How I Train an AI Editor to Cut Through Stock Market Noise

Every morning, I do the same thing: I grab my phone and scroll. There are dozens of unread messages in my WeChat, the trending topics on Weibo have shifted overnight, and my news apps push a wall of 'important updates.' I spend fifteen minutes flipping through headlines, and when I put the phone down, I feel oddly empty. I know a lot happened, but I can't name one thing that actually matters.

The problem isn't scarcity anymore. It's overload. A company raises guidance, an analyst downgrades a stock, a central bank hints at a rate move—each one looks urgent, but stringing them together leaves you with nothing you can act on. The information flood doesn't just eat your time; it erodes your judgment about what's worth your attention.

So I ran an experiment: I built an AI agent to run my own stock market briefing system, pulling from a carefully curated set of sources I trust. The goal was to take back control of 'what I read each morning.'

Start with the Right Sources, Not a Net

My first attempt was naive. I set up a daily 8 a.m. task that would scrape the web for the top five financial stories and drop them into a message. It worked, sort of. I'd wake up, make coffee, and there was my briefing. But within days, the novelty wore off.

The same earnings report would show up three times, rewritten by three different outlets. A story I'd already digested would resurface as 'breaking' because another site had put a new spin on it. The agent wasn't lazy—it was feeding on a pile of raw, unvetted content. Official statements, media coverage, second-hand analysis, and clickbait all look like 'news' to an algorithm.

The fix starts upstream. I've spent years accumulating 161 RSS feeds, but quantity doesn't equal quality. To stay sane in this flood, I needed a system.

Build a Tiered Source Pool

In an age where content gets endlessly recycled and AI-generated fluff is everywhere, the core principle is traceability. Information loses context every time it's passed along, so you want to get as close to the original source as possible.

I sorted my sources by credibility:

• Primary sources: official company press releases, SEC filings, central bank statements. These are the raw facts, unadulterated.
• Established media: Bloomberg, Reuters, WSJ, The Information. They have the resources to verify and the reputation to protect.
• Secondary and aggregators: Techmeme, The Verge, and niche financial blogs. These are great for interpretation and context, but they're one step removed.
• Bloggers and KOLs: independent analysts and commentators. Useful for product insights and contrarian takes, but treat them as seasoning, not the main course.

If you're not already subscribed to some of these, you should be. The point is to have a pyramid of trust, not a flat pile.

Organize Your Feed Like a Magazine

Once you're juggling over a hundred feeds, a single unorganized list is useless. I use a tool called Folo, which is essentially an RSS reader with a twist. RSS might sound old-school in the age of algorithmic feeds, but it's still the best way to actively pull content on your terms.

With Folo, I've arranged my subscriptions into six categories—stocks, macro, tech, energy, crypto, and opinion. It's like flipping through a magazine I've custom-assembled. No random noise, just the sections I care about.

Give Your AI Agent a Backdoor to Your Feed

The real game-changer is Folo's CLI. It lets my AI agent read the unread items directly from my curated feeds. Instead of scraping the entire internet, the agent gets a pre-filtered stream of sources I've already vetted. Each item even includes a direct link back to the original, so there's no risk of the AI hallucinating URLs.

My upgraded morning briefing prompt now says: 'Read my Folo subscriptions from the past 24 hours. Merge duplicate stories, cross-verify details, and prioritize official announcements. Mark items as multi-source verified when appropriate.'

Teach the AI Your Taste (Yes, You Have to Yell at It)

A good assistant doesn't just filter—it learns. After a few days, I noticed my briefing was still too heavy on AI model news. I'd already seen enough of those stories; I wanted more on consumer stocks and hardware earnings. So I told my agent: 'Too much AI chatter. I want more on consumer electronics and hardware. Remember that for future briefings.'

The agent wrote a MEMORY.md file and started adjusting its picks. The next morning, the AI model stories were gone, replaced by updates on chipmakers and device makers. It was a small victory, but it showed the system could adapt. You don't have to be patient with AI—you just have to be specific.

Turn Your Briefing into a Visual Dashboard

Chat bubbles get old fast. I wanted something I could glance at and actually read. So I asked the agent to render the final five stories as a clean HTML page—card-based, minimal, with a title, core facts, why it matters, and buttons linking to sources. It looks like a newspaper, but for me.

That same approach works for tracking a long-term story. Take the endless rumors about a foldable iPhone. Each leak looks like a big deal, but together they're just variations on the same question: when will Apple actually ship one? I challenged my agent to build a 'dynamic encyclopedia' for this topic, complete with a timeline, a tree diagram of claims, a chart showing how many independent sources support each claim, and cards that label each rumor as 'confirmed,' 'corroborated,' 'single source,' or 'unverified.'

The result was a deep-dive page that didn't just list rumors—it showed how they evolved and which ones had legs. The anxiety of missing something disappeared, because I could see exactly what was solid and what was fluff.

Why This Matters for Stock Market Investors

This isn't just a productivity hack. It's a survival skill for anyone who trades or invests. The financial world is drowning in what Merriam-Webster named its 2025 Word of the Year: 'slop.' AI-generated content that looks plausible but adds nothing. In that environment, your edge comes from your ability to filter.

AI can collect, deduplicate, and organize—but it can't outsource your judgment. The point isn't to let an algorithm decide what's important. It's to build your own dam upstream, so that what reaches you is already settled. Then you can decide what deserves a closer look and what to skim past.

When you can see the difference between a real consensus and a single source's guess, the noise fades. You stop chasing every headline and start paying attention to the ones that actually move the needle. That's the whole point: to extract signal from noise, and to do it on your own terms.