Mirror of
feat: round time windows, add colors and standardize summary length
+27
-10
+23
-4
scripts/generate-digest.ts
+23
-4
scripts/generate-digest.ts
···
38
38
console.log("\n\x1b[1m🚀 Generating Intelligent Topic Digest\x1b[0m");
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const now = new Date();
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+
const marks = [6, 14, 22];
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+
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+
const candidates: Date[] = [];
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+
[-1, 0, 1].forEach((dayOffset) => {
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+
marks.forEach((hour) => {
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const d = new Date(now);
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d.setUTCDate(d.getUTCDate() + dayOffset);
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+
d.setUTCHours(hour, 0, 0, 0);
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+
candidates.push(d);
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+
});
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+
});
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+
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+
const nearestMark = candidates.reduce((prev, curr) =>
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+
Math.abs(curr.getTime() - now.getTime()) <
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+
Math.abs(prev.getTime() - now.getTime())
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+
? curr
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+
: prev,
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+
);
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+
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60
const windowSize = 8 * 60 * 60 * 1000;
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-
const startTime = new Date(now.getTime() - windowSize);
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+
const startTime = new Date(nearestMark.getTime() - windowSize);
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try {
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const [gh, bs] = await Promise.all([
···
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77
const heroTopic = pickWeightedTopic(topics);
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const catchyTitle = await generateCatchyTitle(heroTopic);
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-
const type = getPostType(now);
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-
const dateStr = now.toISOString().split("T")[0];
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+
const type = getPostType(nearestMark);
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+
const dateStr = nearestMark.toISOString().split("T")[0];
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const slug = `${dateStr}-${type}`;
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const postData = {
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title: catchyTitle,
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-
date: now.toISOString(),
86
+
date: nearestMark.toISOString(),
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type,
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topics,
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};
+1
-6
src/lib/events.ts
+1
-6
src/lib/events.ts
···
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);
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const prompt = `You are a technical analyst for npmx. Group these events into 5-6 logical "Topics".
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+
Each summary should be around 50 words long.
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Return ONLY JSON: { "topics": Topic[] }.
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Topic Structure:
···
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response_format: { type: "json_object" },
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});
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-
// Cast the parsed content to our interface
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const parsed = JSON.parse(data.choices[0].message.content) as {
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topics: Topic[];
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};
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-
// Verify the topics array exists before processing
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if (!parsed.topics || !Array.isArray(parsed.topics)) {
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throw new Error("AI response missing 'topics' array");
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}
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const topics = parsed.topics.map((t) => {
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-
// Step 1: Runtime validation via Zod
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-
// This is where it would have failed if the AI output was "messy"
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const validated = TopicSchema.parse(t);
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187
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-
// Step 2: Logic & Refinement
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const hasBluesky = validated.sources.some(
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(s) => s.platform === "bluesky",
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);
···
208
204
);
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return topics.sort((a, b) => b.relevanceScore - a.relevanceScore);
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} catch (err: any) {
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-
// If Zod fails, err.errors will contain exactly which field was missing/wrong
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LOG.error(`AI Clustering failed: ${err.message}`);
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return [];
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}