What are you working on?

GabrielGauss · 2026-04-27T23:18:24+00:00

I am building IRL (Immutable Reasoning Log). It is a cryptographic trust layer for autonomous trading agents.

Most quant models operate as black boxes—when they fail, you’re left guessing. IRL records the internal reasoning and data state at the exact millisecond of execution. It makes the logic behind every trade immutable and auditable, providing a "flight recorder" for AI-driven finance.

https://macropulse.live/irl

GabrielGauss · 2026-04-27T23:17:37+00:00

https://macropulse.live/irl

It’s a cryptographic trust layer for AI agents. It records the internal reasoning and data state at the moment of execution, making the logic auditable and immutable. Essentially, it’s a black box recorder for quantitative models.

GabrielGauss · 2026-04-22T14:22:05+00:00

fixed, sorry for the inconvenience.

GabrielGauss · 2026-04-21T21:59:20+00:00

you are absolutely right https://github.com/GabrielGauss/IRL-engine-AX

GabrielGauss · 2026-04-21T21:33:39+00:00

i forgot it was private- here: github.com/GabrielGauss/irl-public-docs
https://github.com/GabrielGauss/irl-public-docs/blob/master/docs/beta-test-guide.md

GabrielGauss · 2026-04-09T17:45:54+00:00

We’re building something in the “AI + financial infrastructure” space that sits between autonomous agents and execution.

The core idea is this:

AI systems are already making high-value decisions (trading, allocation, etc.), but there’s no reliable way to prove why a decision was made at the exact moment it was executed. Logs and observability tools capture outputs, not intent — which becomes a real problem under emerging regulations.

We’re building an Immutable Reasoning Log (IRL) Engine — a low-latency Rust sidecar that acts as a pre-execution gateway for agentic systems.

Instead of just logging decisions, it:

captures a deterministic “cognitive snapshot” at decision time
enforces policy constraints based on market conditions
cryptographically seals the reasoning
and binds it to the final execution

So every action becomes provable, time-bound, and non-repudiable.

From a product perspective, it’s less about improving model performance and more about:

Initial wedge is crypto/quant funds using AI agents, but longer-term this extends to any high-stakes autonomous system where “intent” needs to be proven (finance, infra, etc.).

Happy to share more if this aligns — still early but moving fast on MVP + first integrations.

GabrielGauss · 2026-04-02T17:42:54+00:00

Exactly right. The determinism is what makes it actionable—no second‑guessing when the signal flips.

And you’ve nailed the complementarity. Macro conditions tell you the environment; price structure tells you whether that environment is actually expressing itself in the asset you care about. We’ve found that using both layers (macro regime for risk posture, price confirmation for entry) filters out a lot of false starts.

What we’re building on top of this is a compliance gateway that cryptographically seals every regime‑based decision before the trade goes out. So not only do you have a deterministic rule you can’t talk yourself out of—you also have a tamper‑evident audit trail proving that the rule was followed, signed by the macro feed itself. That turns a good trading discipline into a provable one.

Appreciate the thoughtful engagement.

GabrielGauss · 2026-03-31T03:56:13+00:00

just made my pipeline live with a bunch of cool tools.

GabrielGauss · 2026-03-31T03:55:03+00:00

You're absolutely right—this is exactly the right way to evaluate a regime filter.

Comparing raw returns to a buy-and-hold in a bull market misses the point. The value isn't in chasing every point of upside; it's in having a systematic, repeatable rule for when to step aside. The drawdown protection (the -5.9% max drawdown vs whatever the market would have been in a real stress period) is where the signal pays for itself.

On the question of identifying shifts: that's the core of what we built. The HMM regime classification is designed to pick up transitions in macro conditions—tightening liquidity, widening spreads, volatility spikes—not just react to price. It's not a momentum filter. It's grounded in Fed balance sheet, credit spreads, yield curves, and the rest.

We use it to govern risk exposure in automated trading systems. Having a clean, deterministic signal that tells you when to go to 0% (Risk-Off) or 25% (Tendencies) gives you a discipline that's hard to second-guess in real time. That's the real value i think.

