模型
介绍 afaxxaq-000 — unname：我们正在准备的模型
关于 afaxxaq-000 — unname 的发布前说明：这是一款正在测试并等待公开发布的 Navin Research 多模态 open-weight 模型。
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智能体
Multi-Agent Orchestration Patterns That Work in Production
Choose among manager-worker, parallel fan-out, handoff, and evaluator loops without building an uncontrolled agent swarm.
智能体
How to Design Reliable Handoffs Between AI Agents
Use typed handoff packets, acceptance checks, and clear responsibility boundaries between specialist agents.
智能体
Design Better Tools for AI Agents
Make agent tools easier to select, call, validate, and recover from with narrow schemas and actionable errors.
智能体
AI Agent Memory: What to Store and What to Recompute
Separate working context, durable user facts, episodic history, and external knowledge to reduce cost and privacy risk.
智能体
Secure an MCP Server Before Connecting AI Agents
A deployment checklist for authentication, authorization, consent, validation, and least-privilege Model Context Protocol tools.
智能体
A2A vs MCP: Different Layers for Agent Systems
Understand when Agent2Agent and Model Context Protocol complement each other instead of competing.
智能体
Human Approval Gates for High-Risk AI Agent Actions
Place approval at the last responsible moment and show people exactly what the agent intends to do.
智能体
Build Long-Running AI Agent Jobs That Can Resume Safely
Use checkpoints, idempotent tools, bounded retries, and cancellation for tasks that outlive one request.
智能体
AI Agent Observability: Traces That Help Debug Failures
Capture decisions, tool calls, latency, cost, and evidence without turning traces into a privacy liability.
推理
Reasoning Models vs Chat Models: A Practical Selection Guide
Choose the least expensive model behavior that reliably solves the task instead of enabling heavy reasoning everywhere.
推理
Route Reasoning Effort by Task Difficulty
Build a simple difficulty router with escalation based on confidence and verification rather than one expensive default.
推理
Verify AI Reasoning Without Requesting Chain-of-Thought
Check final answers with tests, constraints, citations, and independent computation instead of collecting private reasoning traces.
推理
Structured Outputs for Reliable AI Pipelines
Use JSON Schema for machine-consumed responses while preserving server-side validation and business rules.
推理
Reasoning With Tools: Separate Planning From Execution
Let models choose actions while deterministic code validates, executes, and records each operation.
推理
Long Context Is Not the Same as Memory
Use large context windows deliberately; retrieval, summaries, and durable state still matter.
推理
A Context Engineering Checklist for AI Agents
Curate instructions, tools, evidence, state, and history as a limited resource on every model turn.
推理
Build a Small Reasoning Evaluation Dataset That Matters
Create a compact, versioned eval set from real failures before chasing broad benchmark scores.
模型
How to Choose an AI Model for a Production Application
Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload.
模型
Open-Weight vs Hosted AI Models: A Deployment Decision
Compare control, operations, licensing, data handling, and performance before self-hosting or buying an API.
模型
Integrate the DeepSeek API Without Locking Your Application
Use an adapter, capability checks, strict validation, and live documentation for a resilient DeepSeek integration.
模型
A Deployment Checklist for Kimi Open-Weight Models
Verify the model card, hardware fit, serving stack, tool format, and license before deploying Kimi models.
模型
A Practical Qwen Model Deployment Checklist
Select the right Qwen variant, template, precision, and serving engine while preserving reproducibility.
模型
Self-Hosting Llama: What to Validate Before Production
Cover licensing, weights, serving, safety, evaluation, and operations for a responsible Llama deployment.
模型
Reliable Function Calling With Mistral Models
Build a provider adapter, validate arguments, and test tool selection with official Mistral capabilities.
模型
An AI Model Upgrade Playbook With Safe Rollback
Change models through pinned versions, regression evals, shadow traffic, canaries, and reversible configuration.
多模态
Build a Reliable Image Understanding Pipeline
Prepare images, preserve provenance, structure outputs, and verify visual claims before using multimodal results.
多模态
Extract Structured Data From Documents With Multimodal Models
Combine file parsing, page images, schemas, citations, and validation for dependable document extraction.
多模态
Practical Video Understanding With Multimodal AI
Sample video deliberately, keep timestamps, separate audio and visual evidence, and evaluate event recall.
多模态
Audio Understanding Beyond Transcription
Design an audio pipeline for transcription, speaker turns, acoustic events, and grounded summaries.
多模态
Build Safer Real-Time Voice Agents
Handle latency, interruptions, turn detection, tool calls, consent, and fallback in live voice systems.
多模态
Multimodal RAG for Text, Images, and Tables
Index each modality with provenance, retrieve at the right granularity, and cite original evidence.
多模态
Defend Multimodal Agents Against Visual Prompt Injection
Treat text inside images and documents as untrusted data, not system instructions.
多模态
Evaluate Multimodal AI With Evidence-Level Metrics
Measure perception, grounding, extraction, reasoning, and abstention separately.
推理服务
Reduce LLM Latency Before Changing Models
Cut sequential calls, shorten outputs, stream useful progress, cache stable prefixes, and parallelize independent work.
推理服务
Prompt Caching: Structure Requests for Reuse
Place stable prefixes first, separate volatile data, monitor cache hits, and protect tenant boundaries.
推理服务
When to Use Batch Inference for AI Workloads
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
推理服务
Quantization for LLM Serving: Measure the Quality Trade-Off
Reduce memory and increase throughput only after evaluating the exact quantized artifact on your tasks.
推理服务
KV Cache Planning for LLM Inference
Estimate memory from context, concurrency, model architecture, precision, and reuse before promising capacity.
推理服务
Prefill and Decode Disaggregation: When It Helps
Separate compute-heavy prompt processing from memory-bandwidth-heavy token generation only for the right workload and scale.
推理服务
Cost-Aware Model Routing Without Silent Quality Loss
Route tasks among models using validated task classes, hard constraints, and escalation rather than price alone.
推理服务
Benchmark an LLM Inference Server Correctly
Report workload, hardware, concurrency, prompt and output lengths, latency percentiles, throughput, and quality together.
安全评估
Prompt Injection Defense for Tool-Using AI Agents
Combine trust separation, least privilege, validation, approvals, and monitoring because prompts alone cannot secure agents.
安全评估
Evaluate AI Agents End to End
Measure task success, policy compliance, tool behavior, efficiency, and recovery across complete trajectories.
安全评估
Build Tool-Call Evaluations for AI Agents
Test tool selection, arguments, sequencing, authorization, recovery, and unnecessary calls with executable fixtures.
安全评估
Evaluate RAG Before Tuning the Generator
Separate retrieval recall, ranking, context quality, answer support, and citation correctness.
安全评估
Use LLM-as-a-Judge Carefully
Design rubrics, randomize ordering, calibrate against humans, and keep deterministic checks first.
安全评估
Red-Team an AI Agent With Realistic Attack Paths
Test untrusted content, tool abuse, data boundaries, identity, and recovery in a controlled environment.
安全评估
Privacy Engineering for AI Applications
Minimize data, define retention, isolate tenants, control logs, and make deletion testable.
安全评估
Continuous AI Evaluations in CI and Production
Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data.