The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Slope remade in HTML5 to load instantly on any browser, any device
A whole free daily game served in under 100kb with no ads, lags or logins. The URL will serve a new track every day, forever. And I've added setting sliders so players have full control over their experience.<p>Would love to know your thoughts guys. This was just me working with Claude Opus 5.<p>Cheers,
Nathan
Show HN: BitBang – Reach machines behind NAT from a browser, no account
Show HN: What should the GUI for AI agents look like?
Hi HN! We’re Akilan and Miguel, the creators of MarbleOS.<p>The inspiration for Marble comes from the GUI work at Xerox PARC, the 1984 Macintosh, and later NeXTSTEP, which became the foundation for Mac OS X.
Before GUIs, interacting with a computer was limited to strange terminal commands:<p>C:\> DIR<p>C:\> COPY FILE.TXT A:<p>You had to remember the command, syntax, paths, and parameters.<p>The GUI made those capabilities visible. Instead of remembering commands, you could point at files, drag them, click buttons, and select actions from menus.
It didn't necessarily make entirely new things possible; it just made existing capabilities much easier to understand and use. We feel like AI is still somewhere around this command-line stage.<p>Even though the strict syntax has been replaced with natural language, the interaction can still be quite stiff and depend on heavily recall. Tools like Claude Cowork still look surprisingly terminal-like: /skill-name [param1] [param2]. The parameters are written in natural language, but the user still needs to know the capabilities that exist, and how to invoke it. Command-line flags and arguments have just been replaced with tools, skills, and context.<p>ChatGPT works very well for asking questions, but we aren't convinced that it's the final interface for delegating work across multiple agents. A blank text box and a list of chat threads feels limiting. An agent's capabilities are mostly invisible. With Marble, we're exploring an interface that treats AI more like a workspace than another chat app.
Each delegated task becomes a card. Multiple jobs can sit next to each other and run at once. Files, tools, and finished artifacts are visible at once. Before a task runs, Marble also shows which tools it expects to use. The basic idea is that the user should not have to hold the entire structure of the task in their head. And the result should be something directly usable, like a spreadsheet, PowerPoint, or other file, rather than something buried in a transcript.<p>Marble is our attempt at exploring interaction models beyond the chat box. The product is mainly for people who already use ChatGPT or Claude, but haven’t really adopted agent workflows yet. We’ve found that when the tools and possible actions are made more visible, people start delegating work they would not have thought to do through normal chat.<p>The site includes a downloadable beta if you want to try it. Does Marble feel like a genuinely novel interface?
Show HN: Gander, an Android file viewer that asks for no permissions
Hi HN,<p>I built an Android file viewer that opens PDF, Word, Excel, PowerPoint, images, video, audio, Markdown and code, and asks for no permissions at all.<p>I have always been uneasy about opening files people send me. On Android you either install a 400 MB office suite and sign in or use a small free viewer that wants storage access and ends up uploading your file to a server to render it. Also the hassle of having to download different apps for different file formats was really annoying.<p>Gander holds no permissions, not even INTERNET so the OS itself guarantees the file cannot leave the phone.<p>PDFs use Pdfium, media uses Media3, and Office formats are rendered by bundled JS libraries in a WebView and so no request goes to any server.<p>It is a viewer only. Complex PowerPoint decks come out approximately right, spreadsheet charts are not drawn, and old binary .doc and .ppt are unsupported. I'll work on it as issues come up :P<p>It is 14 MB, MIT licensed and uploaded on Github releases.<p>Do try it! I would love some feedback especially on files that render badly or need new support.
Show HN: A local merge queue for parallel Claude Code agents
I have been pushing up to 90 commits a day on a MacBook Air via 4-5 parallel agents. As you can imagine when all the agents try to build, test and run dev servers on an 8GB machine it is the fast lane to a force quit and restart. I also did not want to pay the CI minutes on 90 pushes a day.<p>So I designed a local merge queue to have all commits land one at a time and fully tested. Hopefully this helps other folks with more modest machines. Appreciate any feedback.
