AI agents · Decision intelligence · Financial research · Efficient computation
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I am building SignalLayer Labs as an independent open-source technology lab. I design and develop practical systems for AI agents, financial intelligence, decision support and intelligent automation. My goal is to make advanced AI more efficient, transparent, measurable and useful in real-world environments. I do not want to build black-box promises. I want to build software that people can inspect, test, challenge and improve. |
Dante — coding companion and mascot |
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Compute capital allocation for AI agents. I am building MARGINAL to treat tokens, model calls, tools, latency and risk as scarce computational capital. Instead of allowing an agent to execute every possible action, MARGINAL funds only the next action whose expected marginal value justifies its cost. Current capabilities
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Open financial intelligence and market research. I am developing BLUM as an open research platform for transparent financial analysis, market reasoning and AI-assisted decision support. My goal is not to create another black-box trading bot. I want BLUM to produce evidence-based, inspectable and human-verifiable financial intelligence. Core direction
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I build runtime components for more efficient, observable and reliable AI agents. |
I create tools that turn evidence, uncertainty and constraints into better decisions. |
I explore open workflows for financial research, market analysis and signal evaluation. |
I publish reproducible benchmarks, transparent metrics and inspectable experiment traces. |
I believe every important performance claim should be connected to:
- a public or documented dataset;
- a reproducible execution protocol;
- a clearly defined baseline;
- machine-readable results;
- explicit limitations;
- preserved outcome quality.
My benchmark philosophy is simple:
Efficiency gains are meaningful only when task success remains verifiable.
For agent optimization projects, I focus on:
Verified success rate
Token consumption
Cost per solved task
Tool calls per task
Latency
Premature stopping rate
Regression rate
Confidence intervals
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I publish specifications, source code, benchmarks and limitations whenever possible. |
I treat an idea as a hypothesis until reproducible evidence supports it. |
I do not assume that more agents, tokens or tool calls automatically create more value. |
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I clearly separate synthetic demonstrations from results obtained on public benchmarks. |
I want infrastructure to be easy to adopt without forcing developers to rebuild their systems. |
I build in public so that other people can inspect, challenge and improve the work. |
01 Validate MARGINAL on public agent benchmarks
02 Expand BLUM as an open financial research platform
03 Publish reproducible experiments and evaluation traces
04 Build integrations with existing agent frameworks
05 Grow a credible independent open-source laboratory
I welcome contributions from developers, researchers, financial professionals and practitioners working with real-world AI systems.
You can help by:
- reporting reproducible issues;
- improving integrations;
- proposing benchmark tasks;
- reviewing documentation;
- challenging technical assumptions;
- contributing code or experiments;
- reproducing published results.
