Show HN: Dap-mux – Connect your editor and REPL to the same debug session https://ift.tt/xNLWFYM

Show HN: Dap-mux – Connect your editor and REPL to the same debug session 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/psEKtxv ), 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/UEmyBv8 June 7, 2026 at 01:13AM

Show HN: I ported Xonotic (arena FPS) to WebAssembly with full P2P multiplayer https://ift.tt/bJ1xesK

Show HN: I ported Xonotic (arena FPS) to WebAssembly with full P2P multiplayer https://dpgame.xonotic.workers.dev/ June 6, 2026 at 11:29PM

Show HN: Typedframes – Pandas/polars column name checking at lint time https://ift.tt/L3j7NV6

Show HN: Typedframes – Pandas/polars column name checking at lint time https://ift.tt/xMUuwnX June 7, 2026 at 12:32AM

Show HN: I nerfed our coding agents on purpose https://ift.tt/DugVRFl

Show HN: I nerfed our coding agents on purpose 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. June 6, 2026 at 03:19AM

Show HN: OWASP VulnerableApp Modern Extensible and Scalable vulnerable app https://ift.tt/MGH63uV

Show HN: OWASP VulnerableApp Modern Extensible and Scalable vulnerable app https://ift.tt/ONXVEiL June 6, 2026 at 12:19AM

Show HN: I rebuilt a tiny old volleyball game I loved https://ift.tt/TzkM0Zx

Show HN: I rebuilt a tiny old volleyball game I loved https://volleyhop.com/ June 6, 2026 at 12:12AM

Show HN: Bash Runtime for AWS Lambda https://ift.tt/s8WHJA7

Show HN: Bash Runtime for AWS Lambda Hi HN, I built a Bash runtime for AWS Lambda to make writing glue code simpler and faster. Sometimes, all you need is a bit of `sed`, `awk`, maybe a loop and a few HTTP API calls, and this runtime gives you all the tools to do that. It comes bundled with `jq` and `curl` so you can handle JSON payloads and string together HTTP API calls right out of the box, including calling AWS services with `curl --aws-sigv4`. In keeping with the theme, the Lambda handler contract is also made as simple as practical: read from stdin, write to stdout, return 0 for success and non-0 for error. You can run shell scripts, call binaries (either what's available in `al2023.provided` or you can package your own static binaries with your handler), or a combination of both. If you remember nodding along to Adam Drake's post about how bash and coreutils can be faster than a Hadoop cluster, I hope you give this a whirl and find it useful. The runtime is packaged as a Lambda layer, so it should drop right into your normal AWS infrastructure. https://ift.tt/4Kk5Ryx June 5, 2026 at 11:12PM