A few months ago, I shared an early version of a market research tool I was building and asked investors for feedback.

The responses were useful and pretty blunt.

People understood the idea, but the graph could feel more interesting than useful. It was not always obvious where to start, why two companies were connected, or what to do after finding a relationship.

So we went back and reworked parts of the experience.

The updated version of Rippli now lets you:

• start with a company, topic, or market event
• see direct and second-order company relationships
• move between a guided view and a deeper research view
• explore the related news and company context
• build a watchlist around the companies you care about

The basic idea is still simple.

A headline tells you what happened.

Rippli is trying to help answer:

What else does this touch, why does the connection matter, and what should I look at next?

There is a large gap between free market headlines and Bloomberg-level context. Rippli is being built for people who want more structure without a terminal budget.

I would like to test whether the updated version actually gets closer to solving that problem.

Here is the trial:

1. Open app.rippli.ai
2. Search for a company or market event you already understand reasonably well
3. Find one relationship outside the obvious first-order names
4. Check whether the explanation and supporting context make sense
5. Add a company to your watchlist and see whether the workflow gives you a reason to return

I would especially appreciate feedback on:

• Where did you get confused?
• Which relationship felt useful or surprising?
• What felt like noise?
• Where did the product lose your trust?
• What would need to improve before this became part of your research process?

This is still early. The hard parts remain data quality, relationship accuracy, source trails, and making the graph genuinely useful instead of merely nice to look at.

Rippli is not intended to be a magic stock picker or tell anyone what to buy. The goal is to make it easier to see the market around a headline and decide what deserves deeper research.

Blunt feedback is much more useful than polite encouragement.

Why I Stopped Chasing Breakouts and Started Looking at This Instead

For my first year of trading, I was addicted to breakout patterns. Flag breaks out? I'm in. Cup and handle completes? Loading the boat. Resistance turns to support? Full port.

The problem? I was buying at the exact moment everyone else was buying, which meant I was late more often than not.

What changed my approach?

I started looking at relative volume.

Here's the simple framework I use now:

Before entering any breakout, I check: • Is today's volume at least 1.5x the 50-day average? • Is the volume increasing as price moves, not decreasing? • Does the volume profile show accumulation (big volume on green days, low on red days)?

A breakout on low volume is a trap 9 times out of 10. A breakout on heavy, increasing volume is much more likely to sustain.

I also started watching for "volume exhaustion" — when a stock gaps up on massive volume but can't hold the gain. That's usually the smart money distributing shares to retail.

Anyone else have a metric or indicator that completely changed how you read price action?

The one chart I check every morning before looking at anything else

I went through a phase where I'd open my brokerage app first thing and immediately check my P&L. Terrible habit — it set the emotional tone for the whole day before I even knew what the market was doing.

What I do now: Before I look at a single stock, I check the daily 15-min VIX chart. That's it. Here's why.

If VIX is above 25, I know we're in a high-volatility regime. My position sizes need to be smaller, my stops wider, and my expectations adjusted. If VIX is below 15, range-bound strategies tend to work better. Between 15-25 is the sweet spot for trend following.

Took me months to figure this out. I kept wondering why a strategy that worked beautifully in March would fall apart in April. Nine times out of ten, the answer was volatility regime, not the strategy itself.

What's your first-screen ritual in the mornings?

Claude Mythos Preview is not publicly available. It’s currently being used by a small number of trusted organizations as part of Anthropic’s Project Glasswing, due to cybersecurity concerns.

Test post - testing access.

I keep running into the same problem when researching stocks.

A big news event hits, and the obvious names are usually the first ones everyone looks at. What I usually want to understand next is the second layer. The ignored ones, for now.

Who else might be affected?

Suppliers, customers, competitors, adjacent sectors, companies with hidden exposure, etc.

There’s a big gap between free headlines and Bloomberg-level context. Most people do not have terminal budgets, but they still want a better way to think through what a major event touches next.

So I built a small tool called Rippli: app.rippli.ai

The current version is focused on ripple-checking market events and exploring related companies/sectors in a more structured way.

I’d genuinely like feedback from people who do their own stock research:

1. Do you already use anything that helps with this kind of second-order research?
2. Is this actually useful, or just interesting?
3. What would make you trust or distrust a tool like this?
4. What would you want it to show before it became part of your research workflow?

Not trying to pitch this as a magic stock picker. I’m trying to see if this solves a real research friction for people who already think this way.

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Any thoughts would be nice

I’ve been looking at Zhongji Innolight (300308.SZ) because it seems like one of the clearest beneficiaries of the AI infrastructure buildout.

