation, commonly used for AI training and inference.
GPU capacity planning considers workload shape, memory, power, cooling, and software compatibility.
Hypothetical: a batch inference workload uses a GPU to process many inputs in parallel.
A material and device technology used to build processors, memory, and other electronic components.
Hardware availability and component design affect computing capacity and supply risk.
Hypothetical: a new accelerator chip changes the available compute per watt.
The rate at which a processor can read from or write to memory.
Memory bandwidth can limit model throughput when moving data is slower than computation.
Hypothetical: an inference workload slows because model weights cannot be supplied to the processor quickly enough.
The electrical power a facility or system can safely supply to equipment.
Power capacity is a planning constraint for compute hardware alongside cooling, space, and reliability.
Hypothetical: a data center rack cannot add another accelerator without available electrical capacity.
Policies, roles, controls, and review processes that guide how AI systems are selected, used, monitored, and retired.
Governance should connect technical controls to accountable decision-makers and documented evidence.
Hypothetical: a policy requires evaluation and owner approval before a model is used for a high-impact task.
A model released with downloadable trained parameter weights under terms set by its publisher.
Open weights do not by themselves establish permission to use, modify, distribute, or deploy a model.
Hypothetical: a team reviews a model license and evaluation evidence before testing it.
Documented origin and history of a model, including its publisher, version, artifacts, training claims, and modifications.
Provenance records support review of what model artifact was evaluated or used.
Hypothetical: an evaluation record names the model version and artifact digest.
Controls that reduce risk from compromised or untrusted software, dependencies, build systems, models, and hardware inputs.
Supply-chain review should verify sources, integrity, updates, and ownership across dependencies.
Hypothetical: a build accepts a dependency only after its version and integrity information are checked.
Common Vulnerabilities and Exposures: an identifier for a publicly disclosed cybersecurity vulnerability.
A CVE identifier helps teams track a reported issue but does not by itself prove exploitability in a specific system.
Hypothetical: a team checks whether a CVE affects the exact version it uses.
A policy expressed as rules that produce the same result for the same inputs under the same conditions.
Deterministic checks can enforce clear boundaries around model output and tool authorization.
Hypothetical: a rule rejects a tool request whose scope is not on an approved allowlist.
Records that show whether an approved action was attempted, what inputs and targets it used, and what result occurred.
Execution evidence distinguishes a proposal or model statement from an observed system outcome.
Hypothetical: a deployment record includes target, timestamp, validator result, and resulting version.
A defined function or outcome that a system is designed or evaluated to perform.
Capability language should name the intended outcome and the evidence needed to show whether it works as described.
Hypothetical: a model plan identifies document classification as a capability and defines its evaluation criteria.
Testing checks expected behavior; observability uses runtime signals to understand a system’s actual behavior and condition.
Both practices provide complementary evidence for quality, reliability, and investigation.
Hypothetical: automated tests check an API response while metrics expose failures after release.
A JavaScript library for composing user interfaces from components.
IZIOS uses React for customer and administrative interface composition.
A search result, filter control, and inventory review panel can share reusable interface components.
A typed language that extends JavaScript and checks many data-shape mistakes before code runs.
IZIOS uses TypeScript to make client and server contracts clearer to maintainers.
A product response type can show which fields an interface may expect from an API.
A JavaScript runtime used for server applications and build tooling.
IZIOS uses Node.js for its server-side application runtime and supporting tooling.
A server process receives an API request, applies application rules, and returns a defined response.
A Node.js framework for HTTP routes and request middleware.
IZIOS uses Express to define server API routes and request handling.
A route can validate a request, call product services, and return a documented response.
A relational database for durable structured records, queries, and transactions.
IZIOS uses PostgreSQL-backed records for catalog and operational data.
Catalog records can join source identifiers, specifications, certificates, and availability evidence.
A PostgreSQL extension that stores vectors and supports similarity search.
IZIOS uses pgvector-backed retrieval for embeddings alongside structured product data.
A broader search phrase can retrieve related product records while filters retain exact constraints.
A frontend development server and build tool for modern web applications.
IZIOS uses Vite in its frontend build path.
A developer can update an interface component and see the browser bundle refresh during development.
The controlled intake of external records so they can be normalized, validated, and used by a product.
IZIOS ingests supplier inputs before they become searchable catalog records.
A supplier file is parsed, checked, normalized, and connected to its source identity.
An application programming interface: a defined contract for software to request data or actions from another system.
IZIOS exposes reusable product data through API boundaries instead of binding all business logic to one storefront.
A connected client requests catalog data through a documented route and response shape.
Numeric representations of content that let software compare related meaning.
IZIOS stores versioned embeddings for vector retrieval alongside source-aware product records.
A natural-language search can compare its embedding with catalog embeddings to find related listings.
A grouped database change that either completes as one consistent unit or is safely rolled back.
IZIOS keeps commerce transactions distinct from supplier availability truth and discovery results.
A hold workflow can write related records together while preserving the source evidence used for a decision.
The software layer that manages a computer's hardware and provides the environment where applications run.
LNSAT lists operating-system targets separately from runtime qualification evidence.
A planned Linux, Windows, or macOS target still needs its own architecture, lifecycle, storage, and recovery evidence.
An organization that offers models or model APIs that a system may evaluate as a target.
Rangoon uses provider references as inspectable planning context for model targets.
A capability plan can state its target assumptions without claiming an integration is shipped or supported.
A provider-neutral semantic view for describing capabilities, observations and relationships while preserving native meaning.
VUSL describes capabilities and observations across platforms. Rangoon is first consumer; an independent core is planned.
A semantic observation identifies its native source and translation limits without granting permission.
A systems programming language with ownership and type rules that help control memory use and make interfaces explicit.
The pinned VUSL conformance guide describes a Rust library that inspects supplied, bounded JSON offline.
The inspector can return an accepted document or a fixed diagnostic; language choice alone does not prove security.
A general-purpose programming language used for scripting, data processing and independent reference implementations.
VUSL conformance documentation describes Python reference checks for the same bounded subset as the Rust inspector. This is not a Python SDK.
Shared fixtures can compare accepted canonical bytes and refusals across implementations.
Converting accepted data into one precisely defined byte representation so equivalent accepted inputs can be compared consistently.
The experimental VUSL observation profile defines a bounded canonical representation and exact conformance vectors.
A different whitespace layout may yield the same canonical bytes when the profile accepts both inputs; unknown fields still refuse.
Information or meaning that is omitted, transformed or cannot be preserved when one system represents another system’s records.
VUSL keeps native identity and translation limits visible rather than implying that every semantic view is lossless.
A projected policy report can preserve a reported decision without carrying engine authorization or proving an external effect.
Refusing an interpretation when required checks fail instead of guessing, truncating or treating missing evidence as success.
The current VUSL profile rejects unsupported versions, duplicate fields, unknown core fields and inputs outside its bounds.
A document over the byte limit returns a refusal rather than a partially accepted observation.
A computed hash of document bytes that helps identify content; a hash by itself does not authenticate its author or grant authority.
The VUSL source describes canonical SHA-256 expectations and a separately domain-separated document identity. These remain distinct from approval and execution evidence.
Matching digests help compare content but cannot establish a cross-provider relationship or prove a resource changed.