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

Show HN: Digger Solo – Local AI File Explorer https://ift.tt/o1ZqRJH

Show HN: Digger Solo – Local AI File Explorer 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/3W9VH04 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. https://solo.digger.lol June 4, 2026 at 11:25PM

Show HN: Bot or Not – Spot AI-generated randomness https://ift.tt/tNSDq9A

Show HN: Bot or Not – Spot AI-generated randomness https://play-bot-or-not.vercel.app/ June 4, 2026 at 11:56PM

Show HN: Fork of Rsync https://ift.tt/J0mfCWZ

Show HN: Fork of Rsync Hello. After hearing of the problematic LLM commits in rsync, I made a fork of rsync. I decided to fork it off release 3.4.1, since I heard that's the last release without the LLM code. https://ift.tt/gpsnQ6m June 4, 2026 at 02:20AM