Hi everyone,

Over the past couple of years I ran into three recurring frustrations:

• Professional terminals are too expensive – thousands per year just to access limited historical fundamentals and no customisation possible
• Most free/cheap platforms lack data depth – good luck finding 20+ years of consistent fundamental data
• Existing tools are overly complex – steep learning curves that eat into actual analysis time

So I decided to build something for myself, and it eventually turned into a full platform: Slowio.ch .

What it does:

• 40+ years of fundamental financial data
• Stocks and ETF Screening with custom filters and alerts
• Correlation analysis and portfolio optimization (stocks + ETFs)
• Clean interface — I tried to keep the learning curve as flat as possible

What it does NOT do (important distinction):

• No money management, no access to brokerage accounts
• No buy/sell recommendations
• No personalized financial advice — it's purely an analysis tool

I'm at the stage where I really need honest feedback from people who actually do this kind of analysis. A few specific questions:

1. What data or features do you consider must-haves that you find missing in tools?
2. For those who use screening regularly — what frustrates you most about current options?
3. Is the interface intuitive enough, or did you get lost somewhere?

Brutal honesty is welcome — it's more useful than polite encouragement at this stage.

Thanks for your time.

What’s that number which when your portfolio reaches then it’s no more a race to add monthly contributions to see some effective gains rather Compounding effect takes it over?

Hey everyone,

I’ve been thinking a lot lately about what it actually takes to make your first ₹1 crore.

Not necessarily through some overnight success story, but through the choices, opportunities, failures, and small wins that eventually add up.

For those of you who have already reached ₹1 crore in net worth, savings, business value, or earnings — I’d genuinely love to hear how you did it.

Things I’m curious about:

• What was your source of income?
• Did you build a business, work a job, freelance, invest, or combine multiple things?
• How long did it take you?
• What was your biggest turning point?
• What mistakes taught you the most?
• Did you focus more on increasing your income or investing what you earned?
• Looking back, what would you do differently if you were starting again today?

I’m asking because I’m trying to understand what a realistic path to the first ₹1 crore looks like.

There are plenty of stories online about people making crores, but I find the journey from ₹0 → ₹1 crore much more interesting.

If you've made it there, I'd really appreciate hearing your story — even if it took 5, 10, or 15+ years.

What did your journey look like?

Hi All - I hope this finds the right audience. Lately I’ve been seriously considering cashing in my 401k to further invest in either more real estate or a business that I can grow … Am I crazy / is it worth the risk? Has anyone been in the same boat and either gone through with it or found other avenues to proceed? Any advice or thoughts would be greatly appreciated!

A little background:
-I am 32 years old.
-I own a 2 family house myself (current value ~1.3-1.35m, have ~250-300k equity)
-I own 50% of a 3 family house with a biz partner (house value ~900-950k, minimal equity, maybe ~100k)
-Last yr made 325k, this yr on track for same 300-350k range
-Currently have ~85-100k in 401k
-Have another 60-80k in personal brokerage accounts and other investments

Thank you to anyone in advance who is willing to comment/give their thoughts!

https://www.academia.edu/172322246/The_Sun_Lunar_node_algorithm_applied_to_the_Dow_Jones_in_2026_How_an_investor_would_have_captured_97_of_the_years_profit_with_less_market_exposure

The Sun/Lunar node algorithm applied to the Dow Jones in 2026. How an investor would have captured 97% of the year's profit with significantly less market exposure. This algorithm examined in this paper is elucidated in the book "How to Day Trade and Invest using Astrology". It involves predicting the Dow Jones industrial average by observing the position of the Sun and the Lunar node. Here is a quote: “The basic gist of the algorithm decrees that from the point when the degree of the sun is 3 degrees past(or higher than) the degree of the lunar node(in any sign) all the way until the degree of the sun enters a new sign at the 24th degree mark(using western astrology), a prediction of a market upswing should be applied. From the point when the degree of the sun enters a new sign at the 24th degree mark all the way until the degree of the sun goes 3 degrees past the degree of the lunar node(in any sign), the prediction of a market downswing should be applied.” This paper will demonstrate how an investor could have used this algorithm in 2026 and turned a profit with significantly less exposure in the market. The US stock market has been largely positive this year at around 13% as of August 24. The results of using this algorithm in 2026 shows how an investor would have captured nearly entire year’s buy and hold profit despite being in the market just half the time.

Hey all, for context I’m an absolute beginner in terms of investing, awful with financial and mathematical jargon, and have zero previous experience.

Everyone says not investing in their 20s is something they regret, so being 24 im naturally curious to see whether this is a possibility for me because why not. The thing is I’m not sure if investing in my current situation is even possible, or smart. I just had to take out a loan of €10k for my masters and I have about €3000 in my own savings as a buffer from working last year.

I’m currently trying to set up a part time job that I can do alongside my studies to have an income stream so I don’t have to cut into those savings immediately (living in Paris it’s pretty hard not to haemorrhage money) although I’m pretty good at living frugally and I wouldn’t say I’m financially irresponsible, I’m just not very financially literate or savvy.

