UniverseIQ
AI-powered market research, simplified
What it does
UniverseIQ is an AI-powered market research platform that helps you analyze stocks, crypto, sports data, and market news in one place. Upload a screenshot, chart, or PDF to get an AI-generated analysis with probabilities, market context, confidence levels, and key factors to consider. Start free for 7 days.
Upload a bet slip, chart or PDF and get AI probability, market probability, estimated edge and a straight bet-or-pass verdict. Start free for 7 days.
Upload a screenshot, PDF or link. UniverseIQ names the exact outcome, prices it against the market, and tells you plainly whether to bet it — with the probability of success attached. Yes — this is a bet. The market is pricing Denver as a coin flip on the spread while our model has them nine points of probability clear, driven by rest advantage and a rebounding edge that has held all season. The line has not moved despite the injury news, which is where the value is. One workspace for every market you touch, with the same probability language across all of them. Drop in a screenshot, PDF or URL. Get the exact outcome, edge and a plain bet / don't bet verdict. A fresh slate every day across…from universeiq.app
Does the same job
all alternatives →
- FAFactIQ – a realtime econ+finance database for AI agentsJul 2026 · github.com · ▲7
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…
Stock Market GPT for Investment Research2024 · ▲127AI powered stocks, balance sheets, analyst report comparison
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com