C++ says “We have try at home”
22 by ibobev | 2 comments on Hacker News.
New top story on Hacker News: Ask HN: By what percentage has AI changed your output as a software engineer?
Ask HN: By what percentage has AI changed your output as a software engineer?
9 by nomilk | 12 comments on Hacker News.
Compared to the era before AI coding tools (say, ~2 years ago), if you had to put a number on it, how much has your productivity as a SWE changed? I think about this a lot; am keen to hear what others' perceptions are. For me; the short answer: about 2x (i.e. 100% faster than pre LLMs). Long answer: When I thoroughly understand the domain (i.e. business logic and real world problem I'm solving), and am familiar with the tech stack, I'm about ~10x faster for the same or better code quality. When I don't understand the domain, prompts will be ambiguous or inadequate, the LLM will guess, it will do a month's work in a day, but I'll spend the next 3 weeks refactoring and realising how trash the code was, due to how trash the prompt was. All in all, it's probably still faster than pre AI, but can give a demoralising psychological phenomena where you think something's nearly completed only to spend weeks debugging it, refactoring, and often tossing it away and starting over. In an unfamiliar tech stack, I can't always spot obvious mistakes (mistakes caused by the AI or the prompt), so it's less productive and more risky. 10-15% of the productivity improvement is due to improvements in the dev environment. I open ~/dotfiles with cursor and tell it a problem I have or ask for a specific improvement. It usually modifies .zshrc, .vimrc or similar (and iterates as necessary if the first attempt didn't work). Due to how fast this is (e.g. 5 minutes), I've made about 20 little tweaks that previously didn't justify the time. They definitely make me happier as well as a bit more productive. But overall, taking everything into account, I'd say I'm about 2x as productive as before LLMs.
9 by nomilk | 12 comments on Hacker News.
Compared to the era before AI coding tools (say, ~2 years ago), if you had to put a number on it, how much has your productivity as a SWE changed? I think about this a lot; am keen to hear what others' perceptions are. For me; the short answer: about 2x (i.e. 100% faster than pre LLMs). Long answer: When I thoroughly understand the domain (i.e. business logic and real world problem I'm solving), and am familiar with the tech stack, I'm about ~10x faster for the same or better code quality. When I don't understand the domain, prompts will be ambiguous or inadequate, the LLM will guess, it will do a month's work in a day, but I'll spend the next 3 weeks refactoring and realising how trash the code was, due to how trash the prompt was. All in all, it's probably still faster than pre AI, but can give a demoralising psychological phenomena where you think something's nearly completed only to spend weeks debugging it, refactoring, and often tossing it away and starting over. In an unfamiliar tech stack, I can't always spot obvious mistakes (mistakes caused by the AI or the prompt), so it's less productive and more risky. 10-15% of the productivity improvement is due to improvements in the dev environment. I open ~/dotfiles with cursor and tell it a problem I have or ask for a specific improvement. It usually modifies .zshrc, .vimrc or similar (and iterates as necessary if the first attempt didn't work). Due to how fast this is (e.g. 5 minutes), I've made about 20 little tweaks that previously didn't justify the time. They definitely make me happier as well as a bit more productive. But overall, taking everything into account, I'd say I'm about 2x as productive as before LLMs.
New top story on Hacker News: Show HN: Ez FFmpeg – Video editing in plain English
Show HN: Ez FFmpeg – Video editing in plain English
28 by josharsh | 8 comments on Hacker News.
I built a CLI tool that lets you do common video/audio operations without remembering ffmpeg syntax. Instead of: ffmpeg -i video.mp4 -vf "fps=15,scale=480:-1:flags=lanczos" -loop 0 output.gif You write: ff convert video.mp4 to gif More examples: ff compress video.mp4 to 10mb ff trim video.mp4 from 0:30 to 1:00 ff extract audio from video.mp4 ff resize video.mp4 to 720p ff speed up video.mp4 by 2x ff reverse video.mp4 There are similar tools that use LLMs (wtffmpeg, llmpeg, ai-ffmpeg-cli), but they require API keys, cost money, and have latency. Ez FFmpeg is different: - No AI – just regex pattern matching - Instant – no API calls - Free – no tokens - Offline – works without internet It handles ~20 common operations that cover 90% of what developers actually do with ffmpeg. For edge cases, you still need ffmpeg directly. Interactive mode (just type ff) shows media files in your current folder with typeahead search. npm install -g ezff
28 by josharsh | 8 comments on Hacker News.
I built a CLI tool that lets you do common video/audio operations without remembering ffmpeg syntax. Instead of: ffmpeg -i video.mp4 -vf "fps=15,scale=480:-1:flags=lanczos" -loop 0 output.gif You write: ff convert video.mp4 to gif More examples: ff compress video.mp4 to 10mb ff trim video.mp4 from 0:30 to 1:00 ff extract audio from video.mp4 ff resize video.mp4 to 720p ff speed up video.mp4 by 2x ff reverse video.mp4 There are similar tools that use LLMs (wtffmpeg, llmpeg, ai-ffmpeg-cli), but they require API keys, cost money, and have latency. Ez FFmpeg is different: - No AI – just regex pattern matching - Instant – no API calls - Free – no tokens - Offline – works without internet It handles ~20 common operations that cover 90% of what developers actually do with ffmpeg. For edge cases, you still need ffmpeg directly. Interactive mode (just type ff) shows media files in your current folder with typeahead search. npm install -g ezff
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