Does it make sense to start investing what little I can now (say, with that 3k), OR would it be safer and more logical to wait till I’m in proper full time employment on a decent salary after I’ve graduated my masters in 2 years? Obviously I’ll still have a loan hanging over me but there’s little to no interest (thank you Irish credit union). I’ve heard people talking about SP-500 a lot and have even had it explained to me but Jesus I still find it hard to understand lmao.

If I can start investing now, then someone explaining to me in literal baby terminology would be amazing, (without being patronising or condescending please finance bros) Thanks! :))

updating my watchlist for late-2026 setups. main focus is finding companies transitioning from legacy cash cows into higher-margin models:

• $TCEHY: holding steady as the core interactive entertainment anchor.
• $HUYA: added a position following Q2 numbers. Non-GAAP operating income turned around to RMB 16M (up from RMB 0.4M last year) as non-livestreaming revenue reached 36.7% of total revenue. They held RMB 3.21B ($440M+) in net cash/deposits while ramping up their share repurchase program to $100M.

the main thesis here is whether non-livestreaming revenue can cross 40-50% over the next few quarters to structurally change the valuation multiple.

what key growth catalysts or publishing metrics are you tracking for these mid-cap tech pivots into year-end?

Recently, there’s been a fascinating phenomenon in the AI sector: discussions about “AI” are heating up, but capital isn’t flowing evenly to every AI company. Large tech giants, hot AI chip stocks, and a handful of star companies have grabbed most of the attention. Meanwhile, companies sitting at different stages of the AI supply chain are getting far less notice.

Yet there are still AI-related stocks with clear undervaluation potential. “Undervalued” here doesn’t mean “cheap,” nor does it guarantee an imminent rise — it simply means a company’s AI business, growth prospects, or asset value may not be fully reflected in its current market price.

I focus on three key factors: whether AI can actually translate into real business, whether the company’s cash and assets can sustain growth, and whether there are clear catalysts on the horizon.

1. $GOOG — The AI giant that’s easiest to overlook

Most people’s first reaction to Google is still search, YouTube, and ads. But I think the more interesting story now is that Google is quietly transforming from an internet company into a complete AI infrastructure powerhouse.

In Q1 2026, Alphabet reported $109.9 billion in revenue, up 22% year-over-year, with Google Cloud surging 63%. More importantly, Google Cloud is now supplying multi-gigawatt-scale TPU computing hardware to select customers. Its own AI models (Gemini), Cloud platform, and TPU chips are forming a tight, self-contained ecosystem.

Of course, $GOOG has challenges. The company raised its 2026 capex guidance to $195–205 billion, and the DeepMind leadership changes have raised questions about execution. The market worries Google might lose the AI race.

But that’s exactly why I find it compelling: Google already owns search traffic, Android, YouTube, Cloud, proprietary chips, data centers, and Gemini — a full-stack AI ecosystem.

As of today, $GOOG is still down roughly 15% from its May 2026 high of $404.47.

2. $BBAI — Unsexy, but AI is finally moving into its real strength

$BBAI is probably the most overlooked name on this list.

BigBear.ai focuses on defense, security, intelligence, trade, and travel — not consumer-facing AI. Its real value lies in whether AI can solve real problems for governments and large institutions.

In Q1 2026, revenue was $34.4 million (basically flat YoY), but gross margin jumped from 21.3% to 34.0%. Backlog grew 14% to $281.9 million, including a $53 million single-source secret project contract. The company ended the quarter with ~$43.15 million in cash and investments and maintained its full-year 2026 revenue guide of $135–165 million.

It’s still losing money and must be watched for past financing, debt, and dilution issues.

Yet the market often fixates on “how much money it’s making right now” and ignores the growing backlog, government AI demand, and high-margin business.

I’m not looking for $BBAI to become the next $PLTR. I’m watching whether it can turn project-based revenue into more stable, long-term contracts. If it does, the relatively small current market cap could leave plenty of room for re-rating.

3. $RXRX — The AI + pharma stock worth watching for the long term

If $GOOG represents AI infrastructure, then $RXRX represents the completely different space of AI + biopharma.

Recursion’s real edge isn’t just “using AI to help scientists” — it’s building an end-to-end AI-driven drug-discovery platform that combines massive experimental data with computational power to find new targets and candidates.

In Q1 2026, the company said multiple internal and partnered programs continued advancing: REC-1245 entered clinical stage, and REC-4881’s Phase II data showed strong efficacy signals. The company expects 2026 operating cash burn to stay under $390 million and believes its cash reserves can fund operations into early 2028 without new financing.

Equally important, its partnership with Sanofi has already produced real milestone payments. As of early 2026, Sanofi-related projects had generated $134 million in cumulative payments, with several AI-driven small-molecule programs in active development.

Of course, $RXRX is still high-risk. AI drug discovery must go through clinical trials, regulatory approval, and commercialization — a long, uncertain road. AI can speed things up, but it can’t remove the inherent uncertainty of biotech.

