Show HN: Open-source X Bookmark Manager https://ift.tt/CaSFKrg

Show HN: Open-source X Bookmark Manager I built an open source bookmark manager for X (Twitter). Features: - Search bookmarks - Tag bookmarks - Organize saved content - Self-hostable GitHub: https://ift.tt/4wjY72O June 6, 2026 at 07:48AM

Show HN: My Terminal Waifu https://ift.tt/1HIkZc3

Show HN: My Terminal Waifu made this AI generated song using Suno + Wang Video. enjoy :) https://www.youtube.com/watch?v=PLMLH1JLTXk June 6, 2026 at 07:27AM

Show HN: NEP – Ethereum JSON-RPC transform that beats ZSTD by 12% https://ift.tt/YAkXZb7

Show HN: NEP – Ethereum JSON-RPC transform that beats ZSTD by 12% https://ift.tt/Kh4uZfJ June 6, 2026 at 12:00AM

Show HN: Nanocode-CLI – A lightweight terminal-based AI coding assistant https://ift.tt/x2ktdj3

Show HN: Nanocode-CLI – A lightweight terminal-based AI coding assistant https://ift.tt/BG9z6Dq June 6, 2026 at 01:54AM

Show HN: Lowfat – pluggable CLI filter that saved 91.8% of my LLM tokens https://ift.tt/FA3Skys

Show HN: Lowfat – pluggable CLI filter that saved 91.8% of my LLM tokens Hi HN, Not sure if anyone would be interested. But, just wanted to share that I've been maintaining my small tool called 'lowfat' that helps me filters some of my verbose CLI output. It's a single binary, works as an agent hook or a shell wrapper. It has a plugin system to customize filters per command. The idea is pretty simple: agents don't need the full kubectl get -o yaml or any 10k-line dump to make decisions. So that lowfat sits in between, strips the noise, and passes through what matters. Here's my real report after 2 months of personal use: lowfat history --all lowfat plugin candidates ───────────────────────────────────────────────────────── # command runs avg raw cost savings source status 1 kubectl get 101x 14.4K 1.5M 93.9% plugin good 2 grep 103x 13.5K 1.4M 96.2% plugin good 3 git diff 81x 995 80.6K 57.9% built-in good 4 kubectl 90x 485 43.6K 33.6% plugin good 5 docker 127x 5.5K 693.6K 96.1% built-in good 6 ls 489x 117 57.3K 56.2% built-in good 7 find 30x 16.5K 495.0K 95.5% plugin good 8 git show 63x 490 30.9K 38.0% built-in good 9 git 177x 368 65.2K 76.1% built-in good 10 git log 86x 556 47.8K 78.5% built-in good 11 kubectl logs 5x 3.6K 17.8K 43.0% plugin good 12 git status 86x 152 13.1K 58.0% built-in good 13 docker ps 20x 467 9.3K 52.8% plugin good 14 kubectl describe 6x 656 3.9K 1.2% plugin weak 15 docker images 9x 940 8.5K 61.8% built-in good 16 k get 2x 2.1K 4.2K 35.9% plugin good 17 terraform 10x 395 3.9K 32.1% plugin good 18 git commit 32x 77 2.5K 0.0% built-in weak 19 docker build 8x 487 3.9K 37.6% built-in good 20 docker compose 22x 979 21.5K 89.4% built-in good total: 4.4M raw → 4.1M saved (91.8%) My toolset above is kind limited, but it works pretty well for my usecase without any interruption Kinda help me not reaching the token limit for my company Bedrock limit usage and keep optimizing the saving on the go for later usage. But, why not alternatives ( https://ift.tt/2uRDeja ) ? The answers are: - My goal is to make the core lightweight but extensible via plugins i.e. not trying to bundle every command in the installed binary so that people own their output filters. - Customizable per usecase via plugin or filter pipelines as I am using my own toolset. - Customizable for non-public CLI tools, for example, some enterprise might have their interal CLI tools that public won't have access. - People should own their data. So the design is local-first, No telemetry forever. - I kinda love UNIX-style composible pipes, so lowfat-filter has implemented this style. - Be able to adjust aggressiveness of the filter, so we can control that we won't strip something the agent needed. GitHub: https://ift.tt/G3ZPzpO Anyway, if anyone is interested, feedbacks and questions are welcome! Thanks! https://ift.tt/G3ZPzpO June 5, 2026 at 06:10AM

Show HN: CentProof – Local-first bank statement reconciliation for macOS https://ift.tt/64Xdfgy

Show HN: CentProof – Local-first bank statement reconciliation for macOS https://centproof.com June 5, 2026 at 02:31AM

Show HN: Lessons learned from running Claude Code swarms at scale https://ift.tt/lwLZsmu

Show HN: Lessons learned from running Claude Code swarms at scale Some time ago I built a simple app to run swarms of coding agents — I call it fleet ( https://ift.tt/OBmudWG ). It's based on centralized beads with a Python orchestrator and can run any coder (Claude, agy, Codex). Recently I added a UI to manage the whole agent lifecycle: adding new tasks, monitoring running ones, and a chat interface built on MCP with a centralized SQLite DB. From the UI I can spawn agents to run in any directory, define dependencies on other tasks, and specify which coder/model should do the job. Today I can run 10–15 agents concurrently. At that scale you burn through limits very fast, so I spent some time investigating where those limits go and how to maximize efficiency. Here are the lessons learned after a few weeks of running the fleet: - CLAUDE.md is a terrible abstraction. These files load unconditionally, they often contain descriptions irrelevant to the task at hand, and they stack from your working directory upward. The result is wasted tokens and confusion from injecting irrelevant instructions into the session. - Skills are bad, but not as bad as CLAUDE.md. They use a progressive disclosure approach: only the skill description goes into the session, and Claude loads the full skill text with a tool when it's needed. That's one level better, but it still doesn't let you scale — you can't create 10K skills, as that would eat your entire usable context. Claude recently introduced a skills budget that silently drops less frequently used skills from the session entirely. You can still invoke them in an interactive session, but the model can't invoke them in a background session. - Some plugins may be installed more than once. During cleanup I found that a few of mine were installed in multiple locations, consuming double the tokens on duplicated instructions. - Attaching plugins to every session is a bad idea at scale. You want to be precise about which plugins are actually useful and attach them per task. - Use a hierarchical knowledge base instead of CLAUDE.md / skills / plugins. It lets you benefit from real progressive disclosure: keep your instructions and tool descriptions in it and let Claude navigate through it quickly and cheaply. - System tools consume ~15K tokens (7% of the session). You can't manage this — they're just attached, and disabling tools doesn't remove them from the context. - AskUserQuestion isn't available in background sessions. You need to implement your own tool — MCP- or CLI-based — to give `claude -p` the ability to talk to you. - You become selective about which model handles each task. Decompose work into harder and simpler subtasks so you can route the simpler ones to weaker, cheaper models and save tokens. - Your context-switching skill improves over time. Fleet repo: https://ift.tt/wG8cWl5 June 5, 2026 at 01:34AM