feat: deepened M7+M8, download page, healthcheck endpoint, analytics stub

api/checkout.js

@@ -54,6 +54,13 @@ const server = http.createServer(async (req, res) => {
   res.setHeader('Access-Control-Allow-Methods', 'POST, OPTIONS')
   res.setHeader('Access-Control-Allow-Headers', 'Content-Type')
 
+  // Healthcheck endpoint
+  if (req.url === '/health' && req.method === 'GET') {
+    res.writeHead(200, { 'Content-Type': 'application/json' })
+    res.end(JSON.stringify({ status: 'ok', uptime: process.uptime() }))
+    return
+  }
+
   if (req.method === 'OPTIONS') {
     res.writeHead(204)
     res.end()

site/.vitepress/config.mjs

@@ -18,6 +18,8 @@ export default defineConfig({
     ['link', { rel: 'preconnect', href: 'https://fonts.gstatic.com', crossorigin: '' }],
     ['link', { href: 'https://fonts.googleapis.com/css2?family=DM+Sans:opsz,wght@9..40,400;9..40,500;9..40,700;9..40,800&family=DM+Mono:wght@400;500&display=swap', rel: 'stylesheet' }],
     ['link', { href: 'https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20..48,100..700,0,0', rel: 'stylesheet' }],
+    // Analytics — uncomment and configure when ready
+    // ['script', { src: 'https://analytics.example.com/script.js', 'data-domain': 'git.fdsa.agency', defer: '' }],
   ],
 
   appearance: true,

site/.vitepress/dist/assets/modules_m7-advanced.md.FtCudlFk.js

@@ -1,60 +0,0 @@
-import{c as a,Q as i,j as e,m as t}from"./chunks/framework.BPKcPtvA.js";const c=JSON.parse('{"title":"Module 7: Advanced Topics","description":"","frontmatter":{},"headers":[],"relativePath":"modules/m7-advanced.md","filePath":"modules/m7-advanced.md","lastUpdated":1780491906000}'),n={name:"modules/m7-advanced.md"};function l(h,s,p,r,o,d){return i(),e("div",null,[...s[0]||(s[0]=[t(`<h1 id="module-7-advanced-topics" tabindex="-1">Module 7: Advanced Topics <a class="header-anchor" href="#module-7-advanced-topics" aria-label="Permalink to &quot;Module 7: Advanced Topics&quot;">​</a></h1><h2 id="lesson-7-1-autoresearch" tabindex="-1">Lesson 7.1: Autoresearch <a class="header-anchor" href="#lesson-7-1-autoresearch" aria-label="Permalink to &quot;Lesson 7.1: Autoresearch&quot;">​</a></h2><h3 id="the-core-loop" tabindex="-1">The Core Loop <a class="header-anchor" href="#the-core-loop" aria-label="Permalink to &quot;The Core Loop&quot;">​</a></h3><p>Agents that improve themselves. The experiment loop:</p><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>1. Run: Execute the agent with current configuration</span></span>
-<span class="line"><span>2. Measure: Collect metrics (latency, cost, success rate)</span></span>
-<span class="line"><span>3. Log: Record experiment results to JSONL</span></span>
-<span class="line"><span>4. Decide: Keep if improvement, discard if regression</span></span>
-<span class="line"><span>5. Repeat: Try next experiment</span></span></code></pre></div><h3 id="brand-monitor-example" tabindex="-1">Brand Monitor Example <a class="header-anchor" href="#brand-monitor-example" aria-label="Permalink to &quot;Brand Monitor Example&quot;">​</a></h3><p>From your own autoresearch system:</p><div class="language-json vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang">json</span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">{</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;run&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">1</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;metric&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: {</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;name&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;home_ms&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;value&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">52</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">}, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;description&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;Baseline&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">}</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">{</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;run&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">2</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;metric&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: {</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;name&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;home_ms&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;value&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">46</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">}, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;deltaPct&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">-11.5</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">&quot;description&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;Remove N+1 count queries&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">}</span></span></code></pre></div><hr><h2 id="lesson-7-2-the-experiment-loop-—-integrity" tabindex="-1">Lesson 7.2: The Experiment Loop — Integrity <a class="header-anchor" href="#lesson-7-2-the-experiment-loop-—-integrity" aria-label="Permalink to &quot;Lesson 7.2: The Experiment Loop — Integrity&quot;">​</a></h2><h3 id="three-threats-to-integrity" tabindex="-1">Three Threats to Integrity <a class="header-anchor" href="#three-threats-to-integrity" aria-label="Permalink to &quot;Three Threats to Integrity&quot;">​</a></h3><ol><li><p><strong>Reward hacking</strong> — Model moves computation outside the timing function. The &quot;timed call&quot; becomes a lookup. (Found in Mythos paper.)