Mô hình
Giới thiệu afaxxaq-000 — unname, mô hình chúng tôi đang chuẩn bị
Ghi chú trước phát hành về afaxxaq-000 — unname, một mô hình đa phương thức open-weight của Navin Research đang được kiểm thử và chờ công bố.
Ghi chú
Ghi chú mới
Ghi chú mới
Ghi chú nổi bật
Mô hình
Moyi: Công nghệ giọng nói với ranh giới rõ ràng
Moyi là hướng nghiên cứu về model nền tảng giọng nói, runtime cục bộ và dịch giọng nói trong bối cảnh vận hành đa ngôn ngữ.
Tác nhân
Start With One AI Agent Before Building a Multi-Agent System
A practical decision rule for choosing one agent, a workflow, or several collaborating agents.
Ghi chú cũ hơn
Ghi chú cũ hơn
Tác nhân
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.
Tác nhân
How to Design Reliable Handoffs Between AI Agents
Use typed handoff packets, acceptance checks, and clear responsibility boundaries between specialist agents.
Tác nhân
Design Better Tools for AI Agents
Make agent tools easier to select, call, validate, and recover from with narrow schemas and actionable errors.
Tác nhân
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.
Tác nhân
Secure an MCP Server Before Connecting AI Agents
A deployment checklist for authentication, authorization, consent, validation, and least-privilege Model Context Protocol tools.
Tác nhân
A2A vs MCP: Different Layers for Agent Systems
Understand when Agent2Agent and Model Context Protocol complement each other instead of competing.
Tác nhân
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.
Tác nhân
Build Long-Running AI Agent Jobs That Can Resume Safely
Use checkpoints, idempotent tools, bounded retries, and cancellation for tasks that outlive one request.
Tác nhân
AI Agent Observability: Traces That Help Debug Failures
Capture decisions, tool calls, latency, cost, and evidence without turning traces into a privacy liability.
Suy luận
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.
Suy luận
Route Reasoning Effort by Task Difficulty
Build a simple difficulty router with escalation based on confidence and verification rather than one expensive default.
Suy luận
Verify AI Reasoning Without Requesting Chain-of-Thought
Check final answers with tests, constraints, citations, and independent computation instead of collecting private reasoning traces.
Suy luận
Structured Outputs for Reliable AI Pipelines
Use JSON Schema for machine-consumed responses while preserving server-side validation and business rules.
Suy luận
Reasoning With Tools: Separate Planning From Execution
Let models choose actions while deterministic code validates, executes, and records each operation.
Suy luận
Long Context Is Not the Same as Memory
Use large context windows deliberately; retrieval, summaries, and durable state still matter.
Suy luận
A Context Engineering Checklist for AI Agents
Curate instructions, tools, evidence, state, and history as a limited resource on every model turn.
Suy luận
Build a Small Reasoning Evaluation Dataset That Matters
Create a compact, versioned eval set from real failures before chasing broad benchmark scores.
Mô hình
How to Choose an AI Model for a Production Application
Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload.
Mô hình
Open-Weight vs Hosted AI Models: A Deployment Decision
Compare control, operations, licensing, data handling, and performance before self-hosting or buying an API.
Mô hình
Integrate the DeepSeek API Without Locking Your Application
Use an adapter, capability checks, strict validation, and live documentation for a resilient DeepSeek integration.
Mô hình
A Deployment Checklist for Kimi Open-Weight Models
Verify the model card, hardware fit, serving stack, tool format, and license before deploying Kimi models.
Mô hình
A Practical Qwen Model Deployment Checklist
Select the right Qwen variant, template, precision, and serving engine while preserving reproducibility.
Mô hình
Self-Hosting Llama: What to Validate Before Production
Cover licensing, weights, serving, safety, evaluation, and operations for a responsible Llama deployment.
Mô hình
Reliable Function Calling With Mistral Models
Build a provider adapter, validate arguments, and test tool selection with official Mistral capabilities.
