Show HN: Everything a web page can learn about you, in plain English https://bit.ly/4yAdVRc

Show HN: Everything a web page can learn about you, in plain English https://bit.ly/4yuuWfq September 12, 2026 at 11:50PM

Show HN: See Sounds on Your Webcam https://bit.ly/4illDK2

Show HN: See Sounds on Your Webcam https://bit.ly/4imABPW September 10, 2026 at 03:43PM

Show HN: We built a local-first Android agent. Then Meta launched Muse https://bit.ly/4iZAhXt

Show HN: We built a local-first Android agent. Then Meta launched Muse https://bit.ly/4xR2CEh September 12, 2026 at 10:26AM

Show HN: Don't Hit Send – the model answers while you type https://bit.ly/3UPgv7c

Show HN: Don't Hit Send – the model answers while you type Type. The model is already answering. There is no send button. Left pane is one long draft. Right pane is a stack of replies. Pause for ~350ms and it fires a normal streaming chat completion with the whole draft. Type again and it aborts the last request if that reply never produced text; if it did, that bubble stays and a new one stacks. Bubbles never rewrite. Enter is a newline. That is overlapping unary streams, not a duplex socket. Same shape as ghost-text, pointed at a conversation instead of a code line. The bit that took the work is the hold: do not fire on "and N" while someone is still typing "and NASA". git clone https://bit.ly/3USgQpE cd dont-hit-send export SCALATTICE_API_KEY=slt_... # or OPENAI_API_KEY + OPENAI_BASE_URL ./run.sh # https://bit.ly/3T0aCDG Stdlib Python, MIT, key stays on your machine. Defaults to Scalattice OpenAI-compat; any host that speaks /v1/chat/completions works from Settings or env. Browser demo on our inference platform (sign-in after a short try): https://bit.ly/4hmG88a Why've we built this? The conventional AI chat interface is overdone and lacks innovation, I've personally been building agentic software for a while now and feel a lack of innovation in the interactivity. This is a step towards trialling some different inference interfaces! https://bit.ly/4h0mnBV September 12, 2026 at 06:06AM

Show HN: Next Notes – local dictation, meeting notes, and actions app https://bit.ly/46ZTUrb

Show HN: Next Notes – local dictation, meeting notes, and actions app https://bit.ly/4xoO4ur September 12, 2026 at 03:27AM

Show HN: Graphify C# – Compiler-accurate Find Usages for coding agents https://bit.ly/4radWZu

Show HN: Graphify C# – Compiler-accurate Find Usages for coding agents https://bit.ly/3ToX2Kc September 12, 2026 at 01:16AM

Show HN: Kern Agent – See inside your agent's brain https://bit.ly/3UQ1aTQ

Show HN: Kern Agent – See inside your agent's brain https://bit.ly/4lTogTc September 11, 2026 at 02:57AM

Show HN: Learn programming by building something you want https://bit.ly/4xi5tVo

Show HN: Learn programming by building something you want https://bit.ly/3UQ8dfi September 11, 2026 at 01:20AM

Show HN: Dbmask – Discover, mask, and verify sensitive data in SQL databases https://bit.ly/4xh0vII

Show HN: Dbmask – Discover, mask, and verify sensitive data in SQL databases Hi HN I developed *dbmask*, an open-source Python tool designed to discover sensitive columns in SQL databases, masking them with deterministic fake values, and validate the masking results. The motivation behind this tool stems from a common issue I encountered when handling test data: existing masking tools often assume the user already knows exactly which columns contain sensitive information. Furthermore, the successful completion of a masking task does not guarantee that all sensitive values have actually been replaced. dbmask aims to integrate these steps into a unified workflow rather than treating them as three separate tasks. During the detection phase, the tool makes determinations by combining column names, sampled values, database type/context, historical decisions, and optionally LLMs. While LLM functionality is disabled by default, it supports local execution via Ollama or other services compatible with the OpenAI API. If the tool cannot determine a column's nature with confidence, it marks it as `UNKNOWN` rather than simply classifying uncertain cases as "non-sensitive." I also wanted to incorporate human decision-making into the system. Detection results can be exported for manual review, and confirmed decisions can be saved in CSV, XLSX, or Markdown formats, or stored in a SQL history decision table. The system logs information about both the analyst and the reviewer and re-evaluates past decisions if column types change or review results expire. The masking process employs deterministic mapping, ensuring that identical original values consistently map to the same synthetic values across different tables. Once masking is complete, the `dbmask validate --strict` command compares the masked copy with the original data, checking row counts and table structures, and verifying sensitive values row-by-row using primary keys wherever possible. The project is currently in its early stages (version 0.1.x). The current CI (Continuous Integration) pipeline covers SQLite testing; integration tests for PostgreSQL and MySQL, as well as public detection benchmarks, are included in the future development roadmap. I would particularly appreciate feedback on the detection rules, the manual review/history tracking workflows, and the verification methods. Please let me know if you encounter any usability issues or potential security risks while testing on the temporary database. Install: pip install dbmask https://bit.ly/3Vnsmtg September 10, 2026 at 04:15PM

Show HN: What if the speed of light was 5 km/h? https://bit.ly/4ilrAqo

Show HN: What if the speed of light was 5 km/h? I've always wanted to make a visualization where the speed of light was scaled down to human speeds, so that we could intuit relativistic effects with everyday objects. And here is the first version of it! https://bit.ly/4Ak5Sts September 10, 2026 at 02:58AM

Show HN: Browser-based Nginx config generator with live preview https://bit.ly/4hkqMQz

