We still can't stop plagiarism in undergraduate computer science (2018)
17 by wonger_ | 26 comments on Hacker News.
Saturday, May 31, 2025
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New top story on Hacker News: Show HN: Maestro – A Framework to Orchestrate and Ground Competing AI Models
Show HN: Maestro – A Framework to Orchestrate and Ground Competing AI Models
4 by defqon1 | 0 comments on Hacker News.
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://ift.tt/ozg5SUl (Narrative version here: https://ift.tt/JfVDQlA... ) 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.
4 by defqon1 | 0 comments on Hacker News.
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://ift.tt/ozg5SUl (Narrative version here: https://ift.tt/JfVDQlA... ) 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.
New top story on Hacker News: Show HN: Free mammogram analysis tool combining deep learning and vision LLM
Show HN: Free mammogram analysis tool combining deep learning and vision LLM
5 by coolwulf | 3 comments on Hacker News.
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: https://ift.tt/0l3eaYu
5 by coolwulf | 3 comments on Hacker News.
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: https://ift.tt/0l3eaYu
Monday, May 26, 2025
Sunday, May 25, 2025
New top story on Hacker News: Show HN: Zli – A Batteries-Included CLI Framework for Zig
Show HN: Zli – A Batteries-Included CLI Framework for Zig
13 by caeser | 0 comments on Hacker News.
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://ift.tt/EJ2hw8t
13 by caeser | 0 comments on Hacker News.
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://ift.tt/EJ2hw8t
Saturday, May 24, 2025
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New top story on Hacker News: Show HN: Vaev – A browser engine built from scratch (It renders google.com)
Show HN: Vaev – A browser engine built from scratch (It renders google.com)
14 by monax | 4 comments on Hacker News.
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.
14 by monax | 4 comments on Hacker News.
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.
Saturday, May 17, 2025
Friday, May 16, 2025
New top story on Hacker News: Show HN: KVSplit – Run 2-3x longer contexts on Apple Silicon
Show HN: KVSplit – Run 2-3x longer contexts on Apple Silicon
80 by dipampaul17 | 9 comments on Hacker News.
I discovered that in LLM inference, keys and values in the KV cache have very different quantization sensitivities. Keys need higher precision than values to maintain quality. I patched llama.cpp to enable different bit-widths for keys vs. values on Apple Silicon. The results are surprising: - K8V4 (8-bit keys, 4-bit values): 59% memory reduction with only 0.86% perplexity loss - K4V8 (4-bit keys, 8-bit values): 59% memory reduction but 6.06% perplexity loss - The configurations use the same number of bits, but K8V4 is 7× better for quality This means you can run LLMs with 2-3× longer context on the same Mac. Memory usage scales with sequence length, so savings compound as context grows. Implementation was straightforward: 1. Added --kvq-key and --kvq-val flags to llama.cpp 2. Applied existing quantization logic separately to K and V tensors 3. Validated with perplexity metrics across context lengths 4. Used Metal for acceleration (with -mlong-calls flag to avoid vectorization issues) Benchmarked on an M4 MacBook Pro running TinyLlama with 8K context windows. Compatible with Metal/MPS and optimized for Apple Silicon. GitHub: https://ift.tt/XIjvkOA
80 by dipampaul17 | 9 comments on Hacker News.
I discovered that in LLM inference, keys and values in the KV cache have very different quantization sensitivities. Keys need higher precision than values to maintain quality. I patched llama.cpp to enable different bit-widths for keys vs. values on Apple Silicon. The results are surprising: - K8V4 (8-bit keys, 4-bit values): 59% memory reduction with only 0.86% perplexity loss - K4V8 (4-bit keys, 8-bit values): 59% memory reduction but 6.06% perplexity loss - The configurations use the same number of bits, but K8V4 is 7× better for quality This means you can run LLMs with 2-3× longer context on the same Mac. Memory usage scales with sequence length, so savings compound as context grows. Implementation was straightforward: 1. Added --kvq-key and --kvq-val flags to llama.cpp 2. Applied existing quantization logic separately to K and V tensors 3. Validated with perplexity metrics across context lengths 4. Used Metal for acceleration (with -mlong-calls flag to avoid vectorization issues) Benchmarked on an M4 MacBook Pro running TinyLlama with 8K context windows. Compatible with Metal/MPS and optimized for Apple Silicon. GitHub: https://ift.tt/XIjvkOA
Thursday, May 15, 2025
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New top story on Hacker News: Show HN: Feedsmith — Fast parser & generator for RSS, Atom, OPML feed namespaces
Show HN: Feedsmith — Fast parser & generator for RSS, Atom, OPML feed namespaces
10 by macieklamberski | 3 comments on Hacker News.
Hi HN! While working on a project that involves frequently parsing a lot of feeds, I needed a fast JavaScript-based parser to extract specific fields from feed namespaces. Existing Node packages were either too slow or merged all feed formats, losing namespace information. So I decided to write it myself and created this NPM package with a simple API. Feedsmith supports all feed formats and many popular namespaces, including: Podcast, Media, iTunes, Dublin Core, and more. It can also parse and generate OPML files. I am currently adding support for more namespaces and feed generation for RSS, Atom and RDF. The library grew into something bigger than I initially anticipated, so I also started creating a dedicated documentation website to describe all the features.
10 by macieklamberski | 3 comments on Hacker News.
Hi HN! While working on a project that involves frequently parsing a lot of feeds, I needed a fast JavaScript-based parser to extract specific fields from feed namespaces. Existing Node packages were either too slow or merged all feed formats, losing namespace information. So I decided to write it myself and created this NPM package with a simple API. Feedsmith supports all feed formats and many popular namespaces, including: Podcast, Media, iTunes, Dublin Core, and more. It can also parse and generate OPML files. I am currently adding support for more namespaces and feed generation for RSS, Atom and RDF. The library grew into something bigger than I initially anticipated, so I also started creating a dedicated documentation website to describe all the features.
Monday, May 5, 2025
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