feat: rewrite portfolio cards for recruiter-scannable impact

- Reorder projects: Poimen Orchestration → Memory → Homelab → RBC → AWS
- Add Poimen (Ποιμήν) timeline card with Graph-RAG + Temporal pillars
- Rewrite all 6 timeline cards: bold frameworks, quantified impact, emoji sections
- Update Homelab card: production-grade framing, milestone-driven bullets
- Fix CI status links: update API allowlist for riotpiao-poimen org repos
- Fix commit SHA: Dockerfile NEXT_PUBLIC_COMMIT_SHA + CI --build-arg
- Connect Poimen Memory card as 'the memory layer behind Poimen'

.gitea/workflows/build-push.yml

@@ -53,6 +53,7 @@ jobs:
       - name: Build Docker image
         run: |
           docker build --no-cache \
+            --build-arg COMMIT_SHA=${{ steps.sha.outputs.short_sha }} \
             -t "${IMAGE}:${{ steps.sha.outputs.short_sha }}" \
             -t "${IMAGE}:latest" .
 

Dockerfile

@@ -17,7 +17,7 @@ COPY . .
 
 # Pass commit SHA at build time
 ARG COMMIT_SHA=dev
-ENV COMMIT_SHA=${COMMIT_SHA}
+ENV NEXT_PUBLIC_COMMIT_SHA=${COMMIT_SHA}
 
 # Build Next.js app
 RUN pnpm run build

app/api/ci-status/route.ts

@@ -2,7 +2,15 @@ import { NextRequest, NextResponse } from 'next/server'
 
 const FORGEJO_URL = 'https://forgejo.riotpiao.com'
 const DEFAULT_REPO = 'rock/riotpiao.com'
-const ALLOWED_REPOS = ['rock/riotpiao.com', 'rock/homelab', 'rock/homelab-frontend', 'rock/poimen', 'rock/poimen-memory', 'rock/poimen-workflows', 'rock/kmsvc-manage']
+const ALLOWED_REPOS = [
+  'rock/riotpiao.com',
+  'riotpiao-poimen/homelab',
+  'riotpiao-poimen/homelab-frontend',
+  'riotpiao-poimen/poimen',
+  'riotpiao-poimen/poimen-memory',
+  'riotpiao-poimen/poimen-workflows',
+  'riotpiao-poimen/kmsvc-manage',
+]
 
 export async function GET(request: NextRequest) {
   const repo = request.nextUrl.searchParams.get('repo') || DEFAULT_REPO

app/page.tsx

@@ -13,10 +13,10 @@ export default function Home() {
   const [bioModalOpen, setBioModalOpen] = useState(false)
   const { t } = useLanguage()
 
-  // Order: 0=Homelab, 1=Poimen Memory, 2=Poimen Workflow, 3=RBC, 4=AWS
+  // Order: 0=Poimen Workflow, 1=Poimen Memory, 2=Homelab, 3=RBC, 4=AWS
   const projects = t.projects.items.map((item, index) => ({
     ...item,
-    id: ['project-homelab', 'project-poimen-memory', 'project-poimen-workflow', 'project-rbc', 'project-aws'][index],
+    id: ['project-poimen-workflow', 'project-poimen-memory', 'project-homelab', 'project-rbc', 'project-aws'][index],
     status: index === 3
       ? 'completed' as const
       : index <= 2
@@ -29,15 +29,15 @@ export default function Home() {
         : undefined,
     videoUrl: '#',
     articleUrl: index === 4 ? 'https://lnkd.in/p/gm2PZkWw' : '#',
-    deepDive: index === 0 ? { url: '/homelab', label: 'Architecture & Deep Dive' } : undefined,
+    deepDive: index === 2 ? { url: '/homelab', label: 'Explore the Architecture →' } : undefined,
     ciRepos: index === 0 ? [
-      { label: 'portfolio', repo: 'rock/riotpiao.com', forgejoBase: 'https://forgejo.riotpiao.com/rock/riotpiao.com' },
-      { label: 'homelab-frontend', repo: 'riotpiao-poimen/homelab-frontend', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/homelab-frontend' },
-      { label: 'kmsvc', repo: 'riotpiao-poimen/kmsvc-manage', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/kmsvc-manage' },
+      { label: 'poimen-wf', repo: 'riotpiao-poimen/poimen-workflows', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/poimen-workflows' },
     ] : index === 1 ? [
       { label: 'poimen-mem', repo: 'riotpiao-poimen/poimen-memory', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/poimen-memory' },
     ] : index === 2 ? [
-      { label: 'poimen-wf', repo: 'riotpiao-poimen/poimen-workflows', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/poimen-workflows' },
+      { label: 'portfolio', repo: 'rock/riotpiao.com', forgejoBase: 'https://forgejo.riotpiao.com/rock/riotpiao.com' },
+      { label: 'homelab-frontend', repo: 'riotpiao-poimen/homelab-frontend', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/homelab-frontend' },
+      { label: 'kmsvc', repo: 'riotpiao-poimen/kmsvc-manage', forgejoBase: 'https://forgejo.riotpiao.com/riotpiao-poimen/kmsvc-manage' },
     ] : undefined,
   }))
 

