Show HN: Isit2026yet.com – A single-serving site for the New Year https://ift.tt/umLhCez

Show HN: Isit2026yet.com – A single-serving site for the New Year https://ift.tt/2w1vQZl December 31, 2025 at 03:20AM

Show HN: Perfetto2LLM - A tool to pass system traces to an LLM https://ift.tt/EwQJncD

Show HN: Perfetto2LLM - A tool to pass system traces to an LLM I work with perfetto traces for ML/GPU optimization a lot. Copy pasting trace info to LLM is not easy. As usually the traces are gzip compressed, have very large file sizes. Also there is no way to select a certain section and sending it to LLM easily (can maybe write a query to do this but not ergonomic). So vibe coded this tool to quickly select the kernels/slices/threads I want to ask about and one click to get a text/json/markdown in my clipboard to paste for LLMs. I think an MCP server for this might also be useful (tried a few but all miss certain things i wanted so chose to just build this quickly) https://perfetto-to-llm.vercel.app/ December 31, 2025 at 02:38AM

Show HN: Client-side encrypted AI detector using model ensembling https://ift.tt/EGlWrOv

Show HN: Client-side encrypted AI detector using model ensembling Hi HN, I’m Oscar, a Year 8 student from Australia who enjoys messing around with computers and AI. I recently built an AI detector to build on my skills in computer science. I entered the prototype into the Oliphant Science Awards which is a local science competition (writing a 4000-word report on the methodology) and ended up winning, so I decided to polish it into a real web service that the world can make use of. I noticed that schools and businesses are rushing to use AI detectors, but most commercial tools require you to send full, plaintext documents to cloud servers. For researchers or IP-sensitive work, sending data unencrypted to a third party (who might use it for training) is a major privacy risk. Additionally, current commercial AI detectors aren't very transparent and are unable to tell you why they come to a conclusion. I wanted to build something that helps people make informed decisions based on as much information as possible, not tell them a simple percentage with nothing to back it up. I built Veredict to be secure and private: 1. The browser generates a one-time AES-256 key and encrypts the text locally using the Web Crypto API. 2. This AES key is encrypted using the server’s RSA public key. 3. The encrypted payload is sent to my backend (Python/FastAPI running on Modal serverless GPUs). 4. We decrypt in memory only for the split-second of inference. The plaintext is never saved to a database. The detection logic uses an ensemble of 4 models (including statistical analysis of perplexity/burstiness and a fine-tuned BERT model) to output a confidence score. A note on the login: I know HN prefers demos without sign-ups. Since I am a student paying for the GPU compute out of a limited budget, I strictly require Google Auth to prevent bots from draining my credits. I hope you understand. The app provides a free daily quota (250 words) so you can test the architecture. Link: https://ift.tt/TgJcjih I'd really appreciate feedback on basically anything regarding my project :) https://ift.tt/TgJcjih December 30, 2025 at 11:18PM

Show HN: Euclidle – Guess the Coordinates in N‑Dimensional Space https://ift.tt/2mjIvUH

Show HN: Euclidle – Guess the Coordinates in N‑Dimensional Space A small web puzzle game where you guess coordinates in n-dimensional space. Tutorial and manual are available. Available in 17 languages. Play here: https://euclidle.com/ Note: Google Analytics and AdSense are used. Tutorial: https://ift.tt/0SEsp6v Manual: https://ift.tt/FAERZtc Bluesky: https://ift.tt/duzTPgZ https://euclidle.com/ December 26, 2025 at 05:09AM

Show HN: Signing Room – Stateless Bitcoin Multisig Coordinator https://ift.tt/5ZglqOY

Show HN: Signing Room – Stateless Bitcoin Multisig Coordinator https://signingroom.io December 30, 2025 at 12:44AM

Show HN: AI-assisted approach to detecting patterns in network traffic https://ift.tt/ZpXS6E8

Show HN: AI-assisted approach to detecting patterns in network traffic I’ve been experimenting with an AI-assisted approach to detecting patterns in network traffic. For context, I maintain a project called Phone Home Detector, which analyses traffic between IP address pairs. It aggregates traffic into one-minute buckets and applies a set of fixed rules based on byte counts and transmission intervals. I recently prototyped an extension that exposes transmission size and timing data through an MCP tool, making it queryable by an LLM. The goal is to explore whether an LLM can identify patterns that are difficult to capture using static rules alone. This work is still experimental, and I’m not yet convinced it is an improvement over fixed-rule approaches. That said, I do find it interesting, and it may be a foundation for further exploration. The following is an example of a summary that it generates: The data sizes sent to IP address 91.189.91.49 are mostly consistent at 200, after an initial size of 168, and the intervals at which they are sent vary without any apparent pattern. https://ift.tt/AtGDKI7 December 29, 2025 at 02:21AM

Show HN: Matchstick Puzzle Game in the Browser https://ift.tt/jeBTV7R

Show HN: Matchstick Puzzle Game in the Browser An older family member showed me these puzzle games that he was playing via YouTube videos. I wanted to make them more playable in a frictionless way, so I generated all possible combinations for these types of puzzles and put together an interface for them. https://matchmath.pages.dev/ December 29, 2025 at 01:37AM