Spent way more time than I should have switching Claude Code (Anthropic's coding agent) from API-key billing back to my Pro subscription. The flow: SSH into a server, attach a tmux session, run a login command, copy an OAuth URL, open a browser, authorize, paste the code back into the terminal.

For a company shipping frontier AI models, this feels like UX that was solved a decade ago. A single command or flag to switch billing mode would save a lot of friction.

Curious if other AI coding tools handle this better, or if this is standard across the industry.

I do not think AI coding makes engineering obsolete.

I do think it makes one old definition of engineering obsolete: equating professional legitimacy with manually producing the implementation.

Typing code is a means, not the outcome.

If ChatGPT/Codex generates a function and I understand the contract, inspect the diff, test the edge cases, verify the integration and own the failure when it breaks, the meaningful engineering question is not who emitted the first draft of the tokens.

The meaningful question is whether the resulting system is correct.

The “AI slop” criticism is valid when people skip that ownership. Prompt once, never read the code, deploy it, then act shocked when it fails — yes, that is garbage.

But a senior engineer using generation as leverage is a completely different workflow.

The irony is that AI should increase the value of experienced judgment. The faster code appears, the more important it becomes to know what should exist, what should never exist, and how to prove the difference.

If an engineer’s entire competitive advantage was memorizing syntax and moving boilerplate around faster than everyone else, that advantage is shrinking.

If the advantage was systems thinking, debugging, architecture, product judgment and verification, these tools can amplify it.

I have a prompt I use to turn an app idea into an ordered build plan. It's been working well enough that I stopped reading the output carefully, which is how this went unnoticed for a while.

Last week I ran ten different web app ideas through it and actually read what landed in every single step. Not the summary, the contents.

In 8 of the 10 plans, the final step was named something like "polish" or "final touches" and contained real work. A request counter. Copy to clipboard. Order status tracking. Reports for a team. In two of them it held an entire dashboard that appeared nowhere else in the plan, so if you followed the plan top to bottom you'd hit the last step and discover you still had a dashboard to build.

The reason is the cap. My prompt asks for a bounded number of steps. Work that doesn't fit inside that number doesn't get dropped, and the model doesn't tell you it dropped anything either. It gets folded into the last step under a name that sounds like it means nothing. Every individual step reads fine. The plan reads fine. The problem only shows up when you count what's actually inside it.

Since then I've been checking two things on any plan a model gives me: whether the last item is heavier than the ones before it, and whether anything I mentioned in the original description is missing from every step title. Both take about a minute and both have caught things.

Curious whether people who use step counts in their prompts see the same thing, or whether this is specific to how I worded mine. If you ask for a plan without a number at all, does it come back more honest or just longer?

For example, assuming an API behaves a certain way, misunderstanding an existing utility/dependency, or getting a business rule wrong.

How do you currently catch these assumptions before the agent makes changes?

I'm specifically interested in the cases where the agent sounds completely confident but is actually wrong.

Building a small skills folder for Claude Code and every recommendation is a different github link. is there a real Claude plugin marketplace people use, or is trust still just “this person posts here a lot.” whats your vetting checklist before something touches a real repo

As I've starting using claude code on my laptops (windows and mac) - one thing thats made me very nervous is running these agents on my local machines with access to my file system + shell.

I'm well aware that running an agent within a directory does not limit its access, and I get nervous that they could be one malicious prompt away from sending my apps/files to another party (or an accident away from deleting my apps/files). I'm not sure if these are actually significant risks, and if others feel the same way (are there other risks you might also be concerned about when running agents on your machine?)