Mô hình
An AI Model Upgrade Playbook With Safe Rollback
Change models through pinned versions, regression evals, shadow traffic, canaries, and reversible configuration.
Đa phương thức
Build a Reliable Image Understanding Pipeline
Prepare images, preserve provenance, structure outputs, and verify visual claims before using multimodal results.
Đa phương thức
Extract Structured Data From Documents With Multimodal Models
Combine file parsing, page images, schemas, citations, and validation for dependable document extraction.
Đa phương thức
Practical Video Understanding With Multimodal AI
Sample video deliberately, keep timestamps, separate audio and visual evidence, and evaluate event recall.
Đa phương thức
Audio Understanding Beyond Transcription
Design an audio pipeline for transcription, speaker turns, acoustic events, and grounded summaries.
Đa phương thức
Build Safer Real-Time Voice Agents
Handle latency, interruptions, turn detection, tool calls, consent, and fallback in live voice systems.
Đa phương thức
Multimodal RAG for Text, Images, and Tables
Index each modality with provenance, retrieve at the right granularity, and cite original evidence.
Đa phương thức
Defend Multimodal Agents Against Visual Prompt Injection
Treat text inside images and documents as untrusted data, not system instructions.
Đa phương thức
Evaluate Multimodal AI With Evidence-Level Metrics
Measure perception, grounding, extraction, reasoning, and abstention separately.
Suy luận hệ thống
Reduce LLM Latency Before Changing Models
Cut sequential calls, shorten outputs, stream useful progress, cache stable prefixes, and parallelize independent work.
Suy luận hệ thống
Prompt Caching: Structure Requests for Reuse
Place stable prefixes first, separate volatile data, monitor cache hits, and protect tenant boundaries.
Suy luận hệ thống
When to Use Batch Inference for AI Workloads
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
Suy luận hệ thống
Quantization for LLM Serving: Measure the Quality Trade-Off
Reduce memory and increase throughput only after evaluating the exact quantized artifact on your tasks.
Suy luận hệ thống
KV Cache Planning for LLM Inference
Estimate memory from context, concurrency, model architecture, precision, and reuse before promising capacity.
Suy luận hệ thống
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.
Suy luận hệ thống
Cost-Aware Model Routing Without Silent Quality Loss
Route tasks among models using validated task classes, hard constraints, and escalation rather than price alone.
Suy luận hệ thống
Benchmark an LLM Inference Server Correctly
Report workload, hardware, concurrency, prompt and output lengths, latency percentiles, throughput, and quality together.
Đánh giá an toàn
Prompt Injection Defense for Tool-Using AI Agents
Combine trust separation, least privilege, validation, approvals, and monitoring because prompts alone cannot secure agents.
Đánh giá an toàn
Evaluate AI Agents End to End
Measure task success, policy compliance, tool behavior, efficiency, and recovery across complete trajectories.
Đánh giá an toàn
Build Tool-Call Evaluations for AI Agents
Test tool selection, arguments, sequencing, authorization, recovery, and unnecessary calls with executable fixtures.
Đánh giá an toàn
Evaluate RAG Before Tuning the Generator
Separate retrieval recall, ranking, context quality, answer support, and citation correctness.
Đánh giá an toàn
Use LLM-as-a-Judge Carefully
Design rubrics, randomize ordering, calibrate against humans, and keep deterministic checks first.
Đánh giá an toàn
Red-Team an AI Agent With Realistic Attack Paths
Test untrusted content, tool abuse, data boundaries, identity, and recovery in a controlled environment.
Đánh giá an toàn
Privacy Engineering for AI Applications
Minimize data, define retention, isolate tenants, control logs, and make deletion testable.
Đánh giá an toàn
Continuous AI Evaluations in CI and Production
Run fast regression gates on every change and monitor sampled real-world outcomes without leaking user data.