I have an old Django site I'm maintaining for a long-time customer of mine. They often want to make small changes - things that are only a few lines of code, but would take an hour to just spin up the system, remind myself how it works, commit, push, update the server and all that.
Last week I've moved the whole infrastructure to Railway, and taught the customer to use Jules. They make their own PRs now, and Railway spins up an environment with the changes, so the customer can check it themselves. It works like 75% of the time, and when it doesn't, the customer see that it doesn't before it even reaches me. Only if they're happy with the changes, I step in to review the code and press merge. It's been a such a huge time saver so far.
I can't speak for the OP, but I have customers I still support, because they supported me many years ago when I was a teenager taking my first steps into industry.
Does it make me money? Barely a cent. But I can spare a hour or two a year for the guy who gave me a leg up and trusted a teenager who probably shouldn't have been trusted. And I like the feeling of having something I worked on still going strong 20+ years later, when so much of my later work has been thrown away by the endless corporate rewrite treadmill.
They've always paid me per hour. The fewer hours, the better for me: just like the sibling post, I'm also not in it for the money. I care for both the customer and the project, and I'm happy that we've found a way to get the development going again with really minimal effort from my side.
How expensive are the API charges? Seems like it might be a bit too easy for a customer to rack up a big bill testing out minor changes if things weren't configured correctly.
Literally free. No API - the reason I went for Jules instead of Claude Code / Gemini CLI for example is specifically because of it's relatively polished web-interface, which I assumed that my customer would appreciate. They're using their own Google account and the daily tasks free limit seem to be more than enough for them.
There is a free plan with 15 tasks/sessions. It doesn’t count tokens AFAIK. There would obviously be a runtime limit of some sorts for sure. But it’s not the same as API keys and token situation
The free tier is 15 tasks per day (of gemini-2.5-pro) which is EXTREMELY generous. I've had plenty of tasks run for 1-2 hours. I do think that after 1 or 2 hours it's told it needs to wrap up and just present what it's done; I couldn't get it to keep going longer than 2 hours. But Jules is very slow as it seems to be batch processing on spare capacity, so 15+ hours a day is not quite as absurd as it sounds.
I haven't tried Jules in a couple weeks, but the UI/UX had a lot of issues such as not being given any progress updates for very long times. The worst thing was not being able to see what it was doing and correct it: you only see the state of files (without a usable diff viewer, WTF) at the last point that the agent decided to show you anything (the last time it completed a todo list item I think, and I couldn't get it to update the state when asked, though it will send a PR if you ask), and gemini-2.5-pro can often try really stupid things as it tries to debug. I've also been impressed at its debugging abilities a number of times.
Still, I found Jules far more usable than Gemini CLI (free tier), where Gemini just constantly stops for no reason and needs to be told to continue, and I exhausted the usage limit in minutes.
Aside from the unlimited free tier, probably the best part of Jules are its automated code reviews. Once, I was writing up some extensive comments on its code and then unexpectedly a code review was dropped in the conversation which gave exactly the same feedback I was writing. Unfortunately if it never reaches the point of submitting for review, it doesn't get an automated review. It does often ask for feedback before it's done, which is nice. So probably I needed to prompt better.
No, Jules was able to usually edit the code blind and get things working. If they didn't, the customer saw it on the automatic environment created for the PR, told Jules and Jules fixed it. I think I saw one task or maybe two in which Jules actually ran the HTTP server, set up Postgres, ran all the migrations and created a superuser, only to then write some Playwright code that it used to login and take some screenshots.
In other words, so far it didn't feel like including a database will provide us with much, but I am playing with the idea of creating a tiny mock database and including it in the repo, as the real database is around 15GB and contains passwords and names.
That's honestly incredibly cool, could I perhaps encourage you to write a blog about the details with some examples on what the PR requests from your customer looks like.
That's an interesting idea! It's been just a little bit over a week now that we're doing it, but maybe by the end of the month I'll have some more conclusions to share.
My experience with coding agents leads me to believe using something like this will end up being more noise and work than ROI
I think that depends on how far out your horizon is. If you're only looking one task out, or maybe a few weeks out, then it's not worth investing the time yet. On the other hand, if you're looking at how your engineering team will work in 3 years time it's definitely worth starting to look at it now.
An example that comes to mind: having a bot that automatically spins up an environment when a library is updated, runs through the tests, and identifies why a codebase doesn't work with the update, and fixes it then opens an appropriate PR that passes all the tests for humans to review would be incredibly useful.
The LLMs are a crapshoot, and probably always will be, for reliable automatic fixing of anything. They save me time 50% of the time. The other 50% they just can’t put enough together to grok what the existing code does, but damn if their code doesn’t look like it should work.
In 3 years time this won't be how these tools work. So it feels like you're saying we should invest time in something that doesn't work and will be redundant in 6 months.
