Alternatives
Products that do what GeminIQ does
Research Faster. Invest Smarter.
- 1W3
A live dashboard where you can watch GPT-4, Claude 3, and Gemini analyze market data and make daily stock trades with real money. Each AI explains its reasoning, and you can compare their different approaches to the same data. Link: https://trading.snagra.com?utm_source=hn (no signup required) What you can try right now: - Watch live trades from GPT-4, Claude 3, and Gemini - Read each AI's full analysis and reasoning - Compare their different interpretations of the same market data - Track their real-time performance and win rates - View historical trades and performance metrics…
2024 · trading.snagra.com
- 2DF
My main motivation was that I wanted to be able to drill down and filter across all the available stocks, look at the data for myself, and narrow down on the stocks I am interested based on my own sets of criteria, and make data-driven analysis for my personal investment strategies. I used PostgreSQL as the backend database for ELT data pipelines, and used Citus Data cstore_fdw for columnar compression for the final dataset. All financial data is coming from SEC Edgar, https://www.sec.gov/developer. I used Python for downloading most of the data. I also run the data load…
2023 · tesseractanalytics.ai
- 3IM
(* within a few minutes of SEC filing) Currently does it for 1000+ US companies and specifically earnings related filings. By US companies, I mean the ones that are obliged to file SEC filings. This was the result of almost a year long effort and hundreds of prototypes :) It currently auto-publishes for 1000 ish US companies by market cap, relies on 8-K filing as a trigger. e.g. https://www.signalbloom.ai/news/NVDA will take you to NVDA earnings Would be grateful to get some feedback. Especially if you follow a company, check its reports out. Thank you! Some examples:…
2025 · signalbloom.ai
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- 5IB
Hello everyone, I built this tool on Next/Node to automatically analyze new filings from the SEC and probe the Edgar API for new filings 24/7. We use AI to analyze the filings the second they are released. Free accounts to look at real filings (automatically updated) are available to any who sign up. If you have any questions feel free to ask.
2024 · docdelta.ca
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- 7IM
This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text…
2024 · ithy.com
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- 11F9
Hey, Wanted to share what my friend and I built — Feather. It provides investors with all imaginable financial data, without breaking the bank. Effectively 90 percent of the Bloomberg Terminal, at 5 percent of the price. We just opened sign ups for early access — all you need to sign up is your email address. We’ll open access to the software in order of sign ups, and we’d love to have you onboard. Check it out! https://try-feather.com
2022
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Source of truth SEC filing data for humans and AI
Jul 2026 · castlingfinancial.com
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- 16AA
I'm a solo dev in Taiwan. I built 4 AI agents that handle content, sales leads, security scanning, and ops for my tech agency — all on Gemini 2.5 Flash free tier (1,500 req/day). I use ~105. Monthly LLM cost: $0. Architecture: 4 agents on OpenClaw (open source), running on WSL2 at home with 25 systemd timers. What they do every day: - Generate 8 social posts across platforms (quality-gated: generate → self-review → rewrite if score < 7/10) - Engage with community posts and auto-reply to comments (context-aware, max 2 rounds) - Research via RSS + HN API + Jina Reader → feed…
Mar 2026
- 17IB
I posted this a few weeks ago and the server died under the traffic. Fixed that by adding an in-mem caching layer with Redis/valkey and added CloudFront caching for static content. Also upgraded the server. Also fixed the Firefox bugs, trying again. It's a research tool for US stocks. Financials for ~10k companies pulled from SEC filings. You can chart any metric across companies, filter news by ticker, ask questions in plain English and get a chart back. There's also SQL console against the whole database, which is the part I like to use together with the AI chat (generates an SQL…
Jun 2026 · terminal.tesseractanalytics.ai
- 18DQ
There are a lot of APIs that provide access to these types of data, but there were not any (as far as we're aware of) tools & services that provide access to query the raw data directly for analytical purposes. We get the data across different sources and clean, schematize them in a PostgreSQL databases so folks can query/analyze the data directly. We have ~100 tables of data across stocks, ETFs, economic indicators, US/international census data and plan to keep adding more. More information on the tables are available at https://docs.wizdata.io Access to all of the data…
2022 · wizdata.io
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- 20AF
Hey HN! I've always been fascinated by financial markets. This month, I decided to build a tool to help with the research of stocks and cryptocurrencies. I'm using YFinance as a data source because it's free and provides a wide range of reliable market data. For sentiment analysis, I'm leveraging Google Trends to gauge public interest and sentiment over time. The tool, named Zenith, is a command-line utility with four main features: Market Analysis: Provides insights like moving averages, RSI, and volatility for selected stocks or cryptocurrencies. Sentiment Analysis: Uses Google Trends to…
2024 · github.com
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Analyst-grade stock research, without the analyst cost
Mar 2026 · coresight.one
- 22FA
Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
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