Morning Coffee: Apple and Google just collaborated over a Billion iPhones. On Monday, Apple and Google announced a multi-year partnership that will make Gemini the foundation for the next-generation Siri and broader Apple Intelligence features. Translation: the world's most valuable device ecosystem just outsourced its AI brain to its biggest rival.

The thesis is straightforward: Apple tried to go it alone. It failed. Key engineers defected for $200M+ packages elsewhere. The delayed AI Siri became a PR liability. So Cupertino did what it hates doing—it called in reinforcements. Google is reportedly building a custom 1.2 trillion parameter Gemini model (8x larger than Apple's current models) to power Siri's summariser and planner functions. Apple pays an estimated $1B annually for access.

Why this matters beyond the headlines: This is "platform pairing" becoming the default playbook. Just as device makers choose search defaults, they now choose foundation model partners. OpenAI still powers ChatGPT integrations on iPhone, but Gemini is now the engine behind Apple's own AI stack. The competitive shape of consumer AI is crystallising into partnerships, not pure competition.

For Google, this is a masterstroke. Gemini becomes the default AI engine across both Android and iOS—billions of mobile users, overnight. For Apple, it's a pragmatic admission that building frontier AI internally isn't their game.

The question: when the world's most vertically integrated company outsources its intelligence layer, what does that signal about where value actually accrues in the AI stack?

GROWTH HACK

Build a Remote Jobs Dashboard for One Niche—Expose It as a Single API Endpoint

Here's a side project that solves a real problem: remote job seekers waste hours scrolling generic boards. Recruiters posting niche roles get buried. Neither side finds what they need.

The opportunity isn't building another jobs board. It's building a focused jobs aggregator for one specific niche—then exposing it as an API that others can build on. Here's exactly how to build it.

The Stack (Total Cost: £0-25/month)

Component Tool Cost
Data scraping Bright Data or Apify Free tier
Data storage Supabase or PlanetScale Free
AI enrichment Claude API or GPT-4o-mini ~£5/month
API layer FastAPI or Hono Free
Hosting Railway or Fly.io Free tier

Step 1: Pick Your Niche

The narrower, the better. Examples that work:

  • Remote DevOps/Platform Engineering roles (high-paying, specific skills)

  • Part-time executive positions (fractional CMO, CFO, CTO)

  • Climate tech roles (growing sector, passionate community)

  • AI/ML research positions (hot market, clear ICP)

Step 2: Build the Scraper

Use Apify or Bright Data to pull from multiple sources:

  • LinkedIn Jobs (via API or scraping)

  • RemoteOK, WeWorkRemotely, Otta

  • Company career pages (target 50-100 companies in your niche)

  • Set a daily cron job to refresh

Step 3: Enrich with AI

Pass each job through Claude/GPT to extract and standardise:

You are a job data analyst. Extract from this job posting:
- seniority_level: junior/mid/senior/lead/executive
- salary_range: extract or estimate based on role
- required_skills: array of specific technologies
- remote_type: fully_remote/hybrid/flexible
- company_stage: startup/scaleup/enterprise
Return as JSON.

Store enriched data in Supabase with full-text search enabled.

Step 4: Expose as API

Create a single endpoint:

GET /api/jobs?niche=devops&seniority=senior&salary_min=150000

Return clean JSON. Add rate limiting. Document with OpenAPI spec.

The Monetisation Angle

If you want to turn this into revenue:

  • Freemium API: 100 calls/day free, £29/month for unlimited

  • Recruiter tier: £99/month for posting + analytics

  • White-label: License the API to niche job boards or Slack communities

Why This Works

Generic job boards optimise for volume. Niche aggregators optimise for signal. A DevOps engineer doesn't want to see marketing roles. By going narrow and deep, you become the only source for a specific audience—and APIs are sticky infrastructure.