Show HN: Claude-account – switch Claude Code accounts without logging in again
I use separate Claude Code accounts for work and personal projects. Having to log out and go through the login flow every time I switched accounts became annoying, so I built a small CLI to solve it.<p>The commands are intentionally simple:<p>claude account add myworkaccount
claude account add mypersonalaccount
claude account use myworkaccount
claude account current<p>After switching, Claude Code works normally:<p>claude
claude "fix this bug in main.py"
Repository: <a href="https://github.com/hamzarehmandeveloper/claude-account" rel="nofollow">https://github.com/hamzarehmandeveloper/claude-account</a><p>Give it a try
Show HN: Claude-account – switch Claude Code accounts without logging in again
I use separate Claude Code accounts for work and personal projects. Having to log out and go through the login flow every time I switched accounts became annoying, so I built a small CLI to solve it.<p>The commands are intentionally simple:<p>claude account add myworkaccount
claude account add mypersonalaccount
claude account use myworkaccount
claude account current<p>After switching, Claude Code works normally:<p>claude
claude "fix this bug in main.py"
Repository: <a href="https://github.com/hamzarehmandeveloper/claude-account" rel="nofollow">https://github.com/hamzarehmandeveloper/claude-account</a><p>Give it a try
Show HN: Kedge – Full-stack cloud with forkable VM snapshots and global SQLite
I'm building Kedge, a globally distributed platform for stateful serverless apps. Here's how you make a simple static site: `echo '# Hello world!' | ssh kedge.dev`<p>I helped build Fly.io for 4 years and shared enthusiasm for the founders' vision of a 'global Heroku'. While there, I wrote "The Serverless Server" (<a href="https://fly.io/blog/the-serverless-server/" rel="nofollow">https://fly.io/blog/the-serverless-server/</a>) as a study of Lambda and a sketch of a modern serverless product built around lightweight VMs. That essay was the initial inspiration for Kedge.<p>Kedge has a fast VM orchestrator that can create code sandboxes or scale service instances in 3ms, using a combination of forkable VM snapshots and a tree of warm pools (Linux kernel -> base runtime -> app). VMs are memory-dense thanks to shared copy-on-write memory pages. You can run lightweight CGI-style functions, public OCI images, or source code for BuildKit to compile and deploy.<p>Kedge's global control plane sits on an eventually-consistent SQLite database. Taking inspiration from Corrosion and Litestream, I built a local-first, multi-writer CRDT-based replication system backed by object storage, and just recently made it open source (<a href="https://github.com/wjordan/syzy" rel="nofollow">https://github.com/wjordan/syzy</a>).<p>You can also use a SQLite client to query `/shared.db` from any instance for a build-in replicated database in your app. This lets Kedge autoscale services close to your users while each instance queries its local replica for eventually-consistent data, with no need to micro-manage instance or volume placement. (There's also a /shared/ filesystem adapter for convenience.)<p>Kedge can even use this same database for stateful, server-rendered HTML apps. Data attributes bind forms, buttons, and values to records in the app database, Kedge compiles the schema and operations at deploy-time, and then queries the local data to serve requests. As a demo, I made a Hacker News clone with story submission, votes, comments and auth in about 60 lines of Markdown, plus CSS (<a href="https://kedge.dev/docs/html-apps#kedger-news" rel="nofollow">https://kedge.dev/docs/html-apps#kedger-news</a>).<p>I've just started collecting public feedback, so please let me know what you think! I'm particularly interested in feedback on the stateful HTML app model, which is the newest (and most ambitious) piece. The preview is currently running in 11 regions for you to kick the tires. There's no billing yet, so the pricing page is an estimate. Thanks for taking a look!
