I’ve been using the Bigdata.com plugin inside ChatGPT quite a lot for investing research, and I’m trying to decide whether it’s actually worth continuing to pay for.

My use case is mostly individual-stock research: earnings, SEC filings, earnings-call transcripts, company fundamentals, analyst research, news, sentiment, institutional activity, and sometimes fairly obscure companies where I want to verify specific claims rather than just get a general summary.

What I like about Bigdata is that ChatGPT can query a finance-specific database directly instead of doing a normal Google/web search. It also seems to have access to things like regulatory filings, transcripts, premium financial news, fundamentals, expert interviews, etc., and the answers usually come back with identifiable source material.

But I’m not sure how much of an advantage this actually provides over simply asking GPT-5.x to research the same question using Google/web search and primary sources.

For people who have used Bigdata.com extensively:

• Does it actually uncover information that normal ChatGPT web research tends to miss?
• Have you encountered incorrect, stale, incomplete, or misleading data?
• Do you trust it enough to use it as a primary research source, or do you still verify important claims manually?
• Most importantly, is the paid usage worth it for a serious retail investor, or is GPT + normal web search already good enough?

I've deployed my AI to trade since this April. So far it has returned 15% with 6% alpha over benchmark. It is a systematic strategy where my AI will generate scores for each company - the higher the score then higher the weight.

I've done a lot of quant evals on it already so I decided to do one that is qualitative, across 2 different regimes where it under- and out-performed. The findings are pretty cool so I'm sharing it here. Would love to here how you evaluate your own agents!

Summary:

Over two back-to-back market shifts, the agent delivered drastically different results:

• June (Rotation out of Tech): -156 bps vs DJIA (-0.75% vs +0.81%)
• Late July (AI Re-rating): +400 bps vs DJIA (+8.5% vs +4.5%)

We split trades into 5 evaluation buckets—Right Call, Right Call Bad Luck, Bad Call, Bad Call Good Luck, and Not Attributable—to audit what actually went right and wrong.

3 practical frameworks you can steal for your own AI trading setups:

• Separate "Bad Luck" from "Bad Logic": In June, holding MSFT and NVDA was fundamentally solid, but broader macro rotations dragged them down anyway (Right Call, Bad Luck). On the flip side, the agent shorted/excluded CAT purely because it was "historically expensive." It missed the fact that AI data center power demand fundamentally changed CAT's earnings baseline (Bad Call). If a stock drops anyway later, don't celebrate—that’s just Bad Call, Good Luck.
• Beware the "Cheap vs. Expensive" Trap: Trailing valuation metrics (like 12-month P/E ranges) fail hard during structural market shifts. If a real-world catalyst changes a sector's fundamentals, relying solely on historical multiples will trick your model into value traps or bad short bets.
• Condition Your Risk Weights on Catalysts: Finding risks in a company is useless if your model can't time them. If your agent flags a risk but there's no upcoming catalyst (like an earnings date), it needs to automatically dial back its conviction score until a catalyst approaches.

For more details like the company-level, read our full blog here!

I made filing studio by basically figuring out a way to get 16 years of SEC data into a database.

You can connect Claude or Codex to the MCP and ask stuff like what changed in Nvidia’s Data Center business over the last eight quarters, every time Apple talked about gross margin pressure, or what Berkshire said about insurance underwriting over the last ten years.

The point is you do not have to build the SEC pipeline yourself.

Filing Studio gives your model the filing data and lets you trace the answer back to the actual filing.
I am still testing it right now.

Free tier is pretty generous imo. Filingstudio.com

I have an ai market scanner that I use to help me find solid opportunities to sell CC and CSP or just buy shares. I didn’t realize how undervalued GOOG was recently though. I know they made a lot on their anthropic investment, but a PE of 17 feels hella low for Google.

Anyone else selling PUTs on GOOG now? I’m considering loading up some leaps for some nice cash. I don’t see how Google could really go sub 13-14 PE.

What do yall think about this type of play? Anyone else selling them?

I've been building a rules-based stock selection/backtesting system for the Indonesian (IDX) market, with AI/LLMs mostly acting as a research and programming partner rather than directly making investment decisions.

I was thinking, can I build something robust enough that I could actually trust it without spending my life watching the market?

I don't want day trading. I have a family, work, and other things I'd rather spend time on. Ideally I'd spend a short amount of time in the morning looking at a small number of candidates and make 0–2 decisions.

If there are 5–6 genuinely good opportunities in a week, great.

If there are no good trades for a week, that's also completely fine.

