The best Hacker News stories from Show from the past day
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Show HN: VoxCSS – A DOM based voxel engine
Show HN: Wirebrowser – A JavaScript debugger with breakpoint-driven heap search
Hi HN!<p>I'm building a JavaScript debugger called Wirebrowser. It combines network inspection, request rewriting, heap snapshots, and live object search.<p>The main experimental feature is BDHS (Breakpoint-Driven Heap Search): it hooks into the JavaScript debugger and automatically captures a heap snapshot at every pause and performs a targeted search for the value or structure of interest.
This reveals the moment a value appears in memory and the user-land function responsible for creating it.<p>Another interesting feature is the Live Object Search: it inspects runtime objects (not just snapshots), supports regex and object similarity, and lets you patch objects directly at runtime.<p>Whitepaper:
<a href="https://fcavallarin.github.io/wirebrowser/BDHS-Origin-Trace" rel="nofollow">https://fcavallarin.github.io/wirebrowser/BDHS-Origin-Trace</a><p>Feedback very welcome, especially on whether BDHS would help your debugging workflow.
Show HN: Wirebrowser – A JavaScript debugger with breakpoint-driven heap search
Hi HN!<p>I'm building a JavaScript debugger called Wirebrowser. It combines network inspection, request rewriting, heap snapshots, and live object search.<p>The main experimental feature is BDHS (Breakpoint-Driven Heap Search): it hooks into the JavaScript debugger and automatically captures a heap snapshot at every pause and performs a targeted search for the value or structure of interest.
This reveals the moment a value appears in memory and the user-land function responsible for creating it.<p>Another interesting feature is the Live Object Search: it inspects runtime objects (not just snapshots), supports regex and object similarity, and lets you patch objects directly at runtime.<p>Whitepaper:
<a href="https://fcavallarin.github.io/wirebrowser/BDHS-Origin-Trace" rel="nofollow">https://fcavallarin.github.io/wirebrowser/BDHS-Origin-Trace</a><p>Feedback very welcome, especially on whether BDHS would help your debugging workflow.
Show HN: A 2-row, 16-key keyboard designed for smartphones
Mobile keyboards today are almost entirely based on the 26-key, 3-row QWERTY layout.
Here’s a new 2-row, 16-key alternative designed specifically for smartphones.
Show HN: A 2-row, 16-key keyboard designed for smartphones
Mobile keyboards today are almost entirely based on the 26-key, 3-row QWERTY layout.
Here’s a new 2-row, 16-key alternative designed specifically for smartphones.
Show HN: A 2-row, 16-key keyboard designed for smartphones
Mobile keyboards today are almost entirely based on the 26-key, 3-row QWERTY layout.
Here’s a new 2-row, 16-key alternative designed specifically for smartphones.
Show HN: Automated license plate reader coverage in the USA
Built this over the last few days, based on a Rust codebase that parses the latest ALPR reports from OpenStreetMaps, calculates navigation statistics from every tagged residential building to nearby amenities, and tests each route for intersection with those ALPR cameras (Flock being the most widespread).<p>These have gotten more controversial in recent months, due to their indiscriminate large scale data collection, with 404 Media publishing many original pieces (<a href="https://www.404media.co/tag/flock/" rel="nofollow">https://www.404media.co/tag/flock/</a>) about their adoption and (ab)use across the country. I wanted to use open source datasets to track the rapid expansion, especially per-county, as this data can be crucial for 'deflock' movements to petition counties and city governments to ban and remove them.<p>In some counties, the tracking becomes so widespread that most people can't go anywhere without being photographed. This includes possibly sensitive areas, like places of worship and medical facilities.<p>The argument for their legality rests upon the notion that these cameras are equivalent to 'mere observation', but the enormous scope and data sharing agreements in place to share and access <i>millions</i> of records without warrants blurs the lines of the fourth amendment.
