Industry News → Persona-Analyzed Digest

import os, requests, time, json

NA_KEY = os.environ["NEWSAPI_KEY"]
NA_BASE = "https://newsapi.org/v2"
NA_H = {"X-Api-Key": NA_KEY}
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

INDUSTRY = "technology"
COUNTRY = "us"

# 1. Fetch top headlines
r = requests.get(f"{NA_BASE}/top-headlines", headers=NA_H, params={
    "category": INDUSTRY, "country": COUNTRY, "pageSize": 10,
})
r.raise_for_status()
articles = r.json().get("articles", [])
print(f"Fetched {len(articles)} headlines for {INDUSTRY}/{COUNTRY}")

# 2. Create executive personas
EXECS = [
    {"name": "CMO Lens", "desc": "Chief Marketing Officer. Evaluates news for brand impact, competitive positioning, consumer sentiment shifts, and content opportunities."},
    {"name": "CTO Lens", "desc": "Chief Technology Officer. Evaluates news for technology disruption, infrastructure implications, build-vs-buy decisions, and engineering talent market."},
    {"name": "CFO Lens", "desc": "Chief Financial Officer. Evaluates news for market risk, revenue impact, cost implications, investor sentiment, and regulatory exposure."},
]
persona_ids = []
for ex in EXECS:
    p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
        "name": f"News Digest: {ex['name']}",
        "description": ex["desc"],
    }).json()
    persona_ids.append({"id": p["id"], "name": ex["name"]})
    time.sleep(0.3)

# 3. Run each story through each persona via Speak
digest = []
for article in articles[:7]:
    story_block = f"**{article['title']}**\n{article.get('description','')}\nSource: {article.get('source',{}).get('name','')}"
    perspectives = {}

    for persona in persona_ids:
        speak = requests.post(f"{MV_BASE}/speak", headers=MV_H, json={
            "persona_id": persona["id"],
            "input": [
                {"role": "system", "content": f"You are a {persona['name']}. Analyze this news story in 2-3 sentences from your executive perspective. Focus on actionable implications."},
                {"role": "user", "content": story_block},
            ],
            "mode": "interview",
        }).json()
        answer = ""
        for ex in speak.get("exchanges", speak.get("messages", [])):
            if ex.get("role") == "assistant":
                answer = ex["content"]
        perspectives[persona["name"]] = answer[:300]
        time.sleep(0.3)

    digest.append({"title": article["title"], "source": article.get("source",{}).get("name",""),
                   "perspectives": perspectives})

# 4. Print digest
for i, story in enumerate(digest, 1):
    print(f"\n{'='*60}\n{i}. {story['title']} ({story['source']})\n{'='*60}")
    for lens, analysis in story["perspectives"].items():
        print(f"\n  [{lens}]\n  {analysis}")

const NA_KEY = process.env.NEWSAPI_KEY;
const NA_BASE = "https://newsapi.org/v2";
const NA_H = { "X-Api-Key": NA_KEY };
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const INDUSTRY = "technology";
const COUNTRY = "us";

// 1. Headlines
const articles = (await (await fetch(
  `${NA_BASE}/top-headlines?category=${INDUSTRY}&country=${COUNTRY}&pageSize=10`,
  { headers: NA_H })).json()).articles || [];
console.log(`Fetched ${articles.length} headlines for ${INDUSTRY}/${COUNTRY}`);

// 2. Executive personas
const EXECS = [
  { name: "CMO Lens", desc: "Evaluates for brand impact, positioning, sentiment shifts, content opportunities." },
  { name: "CTO Lens", desc: "Evaluates for tech disruption, infrastructure, build-vs-buy, talent market." },
  { name: "CFO Lens", desc: "Evaluates for market risk, revenue impact, cost, investor sentiment, regulation." },
];
const personaIds = [];
for (const ex of EXECS) {
  const p = await fetch(`${MV_BASE}/personas`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ name: `News Digest: ${ex.name}`, description: ex.desc }),
  }).then(r => r.json());
  personaIds.push({ id: p.id, name: ex.name });
  await new Promise(r => setTimeout(r, 300));
}

// 3. Speak per story × persona
const digest = [];
for (const article of articles.slice(0, 7)) {
  const storyBlock = `**${article.title}**\n${article.description || ""}\nSource: ${article.source?.name || ""}`;
  const perspectives = {};

  for (const persona of personaIds) {
    const speak = await fetch(`${MV_BASE}/speak`, { method: "POST", headers: MV_H,
      body: JSON.stringify({ persona_id: persona.id, mode: "interview",
        input: [
          { role: "system", content: `You are a ${persona.name}. Analyze in 2-3 sentences. Actionable implications.` },
          { role: "user", content: storyBlock },
        ],
      }),
    }).then(r => r.json());
    const answer = (speak.exchanges || speak.messages || [])
      .filter(e => e.role === "assistant").map(e => e.content).join(" ");
    perspectives[persona.name] = answer.slice(0, 300);
    await new Promise(r => setTimeout(r, 300));
  }
  digest.push({ title: article.title, source: article.source?.name || "", perspectives });
}

// 4. Print
for (const [i, story] of digest.entries()) {
  console.log(`\n${"=".repeat(60)}\n${i + 1}. ${story.title} (${story.source})\n${"=".repeat(60)}`);
  for (const [lens, analysis] of Object.entries(story.perspectives))
    console.log(`\n  [${lens}]\n  ${analysis}`);
}