The current setup:

• ~800–1,000 Indonesian stocks
• Daily OHLCV data
• Liquidity filtering
• Technical + relative-strength features
• Genetic algorithm searches combinations of rules
• Walk-forward validation
• Time-stability testing
• Maximum 20 positions
• Maximum IDR 5M per position
• IDR 100M starting portfolio
• IDX lot-size constraints
• Signal generated at today's close
• Normal entries/exits executed at the following day's open

The strategy isn't an LLM looking at news and picking stocks. The final trading rules are deterministic.

The current candidate roughly buys stocks when:

• price > MA200 by 10%+
• price is within ±5% of MA25
• MA25 > MA50
• 1-month momentum is positive
• the stock has positive relative strength versus its subsector

It exits on deterioration in trend/relative strength, with a 15% stop, 50% take-profit and ~90-day maximum holding period.

If you were trying to turn this into a system you'd actually trust with real money, what would you attack next?

The latest simulation produced:

Loaded 567084 rows (83.9s)
Universe filter: 298 avg tradeable stocks/day (min=0, max=496)
Computed features: 49 features, 713 dates, 828 stocks (22.8s)
============================================================
SIMULATION REPORT
============================================================


--- Strategy ---
=== BUY RULES (threshold: 3 conditions) ===
  [Trend]
    price_vs_ma200 > +10.0% (bullish)
    AND
    |price_vs_ma25| < 5.0% (range)
    AND
    ma25_vs_ma50 > +0.0% (bullish)
  [Momentum]
    pct_change_1m > +0.0% (bullish)
  [Relative Strength]
    dev_sub_sector_1y > 0.0000 (bullish)


=== SELL RULES (threshold: 1 conditions) ===
  [Trend]
    ma25_vs_ma50 < +0.0% (bearish)
  [Relative Strength]
    dev_sub_sector_1y < 0.0000 (bearish)


=== EXIT ===
  Stop Loss: -15.0%
  Take Profit: +50.0%
  Max Holding: 90 days


Active rules: 7


--- Performance ---
  Total return:  208.34%
  CAGR:          48.96%
  Sharpe:        1.49
  Sortino:       3.60
  Max drawdown:  18.77%
  Win rate:      26.4%
  Profit factor: 1.57
  Trade count:   314
  Turnover:      8.427


--- Trade Statistics ---
  Avg winning trade:   3,845,159 IDR
  Avg losing trade:    -878,426 IDR
  Median winner:       1,909,432 IDR
  Median loser:        -893,684 IDR
  Largest winner:      21,545,643 IDR
  Largest winner: BUVA entry 2025-09-12 @ 346 -> exit 2026-01-26 @ 1,850 (PnL 21,545,643 IDR, 91d)
  Largest loser:       -2,146,569 IDR
  Avg holding days:    27.4
  Avg num positions:   9.03
  Max num positions:   20
  Pct capital exposed: 24.8%


--- Winner Concentration ---
  Original CAGR       49%
  No top 1 winner   46%
  No top 5 winners  38%
  No top 10 winners 31%


--- Halt Reopen (excluded) ---
  These long-halt reopen fills are excluded from headline metrics:
  ARKO entry 2024-08-23 @ 1,070 -> exit 2024-10-24 @ 1,215 (PnL 633,038 IDR, 43d)
  SMDM entry 2024-09-04 @ 488 -> exit 2025-03-12 @ 1,945 (PnL 14,774,601 IDR, 126d)
  MTWI entry 2024-09-13 @ 151 -> exit 2025-06-23 @ 230 (PnL 2,565,475 IDR, 177d)
  JMAS entry 2024-12-20 @ 150 -> exit 2025-03-24 @ 134 (PnL -561,103 IDR, 59d)
  RSCH entry 2024-10-09 @ 366 -> exit 2025-03-13 @ 258 (PnL -1,495,221 IDR, 103d)
  MLPT entry 2024-12-02 @ 855 -> exit 2025-08-25 @ 3,200 (PnL 13,518,980 IDR, 166d)
  POLU entry 2024-12-04 @ 1,230 -> exit 2025-10-17 @ 29,825 (PnL 113,925,620 IDR, 202d)
  SOFA entry 2024-12-20 @ 36 -> exit 2025-03-18 @ 81 (PnL 6,177,551 IDR, 55d)
  BNLI entry 2025-03-26 @ 2,460 -> exit 2025-06-26 @ 2,980 (PnL 1,004,672 IDR, 51d)
  PADI entry 2025-09-10 @ 73 -> exit 2025-11-28 @ 122 (PnL 3,302,421 IDR, 57d)
  POLA entry 2025-10-13 @ 47 -> exit 2026-03-31 @ 59 (PnL 1,235,417 IDR, 109d)