</p></li><li><p><strong>Grinding</strong> — Running identical code 160 times hoping for a lucky outlier. (Found in Mythos paper — &quot;Pure grind — same code, lucky measurement.&quot;)</p></li><li><p><strong>Test set leakage</strong> — Finding the test set and training on it. (Found in Mythos paper — time series agent found the test set and directly trained on it.)</p></li></ol><h3 id="integrity-guards" tabindex="-1">Integrity Guards <a class="header-anchor" href="#integrity-guards" aria-label="Permalink to &quot;Integrity Guards&quot;">​</a></h3><div class="language-python vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang">python</span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Code hash — detect grind runs</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">code_hash </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;"> hash</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">open</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;agent.py&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">).read())</span></span>
-<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">if</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;"> last_run_code_hash </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">==</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;"> code_hash:</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">    flag_grind_run()</span></span>
-<span class="line"></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Compare against median, not best</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">scores </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;"> [</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">52</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">48</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">47</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">46</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">46</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">51</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">, </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">53</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">]</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">median </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;"> sorted</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(scores)[</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">len</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(scores)</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">//</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">2</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">]  </span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># 48</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">best </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;"> max</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(scores)  </span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># 46 (outlier)</span></span>
-<span class="line"></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Verify test data wasn&#39;t modified</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">test_data_hash_before </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;"> hash</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">open</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;benchmark_data.json&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">).read())</span></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># ... run experiment ...</span></span>
-<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">test_data_hash_after </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;"> hash</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">open</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;benchmark_data.json&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">).read())</span></span>