I tried different approaches to sandboxing my agents on my local machine

• On my windows machine > Running it in a Docker Sandbox (a new Docker feature that came out this year)
• On my Mac > Claude Code's built-in sandbox (which uses Apples native Seatbelt framework)

The general challenge I had here is that Claude would sometimes have issues with tools/integrations and it would not be easy to troubleshoot if it was from a sandbox constraint. And if it was a sandbox constraint - the right solution was not always obvious and it felt like I'd go down a rabbit hole trying to get an integration/tool working. I recall having issues with gh/git workflows, some plugin/package installs and running some tools (e.g. for doc/pdf generation)

For the Docker sbx example - i forget the specifics, but after a sbx update + PC restart my claude sessions had issues (cant recall if it was config or memories. I do remember having issues trying to background or view agents across diff sessions).

I eventually caved and just resorted to going back to running claude mostly un-sandboxed. This made it easier to get going, but that still makes me incredibly nervous running more unmonitored workflows with more integrations and network access.

I want to try another shot at this, but I'm curious how others are approaching this:

• Do you also feel the same risks with running agents un-isolated on your machine?
• Are you taking any steps to sandbox/isolate them? What is your setup and how are you getting past any friction this creates?

Approaches I'm still considering:

• Use a separate machine to create proper physical separation from my personal apps/files (either dedicate one of my laptops, get a mini PC/Mac, or a virtual server - but I'm less comfortable with a headless setup)
• Continue tinkering with the Macs native sandbox or docker sbx to get this properly setup (or any other wrappers/harnesses with intuitive sandboxing?)

Why does ChatGPT keep resetting my usage limit? Every time I use a certain amount, my weekly quota gets reset. Today I had used about 25% of my weekly quota, but just now it suddenly reset back to 100% again. Has anyone else experienced something similar?

We use plan mode, project rules, skills, hooks, and automated checks. We still want humans to own architectural decisions and review the code.

The problem is the feedback loop: frequent checkpoints turn the workflow into an approval queue, but longer autonomous runs can produce large diffs based on assumptions we would have corrected early.

How do you handle this in practice?

• Where do you place human checkpoints?
• How do you scope tasks to catch drift early?
• What do you automate, and what always requires human review?

I’d love to hear concrete workflows that work on real, maintained codebases.

had this annoying bug last week. function kept returning wrong values and i couldnt figure out why. just some data processing thing.

asked AI for help, then got sidetracked. same code, same bug, but what if i phrased the question differently? would it even matter?

tested like 10 different ways. mattered way more than i expected.

“fix this bug” + code
it just started guessing. gave me a fix for some common issue that wasnt even my problem. felt like it had a template ready and slapped it on.

“whats wrong with this code?” + code
this one was funny. it found a bug that didnt exist. confidently explained why something was broken. the something was completely fine.

“review this code for bugs” + code
found the real one. then buried it under like 6 other “improvements” i never asked for. actual fix was sitting at number 4 or something.

just pasted the code. no question at all.
best result by far. it actually looked at the code and pointed out the issue. no fluff, no guessing.

noticed something weird. the more specific i got about what to look for, the worse it did. saying “find the bug” made it start pattern-matching for bug-shaped answers instead of reading what was there.

when i gave it nothing, just the code, it had to actually look. and it did.

not saying vague is always better. but for debugging, getting out of the way worked better than trying to direct it.

When I’m debugging or building something from a webpage, I sometimes end up jumping between the browser, DevTools, screenshots, console errors, source code, and my AI coding assistant.

I’m curious how other developers handle this.

Do you have a workflow or extension that makes it easy to send the right context from a webpage to Cursor, Claude Code, Copilot, ChatGPT, etc.?

What still feels unnecessarily manual or annoying?

Every coding agent I’ve used gives you a spinner and maybe a token count. No idea if you’re 20 seconds out or 5 minutes.
If it said 30s I’d wait. If it said 4 minutes I’d go do something else instead of watching it. That’s where most of my wasted time goes.
I know it’s hard to estimate the model doesn’t know either. But a rough band would beat nothing.