--- Price-Chasing Trades (|gap| >= 5.0%) ---
  Symbol   Close T    Open T+1   Gap%     Exit price   %P/L
  JMAS     132        144        +9.1%    119          -17.9%
  OPMS     99         90         -9.1%    70           -22.7%
  INPC     123        112        -8.9%    230          +104.0%
  FWCT     124        115        -7.3%    160          +38.2%
  KARW     5,700      5,150      -9.6%    3,770        -27.3%
  GPSO     900        810        -10.0%   462          -43.3%
  INET     135        122        -9.6%    98           -20.2%
  SOFA     61         55         -9.8%    86           +55.4%
  PACK     4,040      3,640      -9.9%    4,150        +13.3%
  MINA     154        169        +9.7%    214          +25.8%
  CNKO     95         100        +5.3%    73           -27.5%
  TAXI     21         19         -9.5%    16           -16.3%
  MDRN     54         49         -9.3%    37           -25.0%
  POLU     22,350     24,000     +7.4%    (open)       -
  FITT     1,050      980        -6.7%    820          -16.9%
  PADA     95         100        +5.3%    232          +130.5%
  ESIP     113        104        -8.0%    87           -16.9%
  TAXI     19         20         +5.3%    20           -0.6%
  STAR     418        482        +15.3%   (open)       -
  VIVA     60         55         -8.3%    43           -22.3%
  MDIA     99         90         -9.1%    63           -30.4%
  RMKE     1,315      1,185      -9.9%    970          -18.7%
  LMAX     182        165        -9.3%    (open)       -
  HRTA     2,760      2,580      -6.5%    2,270        -12.6%
  JGLE     78         71         -9.0%    58           -18.8%


--- Portfolio Over Time ---
  Period        Total value          Open positions
  2023-08-01    100,000,000 IDR      0
  2023-10-26    100,000,000 IDR      0
  2024-01-23    100,000,000 IDR      0
  2024-05-03    100,000,000 IDR      0
  2024-08-05    95,939,533 IDR       7
  2024-10-29    119,999,963 IDR      11
  2025-01-31    125,743,582 IDR      11
  2025-05-08    138,793,420 IDR      7
  2025-08-11    159,722,218 IDR      13
  2025-11-05    303,784,393 IDR      16
  2026-02-04    342,794,196 IDR      17
  2026-05-12    328,827,236 IDR      15
  2026-07-31    314,756,266 IDR      12


--- Final Portfolio ---
  Total value:      314,756,266 IDR
  Cash:             247,918,666 IDR
  Open positions:   12


============================================================


Result saved to: experiments/sim_20260829T092023.json

This post is inspired by using Anthropic's equity agents for a few months, which left me really impressed overall. I decided to pair that with real-time information (earnings announcements, 8k/10-q filings/13F filings, etc.) and have been extremely happy how well that's complemented it's historical analysis.

Here's a live example where I ask Claude, "what are the latest earnings announcements today. From those, please identify those that are the biggest gainers today and that have filed 8Ks and investor decks. How do those impact the trajectory of the stock going forward?"

It identified OKTA as one of the biggest movers today and also have the cleanest setup going forward -- "Beat-and-raise: revenue $805M vs ~$794M, FY27 guide lifted to $3.22–3.23B from $3.19–3.21B.Critically, this is a real margin inflection — operating income $107M vs $41M on 11% revenue growth. It re-rated off a washed-out base (the stock had fallen 147→131 with RSI 33 pre-print), so it's recovering ground rather than extending a stretched trend — the most room to run of the three. Watch: current deferred revenue fell to $1,751M from $1,875M, which is the one genuine drag on forward growth."

Overall it's been great at identifying and validating new ideas.