Show HN: Automated license plate reader coverage in the USA
Built this over the last few days, based on a Rust codebase that parses the latest ALPR reports from OpenStreetMaps, calculates navigation statistics from every tagged residential building to nearby amenities, and tests each route for intersection with those ALPR cameras (Flock being the most widespread).<p>These have gotten more controversial in recent months, due to their indiscriminate large scale data collection, with 404 Media publishing many original pieces (<a href="https://www.404media.co/tag/flock/" rel="nofollow">https://www.404media.co/tag/flock/</a>) about their adoption and (ab)use across the country. I wanted to use open source datasets to track the rapid expansion, especially per-county, as this data can be crucial for 'deflock' movements to petition counties and city governments to ban and remove them.<p>In some counties, the tracking becomes so widespread that most people can't go anywhere without being photographed. This includes possibly sensitive areas, like places of worship and medical facilities.<p>The argument for their legality rests upon the notion that these cameras are equivalent to 'mere observation', but the enormous scope and data sharing agreements in place to share and access <i>millions</i> of records without warrants blurs the lines of the fourth amendment.
Show HN: Automated license plate reader coverage in the USA
Built this over the last few days, based on a Rust codebase that parses the latest ALPR reports from OpenStreetMaps, calculates navigation statistics from every tagged residential building to nearby amenities, and tests each route for intersection with those ALPR cameras (Flock being the most widespread).<p>These have gotten more controversial in recent months, due to their indiscriminate large scale data collection, with 404 Media publishing many original pieces (<a href="https://www.404media.co/tag/flock/" rel="nofollow">https://www.404media.co/tag/flock/</a>) about their adoption and (ab)use across the country. I wanted to use open source datasets to track the rapid expansion, especially per-county, as this data can be crucial for 'deflock' movements to petition counties and city governments to ban and remove them.<p>In some counties, the tracking becomes so widespread that most people can't go anywhere without being photographed. This includes possibly sensitive areas, like places of worship and medical facilities.<p>The argument for their legality rests upon the notion that these cameras are equivalent to 'mere observation', but the enormous scope and data sharing agreements in place to share and access <i>millions</i> of records without warrants blurs the lines of the fourth amendment.
Show HN: Persistent memory for Claude Code sessions
Show HN: Detail, a Bug Finder
Hi HN, tl;dr we built a bug finder that's working really well, especially for app backends. Try it out and send us your thoughts!<p>Long story below.<p>--------------------------<p>We originally set out to work on technical debt. We had all seen codebases with a lot of debt, so we had personal grudges about the problem, and AI seemed to be making it a lot worse.<p>Tech debt also seemed like a great problem for AI because: 1) a small portion of the work is thinky and strategic, and then the bulk of the execution is pretty mechanical, and 2) when you're solving technical debt, you're usually trying to preserve existing behavior, just change the implementation. That means you can treat it as a closed-loop problem if you figure out good ways to detect unintended behavior changes due to a code change. And we know how to do that – that's what tests are for!<p>So we started with writing tests. Tests create the guardrails that make future code changes safer. Our thinking was: if we can test well enough, we can automate a lot of other tech debt work at very high quality.<p>We built an agent that could write thousands of new tests for a typical codebase, most "merge-quality". Some early users merged hundreds of PRs generated this way, but intuitively the tool always felt "good but not great". We used it sporadically ourselves, and it usually felt like a chore.<p>Around this point we realized: while we had set out to write good tests, we had built a system that, with a few tweaks, might be very good at finding bugs. When we tested it out on some friends' codebases, we discovered that almost every repo has tons of bugs lurking in it that we were able to flag. Serious bugs, interesting enough that people dropped what they were doing to fix them. Sitting right there in peoples codebases, already merged, running in prod.<p>We also found a lot of vulns, even in mature codebases, and sometimes even right after someone had gotten a pentest.<p>Under the hood:
- We check out a codebase and figure out how to build it for local dev and exercise it with tests.
- We take snapshots of the built local dev state. (We use Runloop for this and are big fans.)
