The best Hacker News stories from Show from the past day
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Show HN: Ourguide – OS wide task guidance system that shows you where to click
Hey! I'm eshaan and I'm building Ourguide -an on-screen task guidance system that can show you where to click step-by-step when you need help.<p>I started building this because whenever I didn’t know how to do something on my computer, I found myself constantly tabbing between chatbots and the app, pasting screenshots, and asking “what do I do next?” Ourguide solves this with two modes. In Guide mode, the app overlays your screen and highlights the specific element to click next, eliminating the need to leave your current window. There is also Ask mode, which is a vision-integrated chat that captures your screen context—which you can toggle on and off anytime -so you can ask, "How do I fix this error?" without having to explain what "this" is.<p>It’s an Electron app that works OS-wide, is vision-based, and isn't restricted to the browser.<p>Figuring out how to show the user where to click was the hardest part of the process. I originally trained a computer vision model with 2300 screenshots to identify and segment all UI elements on a screen and used a VLM to find the correct icon to highlight. While this worked extremely well—better than SOTA grounding models like UI Tars—the latency was just too high. I'll be making that CV+VLM pipeline OSS soon, but for now, I’ve resorted to a simpler implementation that achieves <1s latency.<p>You may ask: if I can show you where to click, why can't I just click too? While trying to build computer-use agents during my job in Palo Alto, I hit the core limitation of today’s computer-use models where benchmarks hover in the mid-50% range (OSWorld). VLMs often know what to do but not what it looks like; without reliable visual grounding, agents misclick and stall. So, I built computer use—without the "use." It provides the visual grounding of an agent but keeps the human in the loop for the actual execution to prevent misclicks.<p>I personally use it for the AWS Console's "treasure hunt" UI, like creating a public S3 bucket with specific CORS rules. It’s also been surprisingly helpful for non-technical tasks, like navigating obscure settings in Gradescope or Spotify. Ourguide really works for any task when you’re stuck or don't know what to do.<p>You can download and test Ourguide here: <a href="https://ourguide.ai/downloads" rel="nofollow">https://ourguide.ai/downloads</a><p>The project is still very early, and I’d love your feedback on where it fails, where you think it worked well, and which specific niches you think Ourguide would be most helpful for.
Show HN: SF Microclimates
<a href="https://microclimates.solofounders.com/" rel="nofollow">https://microclimates.solofounders.com/</a>
Show HN: I wrapped the Zorks with an LLM
I grew up on the Infocom games and when microsoft actually open-sourced Zork 1/2/3 I really wanted to figure out how to use LLMs to let you type whatever you want, I always found the amount language that the games "understood" to be so limiting - even if it was pretty state of the art at the time.<p>So I figured out how to wrap it with Tambo.. (and run the game engine in the browser) basically whatever you type gets "translated" into zork-speak and passed to the game - and then the LLM takes the game's output and optionally adds flavor. (the little ">_" button at the top exposes the actual game input)<p>What was a big surprise to me is multi-turn instructions - you can ask it to "Explore all the rooms in the house until you can't find any more" and it will plug away at the game for 10+ "turns" at a time... like Claude Code for Zork or something
Show HN: We Built the 1. EU-Sovereignty Audit for Websites
Show HN: LemonSlice – Upgrade your voice agents to real-time video
Hey HN, we're the co-founders of LemonSlice (try our HN playground here: <a href="https://lemonslice.com/hn">https://lemonslice.com/hn</a>). We train interactive avatar video models. Our API lets you upload a photo and immediately jump into a FaceTime-style call with that character. Here's a demo: <a href="https://www.loom.com/share/941577113141418e80d2834c83a5a0a9" rel="nofollow">https://www.loom.com/share/941577113141418e80d2834c83a5a0a9</a><p>Chatbots are everywhere and voice AI has taken off, but we believe video avatars will be the most common form factor for conversational AI. Most people would rather watch something than read it. The problem is that generating video in real-time is hard, and overcoming the uncanny valley is even harder.<p>We haven’t broken the uncanny valley yet. Nobody has. But we’re getting close and our photorealistic avatars are currently best-in-class (judge for yourself: <a href="https://lemonslice.com/try/taylor">https://lemonslice.com/try/taylor</a>). Plus, we're the only avatar model that can do animals and heavily stylized cartoons. Try it: <a href="https://lemonslice.com/try/alien">https://lemonslice.com/try/alien</a>. Warning! Talking to this little guy may improve your mood.<p>Today we're releasing our new model* - Lemon Slice 2, a 20B-parameter diffusion transformer that generates infinite-length video at 20fps on a single GPU - and opening up our API.<p>How did we get a video diffusion model to run in real-time? There was no single trick, just a lot of them stacked together. The first big change was making our model causal. Standard video diffusion models are bidirectional (they look at frames both before and after the current one), which means you can't stream.<p>From there it was about fitting everything on one GPU. We switched from full to sliding window attention, which killed our memory bottleneck. We distilled from 40 denoising steps down to just a few - quality degraded less than we feared, especially after using GAN-based distillation (though tuning that adversarial loss to avoid mode collapse was its own adventure).<p>And the rest was inference work: modifying RoPE from complex to real (this one was cool!), precision tuning, fusing kernels, a special rolling KV cache, lots of other caching, and more. We kept shaving off milliseconds wherever we could and eventually got to real-time.<p>We set up a guest playground for HN so you can create and talk to characters without logging in: <a href="https://lemonslice.com/hn">https://lemonslice.com/hn</a>. For those who want to build with our API (we have a new LiveKit integration that we’re pumped about!), grab a coupon code in the HN playground for your first Pro month free ($100 value). See the docs: <a href="https://lemonslice.com/docs">https://lemonslice.com/docs</a>. Pricing is usage-based at $0.12-0.20/min for video generation.<p>Looking forward to your feedback!<p>EDIT: Tell us what characters you want to see in the comments and we can make them for you to talk to (e.g. Max Headroom)<p>*We did a Show HN last year for our V1 model: <a href="https://news.ycombinator.com/item?id=43785044">https://news.ycombinator.com/item?id=43785044</a>. It was technically impressive but so bad compared to what we have today.