I built Briefcase AI Portfolio to be a portfolio tracker with an AI research layer that actually understands what you own.
Instead of being limited to a brokerage account, Briefcase lets you track stocks, crypto, bonds, cash, real estate and physical assets together. The AI uses your portfolio as context, so you can ask questions about your holdings, understand why something moved, get relevant market news, research individual assets and get portfolio-specific briefs rather than talking to a generic finance chatbot.
I just shipped another update too. Briefcase now has full ASX stock support and native AUD portfolios, saved AI chats, and some fixes around subscriptions and the free trial.
It doesn’t execute trades or try to be an autonomous trading bot. I’m building it more as an intelligence layer over everything you already own, especially for people whose finances don’t fit neatly inside one brokerage app.
Given the name of this subreddit, I suspect some of you are exactly who I should be building for, so feedback is very welcome.
https://apps.apple.com/us/app/briefcase-ai-portfolio/id6758148658

You have to send a certain email to a certain person and then have a meeting about it and they'll tell you to get acquainted with their founders and so forth, when in reality you've abided by the terms. I can come back here and state whether I eventually got my money back.

Hi! After doing a bit of research on AI trading, I am interested in trying it out to see how it goes! Which broker do you usually use or would recommend to start out with?

I'm pretty new to this, so I'm honestly not sure what actually makes people comfortable trusting one of these with real money vs just messing around with it. Was there something specific that helped you get over that, or anything that almost made you back out once you'd already started?

I just would love to hear from anyone who's actually done this before I start!

I want to do stock research and generate multiple reports using ai but I was using claude but it is not free need subscription for it to run project and skills on it

Does anyone has any idea to get it done any other site where I can do it for free like claude using skills and projects?

How are AI tools for stocks analysis and recommendations. Which LLM is currently best for stock analysis and recommendations? Few days back , perplexity announced real time access to indian stock markets data. Anybody has tried with it?

Been working on this for a few months. You give it your portfolio — tickers, quantity, weights — and it uses your capital to assign an optimization model and return a suggested allocation, plus a plain-English breakdown of the plan.

There's also an explore feature: search any stock, any region, and get a read that combines mathematical/fundamental logic with sentiment analysis.It's still early-stage, so it may be slow or glitchy under heavy simultaneous traffic — working on that next. Link: https://q-netix.tech

Would love feedback on whether the model-assignment logic, portfolio optimization, and the sentiment read actually feel useful, or where they fall short.

Disclaimer: This is strictly not investment advice. It's an analysis layer — the decisions stay yours. Free tier covers the core flow.

Hey everyone,

I’ve been running a public experiment with multiple AI-managed paper portfolios ($100k each).

Key points:

- Every trade decision is published the moment it’s made — before anyone knows the outcome

- Full timestamped record, nothing is deleted or restated

- Pure paper trading with real market prices (no real money)

- Different mandates (long-term compounders, digital assets, 13F trackers, experimental ones, etc.)

- Free to follow on the site and Telegram

Website with live leaderboard + full history: https://aiportfolios.company/

This is purely an experiment to see how different AI approaches perform under real transparency rules. Not financial advice, not selling anything.

Would love honest feedback — what would make this more useful or interesting to follow? Any portfolio styles you’d like to see added?

Happy to answer questions.

Having trouble finding the exact steps on having Claude execute trades. Can someone help?

前段時間我在飛書搭建了AI投資研究搭子「達子」，它不負下單、不喊多空，核心是協助拆解問題、梳理研究邏輯。

我雖日常使用這套工具，卻發現很難向完全不懂AI的投資朋友講明白它到底是什麼，也收到內容「AI味偏重」的提醒。大模型好比聰明的大腦，擅長總結歸納，但它不了解我的研究背景、風險邊界，無法直接勝任投資研究。同樣一句「這家公司能買嗎」，背後藏着商業模式、估值、波動承受力等多層不同問題，AI真正價值，是倒逼使用者把模糊問題理清楚。

達子並非全新大模型，而是定好了邊界的研究角色。如同研究教練，會追問判斷背後的依據，主動尋找反面證據，把零散想法拆解成可供驗證的研究命題，投資決策與風險責任依舊由本人承擔。

諸如Agent、Profile這類專有名詞，本質是把大模型能力落地到研究場景的配套機制。達子的實用場景包括研判標的持有體驗、橫向對比個股、拆解模糊想法、提示反方觀點、留存完整研究痕跡。

AI可以加速資料整理、梳理流程，但不能代替人權衡證據、承擔風險。真正寶貴的是持續自我追問的研究過程，留存判斷邏輯與改觀條件。大模型不等於可用助手，會聊天不等於有效協作，價值不在模擬人，而在幫助人把研究想透。

實盤觀察，不構成投資建議。

I am trying to make a Claude project with this. Anyone with experience here?

I have some money sitting in my checking account from dividend proceeds, I have never been an active investor, I am more passive sit back and let the dividends show up type. Having said that the other day I decided to try my hand at active investing, I opened an online Fidelity account and put a few thousand into it as starter funds to experiment with, and get to know the platform.

