Show HN: Documenting an Obscure Japanese Wii Game – and-Kensaku
2 by TylerJaacks | 1 comments on Hacker News.
I have been using Claude for the past couple of days this week to document and modify the TR2 game file format for an obscure Japanese-exclusive Wii game called And-Kensaku, or 安藤ケンサク. And-Kensaku is a game related to Googling. There are a few game modes, but the most famous one asks you a question and gives you two answer options, and you win if you choose the most popular Google search. I have been able to do the following: 1. disable signature checks on the files, and 2. allow edits to the Phrases.tr2 file, making it possible to modify the content of the aforementioned game mode. I wanted to go on this little adventure because reverse-engineering file formats is an extremely difficult (at least for me) and time-consuming task, and I wondered how well Claude would do at it. Right now, not everything about this game is documented, but I would like to fully document it and maybe release an English patch.
Hack Nux
Watch the number of websites being hacked today, one by one on a page, increasing in real time.
New Show Hacker News story: Show HN: Omni – Local-first multimodal file search on macOS
Show HN: Omni – Local-first multimodal file search on macOS
2 by artex_xh | 0 comments on Hacker News.
Finally made something I've always wanted, using the model we built. • SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw while indexing − Search is near-instant. Multimodal relevance is sometimes arguable, but the idea is recall (the agentic LLM takes the results and refines for the final answer), so maybe that's fine
2 by artex_xh | 0 comments on Hacker News.
Finally made something I've always wanted, using the model we built. • SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw while indexing − Search is near-instant. Multimodal relevance is sometimes arguable, but the idea is recall (the agentic LLM takes the results and refines for the final answer), so maybe that's fine
New Show Hacker News story: Show HN: I nerfed our coding agents on purpose
Show HN: I nerfed our coding agents on purpose
10 by noahfradin | 8 comments on Hacker News.
Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across the board, but we felt it in Codex more acutely. We’re a startup filled with people who work around the clock and are obsessed with building — naturally our daily bill alone was striking. Luckily we’re going after a big mission and speed matters significantly more than marginal token spend on the edges. Still, it got us thinking about how it was ludicrous that while our product has a side effect of decreasing token spend and speeding up agentic workflows by many orders of magnitude, we were using these top tier models for all types of internal coding tasks without any of those optimizations. The waste felt pretty ridiculous — the most glaring culprit was that we were seemingly using the max intelligence model on max reasoning for every task even when the task clearly didn’t require it. As a company who spends a lot of time on cached intelligence, it was also easy for us to see how there was plenty of other low hanging fruit as well. So, on a recent weekend, I quickly built a tool to optimize our usage. At its core is a very fast classifier that classifies your requests to the least intelligence required for the task and includes some nice token optimizations on top. The result is roughly the same quality for multiples lower token spend. But even more exciting for us, is that the properly bin packed intelligence and reasoning levels meant our speed also went up considerably. This wasn’t negligible. We’ve observed up to 3x savings and hours per day per person in saved time that we would have otherwise been waiting on tool turns and coding agent responses. For us, that means improved engineering velocity and significantly higher usage for the same spend. It also means more usage before getting throttled. As I told friends about this, they also wanted to start using it to maximize the usage they could get out of their coding agent plans. There are now engineers across many of the most cutting edge AI companies using this tool to optimize their token utilization in this way. Not just to save money, but to maximize output. Turns out that the best way to avoid getting nerfed by Claude is to intentionally nerf yourself selectively. We decided to release it for the rest of the builder community to use as well. You can now turn on Nerfguard for yourself and start getting more usage today.
10 by noahfradin | 8 comments on Hacker News.
Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across the board, but we felt it in Codex more acutely. We’re a startup filled with people who work around the clock and are obsessed with building — naturally our daily bill alone was striking. Luckily we’re going after a big mission and speed matters significantly more than marginal token spend on the edges. Still, it got us thinking about how it was ludicrous that while our product has a side effect of decreasing token spend and speeding up agentic workflows by many orders of magnitude, we were using these top tier models for all types of internal coding tasks without any of those optimizations. The waste felt pretty ridiculous — the most glaring culprit was that we were seemingly using the max intelligence model on max reasoning for every task even when the task clearly didn’t require it. As a company who spends a lot of time on cached intelligence, it was also easy for us to see how there was plenty of other low hanging fruit as well. So, on a recent weekend, I quickly built a tool to optimize our usage. At its core is a very fast classifier that classifies your requests to the least intelligence required for the task and includes some nice token optimizations on top. The result is roughly the same quality for multiples lower token spend. But even more exciting for us, is that the properly bin packed intelligence and reasoning levels meant our speed also went up considerably. This wasn’t negligible. We’ve observed up to 3x savings and hours per day per person in saved time that we would have otherwise been waiting on tool turns and coding agent responses. For us, that means improved engineering velocity and significantly higher usage for the same spend. It also means more usage before getting throttled. As I told friends about this, they also wanted to start using it to maximize the usage they could get out of their coding agent plans. There are now engineers across many of the most cutting edge AI companies using this tool to optimize their token utilization in this way. Not just to save money, but to maximize output. Turns out that the best way to avoid getting nerfed by Claude is to intentionally nerf yourself selectively. We decided to release it for the rest of the builder community to use as well. You can now turn on Nerfguard for yourself and start getting more usage today.
New ask Hacker News story: Ask HN: Spent thousands, got no customers. What's wrong with my site?
Ask HN: Spent thousands, got no customers. What's wrong with my site?
4 by petebay | 5 comments on Hacker News.
With the recent surge in AI-generated content, I built a website called Voloshow that generates images and videos using AI. However, it has been live for almost a month and still hasn’t attracted a single user. I’m not sure what I did wrong. website is called Voloshow (https://voloshow.com/).
4 by petebay | 5 comments on Hacker News.
With the recent surge in AI-generated content, I built a website called Voloshow that generates images and videos using AI. However, it has been live for almost a month and still hasn’t attracted a single user. I’m not sure what I did wrong. website is called Voloshow (https://voloshow.com/).
New Show Hacker News story: Show HN: Digger Solo – Local AI File Explorer
Show HN: Digger Solo – Local AI File Explorer
3 by sean_pedersen | 0 comments on Hacker News.
After a lot of work I present Digger Solo 0.5.0 - the AI file explorer that respects your privacy (everything runs locally). Demo video: https://ift.tt/s8P4ugM New features: - LLM Chat with RAG (bring your own OpenAI compatible API key - ideally host a local model) - fresh redesign with light theme available in settings - multi-tabbed GUI: open multiple semantic maps at once - smart music player: auto-plays similar songs Digger Solo offers semantic search and maps that allow you to browse your files intuively - uncovering hidden connections and near duplicates easily. Happy to answer questions.
3 by sean_pedersen | 0 comments on Hacker News.
After a lot of work I present Digger Solo 0.5.0 - the AI file explorer that respects your privacy (everything runs locally). Demo video: https://ift.tt/s8P4ugM New features: - LLM Chat with RAG (bring your own OpenAI compatible API key - ideally host a local model) - fresh redesign with light theme available in settings - multi-tabbed GUI: open multiple semantic maps at once - smart music player: auto-plays similar songs Digger Solo offers semantic search and maps that allow you to browse your files intuively - uncovering hidden connections and near duplicates easily. Happy to answer questions.
New ask Hacker News story: Ask HN: Why are so many Show HNs being flagged?
Ask HN: Why are so many Show HNs being flagged?
2 by 866-RON-0-FEZ | 5 comments on Hacker News.
I've noticed a ton of Show HN submissions in New recently are promptly flagged and killed. They don't appear to be obvious spam at first glance so what gives? Are bots doing this?
2 by 866-RON-0-FEZ | 5 comments on Hacker News.
I've noticed a ton of Show HN submissions in New recently are promptly flagged and killed. They don't appear to be obvious spam at first glance so what gives? Are bots doing this?