Show HN: One Human + One Agent = One Browser From Scratch in 20K LOC
Related: <a href="https://simonwillison.net/2026/Jan/27/one-human-one-agent-one-browser/" rel="nofollow">https://simonwillison.net/2026/Jan/27/one-human-one-agent-on...</a>
Show HN posts p/month more than doubled in the last year
Show HN: TetrisBench – Gemini Flash reaches 66% win rate on Tetris against Opus
Show HN: TetrisBench – Gemini Flash reaches 66% win rate on Tetris against Opus
Show HN: Only 1 LLM can fly a drone
Show HN: Only 1 LLM can fly a drone
Show HN: Netfence – Like Envoy for eBPF Filters
To power the firewalling for our agents so that they couldn't contact arbitrary services, I build netfence. It's like Envoy but for eBPF filters.<p>It allows you to define different DNS-based rules that are resolved in a local daemon to IPs, then pushed to the eBPF filter to allow traffic. By doing it this way, we can still allow DNS-defined rules, but prevent contacting random IPs.<p>There's also no network performance penalty, since it's just DNS lookups and eBPF filters referencing memory.<p>It also means you don't have to tamper with the base image, which the agent could potentially manipulate to remove rules (unless you prevent root maybe).<p>It automatically manages the lifecycle of eBPF filters on cgroups and interfaces, so it works well for both containers and micro VMs (like Firecracker).<p>You implement a control plane, just like Envoy xDS, which you can manage the rules of each cgroup/interface. You can even manage DNS through the control plane to dynamically resolve records (which is helpful as a normal DNS server doesn't know which interface/cgroup a request might be coming from).<p>We specifically use this to allow our agents to only contact S3, pip, apt, and npm.
Show HN: Netfence – Like Envoy for eBPF Filters
To power the firewalling for our agents so that they couldn't contact arbitrary services, I build netfence. It's like Envoy but for eBPF filters.<p>It allows you to define different DNS-based rules that are resolved in a local daemon to IPs, then pushed to the eBPF filter to allow traffic. By doing it this way, we can still allow DNS-defined rules, but prevent contacting random IPs.<p>There's also no network performance penalty, since it's just DNS lookups and eBPF filters referencing memory.<p>It also means you don't have to tamper with the base image, which the agent could potentially manipulate to remove rules (unless you prevent root maybe).<p>It automatically manages the lifecycle of eBPF filters on cgroups and interfaces, so it works well for both containers and micro VMs (like Firecracker).<p>You implement a control plane, just like Envoy xDS, which you can manage the rules of each cgroup/interface. You can even manage DNS through the control plane to dynamically resolve records (which is helpful as a normal DNS server doesn't know which interface/cgroup a request might be coming from).<p>We specifically use this to allow our agents to only contact S3, pip, apt, and npm.
Show HN: C From Scratch – Learn safety-critical C with prove-first methodology
Seven modules teaching C the way safety-critical systems are actually built: MATH → STRUCT → CODE → TEST.<p>Each module answers one question: Does it exist? (Pulse), Is it normal? (Baseline), Is it regular? (Timing), Is it trending? (Drift), Which sensor to trust? (Consensus), How to handle overflow? (Pressure), What do we do about it? (Mode).<p>Every module is closed (no dependencies), total (handles all inputs), deterministic, and O(1). 83 tests passing.<p>Built this after 30 years in UNIX systems. Wanted something that teaches the rigour behind certified systems without requiring a decade of on-the-job learning first.<p>MIT licensed. Feedback welcome.
