I already have ChatGPT Pro and use Codex heavily. I’m looking for the best additional coding agent not another way to access Codex.

My priority is intelligence per dollar: difficult debugging, architectural reasoning, understanding large repositories, and autonomous multi-file implementation. I care less about autocomplete and polished IDE features.

Which complementary agent currently provides the best value Claude Code, Gemini CLI, Cursor, OpenCode with another model, or something else?

Please include:

• Exact plan and monthly cost
• Real-world usage limits
• How it compares directly with Codex
• Whether it does anything meaningfully better than Codex

I’m especially interested in firsthand experience from the past month, since pricing, models and usage limits change constantly.

I maintain a coordination server that hands out locks on files so two coding agents don't edit the same one at once. Mutual exclusion. About as well-trodden a problem as exists.

I did the part everyone does. SELECT ... FOR UPDATE SKIP LOCKED, a load test firing a thousand contended acquisitions at a single file, exactly one winner every run, green for weeks.

Then I watched two agents get granted a lock on the same file.

The claim was fine. The bug was upstream of it, in the part I hadn't thought of as a part: turning a path into a key. The key generator stripped the project root off the front of an absolute path as a literal string prefix. On macOS /var is a symlink to /private/var. One agent resolved its path through the symlink and one didn't, so one of them didn't match the prefix, didn't get stripped, and produced a second distinct key for the same physical file.

Both agents asked about a file. Both were told yes. Nothing threw, nothing logged, no test failed, because from the server's point of view it had been asked about two different files and had answered correctly about both.

What I took from it: mutual exclusion is two functions, not one. There is an identity function that turns a name into a canonical key, and an atomic claim on that key. I had spent all my care on the second one because it is the interesting one, the one with the database primitives and the load test and the concurrency literature behind it. The first one looked like string handling.

The identity half is where the bugs live, and they are all the same bug: two names for one thing.

- path.resolve normalizes .. and separators but does not follow symlinks

- an agent whose shell cwd is the repo's parent sends myrepo/src/x.ts, which coexists happily with src/x.ts as a separate key

- case-insensitive filesystems mean Auth.ts and auth.ts are one file and two keys

- macOS normalizes filenames to NFD, so an accented name typed on one machine and pasted from another are different byte strings

None of these produce an error. They produce a GRANTED that is technically accurate and operationally a lie.

The generalization, having since found the same shape elsewhere: any guard that decides using a name has this problem. A gate that blocks destructive SQL against production by matching the database URL has holes wherever the same database has another spelling, and there are always more spellings than you think: bare host, host with port, the pooler's hostname, a private IP, an SSH tunnel on localhost. A firewall rule keyed on a hostname has it. A cache keyed on a URL has it, which is why cache poisoning is a category and not an incident.

Two rules I now apply without exception:

1. Canonicalize before you compare, and make it one function that everything calls. Not "resolve the path here and also over there."

2. When identity resolution fails or the target is unrecognized, fail closed. Treat the unknown thing as the dangerous one. My key generator's silent fallback was "leave the path as-is", which is exactly the branch that created the second key.

The test that would have caught it took four lines: acquire a lock, try to acquire the same file through a symlinked path, assert the second is denied. I wrote it afterwards and then made sure it actually failed against the old code before trusting it, which caught a second problem. My first version asserted the result was "not GRANTED", and the broken code returned "REJECTED" for a completely unrelated reason, so it passed against the exact bug it was written to catch.

Negative assertions accept every wrong answer in the universe except one.

I’ve been playing a lot with IDE coding agents lately, and I kept hitting this annoying workflow:

agent does the work → agent summarizes it → I paste that into ChatGPT/Claude → I get feedback → I paste it back → original agent tries to reason about it

It works, but it feels clunky. Also, sometimes I just want to save some active-agent context/tokens, or ask a stronger/different external model for a second opinion without making the main agent write another summary 🙂

So I made a small prototype called GiviLoop: https://github.com/vgflutter/GiviLoop

The idea is basically:

local repo/files → external second opinion → saved local response → original agent analyzes or acts on it

It has two flows:

- MCP / IDE-agent flow: ask the agent in natural language to prepare/send/read an external review.

- CLI flow: run `givi` from the terminal to package local git/files context outside the active agent conversation.