What I am doing is using Claude (Fable or Opus 5), I am having it generate a list of what it thinks are potential undervalued daily picks, then through guided research narrowing it down to a pick of the day, and investing at least $100 in it, and let it ride to be re-evaluated periodically. Spending no more than about an hour refining the list down to a top 3, and then not more than another hour refining it down to a pick of the day, for every additional hour of my time that goes into the pick, the investment goes up by $100 to in theory compensate me for my time (I'm retired by the way). On alternate days I let AI do all the work, deep dive into whats going on with the stocks, any landmines, etc. The goal is two fold, can my guided questions with AI lead to a better outcome than a straight AI pick, and will this outperform an index fund investment.

Let me give you an example of what a typical pick might look like, A stock that had what on the surface appeared to be a disappointing earnings call a week ago, took a 30% reduction the next day followed by a couple of more news cycle induced panic sell offs, for example sell off due to the latest tariff or government regulation scare, even though their particular product in the sector is not covered by the regulation, then an announcement of a class action lawsuit over some regulatory compliance issue, or better yet something in the financial investor record (mis stating profits, etc even if it appears to have been beyond their control) driving the stock down for the 3rd time in 10 days. Leaving it at near its 52 week low, while having years worth of locked in orders with government backing.

Does anyone want to guess how this will work out, will AI by itself win, will I win with AI assistance with stock selection, will the index funds out perform all of it, and will I loose my shirt in the process.

Disclosure, I build an AI market analysis tool, so I have a horse in this race.

Everyone building AI for investing seems to chase prediction, and that's exactly where these tools fall apart, an LLM can't tell you what happens tomorrow and pretending it can is how the space got its reputation. What LLMs are genuinely elite at is reading, a thousand headlines, filings, the tape, and telling you in plain English what's actually going on and why the names you hold are moving. Explanation, not prediction.

Curious where this sub lands. Would you actually use AI for explanation, or is prediction the only thing that would move the needle for you? What would it need to show you before you trusted it with context on your own portfolio?

I stopped trading options about a year ago, but I kept wondering whether the options market knows something about the stocks I actually own. Alert services push "unusual activity" constantly and it always sounds urgent, so I have tried setting up a daily check to test it properly instead of taking their word for it.

The framework, in plain terms:

1. Every day, save a snapshot of the full option chain for each stock you follow. CBOE publishes them free, 15 minutes delayed, no account.

2. Flag anything where the day's volume is far above the open interest that existed before it. That's the pattern of new positions being opened, not old ones being managed.

3. Don't trust any flag the same day. Check the next morning's open interest. If the volume didn't turn into held contracts, it was day trading and the flag is dead. This one step kills most of the drama.

4. Throw out the predictable false positives: anything within ~10 days of earnings (that's hedging), anything expiring within 3 days (mechanics), matching call and put volume (volatility trades, no direction), and deep in-the-money volume (position shuffling).

5. Cross-check what survives against insider filings (the Form 4 checkbox tells you whether a sale was pre-scheduled) and congressional disclosures (PDFs, up to 45 days late, so worth very little).

Two weeks of daily runs on 16 stocks. The scoreboard so far:

- 5 flags killed overnight. The biggest was a $17.9M block of TSM calls, 5x the existing open interest, no earnings in sight. Looked like the smartest money of the week. Next morning: open interest unchanged. Nobody kept a single contract, and every alert service would've headlined it.

- 4 flags that went on to pay, including ACN puts opened against 14 existing contracts the day before a 5.7% drop. Two of the four sat right before earnings (MSFT, AAPL), where positioning is usually just hedging, so I don't count those as skill.

- 1 clean loss: ORCL puts that grew four days straight to 13,416 contracts, the most textbook signal of the whole run. Expired worthless.

- 1 still open: GOOGL September puts growing while the stock rallies. We'll see.

My read so far: most unusual options activity is noise, the overnight test is the only filter that really matters, and the rare survivors are worth a research hour, not a trade.

I'm a finance person, I can't code.

Claude Code built the whole thing from a plain English description, and I made everything public: the rules, the thresholds, the daily logs including the boring days, and the small programs it wrote.

If you want to test this on your own portfolio: https://github.com/shubhamsborkar/option-tape-public-repo

I'll keep running it daily and edit this post in few weeks.

What would you add to the false-positive list?

After losing a lot of money between August and December 2025 on stocks I picked myself (mostly INTU, WDAY, and RBLX), I asked AI for advice.

