We still can't stop plagiarism in undergraduate computer science (2018)
5 by wonger_ | 2 comments on Hacker News.
Saturday, May 31, 2025
Friday, May 30, 2025
New top story on Hacker News: Show HN: Asdf Overlay – High performance in-game overlay library for Windows
Show HN: Asdf Overlay – High performance in-game overlay library for Windows
20 by storycraft | 5 comments on Hacker News.
I am making a open source overlay library. Game overlay is for rendering contents on top of game screen. Representative examples are Discord and Steam in-game overlay. They are complicated because it has to hook rendering part of a game. Asdf overlay provides easy to use interfaces for rendering on top of game screen. I recognize game performance degradation due to overlay rendering, so GPU shared texture was used to avoid CPU framebuffer copy. Asdf Overlay is capable of rendering full screen overlay without noticeable performance loss.
20 by storycraft | 5 comments on Hacker News.
I am making a open source overlay library. Game overlay is for rendering contents on top of game screen. Representative examples are Discord and Steam in-game overlay. They are complicated because it has to hook rendering part of a game. Asdf overlay provides easy to use interfaces for rendering on top of game screen. I recognize game performance degradation due to overlay rendering, so GPU shared texture was used to avoid CPU framebuffer copy. Asdf Overlay is capable of rendering full screen overlay without noticeable performance loss.
Thursday, May 29, 2025
Wednesday, May 28, 2025
Tuesday, May 27, 2025
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
8 by coolwulf | 7 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/uK974MX
8 by coolwulf | 7 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/uK974MX
Monday, May 26, 2025
Sunday, May 25, 2025
Saturday, May 24, 2025
Friday, May 23, 2025
Thursday, May 22, 2025
New top story on Hacker News: Show HN: Pi Co-pilot – Evaluation of AI apps made easy
Show HN: Pi Co-pilot – Evaluation of AI apps made easy
8 by achintms | 0 comments on Hacker News.
Hey HN — 2 months ago we shared our first product with the HN community ( https://ift.tt/rYckzUG ). Despite receiving lots of traffic from HN, we didn’t see any traction or retention. One of our major takeaways was that our product was too complicated. So we’ve spent the last 2 months iterating towards a much more focused product that tries to do just one thing really well. Today, we’d like to share our second launch with HN. Our original idea was to help software engineers build high-quality LLM applications by integrating their domain knowledge into a scoring system, which could then drive everything from prompt tuning to fine-tuning, RL, and data filtering. But what we quickly learned (with the help of HN – thank you!) is that most people aren’t optimizing as their first, second, or even third step — they’re just trying to ship something reasonable using system prompts and off-the-shelf models. In looking to build a product that’s useful to a wider audience, we found one piece of the original product that most people _did_ notice and want: the ability to check that the outputs of their AI apps look good. Whether you’re tweaking a prompt, switching models, or just testing a feature, you still need a way to catch regressions and evaluate your changes. Beyond basic correctness, developers also wanted to measure more subtle qualities — like whether a response feels friendly. So we rebuilt the product around this single use case: helping developers define and apply subjective, nuanced evals to their LLM outputs. We call it Pi Co-pilot. You can start with any/all of the below: - a few good/bad examples - a system prompt, or app description - an old eval prompt you wrote The co-pilot helps you turn that into a scoring spec — a set of ~10–20 concrete questions that probe the output against dimensions of quality you care about (e.g. “is it verbose?”, “does it have a professional tone?”, etc). For each question, it selects either: - a fast encoder-based model (trained for scoring) – Pi scorer. See our original post [1] for more details on why this is a good fit for scoring compared to the “LLM as a judge” pattern. - or generates Python functions when that makes more sense (word count, regex etc.) You iterate over examples, tweak questions, adjust scoring behavior, and quickly reach a spec that reflects your actual taste — not some generic benchmark or off-the-shelf metrics. Then you can plug the scoring system into your own workflow: Python, TypeScript, Promptfoo, Langfuse, Spreadsheets, whatever. We provide easy integrations with these systems. We took inspiration from tools like v0 and Bolt: natural language on the left, structured artifacts on the right. That pattern felt intuitive — explore conversationally, and let the underlying system crystallize it into things you can inspect and use (scoring spec, examples and code). Here is a loom demo of this: https://ift.tt/pLuSBrE We’d appreciate feedback from the community on whether this second iteration of our product feels more useful. We are offering $10 of free credits (about 25M input tokens), so you can try out the Pi co-pilot for your use-cases. No sign-in required to start exploring: https://withpi.ai Overall stack: Co-pilot next.js and Vercel on GCP. Models: 4o on Azure, fine tuned Llama & ModernBert on GCP. Training: Runpod and SFCompute. – Achint (co-founder, Pi Labs)
8 by achintms | 0 comments on Hacker News.