- We spin up hundreds of copies of the local dev environment to exercise the codebase in thousands of ways and flag behaviors that seem wrong.
- We pick the most salient, scary examples and deliver them as linear tickets, github issues, or emails.<p>In practice, it's working pretty well. We've been able to find bugs in everything from compilers to trading platforms (even in rust code), but the sweet spot is app backends.<p>Our approach trades compute for quality. Our codebase scans take hours, far beyond what would be practical for a code review bot. But the result is that we can make more judicious use of engineers’ attention, and we think that’s going to be the most important variable.<p>Longer term, we think compute is cheap, engineer attention is expensive. Wielded properly, the newest models can execute complicated changes, even in large codebases. That means the limiting reagent in building software is human attention. It still takes time and focus for an engineer to ingest information, e.g. existing code, organizational context, and product requirements. These are all necessary before an engineer can articulate what they want in precise terms and do a competent job reviewing the resulting diff.<p>For now we're finding bugs, but the techniques we're developing extend to a lot of other background, semi-proactive work to improve codebases.<p>Try it out and tell us what you think. Free first scan, no credit card required: <a href="https://detail.dev/" rel="nofollow">https://detail.dev/</a><p>We're also scanning on OSS repos, if you have any requests. The system is pretty high signal-to-noise, but we don't want to risk annoying maintainers by automatically opening issues, so if you request a scan for an OSS repo the results will go to you personally. <a href="https://detail.dev/oss" rel="nofollow">https://detail.dev/oss</a>
Show HN: Detail, a Bug Finder
Hi HN, tl;dr we built a bug finder that's working really well, especially for app backends. Try it out and send us your thoughts!<p>Long story below.<p>--------------------------<p>We originally set out to work on technical debt. We had all seen codebases with a lot of debt, so we had personal grudges about the problem, and AI seemed to be making it a lot worse.<p>Tech debt also seemed like a great problem for AI because: 1) a small portion of the work is thinky and strategic, and then the bulk of the execution is pretty mechanical, and 2) when you're solving technical debt, you're usually trying to preserve existing behavior, just change the implementation. That means you can treat it as a closed-loop problem if you figure out good ways to detect unintended behavior changes due to a code change. And we know how to do that – that's what tests are for!<p>So we started with writing tests. Tests create the guardrails that make future code changes safer. Our thinking was: if we can test well enough, we can automate a lot of other tech debt work at very high quality.<p>We built an agent that could write thousands of new tests for a typical codebase, most "merge-quality". Some early users merged hundreds of PRs generated this way, but intuitively the tool always felt "good but not great". We used it sporadically ourselves, and it usually felt like a chore.<p>Around this point we realized: while we had set out to write good tests, we had built a system that, with a few tweaks, might be very good at finding bugs. When we tested it out on some friends' codebases, we discovered that almost every repo has tons of bugs lurking in it that we were able to flag. Serious bugs, interesting enough that people dropped what they were doing to fix them. Sitting right there in peoples codebases, already merged, running in prod.<p>We also found a lot of vulns, even in mature codebases, and sometimes even right after someone had gotten a pentest.<p>Under the hood:
- We check out a codebase and figure out how to build it for local dev and exercise it with tests.
- We take snapshots of the built local dev state. (We use Runloop for this and are big fans.)
- We spin up hundreds of copies of the local dev environment to exercise the codebase in thousands of ways and flag behaviors that seem wrong.