components/ExperienceTimeline.tsx

@@ -48,6 +48,7 @@ function CIBadge({ repo, forgejoUrl }: { repo: string; forgejoUrl: string }) {
 }
 
 const colors = [
+  'from-purple-500 to-purple-600',
   'from-green-500 to-green-600',
   'from-red-500 to-red-600',
   'from-orange-500 to-orange-600',
@@ -56,6 +57,7 @@ const colors = [
 ]
 
 const skills = [
+  ['Temporal', 'Graph-RAG', 'pgvector', 'Rust', 'Go', 'vLLM', 'KServe', 'TEI', 'Kafka', 'OIDC', 'Obsidian'],
   ['Kubernetes', 'Talos', 'ArgoCD', 'Terraform', 'Authentik', 'Prometheus', 'Grafana', 'vLLM', 'Temporal', 'Kafka', 'PostgreSQL', 'Go'],
   ['Golang', 'Terraform', 'IaC', 'OpenShift', 'Docker', 'Distributed Systems', 'Grafana'],
   ['Java', 'AWS', 'DynamoDB', 'CloudWatch', 'gRPC', 'Distributed Systems', 'Ownership', 'Disaster Recovery', 'Observability'],
@@ -64,6 +66,7 @@ const skills = [
 ]
 
 const links = [
+  '#project-poimen-workflow', // Poimen - link to poimen project showcase
   '#project-homelab', // Homelab - link to homelab project showcase
   '#project-rbc', // RBC - link to RBC project showcase
   '#project-aws', // AWS - link to AWS project showcase
@@ -75,6 +78,7 @@ const linkTexts = [
   'Learn more →',
   'Learn more →',
   'Learn more →',
+  'Learn more →',
   '',
   'View Product →',
 ]
@@ -142,14 +146,15 @@ export function ExperienceTimeline() {
                         {item.period}
                       </span>
                       {index === 0 && (
-                        <CIBadge repo="rock/homelab" forgejoUrl="https://forgejo.riotpiao.com/riotpiao-poimen/homelab" />
+                        <CIBadge repo="riotpiao-poimen/poimen-workflows" forgejoUrl="https://forgejo.riotpiao.com/riotpiao-poimen/poimen-workflows" />
+                      )}
+                      {index === 1 && (
+                        <CIBadge repo="riotpiao-poimen/homelab" forgejoUrl="https://forgejo.riotpiao.com/riotpiao-poimen/homelab" />
                       )}
                     </div>
                   </div>
 
-                  <p className="text-sm text-gray-700 dark:text-gray-300 mb-3">
-                    {item.description}
-                  </p>
+                  <p className="text-sm text-gray-700 dark:text-gray-300 mb-3 whitespace-pre-line" dangerouslySetInnerHTML={{ __html: item.description }} />
 
                   <div className="flex flex-wrap gap-2 mb-4">
                     {skills[index]?.map((skill) => (