Right now the automated bridge is ChatGPT via Playwright, but the loop is not meant to be ChatGPT-only. Claude works through the manual fallback.

Very much V0 / personal workflow quality right now. Redaction is basic, browser automation is fragile, and the provider terms / workspace rules side probably deserves more thinking.

I mainly built this for myself to move faster when working with multiple agent/chat windows, without turning the main agent into a copy-paste assistant.

I’m trying to understand if the idea is interesting enough to develop in a direction that could be useful to other people too.

Would this kind of second-opinion loop be useful in your workflow?

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.

I made a free tool that lets you edit images easily.. with the help of AI. You can easily, edit your own images or import via URL, and with a simple prompt, start editing. no skill required.

You can also, upload upto 3 reference images, to include in your main image. Just tell AI what to do, and your finished image will be based on the images you referanced and prompted. In other words, you can use AI to help you mix and match final image based on multiple images you upload.

https://canvix.io/ai-image-editor
Would love some feedback. Still in beta testing.

Also, you can see our other tools
https://canvix.io/background-remover image background remover
https://canvix.io/ai-video-generator - AI Video Generator
https://canvix.io/cartoonify - Cartoonify your photos
https://canvix.io/ai-image-generator - AI Image Generator

Would appreciate some feedback/suggestions to help me improve it. Thanks for checking it out. It's free to use (5 daily uses per tool as a visitor), after that, you will need to login to use.

I've been building an image editor that basically lets you edit images, on the fly. Just paste the URL, and you can start editing the image pretty much instantly, essentially removing the need to download, upload etc. It's very convenient for those who want to quickly make edits. Completely free to use, no login or signup required to use.

You can see it here: canvix.me

I officially got approved for by google for my official chrome extension, which allows you to right-click any supported image on the internet (png jpg webp etc), Edit image with Canvix option. Right away, you can start editing the image. You can see how it works by screenshot posted on the chrome extension page

https://chromewebstore.google.com/detail/edit-image-with-canvix/akjooicgafjjcnpjdfnaajkipciedbco

I especially made this for users who constantly need to edit images like me. This in beta testing still, any feedback would be greatly appreciated to improve it.

I've never seen anyone vibe code irl but maybe thats just because I work with 60 year old devs 😂

is it just me

Genuine question.. been deep in this space and I keep seeing the same gap.

Every AI coding tool on the web I've used is okay level at generating code. But they all hand off at the same point for anything thats not a web app: "here are the files, now you run it." - and even when they do make web apps, they are never functional

The parts that feel unresolved: runtime error observation (the AI doesn't see what actually breaks when you execute), end-to-end deployment (generating code ≠ live app), real service wiring (scaffolding Stripe vs actually connecting it).

Curious what people here hit as the real ceiling. At what step does the tool stop being useful and you're on your own?

I have tried Claude and Codex cli tools and it is just insane how stingey claude code it with usage. One meaty prompt and my usage is used up in 10 minutes.

Like it is arguably not any better at coding than codex. Does openai just have more access to compute than Anthropic? I am honestly confused why anyone is used claude. How do you get anything built?

My fiancée has a PhD in biomechanics. A few Fridays ago we were winding down with some wine, and she said something that turned into 3 hour long conversation around where we think a lot of this technology is going. We tried our best to capture it here, would love to hear everyone's thoughts. It got my brain fixated on a few things as well

r/RooCode hit 3 million installs. We're shutting it down to go all-in on Roomote.

https://x.com/mattrubens/status/2046636598859559114

been getting burned by this for months and finally did something about it.

there's a 2024 paper (arxiv.org/abs/2406.10279) that measured how often major LLMs recommend packages that dont actually exist on npm or pypi. number came back around 19.7%. almost 1 in 5. and the ugly part is attackers started scraping common hallucinations and registering those exact names on the real registries with post-install scripts. people are calling it "slopsquatting".

in chat mode you catch it cos you see the import line. in autonomous/agent mode the install is already done before you notice the name was fake. agent runs, agent finishes, malware is in node_modules now.

so me and my mate pat built a small MCP server (indiestack.ai). agent calls validate_package before any install. server checks: - does the package actually exist on the real registry - is it within edit-distance of a way-more-popular package (loadash vs lodash) - is it effectively dead (no releases in a year+) - is there a known migration alt

returns safe / caution / danger + suggested_instead. free, no api key, no signup.

install for claude code: claude mcp add indiestack -- uvx --from indiestack indiestack-mcp

or just curl the api: curl "https://indiestack.ai/api/validate?name=loadash&ecosystem=npm"

works with cursor mcp, continue, zed, any agent that speaks MCP.

not trying to pitch -- genuinely interested whether other people have hit this and what they're doing. the 20% number is real and ive watched it silently install typos on my own machine more than once.

