Daily write-up №001 · video 3:42

I cut our LLM streaming latency by 74%. Here's the exact setup.

billyjf · Jul 6, 2026 · 6 min read

short-form · 9:16 · drop poster.jpg

watch first — the write-up below is the reference copy

Every AI product demo dies the same death: the user hits send and stares at a spinner for four seconds. We shipped a fix in one afternoon — time-to-first-byte went from 3.8s to 0.9s, and completion-abandon rate dropped by a third. The trick isn't a faster model. It's moving the stream boundary to the edge and flushing tokens the moment they exist. Below is the full setup — server, client, and the two failure modes that will bite you.

The edge streaming pattern

Instead of buffering the completion in a serverless function, terminate the SSE connection at an edge worker and pipe the upstream body straight through. The worker owns retries; the client owns rendering.

export default {
  async fetch(req, env) {
    const upstream = await env.AI.stream(req);
    return new Response(upstream.body, {
      headers: { "content-type": "text/event-stream" },
    });
  },
};

Benchmarks

Same model, same prompts, 500 runs each:

metric	before → after	delta
TTFB	3.8s → 0.9s	−74%
abandon rate	22% → 14%	−36%
p95 total	unchanged	perception is the win

Tomorrow’s exercise: wire this into the course starter repo and add abort handling. Takes about twenty minutes, and it’s the foundation for the retry work in Module 03.