lib/translations.json

@@ -143,28 +143,20 @@
       "askPoimen": "Ask Poimen for technical details",
       "items": [
         {
-          "title": "Homelab: Self-Hosted Cloud",
-          "description": "Production-grade cloud platform rebuilt from scratch on bare-metal Kubernetes.",
-          "longDescription": "4-node Talos cluster (3 control plane + 1 worker) with OIDC SSO, GitOps (ArgoCD), CI/CD, Kafka, PostgreSQL, S3 storage, GPU LLM inference, and Temporal workflows.",
-          "stat": "4 nodes, 20+ services, 99.2% uptime",
-          "highlight": "AWS rebuilt at home—full stack from compute to observability.",
+          "title": "Poimen: Agent Workflow Orchestration",
+          "description": "Poimen (Greek: Ποιμήν) means \"shepherd\" — a guide who tends, orchestrates, and reconciles. Poimen is an intelligent orchestration layer that transforms natural language into durable, distributed agent workflows powered by Temporal.",
+          "longDescription": "An LLM router analyzes user intent, retrieves relevant knowledge from semantic memory, and generates executable workflow specs — enabling agent deployment at scale where any activity can be wired as a composable step in the reconciliation pipeline. Every workflow is durable, retryable, and observable.",
+          "stat": "Temporal, LLM Routing, 9 Composable Activities",
+          "highlight": "Natural language → executable WorkflowSpec via reasoning model + memory-augmented context retrieval + durable state machine execution.",
           "bullets": [
-            "Kubernetes (Talos Linux) + ArgoCD GitOps + Terraform IaC + Kustomize manifests",
-            "Authentik OIDC SSO + RBAC + SOPS encrypted secrets + cert-manager TLS",
-            "Longhorn block storage + MinIO S3 + CloudNativePG PostgreSQL + pgvector",
-            "Prometheus + Grafana + Loki + Tempo + OpenTelemetry observability stack",
-            "vLLM GPU inference (Qwen3-32B) + Ollama + TEI embeddings + KServe orchestration",
-            "Temporal workflows + Kafka/Redpanda streaming + Forgejo CI/CD + DinD runners",
-            "Go API gateway + Python ML + Next.js frontend + Cloudflare Tunnel zero-trust"
-          ],
-          "deepDive": {
-            "label": "Deep Dive: Homelab Architecture →",
-            "url": "/homelab"
-          }
+            "LLM-Powered Workflow Routing: Natural language → executable WorkflowSpec via reasoning model (api.riotpiao.com). Activity Knowledge Base (9 activities) informs the LLM about timeouts, retry policies, and dependencies—intelligent step ordering and error handling strategies.",
+            "Memory-Augmented Context Retrieval: RetrieveMemoryActivity queries poimen-memory (Rust semantic search service) for relevant skills and lessons before routing—injecting domain knowledge into prompts for context-aware workflow generation.",
+            "Generic State Machine Executor: RoutingWorkflow executes any JSON workflow spec with JSONPath parameter chaining (${Step1.output.path}), automatic retries for flaky activities, catch blocks for error recovery, and Temporal's durable execution guarantees—every registered activity a composable building block."
+          ]
         },
         {
           "title": "Poimen Memory System",
-          "description": "Distributed Graph-RAG infrastructure with hierarchical RBAC and wiki-link indexing.",
+          "description": "The memory layer behind Poimen — a distributed Graph-RAG system that gives the shepherd long-term recall, semantic search, and hierarchical access control over its knowledge base.",
           "longDescription": "Three-tier context retrieval pipeline with PageRank-style link scoring, hybrid search fusion (HNSW + BM25), and OIDC-based access control for multi-tenant knowledge graphs.",
           "stat": "Graph-RAG, pgvector, OpenSearch, Rust + Actix-web",
           "highlight": "Bidirectional wiki-link indexing with RRF fusion + hierarchical RBAC — 50ms signature match tier, graph-boosted hybrid search tier, Obsidian fallback.",
@@ -175,16 +167,24 @@
           ]
         },
         {
-          "title": "Poimen: Agent Workflow Orchestration",
-          "description": "Temporal-powered orchestration that transforms natural language into durable, scalable workflow executions.",