Worse, your example is one that AI agents are notoriously bad at. Give them an error like that and they're more liable to break the existing functionality to fix it, after littering the code with tons of log statements.
When most of the time the actual fix is a silly little mistake that takes one line to fix.
We are unlikely to see these things progress beyond "a never learning junior that doesn't remember what it did last hour"
It's a limitation inherited from how they are designed. Fine if you babysit them, but they quickly get off the rails and waste my time too. Hence to original question about people actually using something like Jules versus speculating how nice it would be
> why would I want an external tool over an integration?
I do not feel comfortable running agents the same computer I have my photos, email, browser cookies, etc. on my personal computer, so giving Jules access to my GitHub project was a nice experience for me. It was able read my Gemfile and run my Rails app's test suite without me having to worry about all my private data on my machine. The code wasn't great, but it did help with coders block to kick off some features.
The benefit I've found of external vs integration at least with GitHub copilot is in the cloud it auto approves by default and it's working in a sandboxed environment.
I believe it is to make sure that the product remains compliant with the data guarantees that Workspace provides. You aren't paying for the latest and the greatest features, you're paying for the support and compliance guarantees your business expects.
companies want features gated behind controls, they want audit trails, compliance, SLAs, integration with their admin consoles. and they want some certainty that the feature won't change too quickly.
i will never understand why people keep using workspace accounts for personal use, and then being surprised when features hit those accounts more slowly. this is how it's worked for 20 years, it's not going to change. if you want earlier access, create a gmail account for your personal use.
I think they are doing both (in true Google fashion), there is an open source Gemini cli with a generous free tier that more directly competes with Claude code.
https://github.com/google-gemini/gemini-cli
It was pretty rough at launch but has gotten a lot better. So has Claude code though, so I’ve never really switched over.
I've been using AI coding agents since the very early days of Aider and I think this is not quite true.
There's a place for async agents. There's a place for collaborative agents. Collaborative agents may even soon be delegating off to multiple async agents and picking best results. There's so much complexity here and we haven't even begun to explore a corner of the possible design space. We're still trying to plug AIs into human-shaped holes instead of building around their interesting/weird capabilities.
Would you be willing to point me to a primer of how I can get started with building agents?
This week I experimented with building a simple planner/reviewer “agentic” iterative pipeline to automate an analysis workflow.
It was effectively me dipping my toes into this field, and I am so floored to learn more. But I’m unsure of where to start, since everything seems so fast paced.
I’m also unsure of how to experiment, since APIs rack up fees pretty quickly. Maybe local models?
There are a number of free and cheap LLM options to experiment with. Google offers a decent free plan for Gemini (get some extra Google accounts). Groq has a free tier including some good open weight models. There's also free endpoints on OpenRouter that are limited but might be useful for long running background agents. DeepSeek v3.2, Qwen3, Kimi K2, and GLM 4.6 are all good choices for cheap and capable models.
Local models are generally not a shortcut to cheap and effective AI. It's a fun thing to explore though.
I am so sick of these anthropomorphized names that have nothing to do with anything that we’re all supposed to remember now. Why are we giving products first names? The worst offender is probably Amazon Rufus. It’s all so dumb and I hate it. At least attempt to be clever and name it something that relates to the product itself. Even Google Wave, despite its shortcomings, made sense as a product name.
My friend, letting yourself be bothered by this is just pissing into the wind. Humans have been anthropomorphizing machines and other objects for as long as we've been making them, it's a fundamental aspect of human nature. Thousands upon thousands of ships and trains given human names. Tanks, guns, cars, anything that is at least moderately complex or that people find themselves relying on and forming relationships with. AIs have been getting human names since at least 1966 with Eliza, probably earlier, and certainly with many earlier examples in fiction.
Would anyone at Google be willing to tell me how many people are working on this project? I’ve been building something functionally similar for my employer, but it’s a nights and weekends project with only one contributor (me).
Jules can add all it wants and I will still not use it simply because it's a Google product and Google doesn't know how to make products in the past 20 years.
Also, why the heck are Google's offerings so fragmented?! We have `gemini`, `jules`, and we also have two sets of different Gemini APIs (one is more limited than the other), and no API is entirely OpenAI-compatible.
I really hope Google discontinues this project soon (that’s kind of their specialty). I find it frustrating when chatbots/LLMs adopt real names as their brand identities.
Last week I've moved the whole infrastructure to Railway, and taught the customer to use Jules. They make their own PRs now, and Railway spins up an environment with the changes, so the customer can check it themselves. It works like 75% of the time, and when it doesn't, the customer see that it doesn't before it even reaches me. Only if they're happy with the changes, I step in to review the code and press merge. It's been a such a huge time saver so far.
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