DAILY STAT

70% of B2B CRM Data Decays Within 12 Months

B2B contact databases experience catastrophic decay rates of 70.3% per year, meaning nearly three-quarters of your prospect data becomes outdated within 12 months. Yet 90% of companies now use AI enrichment agents—and the decay continues.

The paradox: we have more automation than ever, but data quality isn't improving proportionally. Why? AI enrichment tools can't keep pace with the velocity of change: 30% of employees switch jobs annually, companies rebrand, merge, or disappear, and email addresses churn at 3.6% monthly.

What this means:

  • If you sell B2B SaaS: Your pipeline is 70% fiction by next January. Budget for continuous enrichment, not annual cleanups.

  • If you build tools: "Real-time data validation" is an unsolved problem at scale. There's a product here.

  • If you create content: Data quality is the dirty secret of AI-powered sales. Write about it.

The stat that matters: companies that reduced data investments saw a 75% decline in sales and marketing performance. Those that increased investments reported 94% improvement.

Clean data isn't a cost centre. It's a revenue protection strategy.

Source: Landbase Data Decay Statistics 2025; HubSpot B2B Research

TOOL TIP

Composio — The Integration Layer for AI Agents

URL: composio.dev

What it does: Composio gives your AI agents access to 500+ tools (Slack, GitHub, Notion, Salesforce, Google Workspace, and more) through a single SDK. Instead of building individual API integrations, authentication flows, and error handling for each tool, Composio handles it all. Your agent focuses on reasoning; Composio handles execution.

Pricing:

Plan

Cost

Key Features

Free

$0

Core tools, managed auth, SOC 2 compliant

Startup

$29/month

Priority support, higher limits

Growth

Custom

Volume discounts, dedicated SLA

Enterprise

Custom

VPC deployment, SSO, unlimited calls

Startups can apply for up to $25K in free credits.

Who it's for:

  • AI builders — Connect agents to real-world tools without writing integration code

  • Automation developers — Ship faster by outsourcing OAuth, API mapping, and error handling

  • Product teams — Add "AI that takes action" to your product without building infrastructure

  • Agencies — Build client automations across tools without managing credentials

What makes it different:

  • Framework agnostic — Works with OpenAI, Anthropic, LangChain, CrewAI, Vercel AI SDK

  • MCP support — Hosted MCP servers for all 500+ toolkits

  • Managed auth — OAuth, API keys, and tokens handled from one dashboard

  • 30% fewer failures — Optimised JSON structures and error handling for function calling

  • SOC 2 Type II compliant — Enterprise-grade security out of the box

Use cases that work well:

  • AI sales agents that update Salesforce, send follow-ups via Gmail, and log calls to HubSpot

  • Support bots that create Jira tickets, search Confluence, and notify Slack

  • Research agents that scrape data, update Notion, and trigger webhooks

  • Code assistants that commit to GitHub, update Linear, and notify Discord

Limitations to know:

  • Learning curve for first integration (plan 30-60 mins setup)

  • Some niche tools may require custom integration requests

  • Rate limits on free tier may constrain high-volume use cases

The bottom line: If you're building AI agents that need to do things (not just chat), Composio is the infrastructure layer that saves you months of integration work. The free tier is genuinely usable for prototyping and small-scale production.

TICKER WATCH

Ironwood Pharmaceuticals (NASDAQ: IRWD) — $4.28 The Pharma Play That's Actually About Pricing Strategy

Metric

Value

Current Price

~$4.28

Market Cap

$660M

52-Week Range

$0.53 – $5.78

P/E (Forward)

8.73

2026 Revenue Guidance

$450M–$475M

2026 Adj. EBITDA

$300M+

Cash Position

$200M+

Zacks Rank

1 – Strong Buy

What they do: Ironwood is a commercial-stage biotech focused on gastrointestinal disorders. Their flagship product, LINZESS (linaclotide), treats irritable bowel syndrome with constipation (IBS-C) and chronic idiopathic constipation. It's not a drug-discovery lottery ticket—it's a cash-generating machine with a real pipeline.