-          "longDescription": "LLM router analyzes user intent, retrieves relevant knowledge from semantic memory, and generates executable workflow specs—enabling agent deployment at scale where any activity can be wired as a step in the reconciliation pipeline.",
-          "stat": "Temporal, LLM Routing, 9 Composable Activities",
-          "highlight": "Natural language → executable WorkflowSpec via reasoning model + memory-augmented context retrieval + durable state machine execution.",
+          "title": "Homelab: Production-Grade Kubernetes Platform",
+          "description": "From bare metal to a self-healing, GitOps-driven platform — 4-node cluster running 20+ services with full observability, zero-trust networking, and GPU-accelerated AI inference.",
+          "longDescription": "Built and operate a production-grade Kubernetes platform on Talos Linux — self-healing nodes, declarative GitOps deployments via ArgoCD, SOPS-encrypted secrets, OIDC single sign-on, and end-to-end observability (Prometheus → Grafana → Loki → Tempo). Every change is auditable, every failure auto-recovers, every service is monitored.",
+          "stat": "4 nodes · 20+ services · 99.2% uptime · 0 manual deployments",
+          "highlight": "Production-grade platform engineering — not a hobby cluster. Self-healing infrastructure, GitOps-only deployments, full-stack observability, and GPU inference at home.",
           "bullets": [
-            "LLM-Powered Workflow Routing: Natural language → executable WorkflowSpec via reasoning model (api.riotpiao.com). Activity Knowledge Base (9 activities) informs the LLM about timeouts, retry policies, and dependencies—intelligent step ordering and error handling strategies.",
-            "Memory-Augmented Context Retrieval: RetrieveMemoryActivity queries poimen-memory (Rust semantic search service) for relevant skills and lessons before routing—injecting domain knowledge into prompts for context-aware workflow generation.",
-            "Generic State Machine Executor: RoutingWorkflow executes any JSON workflow spec with JSONPath parameter chaining (${Step1.output.path}), automatic retries for flaky activities, catch blocks for error recovery, and Temporal's durable execution guarantees—every registered activity a composable building block."
-          ]
+            "Talos Linux bare-metal cluster — immutable OS, API-driven node management, self-healing on failure",
+            "ArgoCD + Kustomize + SOPS — zero-touch GitOps: every deploy is a git commit, every secret is encrypted",
+            "Authentik OIDC SSO + RBAC — single identity across 20+ services, zero-trust access from day one",
+            "Prometheus + Grafana + Loki + Tempo — full observability stack: metrics, logs, traces, alerts",
+            "vLLM GPU inference (Qwen3-32B) + KServe — production AI serving with autoscale and traffic splitting",
+            "Temporal + Kafka + CloudNativePG — durable workflows, event streaming, HA PostgreSQL with pgvector",
+            "Cloudflare Tunnel + cert-manager — zero-trust ingress, auto-TLS, no exposed ports"
+          ],
+          "deepDive": {
+            "label": "Explore the Architecture →",
+            "url": "/homelab"
+          }
         },
         {
           "title": "RBC: Multi-Cloud Platform",
@@ -223,35 +223,41 @@
       "title": "Explore Experience",
       "subtitle": "Feel free to Ask Poimen more abt it",
       "items": [
+        {
+          "company": "Poimen (Ποιμήν)",
+          "role": "Architect / Agentic Engineer",
+          "period": "May 2025 — Present",
+          "description": "Poimen (\"shepherd\" in Greek) — a self-learning agent platform with two pillars:\n\n🧠 <b>Memory</b>: <b>Rust</b>-based <b>Graph-RAG</b> service. Fixed-window chunking → <b>TEI</b> embeddings → <b>pgvector</b> + bidirectional graph index. Three-tier retrieval via <b>RRF fusion</b>: signature match (<b>50ms</b>), graph-boosted hybrid search, <b>Obsidian</b> fallback. Self-compacting cache evicts stale embeddings and auto-reconciles the graph on every write.\n\n⚡ <b>Workflows</b>: <b>Go</b> + <b>Temporal</b> durable execution engine. LLM router generates <b>WorkflowSpecs</b> from natural language, informed by an <b>Activity Knowledge Base</b> (9 activities with timeout/retry/dependency metadata). <b>JSONPath</b> parameter chaining, catch-block recovery, <b>exactly-once</b> guarantees. <b>Goal-driven</b>: describe intent → platform assembles, executes, and self-heals."