-<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">assert</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;"> test_data_hash_before </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583;">==</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;"> test_data_hash_after</span></span></code></pre></div><hr><h2 id="lesson-7-3-meta-agents-—-agents-that-build-agents" tabindex="-1">Lesson 7.3: Meta-Agents — Agents That Build Agents <a class="header-anchor" href="#lesson-7-3-meta-agents-—-agents-that-build-agents" aria-label="Permalink to &quot;Lesson 7.3: Meta-Agents — Agents That Build Agents&quot;">​</a></h2><h3 id="the-pattern" tabindex="-1">The Pattern <a class="header-anchor" href="#the-pattern" aria-label="Permalink to &quot;The Pattern&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>User: &quot;I need an agent that monitors our API uptime&quot;</span></span>
-<span class="line"><span>Meta-agent:</span></span>
-<span class="line"><span>  1. Researches API monitoring patterns (parallel experts)</span></span>
-<span class="line"><span>  2. Generates agent persona/system prompt</span></span>
-<span class="line"><span>  3. Creates tool definitions</span></span>
-<span class="line"><span>  4. Writes skill files</span></span>
-<span class="line"><span>  5. Validates the generated agent works</span></span></code></pre></div><h3 id="pi-pi-meta-agent" tabindex="-1">Pi-Pi Meta-Agent <a class="header-anchor" href="#pi-pi-meta-agent" aria-label="Permalink to &quot;Pi-Pi Meta-Agent&quot;">​</a></h3><p>From pi-vs-claude-code: a meta-agent that builds Pi agents using parallel research experts:</p><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>pi-pi agent → dispatches to:</span></span>
-<span class="line"><span>  ├── ext-expert (extension documentation)</span></span>
-<span class="line"><span>  ├── theme-expert (theming conventions)</span></span>
-<span class="line"><span>  ├── skill-expert (skill system)</span></span>
-<span class="line"><span>  ├── config-expert (configuration)</span></span>
-<span class="line"><span>  └── tui-expert (TUI components)</span></span>
-<span class="line"><span></span></span>
-<span class="line"><span>Each expert:</span></span>
-<span class="line"><span>  1. Fetches latest documentation (firecrawl + curl fallback)</span></span>
-<span class="line"><span>  2. Synthesizes patterns</span></span>
-<span class="line"><span>  3. Returns structured guidance</span></span>
-<span class="line"><span></span></span>
-<span class="line"><span>pi-pi → generates complete extension code</span></span></code></pre></div><hr><h2 id="lesson-7-4-beyond-mcp-—-the-context-cost-trade-off" tabindex="-1">Lesson 7.4: Beyond MCP — The Context Cost Trade-off <a class="header-anchor" href="#lesson-7-4-beyond-mcp-—-the-context-cost-trade-off" aria-label="Permalink to &quot;Lesson 7.4: Beyond MCP — The Context Cost Trade-off&quot;">​</a></h2><h3 id="the-matrix" tabindex="-1">The Matrix <a class="header-anchor" href="#the-matrix" aria-label="Permalink to &quot;The Matrix&quot;">​</a></h3><table tabindex="0"><thead><tr><th>Approach</th><th>Context Cost</th><th>Portability</th><th>Agent-Invoked?</th><th>Best For</th></tr></thead><tbody><tr><td>MCP Server</td><td>HIGH (full context per call)</td><td>HIGH</td><td>Yes</td><td>Multi-client, standardized tools</td></tr><tr><td>CLI</td><td>MEDIUM</td><td>HIGH</td><td>No</td><td>80% of new tools</td></tr><tr><td>File Scripts</td><td>LOW (progressive disclosure)</td><td>MEDIUM</td><td>No</td><td>Context-sensitive tools</td></tr><tr><td>Skills</td><td>LOW</td><td>MEDIUM</td><td>Yes (auto-detect)</td><td>Agent-native behavior</td></tr></tbody></table><h3 id="the-insight" tabindex="-1">The Insight <a class="header-anchor" href="#the-insight" aria-label="Permalink to &quot;The Insight&quot;">​</a></h3><p>MCP is not always the answer. For tools used by 1-2 agents, CLI + prime prompt is faster, cheaper on context, and easier to debug. MCP shines for tools used by many agents across many clients.</p><hr><h2 id="lesson-7-5-the-mac-mini-agent-—-physical-sandbox" tabindex="-1">Lesson 7.5: The Mac Mini Agent — Physical Sandbox <a class="header-anchor" href="#lesson-7-5-the-mac-mini-agent-—-physical-sandbox" aria-label="Permalink to &quot;Lesson 7.5: The Mac Mini Agent — Physical Sandbox&quot;">​</a></h2><h3 id="architecture" tabindex="-1">Architecture <a class="header-anchor" href="#architecture" aria-label="Permalink to &quot;Architecture&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Mac Mini (Agent Sandbox)</span></span>
-<span class="line"><span>  ├── Steer (GUI automation) — Swift, 14 commands</span></span>
-<span class="line"><span>  │     see, click, type, hotkey, ocr, find, wait</span></span>
-<span class="line"><span>  ├── Drive (Terminal control) — Python, 6 commands</span></span>
-<span class="line"><span>  │     session, run, send, poll, fanout</span></span>
-<span class="line"><span>  └── Listen (Job server) — Python/FastAPI</span></span>
-<span class="line"><span>        POST /job, GET /job/{id}</span></span>
-<span class="line"><span></span></span>
-<span class="line"><span>Primary Machine (Dev)</span></span>
-<span class="line"><span>  └── Direct CLI client</span></span>
