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ive spent the past few months designing a framework for orchestrating multiple large language models in parallel — not to choose the “best,” but to let them argue, mix their outputs, and preserve dissent structurally.

It’s called Maestro heres the whitepaper https://github.com/d3fq0n1/maestro-orchestrator (Narrative version here: https://defqon1.substack.com/p/maestro-a-framework-for-coher...)

Core ideas:

Prompts are dispatched to multiple LLMs (e.g., GPT-4, Claude, open-source models)

The system compares their outputs and synthesizes them

It never resolves into a single voice — it ends with a 66% rule: 2 votes for a primary output, 1 dissent preserved

Human critics and analog verifiers can be triggered for physical-world confirmation (when claims demand grounding)

The feedback loop learns not only from right/wrong outputs, but from what kind of disagreements lead to deeper truth

Maestro isn’t a product or API — it’s a proposal for an open, civic layer of synthetic intelligence. It’s designed for epistemic integrity and resistance to centralized control.

Would love thoughts, critiques, or collaborators.


Comments URL: https://news.ycombinator.com/item?id=44109664

Points: 3

# Comments: 0



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I've built Neuralrad Mammo AI, a free research tool that combines deep learning object detection with vision language models to analyze mammograms. The goal is to provide researchers and medical professionals with a secondary analysis tool for investigation purposes.

Important Disclaimers: - NOT FDA 510(k) cleared - this is purely for research investigation - Not for clinical diagnosis - results should only be used as a secondary opinion - Completely free - no registration, no payment, no data retention

What it does: 1. Upload a mammogram image (JPEG/PNG) 2. AI identifies potential masses and calcifications 3. Vision LLM provides radiologist-style analysis 4. Interactive viewer with zoom/pan capabilities

You can try it with any mass / calcification mammo images, e.g. by searching Google: mammogram images mass

Key Features: - Detects and classifies masses (benign/malignant) - Identifies calcifications (benign/malignant) - Provides confidence scores and size assessments - Generates detailed analysis using vision LLM - No data storage - images processed and discarded

Use Cases: - Medical research and education - Second opinion for researchers - Algorithm comparison studies - Teaching tool for radiology training - Academic research validation

The system is designed specifically for research investigation purposes and to complement (never replace) professional medical judgment. I'm hoping this can be useful for the medical AI research community and welcome feedback on the approach.

Address: http://mammo.neuralrad.com:5300


Comments URL: https://news.ycombinator.com/item?id=44107758

Points: 3

# Comments: 1



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I built zli, a batteries-included CLI framework for Zig with a focus on DX and composability.

Key features:

- Typed flags with default values and help output - Rich formatting, and layout support - Command trees with isolated execution logic - It’s designed to feel good to use, not just to work. - Built for real-world CLI apps, not toy examples.

Would love feedback, feature ideas, or thoughts from other Zig devs.

repo here: https://github.com/xcaeser/zli


Comments URL: https://news.ycombinator.com/item?id=44089054

Points: 13

# Comments: 0



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We’ve been working on Vaev, a minimal web browser engine built from scratch. It supports HTML/XHTML, the CSS cascade, @page rules for pagination, and print-to-PDF rendering. It even handles calc(), var(), and percentage units—and yes, it renders Google.com (mostly).

This is an experimental project focused on learning and exploration. Networking is basic (http:// and file:// only), and grid layouts aren’t supported yet, but we’re making progress fast.

We’d love your thoughts and feedback.


Comments URL: https://news.ycombinator.com/item?id=44023144

Points: 11

# Comments: 3



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