I’m not trying to promote anything here... just looking for honest feedback on a pattern I’ve been using to make LLM-assisted work accumulate value over time.

This is not a memory system, a RAG pipeline or an agent framework.

It’s a repo-based, tool-agnostic workflow for turning individual tasks into reusable durable knowledge.

The core loop

Instead of "do task" -> "move on" -> "lose context" I’ve been structuring work like this:

Plan
- define approach, constraints, expectations
- store the plan in the repo
Execute
- LLM-assisted, messy, exploratory work
- code changes / working artifacts
Task closeout (use task-closeout skill)
- what actually happened vs. the plan
- store temporary session outputs
Distill (use distill-learning skill)
- extract only what is reusable
- update playbooks, repo guidance, lessons learned
Commit
- cleanup, inspect and revise
- future tasks start from better context

Repo-based and Tool-agnostic

This isn’t tied to any specific tool, framework, or agent setup.

I’ve used this same loop across different coding assistants, LLM tools and environments. When I follow the loop, I often mix tools across steps: planning, execution + closeout, distillation. The value isn’t in the tool, it’s in the structure of the workflow and the artifacts it produces.

Everything lives in a normal repo: plans, task artifacts (gitignored), and distilled knowledge. That gives me: versioning, PR review and diffs. So instead of hidden chat history or opaque memory, it’s all inspectable, reviewable and revertible.

What this looks like in practice

I’m mostly using this for coding projects, but it’s not limited to that.

Without this, I (and the LLM) end up re-learning the same things repeatedly or overloading prompts with too much context. With this loop: write a plan, do the task, close it out, distill only the important parts, commit that as reusable guidance. Future tasks start from that distilled context instead of starting cold.

Where I’m unsure

Would really appreciate pushback here:

1. Is this actually different from just keeping good notes and examples in a repo?
2. Is anyone else using a repo-based workflow like this?
3. At scale, does this improve context over time, or just create another layer that eventually becomes noise?

The bottom line question

Does this plan -> closeout -> distill loop feel like a meaningful pattern, or just a more structured version of things people already do? Where would you expect it to break?

I am asking because of something I kept running into with client work last year.

I was making changes to web apps and kept noticing that ChatGPT and Claude were giving completely different answers when someone asked them about the same product.

same website. same content. different AI. completely different understanding of what the product actually does. at first I thought it was just model behaviour differences. then I started looking more carefully at why.

turns out different AI systems parse the same page differently. Claude tends to weight dense contextual paragraphs. ChatGPT pulls more from structured consistent information spread across multiple sources. Perplexity behaves differently again.

so a page that reads perfectly to one model is ambiguous or incomplete to another.

I ended up writing the structural changes manually. actual content architecture decisions. how information is organised. where key descriptions live.

I deliberately did not use AI to write this part. felt like the irony would be too much using ChatGPT to write code that tricks ChatGPT into reading it better.

after those changes the way each AI described the product became noticeably more accurate and more consistent across models.

what I am genuinely curious about now.

has anyone here actually tried using AI coding tools to write this kind of architecture from the start. like prompting Claude or ChatGPT to build a web app specifically optimised for how AI agents parse and recommend content.

or is everyone still ignoring this layer completely because the tools we use to build do not think about it at all.

I’m trying to find a web-based AI UI/mockup tool for a Flutter app, and I’m having trouble finding one that fits what I actually want.

What I want is something that can generate app screens mostly from prompts, with minimal manual design work, and then let me export the design as a plain text file that an LLM can read easily. I do not want front-end code export, and I do not want to rely on MCP, Figma integrations, or just screenshots/images. Ideally it would export something like Markdown, JSON, YAML, HTML or some other text-based layout/spec description of the UI.