-<span class="line"><span>        start, get, list, latest, stop</span></span></code></pre></div><h3 id="sentinel-pattern" tabindex="-1">Sentinel Pattern <a class="header-anchor" href="#sentinel-pattern" aria-label="Permalink to &quot;Sentinel Pattern&quot;">​</a></h3><p>Makes async terminal work deterministic:</p><div class="language-bash vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang">bash</span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Agent runs command, appends sentinel</span></span>
-<span class="line"><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0;">long_running_task</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">; </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">echo</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> &quot;__DONE_abc123:</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">$?</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;</span></span>
-<span class="line"></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Agent polls for sentinel pattern</span></span>
-<span class="line"><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0;">poll</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> logs</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> for</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;"> &quot;__DONE_abc123&quot;</span></span>
-<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># Extract exit code from sentinel</span></span></code></pre></div><hr><h2 id="lesson-7-6-always-on-agents" tabindex="-1">Lesson 7.6: Always-On Agents <a class="header-anchor" href="#lesson-7-6-always-on-agents" aria-label="Permalink to &quot;Lesson 7.6: Always-On Agents&quot;">​</a></h2><h3 id="voice-to-command-bridge" tabindex="-1">Voice-to-Command Bridge <a class="header-anchor" href="#voice-to-command-bridge" aria-label="Permalink to &quot;Voice-to-Command Bridge&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>User speaks → STT (faster-whisper) → LLM transcribes to command → Execute → TTS response</span></span></code></pre></div><h3 id="job-server-pattern" tabindex="-1">Job Server Pattern <a class="header-anchor" href="#job-server-pattern" aria-label="Permalink to &quot;Job Server Pattern&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Submit → Queue → Worker picks up → Claude Code executes → Result stored → Check later</span></span></code></pre></div><h3 id="heartbeat-execution-from-paperclip" tabindex="-1">Heartbeat Execution (from Paperclip) <a class="header-anchor" href="#heartbeat-execution-from-paperclip" aria-label="Permalink to &quot;Heartbeat Execution (from Paperclip)&quot;">​</a></h3><div class="language- vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang"></span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span>Timer fires → Check for queued work → Wake agent → Agent executes → Agent sleeps</span></span></code></pre></div><p>No continuous running. Agents wake, work, and sleep on a schedule.</p><h3 id="scheduling-strategies" tabindex="-1">Scheduling Strategies <a class="header-anchor" href="#scheduling-strategies" aria-label="Permalink to &quot;Scheduling Strategies&quot;">​</a></h3><table tabindex="0"><thead><tr><th>Strategy</th><th>Pattern</th><th>Use Case</th></tr></thead><tbody><tr><td>Fixed interval</td><td>Every N minutes/hours/days</td><td>Brand monitoring, price tracking</td></tr><tr><td>Event-triggered</td><td>Webhook → wake agent</td><td>CI/CD pipelines, PR reviews</td></tr><tr><td>Predictive</td><td>Learn optimal check times</td><td>Traffic monitoring, anomaly detection</td></tr><tr><td>Cascade</td><td>Agent A finishes → Agent B starts</td><td>Data pipeline, ETL workflows</td></tr></tbody></table><h3 id="openclaw-the-always-on-employee" tabindex="-1">OpenClaw: The Always-On Employee <a class="header-anchor" href="#openclaw-the-always-on-employee" aria-label="Permalink to &quot;OpenClaw: The Always-On Employee&quot;">​</a></h3><p>OpenClaw is a daemon-style agent that runs as a background service:</p><div class="language-yaml vp-adaptive-theme"><button title="Copy Code" class="copy"></button><span class="lang">yaml</span><pre class="shiki shiki-themes github-light github-dark vp-code" tabindex="0"><code><span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;"># openclaw-config.yaml</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">agent</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">:</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  name</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;brand-monitor&quot;</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  heartbeat</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">300</span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D;">  # every 5 minutes</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  max_sessions</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF;">1</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  wake_command</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;claude -p &#39;Check brand mentions since last run&#39;&quot;</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  sleep_command</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;pkill -f &#39;claude.*brand-monitor&#39;&quot;</span></span>