Hey HN — 2 months ago we shared our first product with the HN community ( https://ift.tt/rYckzUG ). Despite receiving lots of traffic from HN, we didn’t see any traction or retention. One of our major takeaways was that our product was too complicated. So we’ve spent the last 2 months iterating towards a much more focused product that tries to do just one thing really well. Today, we’d like to share our second launch with HN. Our original idea was to help software engineers build high-quality LLM applications by integrating their domain knowledge into a scoring system, which could then drive everything from prompt tuning to fine-tuning, RL, and data filtering. But what we quickly learned (with the help of HN – thank you!) is that most people aren’t optimizing as their first, second, or even third step — they’re just trying to ship something reasonable using system prompts and off-the-shelf models. In looking to build a product that’s useful to a wider audience, we found one piece of the original product that most people _did_ notice and want: the ability to check that the outputs of their AI apps look good. Whether you’re tweaking a prompt, switching models, or just testing a feature, you still need a way to catch regressions and evaluate your changes. Beyond basic correctness, developers also wanted to measure more subtle qualities — like whether a response feels friendly. So we rebuilt the product around this single use case: helping developers define and apply subjective, nuanced evals to their LLM outputs. We call it Pi Co-pilot. You can start with any/all of the below: - a few good/bad examples - a system prompt, or app description - an old eval prompt you wrote The co-pilot helps you turn that into a scoring spec — a set of ~10–20 concrete questions that probe the output against dimensions of quality you care about (e.g. “is it verbose?”, “does it have a professional tone?”, etc). For each question, it selects either: - a fast encoder-based model (trained for scoring) – Pi scorer. See our original post [1] for more details on why this is a good fit for scoring compared to the “LLM as a judge” pattern. - or generates Python functions when that makes more sense (word count, regex etc.) You iterate over examples, tweak questions, adjust scoring behavior, and quickly reach a spec that reflects your actual taste — not some generic benchmark or off-the-shelf metrics. Then you can plug the scoring system into your own workflow: Python, TypeScript, Promptfoo, Langfuse, Spreadsheets, whatever. We provide easy integrations with these systems. We took inspiration from tools like v0 and Bolt: natural language on the left, structured artifacts on the right. That pattern felt intuitive — explore conversationally, and let the underlying system crystallize it into things you can inspect and use (scoring spec, examples and code). Here is a loom demo of this: https://ift.tt/pLuSBrE We’d appreciate feedback from the community on whether this second iteration of our product feels more useful. We are offering $10 of free credits (about 25M input tokens), so you can try out the Pi co-pilot for your use-cases. No sign-in required to start exploring: https://withpi.ai Overall stack: Co-pilot next.js and Vercel on GCP. Models: 4o on Azure, fine tuned Llama & ModernBert on GCP. Training: Runpod and SFCompute. – Achint (co-founder, Pi Labs)
Wednesday, May 21, 2025
Tuesday, May 20, 2025
Monday, May 19, 2025
Sunday, May 18, 2025
Saturday, May 17, 2025
New top story on Hacker News: Show HN: I built a knife steel comparison tool
Show HN: I built a knife steel comparison tool
19 by p-s-v | 1 comments on Hacker News.
Hey HN! I'm a bit of a knife steel geek and got tired of juggling tabs to compare stats. So, I built this tool: https://ift.tt/AaZoxXj It lets you pick steels (like the ones in the screenshot) and see a radar chart comparing their edge retention, toughness, corrosion resistance, and ease of sharpening on a simple 1-10 scale. [Maybe attach the screenshot here if HN allows, or link to it] It's already been super handy for me, and I thought fellow knife/metallurgy enthusiasts here might find it useful too. Would love to hear your thoughts or any steel requests! Cheers!
19 by p-s-v | 1 comments on Hacker News.
Hey HN! I'm a bit of a knife steel geek and got tired of juggling tabs to compare stats. So, I built this tool: https://ift.tt/AaZoxXj It lets you pick steels (like the ones in the screenshot) and see a radar chart comparing their edge retention, toughness, corrosion resistance, and ease of sharpening on a simple 1-10 scale. [Maybe attach the screenshot here if HN allows, or link to it] It's already been super handy for me, and I thought fellow knife/metallurgy enthusiasts here might find it useful too. Would love to hear your thoughts or any steel requests! Cheers!