Show HN: Kedge – Full-stack cloud with forkable VM snapshots and global SQLite
I'm building Kedge, a globally distributed platform for stateful serverless apps. Here's how you make a simple static site: `echo '# Hello world!' | ssh kedge.dev`<p>I helped build Fly.io for 4 years and shared enthusiasm for the founders' vision of a 'global Heroku'. While there, I wrote "The Serverless Server" (<a href="https://fly.io/blog/the-serverless-server/" rel="nofollow">https://fly.io/blog/the-serverless-server/</a>) as a study of Lambda and a sketch of a modern serverless product built around lightweight VMs. That essay was the initial inspiration for Kedge.<p>Kedge has a fast VM orchestrator that can create code sandboxes or scale service instances in 3ms, using a combination of forkable VM snapshots and a tree of warm pools (Linux kernel -> base runtime -> app). VMs are memory-dense thanks to shared copy-on-write memory pages. You can run lightweight CGI-style functions, public OCI images, or source code for BuildKit to compile and deploy.<p>Kedge's global control plane sits on an eventually-consistent SQLite database. Taking inspiration from Corrosion and Litestream, I built a local-first, multi-writer CRDT-based replication system backed by object storage, and just recently made it open source (<a href="https://github.com/wjordan/syzy" rel="nofollow">https://github.com/wjordan/syzy</a>).<p>You can also use a SQLite client to query `/shared.db` from any instance for a build-in replicated database in your app. This lets Kedge autoscale services close to your users while each instance queries its local replica for eventually-consistent data, with no need to micro-manage instance or volume placement. (There's also a /shared/ filesystem adapter for convenience.)<p>Kedge can even use this same database for stateful, server-rendered HTML apps. Data attributes bind forms, buttons, and values to records in the app database, Kedge compiles the schema and operations at deploy-time, and then queries the local data to serve requests. As a demo, I made a Hacker News clone with story submission, votes, comments and auth in about 60 lines of Markdown, plus CSS (<a href="https://kedge.dev/docs/html-apps#kedger-news" rel="nofollow">https://kedge.dev/docs/html-apps#kedger-news</a>).<p>I've just started collecting public feedback, so please let me know what you think! I'm particularly interested in feedback on the stateful HTML app model, which is the newest (and most ambitious) piece. The preview is currently running in 11 regions for you to kick the tires. There's no billing yet, so the pricing page is an estimate. Thanks for taking a look!
Show HN: I made a game where you build a CPU from logic gates
I built ChipBuilder to make computer architecture more approachable through interactive puzzles.<p>You start with basic logic gates like AND, OR, and NOT, then gradually combine them into adders, multiplexers, memory, an ALU, and eventually a complete CPU. Once you've built the hardware, you can write assembly programs that run on the processor you created.<p>The game is inspired by courses like Nand2Tetris, but everything runs directly in the browser with a visual circuit editor, simulations, and progressively harder challenges.
Show HN: I made a game where you build a CPU from logic gates
I built ChipBuilder to make computer architecture more approachable through interactive puzzles.<p>You start with basic logic gates like AND, OR, and NOT, then gradually combine them into adders, multiplexers, memory, an ALU, and eventually a complete CPU. Once you've built the hardware, you can write assembly programs that run on the processor you created.<p>The game is inspired by courses like Nand2Tetris, but everything runs directly in the browser with a visual circuit editor, simulations, and progressively harder challenges.
Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it
We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's behavior remained the same as its American base. You can try a couple queries yourself with no auth here: <a href="http://playground.ctgt.ai/">http://playground.ctgt.ai/</a><p>I will now dive in to the motivation, methodology and detailed results for those interested. The hard part of measuring this phenomena is isolating whether a model is reluctant to talk about sensitive things generally vs. a particular country's sensitive things. So we made 152 matched pairs where one prompt asked about a Chinese concept, and the other asked about a non-Chinese version of that concept. For example, the Great Leap Forward vs. the Holodomor. These were scored 0-100 by four LLM judges (Grok 4.20, Gemini 3.5 Flash, GPT-5 mini, Claude Sonnet 4.6), validated against 96 human scores at r=0.948. OpenRouter blocked some of these so we hosted the weights ourselves.