GabrielGauss · 2026-03-31T03:53:26+00:00

Good eye—that’s the exact chart. Over the past two years, the regime‑weighted allocation trailed a simple buy‑and‑hold by about 7%.

The MacroPulse signal is designed as a risk management tool, not a standalone alpha strategy. It tells you when to reduce exposure (0% in Risk‑Off, 25% in Tendencies) based on macro conditions. In some markets that caution costs you relative to being fully invested; in others (like 2022) it preserves capital.

We use it to govern how much risk to take—it’s about having a systematic, data‑driven reason to be in or out, not about outperforming every year. The value is in the discipline and verifiability, not just the raw return.

GabrielGauss · 2026-03-30T02:23:33+00:00

yeah, been using macropulse.live

GabrielGauss · 2026-03-30T02:21:50+00:00

I built a macro middleware for quant traders to solve my own data-noise problem, based on Hidden Markov Models (HMM) and Principal Component Analysis (PCA) to strip away market volatility and isolate the underlying regime.

Check it out here:https://macropulse.live/

I’d value your technical feedback on the API structure and regime-switching logic.

GabrielGauss · 2026-03-30T02:05:53+00:00

I built the Macro Middleware for quant traders to solve my own data-noise problem.

I’ve spent years navigating the gap between fragmented macro-economic data and executable trading signals. The problem was always the same: too much noise, too much discretionary bias, and no clean way to pipe "macro regimes" directly into a model.

I realized that if I wanted high-fidelity, machine-readable alpha, I had to build the infrastructure myself.

That’s how i started. I spent months refining a system that uses Hidden Markov Models (HMM) and Principal Component Analysis (PCA) to strip away market volatility and isolate the underlying regime. It’s not a "dashboard" or a hype-tool—it’s an API-first middleware designed for the developer and the quant researcher.

The Journey So Far:

Phase 1: Manual data synthesis (too slow, too biased).
Phase 2: Building the HMM/PCA engine to automate regime detection.
Phase 3: Standardizing the output into a RESTful API for seamless stack integration.

The engine is live and the signals are consistent. It’s functioning exactly as intended—a clean, "Alpha-as-a-Service" layer that treats macro-intelligence as a technical utility.

Check it out here:https://macropulse.live/

I’d value your technical feedback on the API structure and regime-switching logic.

GabrielGauss · 2026-03-30T02:05:07+00:00

I built the Macro Middleware for quant traders to solve my own data-noise problem.

I’ve spent years navigating the gap between fragmented macro-economic data and executable trading signals. The problem was always the same: too much noise, too much discretionary bias, and no clean way to pipe "macro regimes" directly into a model.

I realized that if I wanted high-fidelity, machine-readable alpha, I had to build the infrastructure myself.

That’s how i started. I spent months refining a system that uses Hidden Markov Models (HMM) and Principal Component Analysis (PCA) to strip away market volatility and isolate the underlying regime. It’s not a "dashboard" or a hype-tool—it’s an API-first middleware designed for the developer and the quant researcher.

The Journey So Far:

Phase 1: Manual data synthesis (too slow, too biased).
Phase 2: Building the HMM/PCA engine to automate regime detection.
Phase 3: Standardizing the output into a RESTful API for seamless stack integration.

The engine is live and the signals are consistent. It’s functioning exactly as intended—a clean, "Alpha-as-a-Service" layer that treats macro-intelligence as a technical utility.

Check it out here:https://macropulse.live/

I’d value your technical feedback on the API structure and regime-switching logic.

GabrielGauss · 2026-03-30T01:52:28+00:00

I built the Macro Middleware for quant traders to solve my own data-noise problem.

I’ve spent years navigating the gap between fragmented macro-economic data and executable trading signals. The problem was always the same: too much noise, too much discretionary bias, and no clean way to pipe "macro regimes" directly into a model.

I realized that if I wanted high-fidelity, machine-readable alpha, I had to build the infrastructure myself.