-<span class="line"><span style="--shiki-light:#22863A;--shiki-dark:#85E89D;">  log_path</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8;">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF;">&quot;/var/log/openclaw/brand-monitor.log&quot;</span></span></code></pre></div><p>It starts, checks for work, executes if needed, then goes back to sleep. No continuous billing, no context window overflow, no runaway loops. This is the pattern for production always-on agents.</p><h3 id="when-not-to-use-always-on" tabindex="-1">When NOT to Use Always-On <a class="header-anchor" href="#when-not-to-use-always-on" aria-label="Permalink to &quot;When NOT to Use Always-On&quot;">​</a></h3><p>Always-on adds complexity. Before building one, verify you actually need it:</p><ul><li><strong>Batch jobs</strong> → Cron is simpler and cheaper</li><li><strong>Real-time</strong> → Event-driven architectures (webhooks, Kafka) outperform polling agents</li><li><strong>One-off</strong> → Just run the agent once</li></ul><p>Always-on makes sense when you need adaptive scheduling, dynamic task generation, or autonomous decision-making about what to work on next.</p><hr><h2 id="lab-7-7-build-an-autoresearch-loop" tabindex="-1">Lab 7.7: Build an Autoresearch Loop <a class="header-anchor" href="#lab-7-7-build-an-autoresearch-loop" aria-label="Permalink to &quot;Lab 7.7: Build an Autoresearch Loop&quot;">​</a></h2><p><strong>Objective</strong>: Agent runs experiment, measures result, logs it, decides keep/discard.</p><p><strong>Starter</strong>: <code>course/labs/L7-autoresearch/starter.py</code></p><p><strong>Checkpoints</strong>:</p><ol><li>Run code, measure baseline metric</li><li>Modify code (agent makes change)</li><li>Re-measure, compare, log</li><li>Discard if regression, keep if improvement</li><li>Include integrity guard (code hashing)</li></ol><h2 id="lab-7-8-meta-agent" tabindex="-1">Lab 7.8: Meta-Agent <a class="header-anchor" href="#lab-7-8-meta-agent" aria-label="Permalink to &quot;Lab 7.8: Meta-Agent&quot;">​</a></h2><p><strong>Objective</strong>: Agent generates a new agent persona from documentation.</p><p><strong>Starter</strong>: <code>course/labs/L7-meta-agent/starter.py</code></p>`,62)])])}const g=a(n,[["render",l]]);export{c as __pageData,g as default};

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site/downloads.md

@@ -0,0 +1,128 @@
+---
+title: Downloads - Agentic Engineering the Hard Way
+---
+
+# Course Downloads
+
+Welcome! Your purchase gives you access to all course materials below. Each package is a ZIP file — extract and start learning.
+
+<div class="download-grid">
+
+<div class="dl-card">
+  <span class="dl-icon material-symbols-outlined">package_2</span>
+  <h3>Full Course Package</h3>
+  <p class="dl-size">147 MB · 415 files</p>
+  <ul>
+    <li>8 modules (markdown + code examples)</li>
+    <li>13 labs (starter + solution files)</li>
+    <li>20 skill kits (7 categories)</li>
+    <li>Install scripts (PowerShell + bash)</li>
+  </ul>
+  <a href="/dist/course-only.zip" class="btn-p btn-primary">Download Course</a>
+</div>
+
+<div class="dl-card">
+  <span class="dl-icon material-symbols-outlined">workspace_premium</span>
+  <h3>Enterprise Bundle</h3>
+  <p class="dl-size">191 MB · all course + extras</p>
+  <ul>
+    <li>Everything in Full Course Package</li>
+    <li>Capstone reference implementation</li>
+    <li>Brand Monitor full source</li>
+    <li>All 7 skill kits as standalone ZIPs</li>
+  </ul>
+  <a href="/dist/enterprise.zip" class="btn-p btn-primary">Download Enterprise</a>
+</div>
+
+</div>
+
+## Individual Skill Kits
+
+| Kit | Size | Download |
+|-----|------|----------|
+| Security Foundation | 9 KB | [Download](/dist/security-foundation.zip) |
+| Multi-Agent Orchestration | 7 KB | [Download](/dist/multi-agent-orch.zip) |
+| Verifier Pro | 4 KB | [Download](/dist/verifier-pro.zip) |
+| Task Discipline | 2 KB | [Download](/dist/task-discipline.zip) |
+| Autoresearch | 3 KB | [Download](/dist/autoresearch.zip) |
+| Observability | 2 KB | [Download](/dist/observability.zip) |
+| CEO Board | 3 KB | [Download](/dist/ceo-board.zip) |
+
+## Getting Started
+
+```bash
+# 1. Extract the course package
+unzip course-only.zip -d agentic-engineering
+cd agentic-engineering
+
+# 2. Install dependencies
+python3 -m venv venv
+source venv/bin/activate  # Windows: venv\Scripts\activate
+pip install anthropic openai
+
+# 3. Start with Lab 1
+cd course/labs/L1-first-agent/
+python starter.py test.txt "What is this file about?"