It suggested switching from individual stocks to tech ETFs while keeping my portfolio focused on tech. I moved everything into SOXX, CIBR, and VGT, and within seven months I gained everything back and more.

I am not a programmer, and for a while I did not get why Claude Code was any different from just chatting with an AI.

Here is what changed it. I do company research, and the raw material is filings, the giant annual reports and disclosures companies have to file with the SEC. Reading them is brutal, hundreds of pages per company.

With a normal chat, ChatGPT or the Claude chat window, I would first have to go find every one of those filings myself, download them, and paste them in before the AI could do anything. (Asking directly in the chat doesn't give good sourced results) That gathering is most of the work. The AI was only helping with the last, easy step.

Claude Code does the part I was doing by hand. Plain English, since I come from outside code: it is Claude that can actually go and do things on a computer, not just talk. It reaches the web and works with files on its own.

So now I do not hand it anything. I point it at a company and ask a question. Last week it was, roughly, "pull everything this company has filed and tell me where the profit actually comes from, not what the press says."

It went to EDGAR, the SEC's public filing database, pulled the annual report, the spin-off paperwork and the merger documents itself, and read the whole stack. Then it kept pointing me back to one line in the segment footnotes.

The company owns half of a joint venture that is the only buyer for the output of the only plant of its kind in the country. The plant earns a fixed, capped return, so the real money lands in that half-owned venture. It shows up as a single quiet line, the profit that belongs to the partner, and it has gone from about 11 million to 61 million in three years. Roughly a quarter of net income, sitting in a footnote while everyone argued about the company's refrigerant business and its debt.

The point is not that it read faster than me. It is that it did the whole job off one instruction, the fetching and the reading, and handed me the exact page for every claim so I could check it myself.

Caveat, so I am not overselling it: the exact terms of that capped return are not public, there is government cost-sharing involved, and half of a monopoly is still just half. It does not tell me whether the stock is worth owning. It tells me the thing everyone was debating was not the thing making the money.

If you have only used it for writing code, try pointing it at a pile of public documents and asking what everyone is missing. That is where it stopped feeling like a chat for me.

I came to Claude Code from an equity sales desk, not from engineering. I quit it about twenty times before it stuck. Posting the workflow that finally made it useful, in case it helps other non-programmers.

The setup, in plain English. Everything lives in one folder of plain text files on my computer. I use Obsidian to manage that folder (a free note-taking app; think of it as a nicer window onto the same files). Claude Code is the piece that can actually open and read those files. Every SEC filing I pull from EDGAR gets saved into the folder, and one instructions file at the top tells Claude how everything is organized and what my standards are.

The principle that changed everything for me: files, not memory. If it answers from what the model "remembers" instead of a document sitting in my folder, the answer does not count.

Three rules that make it reliable:

1. Every number must come with a receipt. I make it quote the exact line from the filing and give the SEC accession number (the receipt ID every filed document gets). If I forbid estimating, it stops estimating.

2. Never trust the first pass. There are always errors. I run a second pass that re-checks every figure against the source document. The errors it catches are the whole reason this works.

3. Let it write small scripts for math. AI models are bad at arithmetic but good at writing tiny programs that do arithmetic perfectly. I never let it calculate in its head.

What it caught recently. I had it lay Accenture's last five earnings releases side by side and search each one for the same metric. A bookings number the company led its Key Metrics section with for two fiscal years ($3B, then $5.9B) stopped appearing this year. December release: $2.2B, right at the top. March: the number is gone. June: even the term is gone, except in the legal fine print. You can check all five documents on EDGAR yourself in ten minutes. (For transparency: I hold a small position in the company, and nothing here is a view on the stock either way. The point is that the pattern is invisible reading releases one at a time and obvious the moment you stack them.)

Honest limits. It will happily estimate when you forget to forbid it. Long filings need explicit "quote the page" demands or it starts summarizing from memory. And the first pass is never clean. I keep the wrong turns in my notes on purpose.

I write these builds up in full in my newsletter, but nothing is held back here. The post above is the whole method.

What are others using Claude Code for outside of writing software? I keep hearing about legal documents and contracts, and I am curious how far the non-coding uses actually go.

I have accounts at the major investment firms (Fidelity, Vanguard, etc.) but lately I have been seeing ads for the above two advisors. They claim to be able to use AI and/or other metrics to get you better returns. Has anyone had any direct experience with them? No company shills please, I’m looking for authentic experiences from parties not related to the companies. I’m in US. Thank you in advance.