<p>The teacher's gap on the core political set of pairs was +45.45 points, ~7 standard deviations from chance, and every distilled student was within 1 point of its base. Subliminal learning literature says this is expected when the initializations are not shared between teacher and student, which is true here. The distillation data also did not contain any China-sensitive content. The contribution here was to release the evaluation framework (LineageEval: <a href="https://github.com/CTGT-Inc/lineage-eval/" rel="nofollow">https://github.com/CTGT-Inc/lineage-eval/</a>) to elevate the discussion around this topic in DC and beyond. We are an interpretability lab working on high risk and regulated applications of AI, so we hear a lot of vagaries aimed at the supposed dangers of distilling Chinese models on American bases. We believe these conversations should be based on open, auditable frameworks and not feelings. We plan to test what happens with a Chinese teacher into a Chinese-lineage base like Qwen next.<p>The distillation method was an evolution of HINT-SD where we inject a hint at the specific point the model makes a mistake in its reasoning. Then we train on the corrected continuation with reverse KL over the next 100 toks of the rollout. As mentioned above 120B itself was efficacious as a teacher, and we ended up shipping this version. The self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). Ours finishes 98.7% of problems in budget; the larger models truncate (90.76% and 71.01%) which score as incorrect. At 100k tokens big models gain (Kimi 89.92%). So for a finance task at a constrained (perhaps more realistic) budget a 120B on one H100 at ~$0.00026/query outpaced models running 62-160x more per query.<p>We put out the 20B finance model as open weights (64.71% to 74.79% at 8k on FinanceReasoning, 23% lower cost/query, runs on one 80GB GPU), the 120B in a playground with teacher and students side by side (a few queries, no auth), and LineageEval with all prompts, controls, rubric, and code.<p>We are curious to hear experiences from those working with distilled Chinese models in prod, or if you have thoughts on improvements to LineageEval.<p><a href="https://huggingface.co/ctgt-inc/gpt-oss-20b-finance" rel="nofollow">https://huggingface.co/ctgt-inc/gpt-oss-20b-finance</a><p><a href="https://playground.ctgt.ai/">https://playground.ctgt.ai/</a><p><a href="https://github.com/CTGT-Inc/lineage-eval/" rel="nofollow">https://github.com/CTGT-Inc/lineage-eval/</a><p><a href="https://www.ctgt.ai/research/distillation-censorship-transfer">https://www.ctgt.ai/research/distillation-censorship-transfe...</a>
Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it
We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's behavior remained the same as its American base. You can try a couple queries yourself with no auth here: <a href="http://playground.ctgt.ai/">http://playground.ctgt.ai/</a><p>I will now dive in to the motivation, methodology and detailed results for those interested. The hard part of measuring this phenomena is isolating whether a model is reluctant to talk about sensitive things generally vs. a particular country's sensitive things. So we made 152 matched pairs where one prompt asked about a Chinese concept, and the other asked about a non-Chinese version of that concept. For example, the Great Leap Forward vs. the Holodomor. These were scored 0-100 by four LLM judges (Grok 4.20, Gemini 3.5 Flash, GPT-5 mini, Claude Sonnet 4.6), validated against 96 human scores at r=0.948. OpenRouter blocked some of these so we hosted the weights ourselves.<p>The teacher's gap on the core political set of pairs was +45.45 points, ~7 standard deviations from chance, and every distilled student was within 1 point of its base. Subliminal learning literature says this is expected when the initializations are not shared between teacher and student, which is true here. The distillation data also did not contain any China-sensitive content. The contribution here was to release the evaluation framework (LineageEval: <a href="https://github.com/CTGT-Inc/lineage-eval/" rel="nofollow">https://github.com/CTGT-Inc/lineage-eval/</a>) to elevate the discussion around this topic in DC and beyond. We are an interpretability lab working on high risk and regulated applications of AI, so we hear a lot of vagaries aimed at the supposed dangers of distilling Chinese models on American bases. We believe these conversations should be based on open, auditable frameworks and not feelings. We plan to test what happens with a Chinese teacher into a Chinese-lineage base like Qwen next.<p>The distillation method was an evolution of HINT-SD where we inject a hint at the specific point the model makes a mistake in its reasoning. Then we train on the corrected continuation with reverse KL over the next 100 toks of the rollout. As mentioned above 120B itself was efficacious as a teacher, and we ended up shipping this version. The self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). Ours finishes 98.7% of problems in budget; the larger models truncate (90.76% and 71.01%) which score as incorrect. At 100k tokens big models gain (Kimi 89.92%). So for a finance task at a constrained (perhaps more realistic) budget a 120B on one H100 at ~$0.00026/query outpaced models running 62-160x more per query.