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 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.
I think they throttle it - they note it is an asynchronous service
I agree that is is generally a pretty useful service.
"Only if they're happy with the changes, I step in to review the code and press merge."
It's pretty easy to introduce something like IDOR when asking LLMs to write the code.
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.
Can't justify spending much time on it now but a DIY no/low code solution for them isn't a bad idea.
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.
My experience with coding agents leads me to believe using something like this will end up being more noise and work than ROI
I suppose it could be effectively the same loop I use in VS Code, but then why would I want an external tool over an integration?
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.
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.
To get agentic coding working 9 months ago you were massaging context, 6 months ago you'd be hooking up MCP servers, 3 months ago you'd be writing a gazillion .claude files.
But all of that is utterly useless knowledge. That's not a transferrable skill.
Using AI is much more effective if you write the code yourself, using AI as a supercharged Stack Overflow.
A programmer + AI supercharged SO > A programmer + agentic coding
So stop wasting your time learning how to get agentic coding working today, especially as the method keeps changing. There's no point learning the voodoo and rain-dancing of this iteration of today's coding agents. Try them, and until they work, discard them.
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
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.
Jules is going to simply be another vendor locked walled garden play.
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?
and then use your existing Anthropic plan. Otherwise yeah you'll have to start using API tokens:
https://www.anthropic.com/engineering/building-agents-with-t...
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.
It was pretty rough at launch but has gotten a lot better. So has Claude code though, so I’ve never really switched over.
A good Jules comparison would be OpenAI Codex.
For a Claude Code Google equivalent there’s Gemini Code Assist CLI
But Jules is more sophisticated though.
The default installation for claude code is hilariously insecure and the only times I've used it is in a fully sandboxed VM.
You're literally putting your own money in the shareholders pockets.
Video here: https://www.youtube.com/watch?v=RIjz9w77h1Q
From my experience Jules is the worst coding agent on the market.
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.
Come on Google...
Recently I moved from repl.it to Claude max to save costs.
There's no stopping it. Just roll with it.
I'll never understand why paying users are so often left behind. It's truly bizarre.
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.
ChrisArchitect•4mo ago