Why IRWD fits HackrLife: This stock jumped 37% in the first week of January—not because of a clinical breakthrough, but because of a pricing strategy decision. That's the kind of business-model alpha this newsletter tracks.

The Thesis:

On January 1, 2026, Ironwood and partner AbbVie cut LINZESS's list price by ~50%. Counterintuitive? Here's the arbitrage: US pharma pricing is broken. Under Inflation Reduction Act rules, drugs that raise prices accumulate inflationary penalty rebates. LINZESS has been on the market for a decade—those rebates were crushing net revenue.

By slashing the list price, Ironwood eliminated the inflationary rebate component. The result: net sales increase despite a lower sticker price. Revenue guidance for 2026 came in at $450M–$475M—41% higher than analyst consensus of $319M.

This isn't a medical breakthrough. It's financial engineering applied to drug pricing. And it works.

The Catalysts:

  • Apraglutide Phase 3 trial starting H1 2026 (short bowel syndrome—$2B+ TAM)

  • Self-funded pipeline (no dilution risk—$300M+ EBITDA covers trial costs)

  • Multiple analyst upgrades in first week of January (Zacks, Citi, Craig Hallum, Wells Fargo)

  • Ferring settlement removes legal overhang, adds $12.5M in milestones

Price Targets:

Source

Target

Upside

Citizens JMP

$8.00

+87%

Wells Fargo

$5.00

+17%

Zacks Consensus

$5.00+

+17%+

The Risks (Be Clear-Eyed):

Risk

Detail

Small-cap volatility

Stock swung from $0.53 to $5.78 in 12 months

Pipeline dependency

Apraglutide must deliver; delays = pain

Regulatory exposure

Medicare pricing rules could change

Concentrated revenue

LINZESS is 90%+ of current sales

Technical Indicators:

  • RSI(14): ~52 (neutral)

  • Recent volume: 7M+ (elevated post-news)

  • Trend: Strong recovery from December lows

Position Sizing: This is a small-cap with binary catalysts. If the pricing strategy continues to deliver and apraglutide advances, significant upside remains. If execution falters, expect volatility. Size accordingly—this isn't a core holding.

Not financial advice. Do your own research.

WORKFLOW

AI-Powered Agentic Customer Support Hub

Setup time: 45 minutes | Weekly value: 10+ hours saved

Instead of just routing tickets, this workflow uses AI to resolve them. The agent searches your knowledge base, finds relevant documentation and ticket history, and either responds immediately or escalates with a complete summary already prepared.

The Architecture:

  1. Trigger: Message arrives via WhatsApp, Slack, Intercom, or email

  2. Retrieve: AI agent searches a vector database (Pinecone, Weaviate, or Supabase Vector) for documentation and previous ticket history

  3. Decide: If confidence is high (>85%), respond automatically. If complex, route to human with AI-generated summary.

  4. Log: Update CRM with interaction, resolution status, and sentiment score

n8n Workflow (Import-Ready JSON)

