Headhunting firm: Aliyun to local server + NAS
A 300-person company running ATS/CRM/Elasticsearch on five ECS instances plus OSS storage.
The target stack kept an overseas VPS and moved internal workloads to Proxmox, TrueNAS, Tailscale, and Zabbix.
AI / CLOUD / TRANSFORMATIONI deliver high-complexity AI systems across
I deliver high-complexity AI systems across
agent efficiency, cloud foundations, and transformation.
Three focused lines: agent efficiency architecture, cloud-exit / private-cloud infrastructure, and intelligent transformation strategy. We start from real systems, business goals, and organizational constraints before recommending change.
3Business Lines
6Case Patterns
2-8 wksTypical Phase
OngoingGovernance
Hands-on across high-concurrency architecture, agent memory, cloud exit, and private deployment
Case AreasAgents · Cloud Exit / Private Cloud · AI Transformation
Education system: vision inference and queues off-cloud
Vue frontend, video/book/exam management, Yolo5 inference, MNS/RocketMQ, OSS, and database migration.
The point was not a savings slogan, but moving compute, storage, queues, and inference into a controllable environment.
Real data first, proposal second
CPU, memory, disk, database, bandwidth, and bills. Confirm the load first, then decide whether to migrate, right-size, or rebuild.
Many systems do not need a grand plan. The first step is finding obvious risks and waste.
AI systems as engineering work
Agent memory, harnesses, multi-agent collaboration, GPU inference, RAGFlow, Langfuse, vector databases, monitoring, and backups.
The focus is deployability, observability, recovery, and reviewability, not one-off demo quality.
View business lines →TECHNICAL NOTESNotes and tool catalog remain available
Open-source notes, deployment catalog, and knowledge base are still here for selection notes, pitfalls, and reusable components.
This part is a selection and methodology asset base, not an entry point for low-end outsourcing.
Open capability assets →[ THREE BUSINESS LINES ]
Not a tool shelf. Three delivery systems.
View capability catalogAGENT EFFICIENCY ARCHITECTURE
Agent Efficiency Architecture
Agents are not model scripts. We design around memory, multi-agent collaboration, harness runtime, and high-concurrency / microservice / cloud-native foundations so agent systems can run, trace, review, and scale.
Memory architectureMulti-agent collaborationHarness runtimeEngineering foundation
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CLOUD INFRASTRUCTURE CONFIGURATION
Cloud Infrastructure Configuration
We help teams move critical systems from unpredictable public-cloud bills into controllable, observable, operable private infrastructure across cloud-exit migration, private cloud, local servers / GPUs, network security, monitoring, backups, and cost governance.
Cloud exitPrivate cloudLocal computeOperations governance
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INTELLIGENT TRANSFORMATION STRATEGY
Intelligent Transformation Strategy
AI transformation is not tool training. We design transformation paths around business goals, core processes, organizational knowledge, skill libraries, agent workspaces, and operating metrics so AI enters decision-making, production, delivery, and review systems.
Strategy diagnosisAI-native workflowsSkill assetsOperating system
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[ HOW I WORK ]
From Diagnosis to Phased Delivery
First identify the right business line, then decide how architecture, migration, or transformation should move.
01
Diagnose System and Business
Start from agent prototypes, cloud bills, architecture, workflows, and business goals to locate the real constraint.
02
Design Target Architecture
Design memory / harness, cloud-exit / private-cloud, or AI-native workflow targets with boundaries, risks, and phase acceptance.
03
Deliver and Govern
Deliver executable assets, pilot systems, or migration plans, then govern them with monitoring, evaluation, review, and operations boundaries.
[ CAPABILITY ASSETS ]
The catalog is an asset base, not low-end outsourcing
The case library, deployment catalog, and knowledge base help evaluate scenario type, business scale, complexity, and availability requirements; delivery is organized around architecture, models, data, and operations boundaries.
PERFORMANCE CASELive
TalentAI
Hiring intelligence performance case. Demo, engineering estimates, benchmark, and evaluation frame latency, throughput, cost, Recall@K, and evidence coverage.
View project case[ SELECTION / METHOD ASSETS ]
LIVE
View technical catalog →AI Signal Board
Curated stream, digest, RSS/API
38
Skills Notes
For selection reference
20
MCP Notes
Search, code, data sources
8
Deployment Items
Scope and ops boundaries
[ CASE LIBRARY ]
The case library shows architecture choices and outcomes
The case library has two layers: scenario cases explain selection by problem type, scale, and complexity; project cases show concrete architecture, problems, and outcomes for Marketing Agent, TalentAI, and similar work.
— ARTICLES
Latest Insights
TECH ARTICLE2026-04-10
AI 工具链实战选型：我们日常在用的工具与点评
覆盖反爬采集、社媒自动化、LLM 可观测性、检索增强、项目管理等领域，附适用场景和踩坑经验。
TECH ARTICLE2026-03-09
AI 知识管道：从信息噪音到结构化知识的完整技术选型
一套基于开源生态的企业级知识管理链路方案，串联 TrendRadar + 爬虫 + RAGFlow + Langfuse，覆盖「热点发现 → 内容采集 → 智能解析 → 知识检索 → 质量监控」全生命周期。
TECH ARTICLE2026-02-24
Manus 技术深度调研（2026）：它到底是怎么把 Agent 跑起来的？
一篇尽量讲人话的 Manus 技术拆解：从上下文工程、并行多 Agent、Sandbox、浏览器自动化，到 Skills、MCP 和 API。重点讲清"它为什么能做成事"，也讲清"哪些地方还不透明"。
If you have a high-complexity problem, send the context.
Useful inputs are current agent / system architecture, cloud bills and utilization, business workflows, organizational goals, and the issue you are most worried about.