Show HN: Browser-based Nginx config generator with live preview Generates Nginx server block configs for static sites, PHP, Node.js and reverse proxy. Live preview updates as you toggle options. Download the config file directly. No signup. Runs in the browser. Part of a free sysadmin tools site — also has SSH config builder, .htaccess generator, DNS and SSL tools. Looking for feedback. https://bit.ly/4ijaglV September 9, 2026 at 01:22AM

Show HN: VolAnti – Open-source acoustic detector for fibre-optic FPV drones https://bit.ly/4cugcVu

Show HN: VolAnti – Open-source acoustic detector for fibre-optic FPV drones This project has been a month in the making, and 9 fully functional units will be sent tomorrow to an undisclosed civilian site across the Israel - Lebanon border. All of my findings are shared online to anyone on the globe who could benefit from them, or make their own units. I'm an engineering student at the University of York, originally from Israel, studying in England. I too often have been hearing about tragic cases involving civilians in war zones being chased and attacked by small FPV drones mounted with an explosive. Especially around the north of Israel, Ukraine and Russian. My intentions for this project were clear: create a device small, portable and cheap enough for civilian use, that could drastically change the outcome of a drone infiltration through early alerting. I decided to dedicate a month of my summer to go back to the university labs and to work on this as personal project. These drone attacks are not run on normal wireless drones, instead they are run on small FPV drones that are connected to the operator through a long spool of fibre optic cable. This simply means that drone detection through Radio, the current industry standard, is not applicable. My solution was to build an acoustic detector. A device that uses 4 MEMS microphones to capture the drone's propellers' emitted noise as they cut through the air, runs that data through several algorithms to enhance the SNR, reduce noise (directional, possible confusers), recognises their comb looking shape when plotted by frequency, and alert the user through multiple outputs (display, beeper, buzzer, led) along with alerting all nearby units using a LoRa module also fitted in the device. The units have been tested against a custom drone rig, housing the exact same specs as the drones used in the Israel-Lebanon border (four 2807 motors, 7 in tri blades, FPV airframe), which was stationed 104.2m away from the device in a street with passer-by's and mild wind. That test ran successfully and the device was able to detect the drone. This device was built for detection and alerting only, so no jamming. Every online resource i could think of in regarding this project exists in the Github, along with a neat website that shows off more media with explanations, also housing a live detector simulation that runs in the browser: https://bit.ly/4xQf1IC https://bit.ly/4gVfWQq September 9, 2026 at 01:08AM

Show HN: Auto-Rig Web – Zero-click 3D humanoid rigging in a Web Worker https://bit.ly/4yjK3IC

Show HN: Auto-Rig Web – Zero-click 3D humanoid rigging in a Web Worker https://bit.ly/4yrPpBp September 8, 2026 at 11:48PM

Show HN: Zero downtime embedding model upgrades https://bit.ly/4ctEEq2

Show HN: Zero downtime embedding model upgrades People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database. However, I figured out an interesting way to forgo that upfront embedding cost. algo: old model/index -> retrieve top-K docs -> score those docs with the new model -> cache/materialize the new embeddings so instead of rebuilding the entire vector store upfront, the old index keeps getting retrieved from, while the new model reranks those candidates. This works surprisingly well for some model pairs, (i tested 63 source-> target migrations on h100s, on upto 1M documents). For example, on a 1M document Natural Questions dataset, native Qwen3-Embedding-8B: 0.6812 nDCG@10 Qwen3-4B -> Qwen3-8B, K=50: 0.6816 Qwen3-0.6B -> Qwen3-8B, K=50: 0.6638 MiniLM -> Qwen3-8B, K=50: 0.6486 (the hard part is determining k, I held the k constant above to give some sense of migratability). You can install it with pip pip install embedflow and the code is on github https://bit.ly/4zXvTyx https://bit.ly/4zXvTyx September 8, 2026 at 03:35AM

Show HN: Browse 27 years of movie ticket stubs in ThreeJS https://bit.ly/4gVMLNe

Show HN: Browse 27 years of movie ticket stubs in ThreeJS This is one of those projects that's been sitting in the back of my mind for years. I made a ThreeJS UI for browsing (read: re-living) the ticket stubs I've been collecting since I was 9 years old. I was going to create a whole form so that people could share their own collections but landed on just opening up a version you can use to DIY with your agent. That part is here: https://bit.ly/46JQf0y Enjoy! Notes and feedback welcome and appreciated. September 8, 2026 at 12:32AM

Show HN: Golden hour API and a 3D globe of the best light https://bit.ly/4r1NiC3

Show HN: Golden hour API and a 3D globe of the best light https://bit.ly/4r3TERy September 7, 2026 at 05:18AM

Show HN: Lantunnel – a P2P-first private mesh for reaching your LANs https://bit.ly/4cw9s9u

Show HN: Lantunnel – a P2P-first private mesh for reaching your LANs https://bit.ly/4gMZ5iU September 7, 2026 at 04:47AM

Show HN: GET Together – A social network where you don't need POST to Post https://bit.ly/3SAswNb

Show HN: GET Together – A social network where you don't need POST to Post https://bit.ly/4zUnrjD September 7, 2026 at 02:41AM

Show HN: Keen Bean – Mac meeting notes that draft the spec while you talk https://bit.ly/4iaEF5N

Show HN: Keen Bean – Mac meeting notes that draft the spec while you talk https://bit.ly/4gMY769 September 6, 2026 at 10:53AM

Show HN: OpenMonitor – vendor neutral web monitoring cloud agent https://bit.ly/4r2ShCE

Show HN: OpenMonitor – vendor neutral web monitoring cloud agent https://bit.ly/4hbAPIC September 6, 2026 at 01:03AM