Show HN: ResolveHQ – A Helpdesk Built on Cloudflare Workers, D1, R2 and Queues
8 by mirza_rizvi | 1 comments on Hacker News.
Hack Nux
Watch the number of websites being hacked today, one by one on a page, increasing in real time.
New ask Hacker News story: Stardust Store – An independent Android app marketplace
Stardust Store – An independent Android app marketplace
4 by zishutron | 0 comments on Hacker News.
Hi HN, I built Stardust Store, an independent Android app marketplace as part of my ZISHU TRON ecosystem. Website: https://ift.tt/FIYi5oA The main idea is to focus on app discovery, rather than simply providing a large list of APKs. The current platform has: Editor's Choice Trending and New Releases Popular Apps and Games Search by name, developer and category Top Free / Paid / Grossing / Trending / Rated rankings App categories User profiles Wishlist Download history Reviews I built the project independently and I'm currently focusing on improving the marketplace based on feedback from users and developers. The Android version is also available here: Download APK: https://ift.tt/3INeSaK Privacy: https://ift.tt/Qfz37rw Terms: https://ift.tt/WXQGCMD I'd especially appreciate feedback on the marketplace UX, discovery model, rankings, and what developers would expect from an alternative Android app marketplace.
4 by zishutron | 0 comments on Hacker News.
Hi HN, I built Stardust Store, an independent Android app marketplace as part of my ZISHU TRON ecosystem. Website: https://ift.tt/FIYi5oA The main idea is to focus on app discovery, rather than simply providing a large list of APKs. The current platform has: Editor's Choice Trending and New Releases Popular Apps and Games Search by name, developer and category Top Free / Paid / Grossing / Trending / Rated rankings App categories User profiles Wishlist Download history Reviews I built the project independently and I'm currently focusing on improving the marketplace based on feedback from users and developers. The Android version is also available here: Download APK: https://ift.tt/3INeSaK Privacy: https://ift.tt/Qfz37rw Terms: https://ift.tt/WXQGCMD I'd especially appreciate feedback on the marketplace UX, discovery model, rankings, and what developers would expect from an alternative Android app marketplace.
New Show Hacker News story: Show HN: Dbmask – Discover, mask, and verify sensitive data in SQL databases
Show HN: Dbmask – Discover, mask, and verify sensitive data in SQL databases
5 by SiyuanFeng | 0 comments on Hacker News.
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
5 by SiyuanFeng | 0 comments on Hacker News.
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
New Show Hacker News story: Show HN: What if the speed of light was 5 km/h?
Show HN: What if the speed of light was 5 km/h?
11 by dmitrybrant | 6 comments on Hacker News.
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!
11 by dmitrybrant | 6 comments on Hacker News.
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!
New ask Hacker News story: My Rebuttal to the Anthropic Extinction Event Hype Train
My Rebuttal to the Anthropic Extinction Event Hype Train
3 by hmichaelson24 | 0 comments on Hacker News.
"Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives." You may have seen a post like this yesterday from an Anthropic researcher named Jacob Coxon who quit the company in protest over fears that AI may be an extinction level event. These are a few of the quotes I saw surrounding it, and I want to share my own takes on them: 1. "The more senior the people, the more scared they are" This says more about Silicon Valley culture and incentives than anything else. The mythology around AI has taken on a life of its own in the Valley, and the financial and regulatory incentives support it growing. 2. "We really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade." Any evidence? Any sort of even implied math behind the 10%? I haven't seen any, only vague illusions to ASI. 3. "Coxon left Anthropic, leaving so much money on the table in spite of the upcoming IPO because he's earnest in his beliefs" First off, earnest doesn't mean correct. And pre-IPO options at Anthropic's current valuation even now aren't exactly a sacrifice, they're still worth an extraordinary amount. I don't believe the martyrdom framing. 4. "Self-improving AI is coming and following the exponential improvement curve" In my opinion, this is a kernel of truth hidden under a mountain of narrative spin. Yes, AI has undoubtedly gotten much better. However, behind every headline such as the Navier-Stokes solution, there is a much more grounded, nuanced, and controversial story than the headline might suggest. 5. "We are approaching the singularity" ...of OpenAI and Anthropic's IPOs The really unfortunate part of these extreme narratives is I believe they are actually counterproductive. It obscures the specific, tangible problems that we can solve in extreme language collapsing nuanced positions into two sides. For example, bioweapons, autonomous weapons systems, AI in critical infrastructure, and large-scale cyberattacks are real risks that deserve serious technical and policy attention right now. When everything is a potential apocalypse, nothing gets fixed.
3 by hmichaelson24 | 0 comments on Hacker News.
"Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives." You may have seen a post like this yesterday from an Anthropic researcher named Jacob Coxon who quit the company in protest over fears that AI may be an extinction level event. These are a few of the quotes I saw surrounding it, and I want to share my own takes on them: 1. "The more senior the people, the more scared they are" This says more about Silicon Valley culture and incentives than anything else. The mythology around AI has taken on a life of its own in the Valley, and the financial and regulatory incentives support it growing. 2. "We really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade." Any evidence? Any sort of even implied math behind the 10%? I haven't seen any, only vague illusions to ASI. 3. "Coxon left Anthropic, leaving so much money on the table in spite of the upcoming IPO because he's earnest in his beliefs" First off, earnest doesn't mean correct. And pre-IPO options at Anthropic's current valuation even now aren't exactly a sacrifice, they're still worth an extraordinary amount. I don't believe the martyrdom framing. 4. "Self-improving AI is coming and following the exponential improvement curve" In my opinion, this is a kernel of truth hidden under a mountain of narrative spin. Yes, AI has undoubtedly gotten much better. However, behind every headline such as the Navier-Stokes solution, there is a much more grounded, nuanced, and controversial story than the headline might suggest. 5. "We are approaching the singularity" ...of OpenAI and Anthropic's IPOs The really unfortunate part of these extreme narratives is I believe they are actually counterproductive. It obscures the specific, tangible problems that we can solve in extreme language collapsing nuanced positions into two sides. For example, bioweapons, autonomous weapons systems, AI in critical infrastructure, and large-scale cyberattacks are real risks that deserve serious technical and policy attention right now. When everything is a potential apocalypse, nothing gets fixed.