+```
+
+## Support
+
+Questions? Email [artale@fdsa.agency](mailto:artale@fdsa.agency)
+
+<style>
+.download-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(320px, 1fr));
+  gap: 24px;
+  margin: 32px 0;
+}
+.dl-card {
+  background: var(--vp-c-bg-soft);
+  border: 1px solid var(--vp-c-border);
+  border-radius: 12px;
+  padding: 28px;
+}
+.dl-icon {
+  font-size: 32px;
+  color: var(--vp-c-brand-1);
+  margin-bottom: 12px;
+  display: block;
+}
+.dl-card h3 {
+  font-size: 18px;
+  margin-bottom: 4px;
+}
+.dl-size {
+  font-size: 12px;
+  color: var(--vp-c-text-3);
+  margin-bottom: 16px;
+}
+.dl-card ul {
+  list-style: none;
+  padding: 0;
+  margin-bottom: 20px;
+}
+.dl-card li {
+  padding: 4px 0;
+  font-size: 13px;
+  color: var(--vp-c-text-2);
+}
+.dl-card li::before {
+  content: "→";
+  color: var(--vp-c-brand-1);
+  margin-right: 8px;
+}
+.btn-p {
+  display: inline-block;
+  padding: 12px 24px;
+  background: var(--vp-c-brand-1);
+  color: #fff;
+  text-decoration: none;
+  border-radius: 8px;
+  font-weight: 600;
+  font-size: 14px;
+}
+.btn-p:hover {
+  background: var(--vp-c-brand-3);
+}
+</style>

site/modules/m7-advanced.md

@@ -141,6 +141,81 @@ poll logs for "__DONE_abc123"
 # Extract exit code from sentinel
 ```
 
+### Why Physical Sandbox Matters
+
+Cloud agents are ephemeral. A Mac Mini agent is persistent — it has files, databases, browser sessions, and GPU state that survive agent restarts. This matters for:
+
+- **Long-running experiments** that take hours or days
+- **Browser automation** with persistent login sessions
+- **Local model inference** where GPU memory is precious
+- **Legacy system access** that can't be moved to cloud
+
+The physical sandbox pattern works with any mini PC — Mac Mini, Intel NUC, Raspberry Pi 5, or repurposed laptop. The key is a dedicated machine that the agent can control without affecting your primary workstation.
+
+---
+
+## Lesson 7.6: Multi-Model Strategies
+
+### Why One Model Isn't Enough
+
+No single model is best at everything. Frontier models (Claude Opus, GPT-4o) excel at reasoning but cost 10-100x more than small models (Haiku, Flash, Nano). The strategy: use the right model for each subtask.
+
+### Model Heterogeneity in Practice
+
+```
+Task: "Analyze this codebase for security vulnerabilities"
+
+Step 1: File Discovery (Haiku — $0.0003/run)
+  → List all files, grep for patterns
+  → Cheap, fast, 100% accuracy needed
+
+Step 2: Vulnerability Analysis (Sonnet — $0.008/run)
+  → Read each suspicious file, classify vulnerability
+  → Needs reasoning, but not frontier-level
+
+Step 3: Report Generation (Opus — $0.03/run)
+  → Synthesize findings into executive report
+  → Needs highest quality output
+```
+
+Total cost: ~$0.04 instead of ~$0.30 if done entirely with Opus. **87% savings** with no quality loss on the critical path.