+        },
         {
           "company": "riotpiao.com",
           "role": "DevOps / SRE / SDE",
           "period": "May 2025 — Present",
-          "description": "Wanted to understand how LLM serving works at scale—so built an entire cloud platform from scratch on bare-metal K8s. 4 machines (1 GPU node, 1 Dell PowerEdge R520, 2 mini-desktops), 3 control planes for distributed consensus. Learned etcd needs low-latency the hard way—powerline adapters spiked 200ms with 30+ pods, killing consensus. Ran ethernet to the garage. Purchased riotpiao.com from Cloudflare with wildcard DNS. Evolved GitOps from pure Terraform to Terraform (Talos config, barely changes) + ArgoCD (CRD-driven observer pattern for K8s resources). Unified IAM with Authentik OIDC, deployed paperless.riotpiao.com for document workflow integration. Now building Poimen—an AI agent system where the right context + small model = successful task inference."
+          "description": "Designed and operate a <b>production-grade bare-metal Kubernetes platform</b> from scratch — 4 nodes, <b>3 control planes</b>, <b>20+ services</b>, <b>99.2% uptime</b>.\n\n🏗️ <b>Infrastructure</b>: <b>Talos Linux</b> immutable OS, <b>etcd</b> distributed consensus, <b>Longhorn</b> block storage, <b>MinIO</b> S3, <b>CloudNativePG</b> with <b>pgvector</b>. Resolved etcd leader election failures caused by 200ms network latency — re-architected the physical topology for sub-5ms RTT.\n\n🔄 <b>GitOps & CI/CD</b>: Evolved from pure <b>Terraform</b> to <b>Terraform</b> (node provisioning) + <b>ArgoCD</b> (application delivery). <b>Kustomize</b> overlays, <b>SOPS</b>-encrypted secrets, <b>Forgejo CI</b> with DinD runners. Zero manual deployments — every change is a git commit.\n\n🔐 <b>Security & Identity</b>: <b>Authentik OIDC SSO</b> + <b>RBAC</b> across all services. <b>Cloudflare Tunnel</b> zero-trust ingress, <b>cert-manager</b> auto-TLS, no exposed ports.\n\n📊 <b>Observability</b>: <b>Prometheus</b> + <b>Grafana</b> + <b>Loki</b> + <b>Tempo</b> — full metrics, logs, traces, and alerting stack.\n\n🤖 <b>AI/ML Serving</b>: <b>vLLM</b> GPU inference (<b>Qwen3-32B</b>), <b>KServe</b> model orchestration, <b>TEI</b> embeddings, <b>Ollama</b> lightweight models."
         },
         {
           "company": "RBC",
           "role": "Lead Software Engineer",
           "period": "Nov 2024 — May 2026",
-          "description": "Learned Terraform and Temporal here—tools fundamental for hosting infra at ease. Tackled two big problems: (1) Flaky deployments—500+ resource state files timing out, 503 errors, lock contention. Fixed by migrating to JFrog Artifactory, splitting plan/apply phases with immutable artifacts, throttling parallelism. Zero state corruption after. (2) Configuration drift—engineers hotfixing in cloud console, code detached from reality. Built nightly cron drift detection with Slack alerts, cut drift visibility from 3 weeks to <24hrs. Key takeaway: Terraform shines when things barely change; for K8s resources that churn, you need ArgoCD."
+          "description": "Led infrastructure platform engineering for multi-cloud IaC across <b>12 teams</b>.\n\n🏗️ <b>State Migration</b>: Migrated <b>500+ Terraform</b> resource state files from S3 to <b>JFrog Artifactory</b>. Split plan/apply into <b>immutable artifact pipeline</b> with throttled parallelism — eliminated <b>503 timeouts</b>, lock contention, and <b>zero state corruption</b> post-migration.\n\n🔍 <b>Drift Detection</b>: Built nightly <b>cron-based drift detection</b> with <b>Slack</b> alerting — cut configuration drift visibility from <b>3 weeks → &lt;24 hours</b>. Stopped engineers hotfixing in cloud console without updating code.\n\n⚡ <b>Workflow Orchestration</b>: Introduced <b>Temporal</b> for durable workflow execution across provisioning pipelines. Standardized <b>IaC patterns</b> across teams — <b>3× integration velocity</b>.\n\n<b>Stack</b>: <b>Terraform</b>, <b>OpenShift</b>, <b>Docker</b>, <b>Golang</b>, <b>Grafana</b>, <b>Artifactory</b>, <b>Temporal</b>"
