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Today SciPhi is open-sourcing Triplex, a SOTA LLM for knowledge graph construction.
Triplex is so small that it can be used with SciPhi's R2R to build knowledge graphs directly from your laptop.
Triplex outperforms few-shot prompted gpt-4o at 1/60th the inference cost.
Very grateful to Dalton and Standard for leading our Series A.
About a year ago, the rapid improvement in LLM capabilities inspired me to build a market-making system from scratch. AI dramatically expanded what I could manage as one person.
We recently led the Series A in Event Horizon Labs, a new quantitate research lab founded by @ocolegro
EHL's research direction is focused on automating the type of work Owen used to do at Citadel with the thesis that this is precisely the sort of work that can be done more effectively by self-improving AI vs humans.
There are a number of teams pursuing this research direction, but Owen stands out to me as one of the most talented AI researchers in the YC community.
We are a small team of researchers and engineers with deep experience in AI and markets. Today, we are still in the loop with our agents. Our goal is to engineer ourselves out of it.
We're hiring exceptional engineers and researchers to build the models, infrastructure, and systems that get us there. These are high-ownership founding roles with meaningful equity. If this is the problem you've been looking for, apply or DM me: ehl.markets/careers.html
Owen Colegrove retweeted
We recently led the Series A in Event Horizon Labs, a new quantitate research lab founded by @ocolegro
EHL's research direction is focused on automating the type of work Owen used to do at Citadel with the thesis that this is precisely the sort of work that can be done more effectively by self-improving AI vs humans.
There are a number of teams pursuing this research direction, but Owen stands out to me as one of the most talented AI researchers in the YC community.
Owen Colegrove retweeted
Applications for the latest Standard Capital Series A funding cycle are now open!
Apply by July 21, hear back by July 31.
We have benchmarks for coding, math, and general reasoning, but few test how well a model can autonomously research in a realistic setting.
Quant research is a perfect stress test: noisy, statistical, code-heavy, and easy to fool yourself.
We recently put this to the test.
This is still a backtest, not evidence of live-trading profitability. Also, the strategy signal used is somewhat fictitious in that it implicitly incorporates cross-exchange arbitrage opportunities that are hard/impossible to realize.
Still, from this work we can learn (1) LLMs are already very good at doing quantitative research (2) quantitative research combines reasoning across various research domains that are representative of some of the highest forms of human thinking in a complex and noisy environment and ultimately it forms a great arena for testing LLM ability.
Owen Colegrove retweeted
R2R from sciphi.ai is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.
ycombinator.com/launches/N4p…











