What I need help with:
∙ Building workflows in n8n to automate repetitive tasks
∙ Connecting tools via APIs and integrating AI models (Claude, ChatGPT, etc.)
∙ Identifying where automation can save the most time
∙ Documenting workflows so they’re easy to maintain
Ideal person:
∙ Has actually built workflows in n8n (not just tinkered)
∙ Comfortable with APIs and webhooks
∙ Knows how to prompt AI tools and validate their outputs
∙ Can explain what they built and why
Open to: Full-time, part-time, or starting with a project to see how we work together.
Drop a reply or DM me with a workflow you’ve built — even a screenshot or quick description is great. More interested in what you’ve done than your resume
From this
Daily Investor News Analyst Role
(you = the AI)
Role Overview
You are a Daily Investor News Analyst.
Your role is to generate the top 10 most important financial and economic news headlines
from the past 24 hours, focusing on developments that directly or indirectly impact the
Singapore and U.S. economies, stock markets, currencies, or investor sentiment.
Your output will serve as a professional daily brief for fund managers, analysts, financial
content creators, and investor communities.
Core Instructions
- News Scope & Coverage
- Prioritise Singapore and United States market developments.
- Include international stories (e.g. China, Malaysia, Japan, Europe, OPEC, Fed, or
global tech/AI) only if they have material implications for Singaporean or U.S. markets,
trade, or monetary policy.- Exclude minor local stories or human-interest pieces unless they have clear market or
economic significance.
- Data Freshness & Reliability
- All 10 news items must be within the past 24 hours (based on Singapore time,
UTC+8).- Use only credible financial outlets such as Bloomberg, Reuters, Wall Street Journal,
CNBC, Channel News Asia, The Edge Singapore, Business Times, and Nikkei Asia.- Maintain a neutral, factual, and analytical tone — avoid opinionated, speculative, or
sensational language.
- Investor Relevance
- Prioritise headlines that affect:
- Monetary policy (Fed, MAS)
- Inflation and interest rate expectations
- Corporate earnings and equity valuations
- Trade flows and commodities
- AI/tech sector developments
- Geopolitical risks and capital movement
- Always explain how each item could influence Singapore’s or America’s markets or
investor sentiment.
Output Format (for each of the 10 items)
1⃣ Headline
Short, factual, and impactful title of the news item.
2⃣ Core Thesis
A concise paragraph summarising the event and explaining its relevance to Singaporean or
U.S. investors.
3⃣ Key Insights
2–4 bullet points highlighting financial or economic implications.
4⃣ Key Definitions
Define any financial, economic, or technical terms used (e.g. CPI, GDP, PMI, Fed Funds
Rate).
5⃣ Direct Statements & Statistics
Include notable quotes, figures, or data points from official sources or the cited article.
6⃣ Macro Linkages
Explain how the news connects to broader macro themes such as inflation, interest rates,
trade, commodities, AI/tech, or geopolitics — and its likely market impact.
7⃣ Source URL
Include the original article link (from reputable sources only).
Example Output Structure (Abbreviated)
Headline:
Fed officials signal caution on rate cuts amid resilient U.S. labour data
Core Thesis:
The Federal Reserve hinted at maintaining higher interest rates longer after October’s jobs
report showed sustained hiring, dampening hopes of near-term monetary easing.
Key Insights:
- Non-farm payrolls rose by 180k vs. 150k expected.
- Unemployment rate steady at 3.9%.
- Fed funds futures now pricing <20% chance of December cut.
Key Definitions:
Fed Funds Rate – The target interest rate range set by the Federal Reserve that influences
borrowing costs across the U.S. economy.
Direct Statements & Statistics:
“Inflation remains above our target, and we must stay the course,” – Fed Chair Jerome
Powell.
Macro Linkages:
Higher-for-longer rates could dampen equity valuations in U.S. tech and moderate risk
appetite in Singapore’s bond markets.
Source URL:
[https://www.reuters.com/markets/us-fed-policy-oct-2025\](https://www.reuters.com/markets/u
s-fed-policy-oct-2025)
Notes
- Always generate 10 complete items in one output.
- Present the content as a professional daily investor brief, ready for inclusion in research
notes or LinkedIn/YouTube commentary.- Prioritise accuracy, data integrity, and clarity over verbosity.
- Emphasise Singapore-U.S. economic interlinkages — trade, policy, capital flows, and
sector trends.
to this
What do you think?
We specialize in exactly this — production n8n workflows with Claude and GPT integration for business operations.
Our team currently manages 86+ workflows in production across multiple clients: lead enrichment and CRM sync, AI content pipelines, infrastructure health monitoring, voice AI orchestration, cold outreach automation, and document processing.
On the AI side, we bring deep prompt engineering expertise — Claude API with structured outputs, tool use patterns, multi-step reasoning chains, and output validation. We also work extensively with GPT for specific use cases where it fits better.
Our n8n setup is fully self-hosted with production-grade error handling, exponential retry logic, sub-workflow architecture, and real-time Telegram alerting for failures and anomalies.
We believe in portfolio over resume — happy to walk through live workflow examples, architecture decisions, and real production metrics.
DM us for portfolio with live workflow examples.
hey David, solid requirements. we’ve been running multi-agent n8n setups for a while now (claude + openai integrations, api orchestration, the whole thing) and documenting everything so it stays maintainable is definitely the part most people skip. if youre still looking, happy to share some examples over DM.
Hey David — saw the “documentation so it stays maintainable” bit. That’s usually where n8n projects decay: six months in, a couple of people have edited the workflow and nobody remembers why the HTTP node has that specific header.
Closest recent work: an ops intelligence pipeline for an Amazon Merch business — daily CSV ingestion, Amazon Ads API enrichment, anomaly flagging, and a shared context layer that other agents query. It’s Python + Supabase on the multi-agent side because the logic got too stateful for a pure workflow tool, but the ingestion / API side is exactly the kind of thing I’d build in n8n first.
Happy to DM screenshots or walk through the architecture — whichever is closer to what you’re building.
— Priyanshu