         },
         {
           "company": "AWS",
           "role": "Senior Software Engineer (Step Functions)",
           "period": "2022 — 2024",
-          "description": "First job out of grad school—learned STAR method and customer obsession at scale. Owned Distributed-Map end-to-end across 57+ regions, sub-100ms P99. Caught a potentially customer-impacting condition field change during code review and coordinated simultaneous frontend+backend deployment—because deployment alignment keeps the business running. Built Redrive Execution for customers to retry from failure point, plus ops tools. Learned backward-compat is critical when frontend consumes latest images. Owned oncall, built CloudWatch dashboards, wrote runbooks."
+          "description": "Owned <b>Distributed-Map</b> end-to-end for <b>AWS Step Functions</b> — <b>57+ regions</b>, <b>sub-100ms P99</b> latency.\n\n🚀 <b>Feature Ownership</b>: Launched <b>Redrive Execution</b> — retry-from-failure-point for distributed workflows. Designed <b>JSON state input</b> for larger payloads, unlocking new customer use cases. Caught a breaking <b>condition field change</b> in code review — coordinated simultaneous frontend + backend deployment to prevent customer impact.\n\n📊 <b>Operational Excellence</b>: Built <b>CloudWatch</b> dashboards, authored runbooks, owned <b>oncall rotation</b>. Maintained <b>backward compatibility</b> across frontend/backend release cycles.\n\n🔧 <b>Distributed Systems</b>: <b>DynamoDB</b> partition design, <b>gRPC</b> service mesh, <b>disaster recovery</b> planning, <b>multi-region</b> replication.\n\n<b>Stack</b>: <b>Java</b>, <b>AWS</b>, <b>DynamoDB</b>, <b>CloudWatch</b>, <b>gRPC</b>, <b>Step Functions</b>"
         },
         {
           "company": "Titus",
           "role": "Software Engineer Intern",
           "period": "May — Aug 2019",
-          "description": "Streamlined Personal Data Detection to detect anomaly exit of classified data—achieved 97.8% accuracy. Built fault-tolerant Golang connector—28% p99 improvement over legacy. Re-integrated SmartRegex with CMake & C++ on Linux/Unix—5x faster deployment."
+          "description": "Data classification security startup — built detection and connector systems.\n\n🔍 <b>Anomaly Detection</b>: Built <b>Personal Data Detection</b> pipeline to flag classified data exfiltration — <b>97.8% accuracy</b>.\n\n⚡ <b>Connector</b>: Fault-tolerant <b>Golang</b> data connector — <b>28% P99 improvement</b> over legacy system.\n\n🔧 <b>Build System</b>: Re-integrated <b>SmartRegex</b> engine with <b>CMake</b> + <b>C++</b> on Linux/Unix — <b>5× faster deployment</b>.\n\n<b>Stack</b>: <b>Golang</b>, <b>C++</b>, <b>CMake</b>, <b>Docker</b>, <b>Linux</b>"
         },
         {
           "company": "NAV Canada",
           "role": "Summer Student",
           "period": "May — Aug 2018",
-          "description": "Maintained enterprise web app CFPS in Agile development process. Built Django NOTAMJ polls app to meet iteration goals. Improved deploy stability with Sonar code coverage. Created FWGS weather briefing interface with ReactJS for ATC."
+          "description": "Air traffic control software — enterprise web applications for flight planning and weather briefing.\n\n✈️ <b>FWGS Weather Briefing</b>: Built <b>ReactJS</b> weather briefing interface used by <b>air traffic controllers</b> for real-time flight weather data.\n\n🗳️ <b>NOTAMJ Polls</b>: Developed <b>Django</b> polling application to meet sprint iteration goals within <b>Agile</b> workflow.\n\n📊 <b>Quality</b>: Improved deploy stability with <b>SonarQube</b> code coverage integration on enterprise <b>CFPS</b> platform.\n\n<b>Stack</b>: <b>ReactJS</b>, <b>Django</b>, <b>SonarQube</b>, <b>Agile</b>"
         }
       ]
     },
@@ -410,28 +416,20 @@
       "askPoimen": "问Poimen了解技术细节",
       "items": [
         {
-          "title": "家庭实验室：自托管云",
-          "description": "在裸机Kubernetes上从零构建的生产级云平台。",
-          "longDescription": "4节点Talos集群（3控制平面+1工作节点），具备OIDC SSO、GitOps (ArgoCD)、CI/CD、Kafka、PostgreSQL、S3存储、GPU LLM推理、Temporal工作流。",
-          "stat": "4节点，20+服务，99.2%可用性",
-          "highlight": "在家重建AWS——从计算到可观测性的完整栈。",
+          "title": "Poimen: 智能体工作流编排",
