“persistence”的搜索结果

Run a persistent research workflow with a defined goal and explicit acceptance checks. It saves progress and finishes only when a recorded result passes either a validation script or an architect review of the research output.

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• Research with explicit acceptance criteria
• Verify research results with a script
• Require review approval before completing research

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

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• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

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• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths

Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.

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• Designing an assistant that remembers across conversations
• Storing and updating facts about people or other entities
• Adding relevant memories to prompts

Review agent-written code changes before merge, focusing on concrete correctness defects. Uses independent subagent reviews, targeted verification, and fresh checks after substantial repairs, with a deeper evidence-based process for security, concurrency, persistence, or data-loss risks.

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• Reviewing agent-written changes before merge
• Rechecking a substantial bug fix
• Examining security, concurrency, or data-loss risks

Create and audit the files and routines that guide coding agents across sessions. It organizes instructions, progress tracking, verification, scope boundaries, and handoffs, with scripts for setup, structural checks, and shareable assessment reports.

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• Creating AGENTS.md or CLAUDE.md and supporting files
• Preserving progress between agent sessions
• Defining checks required before work is marked complete

Publish a built website or web app and provide a shareable URL through Kortix Apps, Cloudflare, or Vercel. Check the running build, security, external service requirements, and data storage limits before deploying with the user's explicit approval.

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• Sharing a temporary demo of a static website
• Deploying an app with Kortix sign-in
• Publishing a framework app or container on Vercel

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

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• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Design infrastructure for agents that run in isolated remote environments. Plan faster session startup, saved work states, shared sessions, authentication, and delivery of code changes and other results back to users.

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• Designing infrastructure for background coding agents
• Reducing delays when agent sessions start
• Saving and restoring session work

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

中文简介正在准备中。当前显示英语简介。

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

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• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Route Convex development and operations requests to the relevant specialist skills and current procedures, covering app setup, backend features, code reviews, authorization checks, production monitoring, data migrations, and cost analysis.

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• Start a new full-stack app with Convex
• Add authentication, billing, or search to an existing app
• Review backend code and authorization

Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.

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• Adding trace context or drain flags to WindowedValue
• Checking metadata preservation through Windmill storage
• Reviewing propagation through grouping and timers

Build dashboard panels in vanilla TypeScript with resizing, saved layout state, loading indicators, and retry controls. Examples also show how to wrap existing data-fetching functions with caching and temporary request pauses after repeated failures.

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• Building dashboard panels in vanilla TypeScript
• Saving panel sizes across page reloads
• Adding retry controls to failed data requests

Build dashboard panels in plain TypeScript with automatic retries for failed requests, error messages, and retry buttons. Save expanded or collapsed state and panel dimensions in localStorage, the browser’s local storage.

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• Building dashboard panels in TypeScript
• Adding automatic retries to failed requests
• Saving panel size and collapsed state

Build TypeScript interface panels with badges, thumbnails, and colors driven by data. Organize shared behavior in a base class, including fetch retries, error recovery, and saved panel size and expansion state.

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• Building a news panel with thumbnails
• Adding badges colored by data source
• Preserving panel size and expansion state

Add a complete dashboard panel in plain TypeScript, from server routes and data services to the visible component and registration. The workflow checks existing routing patterns and includes request failure handling, caching, and saved panel state.

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• Adding a dashboard panel across server and browser code
• Connecting routes to Express or http.createServer
• Adding caching and cooldowns after repeated failures

Build reusable React components that save settings in browser storage and support keyboard selection. The patterns cover settings dialogs and command palettes, with guidance on TypeScript types, storage keys, and managing keyboard focus.

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• Keeping app settings across page reloads
• Building command selection with arrow keys and Enter
• Creating reusable settings dialogs

Build dashboard panels in vanilla TypeScript with shared loading and error displays, saved panel state, and request handling. Examples show retries with increasing delays and CircuitBreaker integration to pause repeated failures and reuse cached results.

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• Building dashboard panels in vanilla TypeScript
• Adding caching and cooldowns to existing service calls
• Retrying requests with increasing delays

Build dashboard panels and sidebars in plain TypeScript. Provides patterns for panels that fetch data on a timer and sidebars updated by external callers, including retries, saved display settings, and styles inserted only once.

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• Build dashboard panels that refresh on a timer
• Create sidebars updated with externally supplied data
• Save and restore panel size and collapsed state

Build dashboard panels in plain TypeScript with automatic retries, saved panel settings, and clear error states. Keep panel-specific helper functions in the same file to make each component easier to understand and maintain.

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• Build a panel that retries failed data requests
• Save panel dimensions and expanded or collapsed state
• Keep time and status formatting helpers beside panel code

Build dashboard panels with patterns for handling failed API requests. TypeScript examples cover retries, cached data, temporary pauses after repeated failures, loading and error displays, and saving panel size and expansion state in the browser.

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• Building dashboard panels that handle API failures
• Adding loading, error, and retry displays
• Preserving panel size and expansion state

Review Windows privilege escalation paths from a standard user session to Administrator or SYSTEM. Examine service permissions, token privileges, library loading, scheduled tasks, and stored credentials, with detection indicators for defensive validation.

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• Investigating privilege escalation in authorized Windows assessments
• Reviewing service and scheduled task permissions
• Checking detection coverage with attack and defense teams