Show HN: Fence – Sandbox CLI commands with network/filesystem restrictions
Hi HN!<p>Fence wraps any command in a sandbox that blocks network by default and restricts filesystem writes. Useful for running semi-trusted code (package installs, build scripts, unfamiliar repos) with controlled side effects, or even just blocking tools that phone home.<p>> fence curl <a href="https://example.com" rel="nofollow">https://example.com</a> # -> blocked<p>> fence -t code -- npm install # -> template with registries allowed<p>> fence -m -- npm install # -> monitor mode: see what gets blocked<p>One use-case is to use it with AI coding agents to reduce the risk of running agents with fewer interactive permission prompts:<p>> fence -t code -- claude --dangerously-skip-permissions<p>You can import existing Claude Code permissions with `fence import --claude`.<p>Fence uses OS-native sandboxing (macOS sandbox-exec, Linux bubblewrap) + local HTTP/SOCKS proxies for domain filtering.<p>Why I built this: I work on Tusk Drift, a system to record and replay real traffic as API tests (<a href="https://github.com/Use-Tusk/tusk-drift-cli" rel="nofollow">https://github.com/Use-Tusk/tusk-drift-cli</a>). I needed a way to sandbox the service under test during replays to block localhost outbound connections (Postgres, Redis) and force the app to use mocks instead of real services. I quickly realized that this could be a general purpose tool that would also be useful as a permission manager across CLI agents.<p>Limitations: Not strong containment against malware. Proxy-based filtering requires programs to respect `HTTP_PROXY`.<p>Curious if others have run into similar needs, and happy to answer any questions!
Show HN: Fence – Sandbox CLI commands with network/filesystem restrictions
Hi HN!<p>Fence wraps any command in a sandbox that blocks network by default and restricts filesystem writes. Useful for running semi-trusted code (package installs, build scripts, unfamiliar repos) with controlled side effects, or even just blocking tools that phone home.<p>> fence curl <a href="https://example.com" rel="nofollow">https://example.com</a> # -> blocked<p>> fence -t code -- npm install # -> template with registries allowed<p>> fence -m -- npm install # -> monitor mode: see what gets blocked<p>One use-case is to use it with AI coding agents to reduce the risk of running agents with fewer interactive permission prompts:<p>> fence -t code -- claude --dangerously-skip-permissions<p>You can import existing Claude Code permissions with `fence import --claude`.<p>Fence uses OS-native sandboxing (macOS sandbox-exec, Linux bubblewrap) + local HTTP/SOCKS proxies for domain filtering.<p>Why I built this: I work on Tusk Drift, a system to record and replay real traffic as API tests (<a href="https://github.com/Use-Tusk/tusk-drift-cli" rel="nofollow">https://github.com/Use-Tusk/tusk-drift-cli</a>). I needed a way to sandbox the service under test during replays to block localhost outbound connections (Postgres, Redis) and force the app to use mocks instead of real services. I quickly realized that this could be a general purpose tool that would also be useful as a permission manager across CLI agents.<p>Limitations: Not strong containment against malware. Proxy-based filtering requires programs to respect `HTTP_PROXY`.<p>Curious if others have run into similar needs, and happy to answer any questions!
Show HN: AutoShorts – Local, GPU-accelerated AI video pipeline for creators
Show HN: AutoShorts – Local, GPU-accelerated AI video pipeline for creators
Show HN: An interactive map of US lighthouses and navigational aids
This is an interactive map of US navigational aids and lighthouses, which indicates their location, color, characteristic and any remarks the Coast Guard has attached.<p>I was sick at home with the flu this weekend, and went on a bit of a Wikipedia deep dive about active American lighthouses. Searching around a bit, it was very hard to find a single source or interactive map of active beacons, and a description of what the "characteristic" meant. The Coast Guard maintains a list of active lights though, that they publish annually (<a href="https://www.navcen.uscg.gov/light-list-annual-publication" rel="nofollow">https://www.navcen.uscg.gov/light-list-annual-publication</a>). With some help from Claude Code, it wasn't hard to extract the lat/long and put together a small webapp that shows a map of these light stations and illustrates their characteristic with an animated visualization..<p>Of course, this shouldn't be used as a navigational aid, merely for informational purposes! Though having lived in Seattle and San Francisco I thought it was quite interesting.
Show HN: An interactive map of US lighthouses and navigational aids
This is an interactive map of US navigational aids and lighthouses, which indicates their location, color, characteristic and any remarks the Coast Guard has attached.<p>I was sick at home with the flu this weekend, and went on a bit of a Wikipedia deep dive about active American lighthouses. Searching around a bit, it was very hard to find a single source or interactive map of active beacons, and a description of what the "characteristic" meant. The Coast Guard maintains a list of active lights though, that they publish annually (<a href="https://www.navcen.uscg.gov/light-list-annual-publication" rel="nofollow">https://www.navcen.uscg.gov/light-list-annual-publication</a>). With some help from Claude Code, it wasn't hard to extract the lat/long and put together a small webapp that shows a map of these light stations and illustrates their characteristic with an animated visualization..<p>Of course, this shouldn't be used as a navigational aid, merely for informational purposes! Though having lived in Seattle and San Francisco I thought it was quite interesting.