- We pick the most salient, scary examples and deliver them as linear tickets, github issues, or emails.<p>In practice, it's working pretty well. We've been able to find bugs in everything from compilers to trading platforms (even in rust code), but the sweet spot is app backends.<p>Our approach trades compute for quality. Our codebase scans take hours, far beyond what would be practical for a code review bot. But the result is that we can make more judicious use of engineers’ attention, and we think that’s going to be the most important variable.<p>Longer term, we think compute is cheap, engineer attention is expensive. Wielded properly, the newest models can execute complicated changes, even in large codebases. That means the limiting reagent in building software is human attention. It still takes time and focus for an engineer to ingest information, e.g. existing code, organizational context, and product requirements. These are all necessary before an engineer can articulate what they want in precise terms and do a competent job reviewing the resulting diff.<p>For now we're finding bugs, but the techniques we're developing extend to a lot of other background, semi-proactive work to improve codebases.<p>Try it out and tell us what you think. Free first scan, no credit card required: <a href="https://detail.dev/" rel="nofollow">https://detail.dev/</a><p>We're also scanning on OSS repos, if you have any requests. The system is pretty high signal-to-noise, but we don't want to risk annoying maintainers by automatically opening issues, so if you request a scan for an OSS repo the results will go to you personally. <a href="https://detail.dev/oss" rel="nofollow">https://detail.dev/oss</a>
Show HN: AlgoDrill – Interactive drills to stop forgetting LeetCode patterns
I built AlgoDrill because I kept grinding LeetCode, thinking I knew the pattern, and then completely blanking when I had to implement it from scratch a few weeks later.<p>AlgoDrill turns NeetCode 150 and more into pattern-based drills: you rebuild the solution line by line with active recall, get first principles editorials that explain why each step exists, and everything is tagged by patterns like sliding window, two pointers, and DP so you can hammer the ones you keep forgetting. The goal is simple: turn familiar patterns into code you can write quickly and confidently in a real interview.<p><a href="https://algodrill.io" rel="nofollow">https://algodrill.io</a><p>Would love feedback on whether this drill-style approach feels like a real upgrade over just solving problems once, and what’s most confusing or missing when you first land on the site.
Show HN: AlgoDrill – Interactive drills to stop forgetting LeetCode patterns
I built AlgoDrill because I kept grinding LeetCode, thinking I knew the pattern, and then completely blanking when I had to implement it from scratch a few weeks later.<p>AlgoDrill turns NeetCode 150 and more into pattern-based drills: you rebuild the solution line by line with active recall, get first principles editorials that explain why each step exists, and everything is tagged by patterns like sliding window, two pointers, and DP so you can hammer the ones you keep forgetting. The goal is simple: turn familiar patterns into code you can write quickly and confidently in a real interview.<p><a href="https://algodrill.io" rel="nofollow">https://algodrill.io</a><p>Would love feedback on whether this drill-style approach feels like a real upgrade over just solving problems once, and what’s most confusing or missing when you first land on the site.
Show HN: AlgoDrill – Interactive drills to stop forgetting LeetCode patterns
I built AlgoDrill because I kept grinding LeetCode, thinking I knew the pattern, and then completely blanking when I had to implement it from scratch a few weeks later.<p>AlgoDrill turns NeetCode 150 and more into pattern-based drills: you rebuild the solution line by line with active recall, get first principles editorials that explain why each step exists, and everything is tagged by patterns like sliding window, two pointers, and DP so you can hammer the ones you keep forgetting. The goal is simple: turn familiar patterns into code you can write quickly and confidently in a real interview.<p><a href="https://algodrill.io" rel="nofollow">https://algodrill.io</a><p>Would love feedback on whether this drill-style approach feels like a real upgrade over just solving problems once, and what’s most confusing or missing when you first land on the site.
Show HN: I built a system for active note-taking in regular meetings like 1-1s
Hey HN! Like most here regular meetings have always been a big part of my work.<p>Over the years I've learned the value of active note taking in these meetings. Meaning: not minutes, not transcriptions or AI summaries, but <i>me</i> using my brain to actively pull out the key points in short form bullet-like notes, as the meeting is going on, as I'm talking and listening (and probably typing with one hand). This could be agenda points to cover, any interesting sidebars raised, insights gotten to in a discussion, actions agreed to (and a way to track whether they got done next time!).<p>It's both useful just to track what's going on in all these different meetings week to week (at one point I was doing about a dozen 1-1s per week, and it just becomes impossible to hold it in RAM) but also really valuable over time when you can look back and see the full history of a particular meeting, what was discussed when, how themes and structure are changing, is the meetings effective, etc.<p>Anyway, I've tried a bunch of different tools for taking these notes over the years. All the obvious ones you've probably used too. And I've always just been not <i>quite</i> satisfied with the experience. They work, obviously (it's just text based notes at the end of the day) but nothing is first-class for this usecase.<p>So, I decided to build the tool I've always felt I want to use, specifically for regular 1-1s and other types of regular meetings. I've been using it myself and with friends for a while already now, and I think it's got to that point where I actually prefer to reach for it over other general purpose note taking tools now, and I want to share it more widely.<p>There's a free tier so you can use it right away, in fact without even signing up.<p>If you've also been wanting a better system to manage your notes for regular meetings, give it a go and let me know what you think!
