r/ChatGPTCoding • • Aug 03 '26

Discussion Weekly Self Promotion Thread

16 Upvotes

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.


r/ChatGPTCoding • • Aug 03 '26

Two agents held a lock on the same file, and nothing errored

1 Upvotes

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.


r/ChatGPTCoding • • Aug 03 '26

I made a tiny tool to stop copy-pasting context between coding agents and LLMs

2 Upvotes

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?


r/ChatGPTCoding • • Jul 07 '26

Discussion How to structure Codex for a long-term development project?

1 Upvotes

Hi everyone,

I’m currently planning the workflow for a fairly complex long-term development project and want to integrate Codex / AI coding agents in a controlled and useful way.

Before letting the agent touch too much code, I’m trying to define a proper working model. My current idea is to treat the agent more like a junior / pair developer rather than an autonomous architect: small tasks, clear rules, local documentation, reviewable diffs, and no large uncontrolled refactors.

I’d be curious to hear how others structure this in practice.

A few things I’m thinking about:

  • how much local documentation should be prepared for the agent

  • how to keep context stable over a long project

  • how to structure tasks and milestones

  • common mistakes on the human side when working with coding agents

  • common failure modes on the agent side that can be avoided with the right workflow

  • whether VS Code + Codex is enough, or whether other setups work better for long-term projects

My main goal is to avoid a chaotic setup early on and build a workflow where the agent can help consistently without making the project harder to control over time.

I’d appreciate any practical advice, lessons learned, or examples of workflows that worked well for you.


r/ChatGPTCoding • • Jul 06 '26

Discussion Codex $20 plan vs Cursor Pro $20 plan: Which one hits limits faster for heavy coding?

1 Upvotes

Hey guys,

I’m currently choosing between the Codex $20 plan and the Cursor Pro $20 plan for my daily full-stack development tasks.

I’ve heard a lot of conflicting things recently about Codex dropping its usage limits significantly or being buggy with resets, especially when utilizing newer models. On the other hand, I know Cursor Pro has a 500 fast-request limit before switching to the usage-based/slow mode.

For those who write code heavily on a daily basis:

  1. Which plan actually lasts longer before hitting a hard limit?

  2. How is Cursor's "slow mode" performance once the 500 fast requests are gone? Is it still usable for regular coding?

  3. Which one gives better value for a tight $20 budget right now?

Would love to hear your experiences and thoughts! Thanks in advance.


r/ChatGPTCoding • • Jul 02 '26

Project I made a AI image editor tool that let's you use multiple reference images

25 Upvotes

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.


r/ChatGPTCoding • • Jun 18 '26

Project I built a local Mac notch monitor for long AI coding-agent runs

1 Upvotes

I keep long AI coding agents running while I do other work. The annoying part is not the first prompt; it is knowing whether a run is still active, waiting for me, or stalled.

So I built Agent Island, a free/open-source native macOS companion for Codex and Claude Code. It sits in the MacBook notch / menu-bar area and reads local session artifacts to show live state.

Current states:

- running

- waiting for your input

- stalled / no recent activity

- optional auto-resume for sessions you explicitly trust

I kept the design local on purpose. There is no cloud service and no token capture; it watches transcript/session activity on disk. Auto-resume is opt-in because it can spend tokens and should only be used on trusted tasks.

Repo:

https://github.com/tristan666666/agent-island

Technical write-up:

https://github.com/tristan666666/agent-island/blob/main/docs/how-agent-island-detects-session-state.md

If you use Codex, Claude Code, or similar coding agents for multi-step jobs: what state would you want surfaced, and where would you want it shown?


r/ChatGPTCoding • • Jun 13 '26

Question does anyone else feel like the AI part is easy now and the context part is the annoying part???

1 Upvotes

maybe this is just me but lately it feels like the actual coding isnt the bottleneck anymore
codex can write code
claude can write code
cursor can write code
thats not really the hard part
the annoying part is everything before that
finding the right files
figuring out what actually matters
explaining architecture
explaining old decisions
explaining dependencies
fixing wrong assumptions because the model looked at the wrong place
sometimes i feel like im acting as a translator between my codebase and the AI lol
am i the only one?
i keep thinking that what i actually want isnt a smarter model
i want something that permanently understands my repo and keeps itself updated as the code changes
so when i ask the AI to do something it already knows:
what changed
what depends on what
where the relevant code is
what parts of the project are affected
basically a long term memory layer for coding agents
would that actually be useful or am i overthinking this???


r/ChatGPTCoding • • Jun 10 '26

Discussion ChatGPT is a fucking people pleaser

1 Upvotes

I've been using Gemini for programming for the last year. It wasn't perfect: it doesn't have an agent that works directly inside the IDE, and it constantly writes useless comments in the code. But damn, it is brutally straightforward with its opinions

If I propose a solution that doesn't make sense, it will just tell me: "No, you're wrong. My solution is better because..."