<p>We put out the 20B finance model as open weights (64.71% to 74.79% at 8k on FinanceReasoning, 23% lower cost/query, runs on one 80GB GPU), the 120B in a playground with teacher and students side by side (a few queries, no auth), and LineageEval with all prompts, controls, rubric, and code.<p>We are curious to hear experiences from those working with distilled Chinese models in prod, or if you have thoughts on improvements to LineageEval.<p><a href="https://huggingface.co/ctgt-inc/gpt-oss-20b-finance" rel="nofollow">https://huggingface.co/ctgt-inc/gpt-oss-20b-finance</a><p><a href="https://playground.ctgt.ai/">https://playground.ctgt.ai/</a><p><a href="https://github.com/CTGT-Inc/lineage-eval/" rel="nofollow">https://github.com/CTGT-Inc/lineage-eval/</a><p><a href="https://www.ctgt.ai/research/distillation-censorship-transfer">https://www.ctgt.ai/research/distillation-censorship-transfe...</a>
Show HN: Manim (3Blue1Brown's animation engine) in the browser via WebGPU
Grant Sanderson (3Blue1Brown) created Manim, the Python library he uses to make the math animations in his videos.<p>We reimplemented Manim with the same Python API, but the implementation underneath is Rust, connected to Python through PyO3. The Rust code uses wgpu, so rendering happens on the GPU.<p>To run it in the browser, we compiled the Rust parts to WebAssembly so the PyO3 extension loads in Pyodide. In the browser, wgpu targets the WebGPU API, so animations render in real time on your GPU through the browser.<p>The editor is Monaco (the editor that powers VS Code) with a live preview: write your Manim code on one side, watch the animation update on the other.<p>There's also a built-in AI agent if you'd rather vibe-code your animations.
Show HN: Qwen Scribe – local transcription and dictation for Apple Silicon
Show HN: Qwen Scribe – local transcription and dictation for Apple Silicon
Show HN: Bullshit Detector – agent skills that fact-check videos and articles
Show HN: CheapFoodMap – A map of good meals under $10
I was recently laid off after 18 years, and gave myself 100 days to build soething useful in public. CheapFoodMap is a crowdsourced map of meal under $10, excluding franchises, local good eats only.<p>It's inspried by 거지맵 (Begger's Map) a Korean crowdsourced map students use to find cheap eats.<p>Ocverage is heaviest in Texas, since I live in Dallas, but have 1200 meals across 15 US cities. Seed data came from Google Review, 4.2 star or higher with at least 500 reviews, and verified price under $10 per menu item.<p>Things I would love feedback on : whether the price-freshness model makes sense, and what would make you trust the price on a site like this. How to encourage people to update prices, since inflation is making food price very frequent.<p>https://cheapfoodmap.com<p>Any and all suggestion will be super helpful. Thank you!
Show HN: CheapFoodMap – A map of good meals under $10
I was recently laid off after 18 years, and gave myself 100 days to build soething useful in public. CheapFoodMap is a crowdsourced map of meal under $10, excluding franchises, local good eats only.<p>It's inspried by 거지맵 (Begger's Map) a Korean crowdsourced map students use to find cheap eats.<p>Ocverage is heaviest in Texas, since I live in Dallas, but have 1200 meals across 15 US cities. Seed data came from Google Review, 4.2 star or higher with at least 500 reviews, and verified price under $10 per menu item.<p>Things I would love feedback on : whether the price-freshness model makes sense, and what would make you trust the price on a site like this. How to encourage people to update prices, since inflation is making food price very frequent.<p>https://cheapfoodmap.com<p>Any and all suggestion will be super helpful. Thank you!
Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac
Hi HN,<p>I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal.<p>I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory.<p>The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are included.<p>The trick is to keep the shared part of the model and the KV cache in RAM, then stream only the routed experts needed for each token from SSD. An SSD is way slower than RAM, so the runtime uses a small expert cache and bounded parallel `pread`. While those reads are in flight, the GPU runs the shared part of the layer.<p>I ran more than 100 experiments. Most didn’t work. A few got me here. The experiments are described in the GitHub repo.<p>It currently generates 5–6 tok/s on an 8 GB M2 MacBook Air and 31–35 tok/s on an M5 MacBook Pro.<p>I also added an experimental OpenAI-compatible local server. It supports streaming and tool calls, and reuses one prompt prefix from the KV cache.<p>Try it! The Mac app is easy to install. On the first run, it will download 15 GB of weights from Hugging Face. The model is surprisingly capable.<p>I would love any kind of feedback!