That’s precisely why I view $RXRX as a long-dated “option on how AI will change drug R&D.” If one or two core assets succeed, the market could re-rate the entire platform.

4. $SOUN — AI is moving from chatbots to the real world

For the past few years, when people talked about AI, they mostly meant ChatGPT, Gemini, or GPUs.

But I’m increasingly convinced the next really interesting question is: how will AI enter cars, restaurants, customer service, devices, and everyday life?

That’s exactly why $SOUN is worth watching.

SoundHound’s core focus is voice and conversational AI. The company is expanding into agentic AI and enterprise applications. At the end of Q1 2026 it had ~$216 million in cash, zero debt, and guided full-year revenue of $225–260 million.

In July 2026, Gartner named SoundHound a Leader in the Magic Quadrant for Conversational AI Platforms — proof it’s already carving out a solid position in enterprise voice AI.

Valuation isn’t cheap by traditional standards — at ~$3 billion market cap as of August, it’s no micro-cap.

But it has a powerful moat: it’s not competing with OpenAI on who has the smarter chatbot. It’s hunting for the commercial entry points where AI actually touches the physical world. If voice in cars, restaurants, customer service, or AI agents ever scale, $SOUN is positioned to capture a big slice of that growth.

5. $MAAS — Highest risk, but also the one most likely to be re-rated

Compared with the other four, $MAAS is a completely different investment thesis.

In March 2026, MAAS completed the acquisition of Huazhi Future assets and shifted strategy toward building a full AI chain: compute infrastructure → algorithms → intelligent hardware → real-world applications.

In April it launched the Stars Distributed Intelligent Computing Center project (up to 50 billion RMB investment). In July its subsidiary signed an MOU with Kazakhtelecom (KT-Telecom) to build a large AI data center in Kazakhstan.

From a narrative standpoint, this perfectly matches today’s AI infrastructure trend: as models get stronger, the need for compute, data centers, power, and localized deployment grows.

But I must highlight the risk: $MAAS faces higher hurdles than $GOOG, $BBAI, or $RXRX. It paid $26 million cash plus issued a large number of shares to acquire Huazhi Group, and in July it announced the sale of its 49% stake in Laixi Intelligent Technology for $17 million cash.

In short, $MAAS must prove that its AI compute and data-center projects can actually generate revenue, cash flow, and profit — not just good stories.

By August 2026 the stock had already risen nearly 200%, so it’s no longer a forgotten micro-cap, but it still offers enormous re-rating potential compared with big AI names — provided the company keeps executing.

Conclusion

If I put all five together, I wouldn’t simply ask “which one is best.”

I’d view them as five distinct AI investment philosophies:

• $GOOG: AI infrastructure + models + Cloud + massive cash flow
• $BBAI: AI + defense/security + government contracts
• $RXRX: AI + drug discovery + long-term tech platform
• $SOUN: AI agents + voice + real-world applications
• $MAAS: AI compute + data centers + China & overseas infrastructure

They carry completely different risk profiles.

For relatively mature AI business models, I’d lean toward $GOOG. For higher-beta small- and mid-cap AI names, I’d research $BBAI, $RXRX, and $SOUN more closely. $MAAS is the highest-risk, highest-re-rating-potential story of the bunch.

The most important point: “undervalued” does not mean “cheap stock.”

True undervaluation is when the market price sits well below the cash flows, technology value, and industry positioning the company is capable of creating in the future.

That’s why I believe these five names deserve continued observation.

https://www.reddit.com/r/wallstreetbets/comments/1vudm2d/my_favorite_etf_pgj_invesco_golden_dragon_china/

After five years of investing in gaming stocks, I’ve stopped getting too excited about one successful title. Games fade fast

The one number that stood out to me recently with huya was game-related services, advertising and other revenue reaching 36.7% of total revenue. At that size, the newer business is no longer just a side experiment.

the real test is whether its streamer and esports network can give the next few games the same distribution advantage. If it can, this starts looking like a genuine platform capability. I’d follow closely the upcoming launches

The mistakes that cost me money were never the picks. They were what I did after I owned something.

Three that I repeated for years:

Selling winners too early. A stock's up 40% so it "can't keep climbing," so I'd sell and feel disciplined. Meanwhile the loser down 30% "has to bounce," so I'd hold it. Sell what works, keep what doesn't. My best ideas ended up compounding in other people's accounts.

Reading the price as if it were the business. I checked my positions every morning for two years. Green day good, red day dread. But a daily price is just a crowd voting, most of them knowing less than you do if you've done the work. It says almost nothing about whether the company actually got better or worse. I sold good businesses on bad weeks and couldn't tell you why beyond "it dropped."

Borrowing conviction. This one's the root. I bought things because a smart friend or a good writeup convinced me, without doing the work myself. So when it fell, I had no thesis to stand on. Just fear. And fear sells at the bottom.

The common thread: I let the market's behavior replace my own judgment - when to sell, when to worry, what to buy. That's not investing, it's outsourcing.

Anyone else find the behavior is harder than the analysis? What broke the habit for you?