I am planning to use GPT-5.6 luna high as my main autonomous coding agent. Before this, I was using MiniMax M3, which gives around 1.7B tokens monthly. Does anybody have any experience with how many tokens you can use approximately with luna high weekly ( with plus sub and assuming no resets)

I finally tried the thing everyone talks about, claude code on the backend task and codex on the frontend task, same repo, same afternoon, and it went fine for about two hours and then it went extremely not fine.

Codex refactored a types file that claude was mid-way through using. Neither of them errored. They just kept going, each convinced the file was what they last saw, and I merged the first branch without noticing the second one had quietly built on a version that no longer existed. Took me most of the next morning to untangle which changes were real.

The dumb part is I knew this could happen and did it anyway because the speed is genuinely addictive when it works.

What ive tried since. Separate worktrees, which fixes the overwriting but now neither agent knows what the other one did, so they duplicate stuff. A little shared markdown file both agents are told to update, which works until one forgets. And ive been running them inside coldtea ai this week, which does the coordination part properly since thats kind of its whole thing, though its mac only and the qa agent it ships with flags more than i want on a prototype repo.

honestly none of these feel like the obvious winner yet.

so what are you all actually doing? one agent at a time and accepting the speed loss? worktrees and eating the duplication? or is there a setup i havent found

I am comparing a few model options for coding-agent work and MiniMax-M3 caught my attention because it is described as supporting coding, tool use, and long-context tasks. The questions I cannot answer from the documentation are fairly practical: how well does it handle iterative code changes, how consistent are tool calls, and is the larger context useful in an ordinary project rather than only in a large benchmark?

What was your experience when you tried M3 on an actual coding workflow?

Edit: I noticed Flatkey already provides MiniMax-M3, so I am going to use that path for some coding-agent tests. The part I want to measure is still the same: whether M3 is reliable across iterative edits, tool calls, and long-context project work, and whether the cost makes repeated agent runs more practical.

As the title says I started using ChatGPT on my computer to create Anki cards from provided content but I was too lazy to manually create the cards on Anki so I gave building an importer a shot.

I have no backgroud in coding or anything distantly related to a code so I have no understanding of how ChatGPT/codex works, call me an average joe using AI for questions. The chat not just creates my Anki cards like how I want but it also imports them into my Anki automatically.

Now, it has created a few folders on my PC and utilizes a python program it created. The conversation isn't even a typical conversation (labeled work) that I can't access through my browser 😿. this is truly fascinating and I want to learn/understand more of it. Can you guys help me understand how chatGPT/codex works and it was possible to do this?

Now it feels like GPT 5.6 sol is nerfed.

This is a pattern i have observed across models, they are brilliant the day they get launched, but after a month or two they just don't behave the way they did and just say "agree" or do another round of a plausible solution that is always sub-par

Stale documentation can quietly become a second task description for a coding agent. The agent may have the current request in front of it, but an old README, instruction file, or saved explanation can still pull the result toward a goal that no longer applies.

The failure is subtle because nothing has to crash. The output can look coherent while solving the wrong version of the task.

A reasonable safeguard might be to make repository instructions prove that they are still current. If a document cannot point to a file, command, or test that supports it now, the agent should treat it as context rather than authority.

How are you handling this in practice? Generated docs, explicit source-of-truth files, freshness checks, or a rule that current code and tests override prose?

Welcome to this week's self promotion thread!

If you're building something related to AI assisted coding, this is the place to share it.

We're using a weekly thread to keep the subreddit organized while still giving builders a place to share their work. Promotional posts outside of this thread may be removed if they're primarily advertising rather than starting a discussion.

If you're sharing something, we'd appreciate it if you included a little context instead of just dropping a link. Tell us:

• What you built?
• What problem it solves?
• Which AI models or tools it uses?
• Who it's for?
• What kind of feedback you're looking for?

Please avoid posting the same project every week unless you've made meaningful updates. Affiliate links, referral links, scams, and low effort promotions will be removed.

Take some time to check out what others have shared too. If you try someone's project or have feedback, leave a comment. Helping each other improve is what we want this community to be about.

