unify experience bullets to past tense and reorder project cards
All timeline bullets now open with past-tense action verbs (Huntington gerunds become Built/Created/Led; stray 'Lead a high-performing team' becomes 'Led'). The Local AI Stack card moves to second position, right after Brain of Reese; the test-locked grid test is updated in the same change. LLM-Generated: true
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@@ -117,14 +117,14 @@
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</div>
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</div>
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<span class="timeline-date">Jun 2026 – Present</span>
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<span class="timeline-date">Jun 2026 – Present</span>
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<ul class="timeline-list">
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<ul class="timeline-list">
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<li>Building an enterprise agentic vulnerability remediation
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<li>Built an enterprise agentic vulnerability remediation toolkit
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toolkit and workflow to resolve millions of vulnerabilities.</li>
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and workflow to resolve millions of vulnerabilities.</li>
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<li>Creating and distributing LLM harness skills that automate
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<li>Created and distributed LLM harness skills that automate
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business problems — including ServiceNow incident response —
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business problems - including ServiceNow incident response -
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at machine speed.</li>
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at machine speed.</li>
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<li>Leading a team of 6 offshore engineers to build a developer
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<li>Led a team of 6 offshore engineers building a developer landing
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landing zone with agentic tools that build pipelines, plan
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zone with agentic tools that build pipelines, plan work, and
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work, and make documentation searchable.</li>
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make documentation searchable.</li>
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</ul>
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</ul>
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</div>
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</div>
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</div>
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</div>
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@@ -137,7 +137,7 @@
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</div>
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</div>
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<span class="timeline-date">Jan 2022 – Apr 2026</span>
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<span class="timeline-date">Jan 2022 – Apr 2026</span>
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<ul class="timeline-list">
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<ul class="timeline-list">
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<li>Lead a high-performing DevOps team building microelectronics design services and
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<li>Led a high-performing DevOps team building microelectronics design services and
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integrating cloud partner architectures with AWS.</li>
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integrating cloud partner architectures with AWS.</li>
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<li>Recruited and led an engineering team to deliver a machine learning platform in <1
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<li>Recruited and led an engineering team to deliver a machine learning platform in <1
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year, utilizing Langchain, PyTorch, and BERT to identify government contract patterns.
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year, utilizing Langchain, PyTorch, and BERT to identify government contract patterns.
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@@ -332,6 +332,45 @@
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="project-card fade-in" role="listitem">
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<div class="project-preview">
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<div class="code-block">
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<div class="code-line"><span class="code-comment"># Inference engines</span></div>
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<div class="code-line"><span class="code-variable">engines</span> = {</div>
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<div class="code-line"> <span class="code-string">"vllm"</span>: <span class="code-string">"cuda"</span>, <span class="code-comment"># high-throughput serving</span></div>
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<div class="code-line"> <span class="code-string">"gufo"</span>: <span class="code-string">"rocm"</span>, <span class="code-comment"># Strix Halo inference</span></div>
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<div class="code-line"> <span class="code-string">"llama.cpp"</span>: <span class="code-string">"gguf"</span>, <span class="code-comment"># CPU/GPU fallback</span></div>
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<div class="code-line">}</div>
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<div class="code-line"> </div>
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<div class="code-line"><span class="code-comment"># LiteLLM proxy for unified API</span></div>
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</div>
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</div>
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<div class="project-info">
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<h3>Local AI Stack</h3>
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<p>Complete local AI infrastructure: vLLM, Gufo, and llama.cpp for inference, LiteLLM as a
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unified API proxy, Bifrost for model routing, and Langfuse for observability. Supports
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both CUDA and ROCm.</p>
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<div class="project-meta">
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<div class="project-tech">
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<span>vLLM</span>
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<span>Gufo</span>
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<span>llama.cpp</span>
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<span>LocalAI</span>
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<span>LiteLLM</span>
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<span>Langfuse</span>
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</div>
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<div class="project-links">
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<a href="https://gitea.reeseapps.com/services/homelab" title="Source" target="_blank"
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rel="noopener">
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<svg width="18" height="18" viewBox="0 0 24 24" fill="currentColor">
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<path
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d="M12 0C5.37 0 0 5.37 0 12c0 5.31 3.435 9.795 8.205 11.385.6.105.825-.255.825-.57 0-.285-.015-1.23-.015-2.235-3.015.555-3.795-.735-4.035-1.41-.135-.345-.72-1.41-1.23-1.695-.42-.225-1.02-.78-.015-.795.945-.015 1.62.87 1.845 1.23 1.08 1.815 2.805 1.305 3.495.99.105-.78.42-1.305.765-1.605-2.67-.3-5.46-1.335-5.46-5.925 0-1.305.465-2.385 1.23-3.225-.12-.3-.54-1.53.12-3.18 0 0 1.005-.315 3.3 1.23.96-.27 1.98-.405 3-.405s2.04.135 3 .405c2.295-1.56 3.3-1.23 3.3-1.23.66 1.65.24 2.88.12 3.18.765.84 1.23 1.905 1.23 3.225 0 4.605-2.805 5.625-5.475 5.925.435.375.81 1.095.81 2.22 0 1.605-.015 2.895-.015 3.3 0 .315.225.69.825.57A12.02 12.02 0 0024 12c0-6.63-5.37-12-12-12z" />
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</svg>
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</a>
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</div>
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</div>
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</div>
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</div>
