For me it feels like roughly a 10–20x change, but mostly because I restructured how I work rather than just adding an “AI helper” on top.
In the last year I’ve shipped a couple of small OSS tools that I almost certainly would not have finished without AI‑assisted “vibe coding”. Everything I build now flows through AI, but in a slightly different way than just chatting with an LLM. I rarely use standalone ChatGPT/Gemini/Claude; almost all of it happens inside GitHub with Copilot and agents wired into my repos.
The big shift was treating GitHub as the interface for almost all of my work, not just code. I have repos for things like hiring, application review, financial reviews, and other operational workflows. There are “master” repos with folders and sub‑folders, and each folder has specific context plus instructions that the AI agent should follow when operating in that scope, essentially global rules and sub‑rules for each area of work.
Because of that structure, AI speeds up more than just the typing of code. Idea → spec → implementation → iteration all compress into much tighter loops. Tasks that would have taken me weeks end‑to‑end are now usually a couple of days, sometimes hours. Subjectively that’s where the 10–20x feeling comes from, even though it’s hard to measure precisely.
On the team side we’ve largely replaced traditional stand‑ups with AI‑mediated updates. KPIs and goals live in these repos, and progress/achievements are logged and summarized via AI, which makes updates more quantitative and easier to search back through. It’s starting to feel less like “AI helps me with code” and more like “AI is the main operating system for how our team works.”
Happy to share more about the repo/folder structure or what has/hasn’t worked if anyone’s curious.
While the multiplier is less for me ( perhaps 3x or 4x ) I think the assumption that productivity gain leads to directly to more money is bit optimistic. Unless you are self employed, or run your own company, and actually get paid by results, being more efficient is seldom paid much. ( with luck you get promotion every two years or so or pay raise every year )
I have worked for too long in the field, but this year and simply thanks to the LLMs I have actually managed to get 4 usable hobby projects done ( as far as I need them to go anyway - personal tools that I use and publish but do not actively maintain unless I need some new feature ), and I have been quite productive with stack I do not normally use at our startup. Most previous years I have finished 0-1 hobby projects.
The ramp up period for new stack was much less, and while I still write some code myself, most of it at least starts as LLM output which I review and adjust to what I really want. It is bit less intellectually satisfying but a lot more efficient way to work for me. And ultimately for work at least I care more about good enough results.
Completely unrelated, but what’s with the spaces inside the parenthesis here? It’s super weird (and leads to incorrect text layout with a standalone parenthesis at the end or beginning of a line…)
When it comes to small focused OSS tools, you can basically code them at the speed of thought with modern LLMs. The slow part is figuring out what you want to do in the first place, the implementing is basically instantaneous. It's literally faster than googling a tool that already does the job.
And yes, that doesn't scale to all problem domains or problem sizes, but in some areas even a 20x speedup would be a huge understatement.
I love this! I mean, (a) I feel I am 20x faster / better. I have not yet reached the stage where I can scientifically put a "x" on how much better I have become.
Am I getting more money>> I think I am currently putting the correct amount of seeds which hopefully will flourish in 2026 with results. But now, it is easier and faster.
But yes, I am sorry for claiming a more "felt like" number.
Our old stand‑up was the classic “what I did yesterday / what I’ll do today / blockers” round‑robin on a call. Now we treat it as an AI‑generated, repo‑driven status report instead of a live status meeting.
Concretely, we:
- Break each person’s day into deterministic, measurable tasks with expected time cost and explicit test/code criteria in the repo.
- At EOD, run the updated repo through an AI agent that checks for those tests/changes, scores how much of each task was actually completed, and produces a per‑person and team summary that replaces the verbal stand‑up.
Happy to elaborate on this more if you would like.
Daily tasks are then deterministic checklists inside the repo, e.g. “Create 3 drafts for next Tuesday with images in /assets/images and entries added to 2025-12-calendar.md under campaign X”.
In the last year I’ve shipped a couple of small OSS tools that I almost certainly would not have finished without AI‑assisted “vibe coding”. Everything I build now flows through AI, but in a slightly different way than just chatting with an LLM. I rarely use standalone ChatGPT/Gemini/Claude; almost all of it happens inside GitHub with Copilot and agents wired into my repos.
The big shift was treating GitHub as the interface for almost all of my work, not just code. I have repos for things like hiring, application review, financial reviews, and other operational workflows. There are “master” repos with folders and sub‑folders, and each folder has specific context plus instructions that the AI agent should follow when operating in that scope, essentially global rules and sub‑rules for each area of work.
Because of that structure, AI speeds up more than just the typing of code. Idea → spec → implementation → iteration all compress into much tighter loops. Tasks that would have taken me weeks end‑to‑end are now usually a couple of days, sometimes hours. Subjectively that’s where the 10–20x feeling comes from, even though it’s hard to measure precisely.
On the team side we’ve largely replaced traditional stand‑ups with AI‑mediated updates. KPIs and goals live in these repos, and progress/achievements are logged and summarized via AI, which makes updates more quantitative and easier to search back through. It’s starting to feel less like “AI helps me with code” and more like “AI is the main operating system for how our team works.”
Happy to share more about the repo/folder structure or what has/hasn’t worked if anyone’s curious.