{
  "name": "Agentic Support Hub",
  "nodes": [
    {
      "parameters": {
        "httpMethod": "POST",
        "path": "support-webhook",
        "responseMode": "responseNode"
      },
      "name": "Webhook Trigger",
      "type": "n8n-nodes-base.webhook",
      "position": [250, 300]
    },
    {
      "parameters": {
        "values": {
          "string": [
            {"name": "customer_message", "value": "={{$json.body.message}}"},
            {"name": "customer_email", "value": "={{$json.body.email}}"},
            {"name": "channel", "value": "={{$json.body.channel}}"}
          ]
        }
      },
      "name": "Extract Message",
      "type": "n8n-nodes-base.set",
      "position": [450, 300]
    },
    {
      "parameters": {
        "url": "https://YOUR_PINECONE_ENDPOINT/query",
        "method": "POST",
        "body": {
          "vector": "={{$json.customer_message}}",
          "topK": 5,
          "includeMetadata": true
        }
      },
      "name": "Search Knowledge Base",
      "type": "n8n-nodes-base.httpRequest",
      "position": [650, 300]
    },
    {
      "parameters": {
        "model": "claude-sonnet-4-20250514",
        "messages": {
          "values": [
            {
              "role": "system",
              "content": "You are a customer support agent. Use the provided context to answer the customer's question. If you can confidently answer (>85% confidence), provide the answer. If unsure, respond with ESCALATE: followed by a summary for the human agent. Always be helpful and concise."
            },
            {
              "role": "user", 
              "content": "Customer message: {{$node['Extract Message'].json.customer_message}}\n\nRelevant documentation:\n{{$node['Search Knowledge Base'].json.matches}}\n\nProvide your response:"
            }
          ]
        }
      },
      "name": "AI Resolution",
      "type": "@n8n/n8n-nodes-langchain.anthropic",
      "position": [850, 300]
    },
    {
      "parameters": {
        "conditions": {
          "string": [{"value1": "={{$json.response}}", "operation": "notContains", "value2": "ESCALATE:"}]
        }
      },
      "name": "Can Resolve?",
      "type": "n8n-nodes-base.if",
      "position": [1050, 300]
    },
    {
      "parameters": {
        "channel": "#support-resolved",
        "text": "✅ Auto-resolved for {{$node['Extract Message'].json.customer_email}}\n\n*Query:* {{$node['Extract Message'].json.customer_message}}\n*Response:* {{$json.response}}"
      },
      "name": "Log Resolution",
      "type": "n8n-nodes-base.slack",
      "position": [1250, 200]
    },
    {
      "parameters": {
        "channel": "#support-escalation",
        "text": "🚨 *Escalation Required*\n\nCustomer: {{$node['Extract Message'].json.customer_email}}\nChannel: {{$node['Extract Message'].json.channel}}\n\n*Query:* {{$node['Extract Message'].json.customer_message}}\n\n*AI Summary:* {{$json.response}}"
      },
      "name": "Escalate to Human",
      "type": "n8n-nodes-base.slack",
      "position": [1250, 400]
    }
  ],
  "connections": {
    "Webhook Trigger": {"main": [[{"node": "Extract Message"}]]},
    "Extract Message": {"main": [[{"node": "Search Knowledge Base"}]]},
    "Search Knowledge Base": {"main": [[{"node": "AI Resolution"}]]},
    "AI Resolution": {"main": [[{"node": "Can Resolve?"}]]},
    "Can Resolve?": {"main": [[{"node": "Log Resolution"}], [{"node": "Escalate to Human"}]]}
  }
}

Setup:

  1. Import JSON into n8n

  2. Connect your vector database (Pinecone, Weaviate, or Supabase)

  3. Add Anthropic API credentials

  4. Connect Slack (or your preferred notification channel)

  5. Configure webhook to receive from your support channels

  6. Populate vector DB with your docs, FAQs, and historical tickets

Expansion Ideas:

  • Add sentiment analysis to prioritise frustrated customers

  • Connect to your CRM (HubSpot, Salesforce) to log interactions

  • Build a feedback loop: when humans correct AI, retrain embeddings

  • Add language detection for multi-lingual support

THE BOTTOM LINE

Platform pairing is the new playbook, and the companies who control foundation models will capture business value regardless of who makes the hardware.

Meanwhile, your CRM decays at 70% per year while AI enrichment tools struggle to keep pace. The solution isn't more automation—it's better infrastructure. Build the niche job board that becomes essential. Use Composio to ship agents that actually do things. And watch Ironwood prove that sometimes the best biotech play is financial engineering, not drug discovery.

Ship daily.

HackrLife Daily is read by growth marketers at Google, Adobe, LinkedIn, and creators building the future.

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