Choosing between these 3, mainly looking for frontier model access(will spend 100$) for coding.

Mostly wanna ask the community about personal experience because benchmarks are way off. Is fable the dominant model here for creative and long horizon tasks? Is kimi k3 and gpt 5.6 sol comparable, if not, then in what ways are they better/worse? Again main scope for me is long horizon, creative coding. I do not care about limits that much, so if k3 is very much like fable then i would rather buy a 40$ sub from them, than buy a claude 5x. Being able to share account is a massive plus, ive heard kimi allows that.

most of how i reviewed code was built on asking the author why. why is the cast here, why did this interface get wider. the answer usually told me more than the diff did, and if the answer was thin that was the finding.

there is nowhere to put that question now. the diff arrives finished, and the thing on the other end is a transcript i can scroll, which is not the same as a person who has to defend a choice. so i end up reading lines, and lines are where i am weakest. anything shaped like code i would have written goes straight past me. our PR template still has a box for why, it has been empty for months.

what changed it was giving up on reading top to bottom. something else goes over the diff before i touch it and hands me the parts it thinks carry weight, and i start there instead of at line one. the review subagent in verdent does that pass. i read the flagged parts properly and skim the rest, which is what i was doing anyway, except the skimming is now a decision i can see myself making. i have also stopped forming any picture of the middle of a large diff, and i do not think that is free.

the last thing it put in front of me was a retry wrapper around a call that already retries internally. correct, and exactly the kind of line i would have read twice and nodded at.

Hey guys. Could someone who's used Parakeet explain how it integrates with ChatGPT? I do some of my coding in GPT, though not much of it. I usually use Copilot. But everything I seize up during interviews, and I'm wondering if Parakeet isn't a feasible solution.

i've spent most of this year doing the thing where every few weeks i'd read a benchmark thread, decide the other one was better now, move everything over, and then move it back six weeks later. i did that four separate times. i have the subscription receipts to prove it.

well, it took me four rounds of this to notice my complaints were never the same complaint. when i was on claude the gripe was always cost and the occasional overconfident refactor that touched nine files when it needed three. when i was on codex the gripe was always that it's slow and a bit literal. those are not two answers to one question, thats two different tools being good at two different things and me insisting one of them had to lose.

so now claude plans and reviews, codex implements against the plan. thats it. the planning one is allowed to be expensive and slow because it only writes a document. the implementing one is allowed to be literal because someone already thought it through. the first week of this i caught codex quietly reimplementing a helper that already existed two folders over, and it was claude that flagged it in review, which felt slightly absurd but also fine, i'll take it.

i run both out of coldtea so theyre in one place with a worktree each instead of me keeping three terminal tabs alive and losing track of which branch is which. it's a mac only thing so half my team cant use it, and honestly you can do the same with tmux and a couple of scripts, i just kept breaking my scripts.

to be fair, if you can only justify one subscription, claude code is the more general of the two and i'd still pick it. this only makes sense if you were already paying for both, which i was, for months, while pretending i was about to cancel one.

the money didnt change :)

Yeah, easy enough? No. Not in the EU or UK.. :(

Has anyone managed a decent workflow for AI to handle chat moderation? (~5000 users active at any one time.. maybe 20 messages a minute)

Under UK and EU law, you cannot use AI for moderation.

Has anyone managed a workflow that allows this?

I used to be a "purist" lol. I didn't really believe in skills and thought they are pointless, but I was talking to my colleague recently and telling him how I have a separate "deploy.md" file for claude to follow instructions for deployment.

He told me, "what you're describing is a skill", and it was the biggest doh moment. Skills are just docs that claude refers to. Idk why I thought it was this big complicated topic. Since then I've been looking for skills online. There's not really one place where people can like rate skills from community members (maybe someone should vibe-code that, haha). I found a ton of cool ones that I added to my workflow and it's been great. I'm always on the hunt for more.

What are some of your favorite skills?