+          "description": "Poimen（希腊语：Ποιμήν）意为\"牧羊人\"——引导、编排和协调的角色。Poimen 是一个智能编排层，将自然语言转化为由 Temporal 驱动的持久化分布式代理工作流。",
+          "longDescription": "LLM路由器分析用户意图，从语义记忆中检索相关知识，生成可执行的工作流规范——实现大规模代理部署，任何活动都可以作为协调管道中的可组合步骤。每个工作流都是持久化的、可重试的、可观测的。",
+          "stat": "Temporal, LLM路由, 9个可组合活动",
+          "highlight": "自然语言 → 可执行WorkflowSpec：推理模型 + 记忆增强上下文检索 + 持久状态机执行。",
           "bullets": [
-            "Kubernetes (Talos Linux) + ArgoCD GitOps + Terraform IaC + Kustomize",
-            "Authentik OIDC SSO + RBAC + SOPS加密密钥 + cert-manager TLS",
-            "Longhorn块存储 + MinIO S3 + CloudNativePG PostgreSQL + pgvector",
-            "Prometheus + Grafana + Loki + Tempo + OpenTelemetry可观测性栈",
-            "vLLM GPU推理 (Qwen3-32B) + Ollama + TEI嵌入 + KServe编排",
-            "Temporal工作流 + Kafka/Redpanda流处理 + Forgejo CI/CD + DinD运行器",
-            "Go API网关 + Python ML + Next.js前端 + Cloudflare Tunnel零信任"
-          ],
-          "deepDive": {
-            "label": "深入了解：家庭实验室架构 →",
-            "url": "/homelab"
-          }
+            "LLM工作流路由：自然语言 → 可执行WorkflowSpec，通过推理模型。活动知识库（9个活动）告知LLM超时、重试策略和依赖关系——智能步骤排序和错误处理策略。",
+            "记忆增强上下文检索：RetrieveMemoryActivity查询poimen-memory（Rust语义搜索服务）获取相关技能和经验——将领域知识注入提示词，实现上下文感知的工作流生成。",
+            "通用状态机执行器：RoutingWorkflow执行任何JSON工作流规格，支持JSONPath参数链接、自动重试、catch错误恢复和Temporal持久执行保证——每个注册活动都是可组合的构建块。"
+          ]
         },
         {
           "title": "Poimen记忆系统",
-          "description": "具有分层RBAC的分布式图RAG基础设施和维基链接索引。",
+          "description": "Poimen 背后的记忆层——分布式 Graph-RAG 系统，为牧羊人提供长期记忆、语义搜索和分层访问控制。",
           "longDescription": "三层上下文检索管道，支持PageRank风格的链接评分、混合搜索融合（HNSW + BM25）和基于OIDC的多租户知识图访问控制。",
           "stat": "图-RAG, pgvector, OpenSearch, Rust + Actix-web",
           "highlight": "双向维基链接索引配RRF融合 + 分层RBAC——50ms签名匹配层、图增强混合搜索层、Obsidian兜底。",
@@ -442,16 +440,24 @@
           ]
         },
         {
-          "title": "Poimen: 智能体工作流编排",
-          "description": "Temporal驱动的编排平台，将自然语言转化为持久、可扩展的工作流执行。",
-          "longDescription": "LLM路由器分析用户意图，从语义记忆中检索相关知识，生成可执行工作流规格——支持大规模智能体部署，任何活动可作为协调管道的步骤。",
-          "stat": "Temporal, LLM路由, 9个可组合活动",
-          "highlight": "自然语言 → 可执行WorkflowSpec：推理模型 + 记忆增强上下文检索 + 持久状态机执行。",
+          "title": "家庭实验室：生产级 Kubernetes 平台",
+          "description": "从裸金属到自愈、GitOps 驱动的平台 — 4节点集群运行20+服务，具备全链路可观测性、零信任网络和GPU加速AI推理。",
+          "longDescription": "在 Talos Linux 上构建并运维生产级 Kubernetes 平台 — 自愈节点、ArgoCD 声明式 GitOps 部署、SOPS 加密密钥、OIDC 单点登录，以及端到端可观测性（Prometheus → Grafana → Loki → Tempo）。每次变更可审计，每次故障自动恢复，每个服务均有监控。",
+          "stat": "4节点 · 20+服务 · 99.2%可用性 · 0次手动部署",
+          "highlight": "生产级平台工程 — 不是业余集群。自愈基础设施、纯GitOps部署、全栈可观测性、家庭GPU推理。",
           "bullets": [
-            "LLM工作流路由：自然语言 → 可执行WorkflowSpec，通过推理模型。活动知识库（9个活动）告知LLM超时、重试策略和依赖关系——智能步骤排序和错误处理策略。",
-            "记忆增强上下文检索：RetrieveMemoryActivity查询poimen-memory（Rust语义搜索服务）获取相关技能和经验——将领域知识注入提示词，实现上下文感知的工作流生成。",
-            "通用状态机执行器：RoutingWorkflow执行任何JSON工作流规格，支持JSONPath参数链接、自动重试、catch错误恢复和Temporal持久执行保证——每个注册活动都是可组合的构建块。"
-          ]
+            "Talos Linux 裸金属集群 — 不可变OS、API驱动节点管理、故障自愈",
+            "ArgoCD + Kustomize + SOPS — 零接触GitOps：每次部署即git提交，每个密钥均加密",
+            "Authentik OIDC SSO + RBAC — 20+服务单一身份，零信任访问",
+            "Prometheus + Grafana + Loki + Tempo — 全链路可观测：指标、日志、追踪、告警",
+            "vLLM GPU推理 (Qwen3-32B) + KServe — 生产级AI服务，自动扩缩与流量分割",
+            "Temporal + Kafka + CloudNativePG — 持久化工作流、事件流、高可用PostgreSQL + pgvector",
+            "Cloudflare Tunnel + cert-manager — 零信任入口、自动TLS、无暴露端口"
+          ],
+          "deepDive": {
+            "label": "探索架构详情 →",
+            "url": "/homelab"
+          }
         },
         {
           "title": "RBC: 多云平台",
@@ -490,35 +496,41 @@
       "title": "工作经历",
       "subtitle": "欢迎向Poimen了解更多",
       "items": [
+        {
+          "company": "Poimen (Ποιμήν)",
+          "role": "架构师 / 智能体工程师",
+          "period": "2025年5月 — 至今",
+          "description": "Poimen（\"牧羊人\"）— 自学习智能体平台，两大支柱：\n\n🧠 <b>记忆</b>：<b>Rust</b> <b>Graph-RAG</b> 服务。固定窗口分块 → <b>TEI</b> 嵌入 → <b>pgvector</b> + 双向图索引。三层 <b>RRF 融合</b>检索：签名匹配（<b>50ms</b>）、图增强混合搜索、<b>Obsidian</b> 回退。自压缩缓存淘汰过时嵌入，每次写入自动协调图。\n\n⚡ <b>工作流</b>：<b>Go</b> + <b>Temporal</b> 持久化执行引擎。LLM 路由器从自然语言生成 <b>WorkflowSpec</b>，基于<b>活动知识库</b>（9个活动，含超时/重试/依赖元数据）。<b>JSONPath</b> 参数链、catch 恢复、<b>精确一次</b>保证。<b>目标驱动</b>：描述意图 → 平台组装、执行并自愈。"
