Show HN: StructOCR – API for parsing global passports, invoices, and containers https://ift.tt/XkfldyJ

Show HN: StructOCR – API for parsing global passports, invoices, and containers Hi HN, I built an AI-powered OCR API designed to extract highly structured JSON data from complex documents like global passports, IDs, receipts, and shipping containers. We recently rolled out our Python and Node.js SDKs. Just wanted to share it with the community. https://structocr.com June 6, 2026 at 02:48AM

Show HN: Open-source X Bookmark Manager https://ift.tt/1iHaEeM

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/2Enrpbl June 6, 2026 at 12:48AM

Show HN: My Terminal Waifu https://ift.tt/tTZroCG

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 12:27AM

Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities https://ift.tt/KVkMXgL

Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities I built a benchmark with 20 real CVEs across 18 Python projects (Pillow, GitPython, yt-dlp, urllib3, etc). I've run it over 5 LLM agents (3 OpenAI, 2 poolside) and 3 different prompts (full advisory, locate, diagnose) with a total of 300 runs. The agents are tasked to fix security vulnerabilities in a sandboxed environment and they are scored against a hidden security tests from the maintainer's own fix. Best solve rate was 50%. On the other 50%, some fixes are sometimes coherent and pass all regression tests, but vulnerability still present. The main differentiator I found between models is cost: gpt-5.5 at 12× more expensive than gpt-5.4-mini while producing statistically similar results. Within-family performance gaps are small, which points out the difference is likely due to model training data. I also did a power analysis and the task count needed to detect a meaningful within-family edge at ~700. Full write-up: https://giovannigatti.github.io/cve-bench Code: https://ift.tt/8yeu63I https://giovannigatti.github.io/cve-bench/ June 4, 2026 at 09:43PM

Show HN: Fast Android File Manager that works https://ift.tt/vmPGYl0

Show HN: Fast Android File Manager that works https://ift.tt/8zDWVPb June 5, 2026 at 02:33AM

Show HN: Local-first fast CPU image to text for screenshots, PDFs, webpages https://ift.tt/BzZ5LY4

Show HN: Local-first fast CPU image to text for screenshots, PDFs, webpages https://ift.tt/rFigKb2 June 5, 2026 at 01:16AM

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

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/iq5RHG6 ) ? 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/lVbLtvf Anyway, if anyone is interested, feedbacks and questions are welcome! Thanks! https://ift.tt/lVbLtvf June 4, 2026 at 11:10PM