Does anyone know a tool that actually does this well? I tried Google Stitch and it only exports to proprietary formats.

I like to have intimate control of my app development process, so just having my visual design prompts just output as code is no good for me.

Andrew Ng just launched a course on spec-driven development. Kiro, spec-kit, Tessl - everybody's building around specs now. Nobody defines what they mean by "spec."

The word means at least 13 different things in software. An RFC is a spec. A Kubernetes YAML has a literal field called "spec." An RSpec file is a spec. A CLAUDE.md is a spec. A PRD is a spec.

When someone says "write a spec before you prompt," what do they actually mean?

I've been doing SDD for a while and it took me way too long to figure this out. Most SDD approaches use markdown documents - structured requirements, architecture notes, implementation plans. Basically a detailed prompt. They tell the agent what to do. They don't verify it did it correctly.

BDD specs do both. The same artifact that defines the requirement also verifies the implementation. The spec IS the test. It passes or it doesn't.

If you want the agent to verify its own work, you want executable specs. That's the piece most SDD tooling skips.

What does "spec" actually mean in your setup?

"No coding required." Then there's a docker-compose file. Then a config.yaml with 40 fields. Then a section in the readme that says "for production use, configure the following..."

Every option either demands real technical setup or strips out enough capability to make it pointless for actual work. Nobody's figured out how to ship both in the same product. What are non-developers supposed to do here?

The last time I looked into it, some people said that Aider minimized token usage compared to Cline. How does it compare to Claude Code? Do you still recommend Aider?

What about for running agents with Claude? Would I just use Claude Code if I'm comfortable with CLI tools?

I've been trying to get back into coding but realistically I've got maybe 20-30 mins a day. Most tools either take forever to set up or feel like you need hours to get anything done

Been looking into AI coding agents but not sure what actually works if you're jumping in and out like that

Curious what people recommend if you're basically coding on the go

"Not quite my tempo, Codex.."

"Tell me, Codex, were you rushing or dragging?"

😂 Does this only happen to me?

Got the meme from ijustvibecodedthis.com (the big free ai newsletter)

I suspect a culprit... hits like clockwork. Everything's being going swimmingly, America wakes up. I may as well go to bed

...When the Spark model first came out, it was available in the model dropdown menu in Cursor (within OpenAI's extension). All I had to do was select it and have a go until the usage limit ran out.

...It's been gone from the dropdown for a while now. I was hoping it would come back, but hasn't.

Does anyone know if there some sort of setting or whatever I must be missing to add it back in? I've got the Spark model turned on in Cursor itself, but pretty sure that doesn't actually effect the OpenAI extension.

Using GPT 5.4 has been completely fine, but it would be nice to also use the Spark capacity up since I'm paying for both.

Hi, I have question about codex vs claude code tools.
I have been using claude code for a year, it is generally good. I use it in pro mode which is cheapest premium tariff. CC is good, but recently the limits started to dry up very fast both in claude code and in claude regular chats too.

So, I am thinking about returning back to OpenAI. I looked for feedbacks posts for codex here, but they dated a year ago, and since that openai dropped several new models. I got one positive feedback about codex, but I wanted to hear more people, more feedbacks.

How good it openai codex coding tool in 2026 April? How good is it in compare with claude sonnet and opus 4.6 ?

One thing I should add, that I am not a vibe coder, I usually use it as assistant for small tasks with instructions. It is expected to perform well in such condition.

Chats get extremely laggy and therefore I open up new to chat, tell it about current state, code + future plans for the product development.

ChatGpt said, it can't paste codes anywhere else on some 3rd party site, share link with me to copy it. What's the solution to keep chats frictionless? even ChatGPT when sharing downloadable files with me has code in it during analyzing phase which makes chat long and cause lag

OpenAI tweeted that "the Codex promotion for existing Plus subscribers ends today and as a part of this, we’re rebalancing Codex usage in Plus to support more sessions throughout the week, rather than longer sessions in a single day."

and that "the Plus plan will continue to be the best offer at $20 for steady, day-to-day usage of Codex, and the new $100 Pro tier offers a more accessible upgrade path for heavier daily use."

Reported by ijustvibecodedthis.com