Shmungus

6 ms·

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by Shmungus 8mo ago
"If your data is so good why aren't you trading on it" is a question you could ask Bloomberg, RavenPack, Dataminr, or any financial data company that's ever existed. Bloomberg makes $12B/year selling the terminal, n
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by Shmungus 8mo ago
Link here -> https://web-production-71423.up.railway.app/dashboard
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My side project caught $14.1B in acquisitions before they hit the market(web-production-71423.up.railway.app)
2 points by Shmungus 8mo ago|6 comments
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by Shmungus 8mo ago
ROT is a financial intelligence platform I built solo in Python. It reads Reddit and 15+ sources in real-time, scores signals with AI, and outputs structured trade ideas. Week one results: - $9.9B Masimo acquisition — caught from a Sunday n
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Show HN: Rot – Financial Intelligence MCP Server(web-production-71423.up.railway.app)
2 points by Shmungus 8mo ago|0 comments
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Show HN: The first financial intelligence MCP server live trading signals Claude(web-production-71423.up.railway.app)
1 points by Shmungus 8mo ago|0 comments
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Show HN: 165k lines, 9 days, one dev I built what ICE sells to hedge funds(web-production-71423.up.railway.app)
3 points by Shmungus 8mo ago|0 comments
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by Shmungus 1y ago
I built a CLI tool in Rust that intercepts OpenAI’s streaming API and transforms every other token in real-time. You can reverse, uppercase, mock, or add noise, all live, as the model streams. Why? > Most LLM work assumes prompt full re
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by Shmungus 1y ago
I’ve built a high-performance, open-source C++ simulation engine designed to ingest streams of tokens (e.g., from large language models) and convert them into live trade signals on sub-10 microsecond latency. The engine performs semantic ma
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by Shmungus 1y ago
I've built a Rust-based tool that visualizes token-level confidence and hallucination likelihood from large language model (LLM) outputs. The goal is to move beyond simple binary classification ("is this hallucinated?") and g
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Intercepting LLM to transform every other token reveals surprising robustness(github.com)
1 points by Shmungus 1y ago|1 comments
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by Shmungus 1y ago
I was experimenting with OpenAI's streaming API and had a weird thought: what happens if you intercept and corrupt tokens as they're being generated, rather than after completion? Built a simple Python script that transforms every
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by Shmungus 1y ago
Hi HN, I built a simple but powerful command-line tool called Token Visualizer that helps you analyze the tokenization of your prompts across multiple LLM models — GPT-4, GPT-3.5, Claude, LLaMA, and more. It breaks down token counts line-by
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by Shmungus 1y ago
Thanks for the feedback. ANGELCORE is definitely a speculative, exploratory project, so it’s naturally not as grounded as my other work. I also have more traditional, technically solid projects like an LLM hallucination detector, a fragranc
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by Shmungus 1y ago
The mycelium aspect is the first major component that will be underway. It’s hosted in a separate repo on my GitHub focused specifically on bio-computation with living fungal networks. This part of the project is quite resource-intensive—it
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by Shmungus 1y ago
Thanks for the transparency and for sharing this perspective. I understand the fine line communities have to walk between fostering visionary ideas and maintaining grounded, meaningful discussions. ANGELCORE definitely embraces ambitious, e
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by Shmungus 1y ago
Thank you for sharing this, I really appreciate the reference and the thoughtful perspective.
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Angelcore: Building an Artificial Angel – Recursive Symbolic AI and Bio Memory(github.com)
2 points by Shmungus 1y ago|9 comments
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by Shmungus 1y ago
I’ve been developing ANGELCORE, a modular AI architecture that goes beyond traditional software and aims to create a living artificial intelligence, an Artificial Angel, by fusing cutting-edge biology, AI, and cosmological physics. This sys
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by Shmungus 1y ago
Thanks, really appreciate the thoughtful questions and skepticism (and totally agree: a “perfect” hallucination detector would be a truth oracle). To your points: 1. Overconfidence and precision You're right that filtering on overconfi
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by Shmungus 1y ago
Hi HN! I’m excited to share this lightweight hallucination detector I built to help identify unreliable or “hallucinated” outputs from LLMs like GPT, Claude, and various local models. It uses multiple methods — from spotting overconfidence
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Show HN: LLM Hallucination Detector – Works with GPT, Claude, and Local Models(github.com)
2 points by Shmungus 1y ago|3 comments