Show HN: Dap-mux – Connect your editor and REPL to the same debug session
3 by YesJustWolf | 1 comments on Hacker News.
I have been coding over four decades, in many languages, on many projects (including Firefox, Final Cut Pro, the Newton, and Fullwrite Professional if you can remember that far back; all these using my "dead-name"). I wrote something small and simple to scratch an itch. It's the UNIX philosophy: small "one-trick ponies", each *really* good at their one trick, then the user can hook them together to solve actual problems. I'm a CLI guy, and for almost everything, I already have this. But not for debugging. The itch I scratched was the connector that enables this philosophy for debugging. That thing is dap-mux. A DAP multiplexer turning a one-to-one protocol into a cooperating session of as many tools as you need to get it done! How it started: Helix and Python for me (and sometimes IPython), with the rest of my team using PyCharm (which I have long loved!). My team's problem is that they want the PyCharm debugger, and so must be satisfied with the JetBrains editor. *My* problem was I could use a full-blown debugger *or* I could have IPython *or* I could have Helix (or sometimes an unsatisfying combination of Helix and the debugger). That was my "itch". DAP (Debug Adapter Protocol) is the tantalizing answer, except it isn't. DAP is what editors (that don't want to write their own debuggers) are starting to adopt. The problem with DAP is it's one-to-one. One editor connects to one debugger. Done. Not a solution to my problem. And then suddenly, it *was* the solution. I realized that a DAP multiplexer would let you connect any DAP-aware editor to any debugger for any language, and simultaneously to a REPL, another session of your editor (or a different editor)! With the side benefit that now, like screen or tmux, since each process is its own thing: sessions are durable. Just restart whatever crashed and you're back where you were! There were hard parts: sequencing, late joiners, state management. Different end-points working on different actions in different sequences but with the same message ids. I solved these problems something like how NAT works. Instead of translating network addresses, though, I'm translating the sequence numbers of each client into something global and ordered, then correctly routing replies back to the end-point awaiting them, while mapping the sequence numbers for those replies back into the space of that end-point. Knowing the current state of the debugger, and replaying that as a message sequence to late joiners lets you start/connect the clients in any order. I chose Python: asyncio fits the I/O-router pattern perfectly, and it lets the IPython extension run in-process rather than over IPC. There are problems not yet solved: for instance, I think configuration in the clients and/or the startup sequence is too complicated. But it functions! I got what I wanted! The combination I use every day: Python + debugpy + Helix + IPython, all connected simultaneously. Step with `%n` or `%s`, evaluate expressions with `%eval`, watch Helix track the current line in real time. Rust with codelldb is the second confirmed combination — I debugged a Dijkstra implementation with Helix and a third-party DAP observer tool both connected to the same codelldb session. A community member, Sean Perry, has already built [dap-observer]( https://ift.tt/rKh6DPv ), which renders the current frame's variables as a navigable terminal tree. *This* was my exact dream! Small, focused, connectable tools all playing together! There's so much left to try: other editors, other debug adapters, Windows, other languages. None of this has been touched yet. The most helpful thing now is people testing it with their own setup and reporting what they find. It's time to play! `uv tool install 'dap-mux[ipython]'` for Python + IPython. `uv tool install dap-mux` for headless use with any language and adapter. No need for any part of the Python ecosystem. https://ift.tt/Dv421dh
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New Show Hacker News story: Show HN: Documenting an Obscure Japanese Wii Game – and-Kensaku
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.
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.
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/).