Show HN: I built a system for active note-taking in regular meetings like 1-1s
Hey HN! Like most here regular meetings have always been a big part of my work.<p>Over the years I've learned the value of active note taking in these meetings. Meaning: not minutes, not transcriptions or AI summaries, but <i>me</i> using my brain to actively pull out the key points in short form bullet-like notes, as the meeting is going on, as I'm talking and listening (and probably typing with one hand). This could be agenda points to cover, any interesting sidebars raised, insights gotten to in a discussion, actions agreed to (and a way to track whether they got done next time!).<p>It's both useful just to track what's going on in all these different meetings week to week (at one point I was doing about a dozen 1-1s per week, and it just becomes impossible to hold it in RAM) but also really valuable over time when you can look back and see the full history of a particular meeting, what was discussed when, how themes and structure are changing, is the meetings effective, etc.<p>Anyway, I've tried a bunch of different tools for taking these notes over the years. All the obvious ones you've probably used too. And I've always just been not <i>quite</i> satisfied with the experience. They work, obviously (it's just text based notes at the end of the day) but nothing is first-class for this usecase.<p>So, I decided to build the tool I've always felt I want to use, specifically for regular 1-1s and other types of regular meetings. I've been using it myself and with friends for a while already now, and I think it's got to that point where I actually prefer to reach for it over other general purpose note taking tools now, and I want to share it more widely.<p>There's a free tier so you can use it right away, in fact without even signing up.<p>If you've also been wanting a better system to manage your notes for regular meetings, give it a go and let me know what you think!
Show HN: I built a system for active note-taking in regular meetings like 1-1s
Hey HN! Like most here regular meetings have always been a big part of my work.<p>Over the years I've learned the value of active note taking in these meetings. Meaning: not minutes, not transcriptions or AI summaries, but <i>me</i> using my brain to actively pull out the key points in short form bullet-like notes, as the meeting is going on, as I'm talking and listening (and probably typing with one hand). This could be agenda points to cover, any interesting sidebars raised, insights gotten to in a discussion, actions agreed to (and a way to track whether they got done next time!).<p>It's both useful just to track what's going on in all these different meetings week to week (at one point I was doing about a dozen 1-1s per week, and it just becomes impossible to hold it in RAM) but also really valuable over time when you can look back and see the full history of a particular meeting, what was discussed when, how themes and structure are changing, is the meetings effective, etc.<p>Anyway, I've tried a bunch of different tools for taking these notes over the years. All the obvious ones you've probably used too. And I've always just been not <i>quite</i> satisfied with the experience. They work, obviously (it's just text based notes at the end of the day) but nothing is first-class for this usecase.<p>So, I decided to build the tool I've always felt I want to use, specifically for regular 1-1s and other types of regular meetings. I've been using it myself and with friends for a while already now, and I think it's got to that point where I actually prefer to reach for it over other general purpose note taking tools now, and I want to share it more widely.<p>There's a free tier so you can use it right away, in fact without even signing up.<p>If you've also been wanting a better system to manage your notes for regular meetings, give it a go and let me know what you think!
Show HN: Gemini Pro 3 imagines the HN front page 10 years from now
Show HN: Gemini Pro 3 imagines the HN front page 10 years from now