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v1.0 - launch: full nav on gallery, chat widget auto-open, comparison logos, checkout fixes

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Will Anderson
2026-05-01 18:13:06 -05:00
parent ff1f9577db
commit 00f2323c98
41 changed files with 4740 additions and 434 deletions
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src/environmental.el
+14 -9
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@@ -9,7 +9,7 @@ fn environmental() -> String {
<section id=\"environmental\" aria-label=\"Environmental impact\" style=\"padding:8rem 2.5rem;background:var(--bg)\">
<div class=\"container-lg\">
<div style=\"display:grid;grid-template-columns:1fr 1fr;gap:6rem;align-items:start\">
<div style=\"display:grid;grid-template-columns:1fr 1fr;gap:6rem;align-items:start\" class=\"env-grid\">
<div>
<div style=\"display:flex;align-items:center;gap:1.5rem;margin-bottom:2rem\">
@@ -18,15 +18,15 @@ fn environmental() -> String {
</div>
<h2 class=\"display-lg reveal\" style=\"transition-delay:80ms;margin-bottom:1.5rem\">
35% fewer tokens.<br><span class=\"gold\">Same work done.</span>
Fewer tokens.<br><span class=\"gold\">Same work done.</span>
</h2>
<p class=\"reveal\" style=\"transition-delay:160ms;font-family:var(--body);font-weight:300;font-size:1rem;color:var(--t2);line-height:1.8;margin-bottom:1.25rem\">
Neuron delivers a 35% reduction in tokens across your AI interactions. Less computation. Lower cost. A smaller footprint. This isn&#39;t a setting you toggle - it&#39;s what persistent context does by default.
Persistent context means shorter, more targeted prompts on every call. Less computation. Lower cost. A smaller footprint. This isn&#39;t a setting you toggle - it&#39;s what the architecture does by default.
</p>
<p class=\"reveal\" style=\"transition-delay:220ms;font-family:var(--body);font-weight:300;font-size:1rem;color:var(--t2);line-height:1.8;margin-bottom:1.25rem\">
Every time you open ChatGPT and explain who you are again, that&#39;s computation that didn&#39;t need to happen. With Neuron, that context tax doesn&#39;t accumulate. Over months of use, 35% fewer tokens compounds into a meaningful reduction in total compute - and a meaningful reduction in what you pay.
Every time you open ChatGPT and explain who you are again, that&#39;s computation that didn&#39;t need to happen. With Neuron, that context tax doesn&#39;t accumulate. Over months of use, the savings compound into a meaningful reduction in total compute - and a meaningful reduction in what you pay.
</p>
<p class=\"reveal\" style=\"transition-delay:280ms;font-family:var(--body);font-weight:300;font-size:1rem;color:var(--t2);line-height:1.8;margin-bottom:2.5rem\">
@@ -41,7 +41,7 @@ fn environmental() -> String {
<span id=\"calc-savings\" style=\"font-family:var(--head);font-size:2.5rem;font-weight:600;color:var(--navy)\">$240</span>
<span style=\"font-family:var(--body);font-size:0.7rem;font-weight:500;letter-spacing:0.15em;text-transform:uppercase;color:var(--t3)\">saved per year</span>
</div>
<p style=\"font-family:var(--body);font-size:0.75rem;color:var(--t3)\">Based on 35% token reduction applied to your monthly spend.</p>
<p style=\"font-family:var(--body);font-size:0.75rem;color:var(--t3)\">Based on estimated token reduction applied to your monthly spend.</p>
</div>
<script>
@@ -65,10 +65,10 @@ fn environmental() -> String {
<div style=\"display:flex;flex-direction:column;gap:1.5rem;padding-top:1rem\">
<div class=\"reveal card-dark\" style=\"transition-delay:100ms;padding:1.75rem 2rem;border-left:3px solid rgba(0,120,84,.40)\">
<p style=\"font-family:var(--body);font-size:0.7rem;font-weight:600;letter-spacing:0.18em;text-transform:uppercase;color:rgba(0,120,84,.70);margin-bottom:0.75rem\">Local inference</p>
<p style=\"font-family:var(--body);font-size:0.7rem;font-weight:600;letter-spacing:0.18em;text-transform:uppercase;color:rgba(0,120,84,.70);margin-bottom:0.75rem\">Local inference - coming</p>
<p style=\"font-family:var(--body);font-weight:400;font-size:0.9375rem;color:var(--t1);margin-bottom:0.5rem\">Your GPU, already powered on</p>
<p style=\"font-family:var(--body);font-weight:300;font-size:0.875rem;color:var(--t2);line-height:1.7\">
When you run inference locally via Ollama, your device&#39;s GPU handles it - hardware that&#39;s already consuming power. No data center spins up a cluster for your query. No round-trip. No idle servers waiting at scale.
The design: when you run inference locally via Ollama, your device&#39;s GPU handles it - hardware that&#39;s already consuming power. No data center spins up a cluster for your query. No round-trip. No idle servers waiting at scale. This is where we&#39;re headed.
</p>
</div>
@@ -84,13 +84,13 @@ fn environmental() -> String {
<p style=\"font-family:var(--body);font-size:0.7rem;font-weight:600;letter-spacing:0.18em;text-transform:uppercase;color:rgba(0,120,84,.70);margin-bottom:0.75rem\">Persistent context = less recomputation</p>
<p style=\"font-family:var(--body);font-weight:400;font-size:0.9375rem;color:var(--t1);margin-bottom:0.5rem\">No re-explaining. No wasted tokens.</p>
<p style=\"font-family:var(--body);font-weight:300;font-size:0.875rem;color:var(--t2);line-height:1.7\">
Neuron surfaces exactly what&#39;s relevant for each conversation - no re-deriving who you are from long histories. Shorter, more targeted prompts. 35% fewer tokens per useful outcome.
Neuron surfaces exactly what&#39;s relevant for each conversation - no re-deriving who you are from long histories. Shorter, more targeted prompts. More with less.
</p>
</div>
<div class=\"reveal\" style=\"transition-delay:400ms;padding:1.25rem 1.75rem;background:rgba(0,0,0,.03);border:1px solid rgba(0,0,0,.07);border-radius:2px\">
<p style=\"font-family:var(--body);font-size:0.8125rem;font-weight:300;color:var(--t3);line-height:1.7\">
<strong style=\"font-weight:500;color:var(--t2)\">The honest picture:</strong> When you use Neuron with BYOK providers (OpenAI, Anthropic, Groq) or Neuron Inference, those queries travel to inference servers - that footprint exists. The savings come from the architecture: persistent memory and local-first design reduce the total computation required to get the same work done.
<strong style=\"font-weight:500;color:var(--t2)\">The honest picture:</strong> When you use Neuron with BYOK providers (OpenAI, Anthropic, Grok) or Neuron Inference, those queries travel to inference servers - that footprint exists. The savings come from the architecture: persistent memory and local-first design reduce the total computation required to get the same work done.
</p>
</div>
@@ -99,5 +99,10 @@ fn environmental() -> String {
</div>
</section>
<style>
@media (max-width: 768px) {
.env-grid { grid-template-columns: 1fr !important; gap: 2rem !important; }
}
</style>
"
}