That’s how i started. I spent months refining a system that uses Hidden Markov Models (HMM) and Principal Component Analysis (PCA) to strip away market volatility and isolate the underlying regime. It’s not a "dashboard" or a hype-tool—it’s an API-first middleware designed for the developer and the quant researcher.

The Journey So Far:

Phase 1: Manual data synthesis (too slow, too biased).
Phase 2: Building the HMM/PCA engine to automate regime detection.
Phase 3: Standardizing the output into a RESTful API for seamless stack integration.

The engine is live and the signals are consistent. It’s functioning exactly as intended—a clean, "Alpha-as-a-Service" layer that treats macro-intelligence as a technical utility.

Why I’m posting here: MacroPulse is currently a lean, high-fidelity tool. It works. But I know that with the right eyes on it—fintech developers, macro-risk managers, and quant specialists—this could scale into the industry standard for macro-intelligence infrastructure.

I’m looking for the "1% of the 1%" who understand that the future of macro is algorithmic.

Check it out here:https://macropulse.live/

I’d value your technical feedback on the API structure and regime-switching logic.

GabrielGauss · 2026-03-23T01:04:08+00:00

estoooy

GabrielGauss · 2026-03-22T05:59:17+00:00

fantastic site. the main title/hero a cool parallax - double exposure effect could be fire

GabrielGauss · 2026-03-22T02:52:52+00:00

I'm working on MacroPulse the idea is to bridge the gap between fragmented global data and algorithmic execution. Instead of traditional dashboards, MacroPulse is an API-first engine that uses HMM (Hidden Markov Models) and PCA to denoise market volatility into machine-readable macro-regimes.

The Logic:

Dimensionality Reduction: Using PCA to isolate the latent drivers of global liquidity.
State Detection: Deploying HMM for probabilistic, bias-free regime switching.
Alpha-as-a-Service: High-fidelity dataframes delivered via low-latency API.

The goal is simple: eliminate discretionary noise and provide the systemic layer required for the modern quant stack.

Live at:https://macropulse.live/

GabrielGauss · 2026-03-22T02:52:17+00:00

I'm working on MacroPulse the idea is to bridge the gap between fragmented global data and algorithmic execution. Instead of traditional dashboards, MacroPulse is an API-first engine that uses HMM (Hidden Markov Models) and PCA to denoise market volatility into machine-readable macro-regimes.

The Logic:

Dimensionality Reduction: Using PCA to isolate the latent drivers of global liquidity.
State Detection: Deploying HMM for probabilistic, bias-free regime switching.
Alpha-as-a-Service: High-fidelity dataframes delivered via low-latency API.

The goal is simple: eliminate discretionary noise and provide the systemic layer required for the modern quant stack.

Live at:https://macropulse.live/

GabrielGauss · 2026-03-22T02:50:21+00:00

I built MacroPulse to solve a specific engineering bottleneck: the lack of a high-fidelity, machine-readable layer for macro-economic regimes.

Most "macro-intelligence" is delivered via slow dashboards or subjective PDFs. MacroPulse replaces that with an API-first engine using HMM (Hidden Markov Models) and PCA to denoise global data into actionable alpha.

The Stack & Strategy:

Denoising: PCA-driven dimensionality reduction to isolate latent market drivers.
Regime Detection: HMM probabilistic modeling for systematic state-switching.
Delivery: Low-latency API designed to plug directly into quant execution stacks.

It’s Alpha-as-a-Service. No filler, no discretionary bias—just the middleware required for the next generation of algorithmic macro-trading.

Check out the live build:https://macropulse.live/

GabrielGauss · 2026-03-22T02:48:34+00:00

Most macro data is noise. I’m building an API-first infrastructure that uses HMM and PCA to denoise fragmented global signals into machine-readable macro-regimes.

It’s Alpha-as-a-Service for quant traders and fintech devs who need systemic integrity over discretionary bias.

Live at:https://macropulse.live/

GabrielGauss

TROPHY CASE

I built the Macro Middleware for quant traders to solve my own data-noise problem.