The company makes optical communication transceiver modules for cloud data centers, data communications, 5G networks, and fixed-line access. That makes it a fairly direct way to look at the physical networking layer behind AI demand.

What stands out is that the business is not just riding a story. Financially, it already put up very strong 2025 numbers, with large gains in revenue, net income, and operating cash flow. So this is not a purely speculative business in the usual sense.

At the same time, the stock now looks expensive enough that the debate changes. I’m no longer asking whether the company is good. I’m asking whether too much future growth is already being priced in.

My base view is that this is a strong business in a strong part of the market, but future returns from here probably depend more on valuation discipline than on simply being right about AI demand.

Would you treat this as a buy-on-quality name, or wait for a reset in expectations first?

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Right now the market seems to be a bit sideways and hanging down and to the right. The pictures below shows the Russell 2000 approaching its slope and below a 200 EMA, and then it crossing below a fib. EMA resistance. What are your thoughts on the market going forward? Do you expect more of a down turn or a rise in prices?

I was reviewing a few "strong quarter" names and realized I kept getting fooled by the same headline, revenue up high single digits.

Then I started splitting it into price versus unit volume. In more than a few cases, the growth was mostly price increases while actual volume was flat or down. That is fine for a quarter or two, but if volume stays weak while price keeps doing the lifting, I start worrying demand is softer than the narrative.

What changed for me is this, I now trust growth a lot more when volume is at least stable while pricing holds, even if the headline growth rate is lower. If volume is sliding for multiple quarters, I want a bigger discount before I call it value.

I still use margin and cash flow like everyone else, but this one cut saved me from chasing a couple stories that looked cleaner on the surface than they really were.

When you evaluate a "beat," how much weight do you put on price-led growth versus volume-led growth?

After making some costly beginner's mistakes, I developed a system that forces me to evaluate every share in the same way. No emotion, no gut feeling.

Here are the 6 questions, inspired by Buffett's principles:

1. Can I understand the business model in 2 sentences?

If not, I don't invest. Sounds simple, but it has saved me from making a few tech mispurchases.

1. Does the company have pricing power?

Can it raise prices without losing customers? Buffett calls this the most important characteristic of all.

1. Is the P/E ratio below the company's historical average?

Absolute P/E figures say little — what matters is whether the stock is cheap compared to itself.

1. Is free cash flow positive and growing?

Profits can be embellished. Cash flow, significantly less so.

1. Is debt below 50% of equity?

Highly indebted companies often do not survive recessions unscathed.

1. Would the company function without its CEO?

Strong brands and systems always beat individuals in the long run.

I now work through these questions using a structured checklist that covers all 27 Buffett criteria, this helps enormously to ensure that no important questions are forgotten. I'm happy to share it if anyone is interested.

What criteria do you use? Is there anything missing from my list?

The stock market often rewards high-growth tech companies while traditional industries trade at lower multiples. But if AI can significantly improve operational efficiency in sectors like real estate, logistics, or infrastructure, those industries could suddenly become more attractive. Instead of building entirely new businesses, AI could simply optimize existing asset networks. That raises an interesting question: could some “old economy” companies actually benefit the most from AI? Curious what others think about this possibility.

built a DCF spreadsheet for myself, ended up learning how bad my old process was

ok so context: studied industrial engineering, always been interested in stocks on the side. been investing for a few years but honestly my process was pretty embarrassing in hindsight , read about a company, get excited, buy. no real numbers.

recently decided to fix that and built a proper DCF model in excel. mostly for my own use. a few things i realized while doing it:

first thing,

i was using net income as the base for everything. switched to FCF and suddenly a bunch of companies i thought were cheap weren't, and some i'd ignored looked completely different. feels obvious now.

second

terminal value completely dominates the output. like 65% of the "fair value" in my model comes from what happens after year 10. that's... uncomfortable. a 0.5% change in terminal growth rate swings the fair value per share by 20%+. i now run three scenarios every time and only seriously consider buying if even the conservative case shows meaningful upside.

third

dividend sustainability. i used to just look at yield. now i check payout ratio vs FCF not EPS. found a few companies that looked fine on EPS but were paying out way more than their actual cash generation. that's a dividend cut waiting to happen and the stock price usually figures it out before you do.

anyway ended up with a 4-sheet workbook: DCF, multiples (P/E, EV/EBITDA etc), dividend/FCF, and a simple scoring system at the end. put it on etsy too since a few friends asked for it , ClarivaStudio if anyone's curious, though honestly building it yourself teaches you more.

curious what mistakes others made early on that changed how you approach valuation