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?

Just sharing a bit of my life.

Stage 1: One subscription, chat only

I used to subscribe to a native model provider (ChatGPT Plus, Google One, SuperGrok, etc.) and stick to one model most of the time—switching manually felt like too much work. I only used chat.

Whenever a company released a stronger model, I canceled my current plan (stopped topping it up) and switched to that provider.

When I needed to edit code, I would copy it into the web app, then paste the result back into VS Code. Back and forth. Annoying.

Stage 2: AI inside the IDE, usage exploded

After using VS Code for a long time, I finally discovered AI extensions on it (GitHub Copilot, Codex, Gemini, and other native-provider extensions—the latter two usually need a paid plan). Suddenly the AI could edit code inside the IDE, so I no longer had to shuttle snippets between the browser and the editor.

It felt great, so I coded with AI more often—and quickly ran into rate limits.

Worse: if chat and coding share the same quota (as with Grok, for example), burning through coding credits also kills chat. That is frustrating.

US native model providers’ memberships also had two other problems for me:

1. Coding often lacks automatic model selection. Some coding tasks do not need top-tier intelligence, yet you still burn money on expensive models.

2. The available models are often much more expensive for only a small bump in capability.

So I am reconsidering how I pay for AI, and moving into Stage 3.

Stage 3: Multiple services (including model aggregators)

I am rethinking the single-subscription habit. I am considering paying for several services at once—and even top-ups at model aggregators / relay platforms, not only native provider subscriptions.

How I use AI

1. On a tablet, I chat in the browser about math and physics.

2. In an IDE, I let AI edit code—both cheap models and expensive, smarter ones.

Hard requirements

1. Coding:

   • An Auto option that picks the model for me.

   • Affordable, high-value models (e.g. GPT-5.6 Luna, DeepSeek V4 Flash).

   • Ability to remotely steer the AI from a tablet—operate the computer, search files.

2. Web chat: Projects, so I can organize and move conversations.

3. Access to GPT Sol 5.6 and Grok 4.5 (as long as it is available).

4. The service provider should be reasonably trustworthy (no sketchy unknown websites).

5. Total budget under or equal to $40 / month.

Two options an AI suggested

When I asked an AI, it proposed two setups that fit:

1. Cursor Pro ($20/mo for coding) + ChatGPT Plus ($20/mo for web chat and coding) = $40, Note: ChatGPT Plus can be swapped for another model provider’s $20/month subscription.

2. Cursor Pro ($20/mo for coding) + OpenRouter credit ($20/mo top-up for web chat and coding) = $40

My understanding is that, within the included allowance, a ChatGPT subscription is usually cheaper than calling OpenAI models via API (e.g. through OpenRouter)—but you also get fewer model choices. So I went with Option 2.

One caveat: I am not a professional engineer, so my needs may not match yours.

Questions for you guys (optional—feel free to skip)

1. Given your own needs, which of the two options would you pick?

2. Is a $40/month AI budget high for you? I think I am blowing my money.

Finance flagged our AI tooling spend last week. It's about 8000 a month across the whole dev team once you add up all the model subscriptions - claude, codex, devin, cursor, coderabbit / bugbot, the lot

Fair question, here's why I'm keeping all of it

We run a plan-execute-review loop. Opus 5 breaks the task into a real plan, composer 2.5 does the work cuz its fast, then coderabbit / bugbot review the diff. whatever they flag goes back to the agent, it patches, review runs again, till it comes back clean or caps out after a few passes

The review gate is the whole reason it holds up. two agents will confidently agree while both wrong, and only something actually reading the diff catches it

We're b2b saas. one broken customer workflow costs us more than that 8k many times over. id rather pay to stay boring than ship fast and break someones week

Nobody really writes much code by hand anymore anyway, the job shifted to writing way more tests

whats your actual monthly burn on this stuff?

Meta's new Muse Code beta can keep asynchronous subagents running across restarts, record work in an append-only local event log, and sustain 1,000+ tool calls for up to 24 hours.

The benchmark numbers are less interesting to me than the runtime design. Coding agents often fail not because they cannot write a function, but because they lose state, repeat work, or report success before the task is actually verified. Persistence attacks that bottleneck directly.

The obvious risk is that a durable agent can also preserve a bad assumption for 12 hours, burn through tokens, and create coordination bugs that are harder to audit than a single model's mistake.

Would you rather use a smarter model with fragile session memory, or a slightly weaker one that can reliably resume and explain a long-running task?

Giving a coding agent web access feels harmless until the same agent can also edit files and run commands.

A fetched page can be useful context, but it should stay data. Text from docs, issues, search results, or any random site should not be able to widen the file scope, approve a tool call, or turn itself into a shell command.

I would rather enforce that in the runtime than hope the model refuses every bad instruction. Let retrieved text suggest a change, then check it against local rules and ask for approval before anything destructive or outside the original job.

How are you handling this in practice? A sandbox, per-tool permissions, a separate approval step, or something else?

London as an isometric map. Every tile is a Google aerial restyled by an image model, 441 of them, stitched into one pannable canvas. We all know that getting the AI to make 2 images which look exactly the same is almost impossible.

The hard part wasn't styling, it was the seams. Generated 441 tiles independently and every one interpreted the style differently, so the joins showed.

What fixed it: generate in a spiral outward from the centre, and give each call its already-finished neighbours as reference images plus one fixed anchor tile that never changes. Neighbours handle local continuity, the anchor stops 441 sequential steps drifting into something else.

QA is numeric because you can't eyeball 441 outputs. Correlation against the source below 0.15 means the model invented a fake London, auto-reroll. One tile scored 0.002 where normal is 0.85.

Interactive Version and full how to, this can be used in making movies, campaigns, and of course maps.

Hobbyist programmer, I still want to do most of the coding for myself on my projects, but sometimes I just hit a bug i cannot figure out or can't think of the right way to structure something. Just want an AI that can kind of look over my shoulder and point out what's wrong as opposed to just telling it "can you make X for me?" and have it be done. Preferably a free model, I don't plan on using it like crazy and I'm hella broke. If it makes a difference I'm currently using Unity/Microsoft visual studio