+
+### The 3-Tier Cascade Pattern
+
+```python
+TIERS = {
+    "cheap": {"model": "claude-haiku",  "max_tokens": 4000,  "cost_per_k": 0.00025},
+    "mid":   {"model": "claude-sonnet", "max_tokens": 8000,  "cost_per_k": 0.003},
+    "premium": {"model": "claude-opus",  "max_tokens": 16000, "cost_per_k": 0.015},
+}
+
+def cascade(prompt, complexity="mid"):
+    """Route to appropriate tier based on task complexity."""
+    if complexity == "low":
+        return call_model(TIERS["cheap"], prompt)
+    elif complexity == "high":
+        return call_model(TIERS["premium"], prompt)
+    else:
+        return call_model(TIERS["mid"], prompt)
+```
+
+### Model Selection Decision Tree
+
+```
+Is the task deterministic? (grep, sort, count)
+  → Use cheap model (Haiku/Flash)
+  
+Does the task need reasoning? (analyze, explain)
+  → Use mid model (Sonnet/4o-mini)
+  
+Is the output customer-facing? (report, email, memo)
+  → Use premium model (Opus/4o)
+
+Will this code be deployed to production?
+  → Run through ALL three tiers in sequence (cascade)
+  → Cheap for bulk work, mid for analysis, premium for final review
+```
+
 ---
 
 ## Lesson 7.6: Always-On Agents

site/modules/m8-capstone.md

@@ -145,6 +145,59 @@ Apply security layer:
 ```
 [ ] Architecture document (one page, with diagram)
 [ ] All agent source code with system prompts
+[ ] Agent configuration files (YAML/JSON)
+[ ] Tool definitions with reasoning parameters
+[ ] Security hooks configuration (L3+)
+[ ] Golden dataset (10+ test cases with expected outputs)
+[ ] Test script showing all agents respond correctly
+[ ] Cost analysis per session
+[ ] README with setup instructions
+[ ] Self-assessment against rubric
+
+---
+
+## Production Readiness Checklist
+
+Before submitting, verify your system meets these production standards:
+
+### Reliability
+- [ ] All tools include error handling (FileNotFoundError, API timeout, etc.)
+- [ ] MAX_ITERATIONS set on every agent loop (no infinite loops)
+- [ ] Retry logic with exponential backoff for API calls
+- [ ] Agent gracefully handles tool failures instead of crashing
+
+### Security
+- [ ] Minimum L3 blacklist hook installed on bash access
+- [ ] Domain locking prevents agents from accessing files outside their scope
+- [ ] Verifier agent has read-only tools only (no edit/bash)
+- [ ] Damage-control rules prevent rm -rf, DROP TABLE, etc.
+
+### Observability
+- [ ] Every tool call logged to file (JSONL format)
+- [ ] Cost tracked per session
+- [ ] Agent loop depth monitored
+- [ ] Errors logged with stack traces
+
+### Economics
+- [ ] Cost estimate per session documented
+- [ ] Cascade routing implemented where appropriate
+- [ ] Iteration limits set to minimize token waste
+- [ ] Model selection justified (why this model for this agent?)
+
+---
+
+## Graduation Criteria
+
+Your capstone passes if it meets ALL of these:
+
+1. **Three or more agents** that communicate and coordinate
+2. **Security at L3+** (blacklist or whitelist hook)
+3. **Working test script** that demonstrates all agents responding
+4. **Documented cost estimate** per session
+5. **Observability** — at least one trace log from a complete run
+6. **Self-assessment** against the rubric with honest notes on what you'd improve
+
+The capstone is not about perfection. It's about demonstrating you understand the full stack — from tool design to security to economics. A passing capstone with honest self-assessment is worth more than a perfect one with no reflection.
 [ ] Mental model files for each agent
 [ ] Security configuration (damage-control rules)
 [ ] Verifier agent (if applicable)