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<div class="project-card fade-in" role="listitem">
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<div class="project-card fade-in" role="listitem">
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<div class="project-preview">
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<div class="project-preview">
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<div class="code-block">
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<div class="code-block">
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@@ -421,45 +460,6 @@
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="project-card fade-in" role="listitem">
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<div class="project-preview">
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<div class="code-block">
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<div class="code-line"><span class="code-comment"># Inference engines</span></div>
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<div class="code-line"><span class="code-variable">engines</span> = {</div>
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<div class="code-line"> <span class="code-string">"vllm"</span>: <span class="code-string">"cuda"</span>, <span class="code-comment"># high-throughput serving</span></div>
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<div class="code-line"> <span class="code-string">"gufo"</span>: <span class="code-string">"rocm"</span>, <span class="code-comment"># Strix Halo inference</span></div>
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<div class="code-line"> <span class="code-string">"llama.cpp"</span>: <span class="code-string">"gguf"</span>, <span class="code-comment"># CPU/GPU fallback</span></div>
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<div class="code-line">}</div>
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<div class="code-line"> </div>
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<div class="code-line"><span class="code-comment"># LiteLLM proxy for unified API</span></div>
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</div>
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</div>
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<div class="project-info">
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<h3>Local AI Stack</h3>
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<p>Complete local AI infrastructure: vLLM, Gufo, and llama.cpp for inference, LiteLLM as a
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unified API proxy, Bifrost for model routing, and Langfuse for observability. Supports
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both CUDA and ROCm.</p>
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<div class="project-meta">
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<div class="project-tech">
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<span>vLLM</span>
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<span>Gufo</span>
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<span>llama.cpp</span>
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<span>LocalAI</span>
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<span>LiteLLM</span>
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<span>Langfuse</span>
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</div>
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<div class="project-links">
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<a href="https://gitea.reeseapps.com/services/homelab" title="Source" target="_blank"
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rel="noopener">
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<svg width="18" height="18" viewBox="0 0 24 24" fill="currentColor">
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<path
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d="M12 0C5.37 0 0 5.37 0 12c0 5.31 3.435 9.795 8.205 11.385.6.105.825-.255.825-.57 0-.285-.015-1.23-.015-2.235-3.015.555-3.795-.735-4.035-1.41-.135-.345-.72-1.41-1.23-1.695-.42-.225-1.02-.78-.015-.795.945-.015 1.62.87 1.845 1.23 1.08 1.815 2.805 1.305 3.495.99.105-.78.42-1.305.765-1.605-2.67-.3-5.46-1.335-5.46-5.925 0-1.305.465-2.385 1.23-3.225-.12-.3-.54-1.53.12-3.18 0 0 1.005-.315 3.3 1.23.96-.27 1.98-.405 3-.405s2.04.135 3 .405c2.295-1.56 3.3-1.23 3.3-1.23.66 1.65.24 2.88.12 3.18.765.84 1.23 1.905 1.23 3.225 0 4.605-2.805 5.625-5.475 5.925.435.375.81 1.095.81 2.22 0 1.605-.015 2.895-.015 3.3 0 .315.225.69.825.57A12.02 12.02 0 0024 12c0-6.63-5.37-12-12-12z" />
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</svg>
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</a>
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</div>
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</div>
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</div>
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</div>
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<div class="project-card fade-in" role="listitem">
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<div class="project-card fade-in" role="listitem">
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<div class="project-preview">
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<div class="project-preview">
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<div class="code-block">
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<div class="code-block">
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@@ -52,9 +52,9 @@ test.describe('Page structure', () => {
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const titles = await cards.locator('.project-info h3').allTextContents();
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const titles = await cards.locator('.project-info h3').allTextContents();
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expect(titles).toEqual([
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expect(titles).toEqual([
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'Brain of Reese',
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'Brain of Reese',
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'Local AI Stack',
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'Reverse Proxy Infrastructure',
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'Reverse Proxy Infrastructure',
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'Dynamic DNS Service',
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'Dynamic DNS Service',
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'Local AI Stack',
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'Deployment Automation',
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'Deployment Automation',
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'ServWaf',
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'ServWaf',
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]);
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]);
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@@ -63,8 +63,8 @@ test.describe('Page structure', () => {
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await expect(brain.locator('.project-tech span')).toHaveCount(4);
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await expect(brain.locator('.project-tech span')).toHaveCount(4);
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await expect(brain).toHaveText(/RAG chatbot/i);
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await expect(brain).toHaveText(/RAG chatbot/i);
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await expect(brain.locator('a[href="https://gitea.reeseapps.com/Experimental/brain-of-reese"]')).toBeAttached();
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await expect(brain.locator('a[href="https://gitea.reeseapps.com/Experimental/brain-of-reese"]')).toBeAttached();
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// Local AI Stack: Ollama replaced by vLLM, Gufo, llama.cpp
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// Local AI Stack: second card, Ollama replaced by vLLM, Gufo, llama.cpp
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const ai = cards.nth(3);
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const ai = cards.nth(1);
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await expect(ai.locator('.project-tech span')).toHaveText(['vLLM', 'Gufo', 'llama.cpp', 'LocalAI', 'LiteLLM', 'Langfuse']);
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await expect(ai.locator('.project-tech span')).toHaveText(['vLLM', 'Gufo', 'llama.cpp', 'LocalAI', 'LiteLLM', 'Langfuse']);
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await expect(ai).not.toContainText('Ollama');
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await expect(ai).not.toContainText('Ollama');
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// ServWaf is closed source — description only, no source link
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// ServWaf is closed source — description only, no source link
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