+        },
         {
           "company": "riotpiao.com",
           "role": "DevOps / SRE / 软件开发",
           "period": "2025年5月 — 至今",
-          "description": "想理解LLM大规模服务的原理——于是在裸机K8s上从零构建了完整的云平台。4台机器（1 GPU节点、1台Dell PowerEdge R520、2台迷你主机），3个控制平面做分布式共识。经历了etcd延迟的教训——电力线适配器在30+pod时延迟飙升200ms，导致共识崩溃。从实践中学习，GitOps从Terraform演进为Terraform（Talos配置）+ ArgoCD（CRD观察者模式）。统一Authentik OIDC身份认证，部署paperless.riotpiao.com用于文档工作流集成。现在正在构建Poimen——一个AI智能体系统，正确的上下文+小模型=成功的任务推理。"
+          "description": "从零设计并运维<b>生产级裸金属 Kubernetes 平台</b> — 4节点、<b>3控制面</b>、<b>20+服务</b>、<b>99.2%可用性</b>。\n\n🏗️ <b>基础设施</b>：<b>Talos Linux</b> 不可变OS、<b>etcd</b> 分布式共识、<b>Longhorn</b> 块存储、<b>MinIO</b> S3、<b>CloudNativePG</b> + <b>pgvector</b>。解决了200ms网络延迟导致的 etcd 选主失败 — 重新设计物理拓扑至 5ms 以下 RTT。\n\n🔄 <b>GitOps & CI/CD</b>：从纯 <b>Terraform</b> 演进为 <b>Terraform</b>（节点配置）+ <b>ArgoCD</b>（应用交付）。<b>Kustomize</b> overlay、<b>SOPS</b> 加密密钥、<b>Forgejo CI</b> + DinD 运行器。零手动部署。\n\n🔐 <b>安全与身份</b>：<b>Authentik OIDC SSO</b> + <b>RBAC</b>。<b>Cloudflare Tunnel</b> 零信任入口、<b>cert-manager</b> 自动TLS。\n\n📊 <b>可观测性</b>：<b>Prometheus</b> + <b>Grafana</b> + <b>Loki</b> + <b>Tempo</b> — 全栈指标、日志、追踪、告警。\n\n🤖 <b>AI/ML 服务</b>：<b>vLLM</b> GPU推理（<b>Qwen3-32B</b>）、<b>KServe</b> 模型编排、<b>TEI</b> 嵌入、<b>Ollama</b> 轻量模型。"
         },
         {
           "company": "RBC",
           "role": "技术主管",
           "period": "2024年11月 — 2026年5月",
-          "description": "在这里学习了Terraform和Temporal——让基础设施托管变得简单的核心工具。解决了两个大问题：(1)不稳定部署——500+资源的状态文件超时、503错误、锁竞争。迁移到JFrog Artifactory，拆分plan/apply阶段为不可变制品，限流并行度。零状态损坏。(2)配置漂移——工程师在控制台热修复，代码与现实脱节。构建夜间cron漂移检测+Slack告警，漂移可见性从3周缩短到<24小时。核心心得：Terraform适合很少变化的东西；K8s资源频繁变动时需要ArgoCD。"
+          "description": "领导多云 IaC 基础设施平台工程，服务 <b>12 个团队</b>。\n\n🏗️ <b>状态迁移</b>：将 <b>500+ Terraform</b> 资源状态文件从 S3 迁移至 <b>JFrog Artifactory</b>。plan/apply 拆分为<b>不可变制品管道</b>，限流并行度 — 消除 <b>503 超时</b>、锁竞争，迁移后<b>零状态损坏</b>。\n\n🔍 <b>漂移检测</b>：构建夜间 <b>cron 漂移检测</b> + <b>Slack</b> 告警 — 配置漂移可见性从 <b>3周 → &lt;24小时</b>。\n\n⚡ <b>工作流编排</b>：引入 <b>Temporal</b> 持久化工作流。标准化 <b>IaC 模式</b> — <b>3倍集成速度</b>。\n\n<b>技术栈</b>：<b>Terraform</b>、<b>OpenShift</b>、<b>Docker</b>、<b>Golang</b>、<b>Grafana</b>、<b>Artifactory</b>、<b>Temporal</b>"
         },
         {
           "company": "AWS",
           "role": "高级软件工程师 (Step Functions)",
           "period": "2022 — 2024",
-          "description": "毕业后第一份工作——学习了STAR方法和大规模组织中的客户至上。端到端拥有Distributed-Map，跨57+区域、P99<100ms。在代码审查中发现潜在影响客户的条件字段变更，协调前后端同步部署——因为部署对齐是业务持续运行的关键。构建Redrive Execution让客户从失败点重试。认识到前端消费最新镜像时后向兼容至关重要。"
+          "description": "端到端负责 <b>AWS Step Functions</b> 的 <b>Distributed-Map</b> — <b>57+ 区域</b>、<b>P99 &lt;100ms</b>。\n\n🚀 <b>功能负责</b>：发布 <b>Redrive Execution</b> — 分布式工作流从失败点重试。设计 <b>JSON 状态输入</b>支持更大负载。在代码审查中发现破坏性<b>条件字段变更</b> — 协调前后端同步部署，防止客户影响。\n\n📊 <b>运维卓越</b>：构建 <b>CloudWatch</b> 仪表盘、编写运维手册、负责<b>值班轮换</b>。保持前后端发布周期<b>向后兼容</b>。\n\n🔧 <b>分布式系统</b>：<b>DynamoDB</b> 分区设计、<b>gRPC</b> 服务网格、<b>灾备</b>规划、<b>多区域</b>复制。\n\n<b>技术栈</b>：<b>Java</b>、<b>AWS</b>、<b>DynamoDB</b>、<b>CloudWatch</b>、<b>gRPC</b>、<b>Step Functions</b>"
         },
         {
           "company": "Titus",
           "role": "软件工程师实习",
           "period": "2019年5月 — 8月",
-          "description": "优化个人数据检测系统，识别机密数据异常流出——准确率97.8%。构建容错Golang连接器——p99延迟比旧版提升28%。用CMake & C++在Linux/Unix上重新集成SmartRegex——部署速度提升5倍。"
+          "description": "数据分类安全初创公司 — 构建检测与连接器系统。\n\n🔍 <b>异常检测</b>：构建<b>个人数据检测</b>管道，标记机密数据外泄 — <b>97.8% 准确率</b>。\n\n⚡ <b>连接器</b>：容错 <b>Golang</b> 数据连接器 — <b>P99 提升 28%</b>。\n\n🔧 <b>构建系统</b>：<b>CMake</b> + <b>C++</b> 重新集成 <b>SmartRegex</b> 引擎 — <b>部署速度提升 5 倍</b>。\n\n<b>技术栈</b>：<b>Golang</b>、<b>C++</b>、<b>CMake</b>、<b>Docker</b>、<b>Linux</b>"
         },
         {
           "company": "NAV Canada",
           "role": "暑期实习生",
           "period": "2018年5月 — 8月",
-          "description": "在敏捷开发流程中维护企业级Web应用CFPS。构建Django NOTAMJ轮询应用达成迭代目标。通过Sonar代码覆盖率检查提升部署稳定性。用ReactJS为ATC创建FWGS天气简报界面。"
+          "description": "空管软件 — 航班计划与气象简报的企业级 Web 应用。\n\n✈️ <b>FWGS 气象简报</b>：构建 <b>ReactJS</b> 气象简报界面，供<b>空管人员</b>查看实时航班气象数据。\n\n🗳️ <b>NOTAMJ 投票</b>：<b>Django</b> 投票应用，<b>Agile</b> 工作流中达成迭代目标。\n\n📊 <b>质量</b>：通过 <b>SonarQube</b> 代码覆盖率集成提升企业 <b>CFPS</b> 平台部署稳定性。\n\n<b>技术栈</b>：<b>ReactJS</b>、<b>Django</b>、<b>SonarQube</b>、<b>Agile</b>"
         }
       ]
     },