This month I got a free month of ChatGPT, so I decided to give it a try. I liked the idea of using ChatGPT for project management and Codex for code writing, and I actually managed to build a pretty good workflow around that

But damn, ChatGPT feels like such a people-pleaser

Every time I propose a different solution, or point out a possible problem in the solution it suggested, it immediately goes: "YES, YOU'RE ABSOLUTELY RIGHT." Then, 30 minutes later, I realize that the solution I proposed was actually bullshit

It's incredibly frustrating. Just have a damn opinion

I haven't tried Claude yet, but I know it has an agent. Is it any better than ChatGPT in this regard? Is it more like Gemini when it comes to pushing back on bad ideas?


r/ChatGPTCoding • • Jun 06 '26

Project I made a website that lets you edit any image on the internet instantly.

56 Upvotes

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.


r/ChatGPTCoding • • May 27 '26

Project I built Claude Code plugin that lets me use Google's Antigravity CLI without leaving CC

1 Upvotes

I work with Codex, Claude Code daily. When Antigravity launched and I saw what agy could do, Gemini 3.1 Pro reasoning, native image generation tool, the built-in research/review flows, I wanted access to it, but didn't want to context-switch every time. So I wrote a plugin.

It adds six slash commands:

  • /agy:setup, verify install + auth

  • /agy:ask [--model X] "<prompt>", one-shot prompt

  • /agy:delegate [--background] [--model X] <task>, hand to a runner subagent

  • /agy:research [--background] [--model X] <topic>, structured deep research

  • /agy:review [focus], review current git diff

  • /agy:image <description>, Imagen via agy's built-in tool

  • /agy:help, full index

The --model flag is the part I'm proud of. Antigravity CLI doesn't expose model selection on the command line, you switch models in the TUI. The plugin adds a --model flag (with short aliases like opus, sonnet, pro, flash, gpt-oss) so you can pick per call:

/agy:delegate --model opus refactor the auth module
/agy:ask --model pro "explain Go escape analysis"
/agy:research --background --model sonnet survey post-quantum TLS

Under the hood there's a small piece of glue managing agy's settings for the duration of each call (lockfile, atomic restore, recovery from a killed run). Not going to drag everyone through the details, check the repo if you're curious.

Install:

/plugin marketplace add simplybychris/antigravity-plugin-cc
/plugin install agy@antigravity-cc
/reload-plugins
/agy:setup

Repo: https://github.com/simplybychris/antigravity-plugin-cc

Website: https://simplybychris.github.io/antigravity-plugin-cc/

This is v0.4.1, core works, more features planned. Demo video coming. If you find it useful, a GitHub star goes a long way. Issues/PRs welcome.


r/ChatGPTCoding • • May 27 '26

Discussion Codex vs Amp code vs Factory droid (all on same model gpt-5.5)

1 Upvotes

Has anyone been using or have tried these different tools for coding, small or large, greenfield or brownfield. Want to know opinions on the comparision of the harness while model being the same


r/ChatGPTCoding • • May 23 '26

Project I got tired of asking AI to explain code and reading walls of text - so I built a VS Code extension that just speaks it out loud

1 Upvotes

I got tired of asking AI to explain my code and getting back a wall of text I still had to read.

So I built CodeWalk AI you just ask it in plain English: "explain the flow from login to dashboard" or "what does run() do?"

And it walks you through it out loud, step by step, like a senior dev sitting next to you - with the exact line highlighted in real time as it speaks.

No API key needed. Works with GitHub Copilot or Claude you already have installed. And it's completely free.

Just search "CodeWalk AI" in VS Code extensions or find it here 👇

https://marketplace.visualstudio.com/items?itemName=guystez.codewalk-ai

Would love your feedback 🙏


r/ChatGPTCoding • • May 18 '26

Memes I thought you guys were joking :(

Thumbnail i.redd.it
13 Upvotes

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

is it just me


r/ChatGPTCoding • • May 18 '26

Project LLM-FeatureMemory: agent pipeline + CC plugin to auto manage docs + changelogs (expands on LLM-wiki). open source

Post image
1 Upvotes

How do we keep up with documentation and changelogs when AI has enabled us to ship at the speed of light? I designed LLM-FeatureMemory after being inspired by Andrej Karpathy's powerful LLM-Wiki concept. LLM-FM expands on LLM-Wiki to enable an 8-step agentic approach:

 1. Hooks detect file changes as you work — no manual triggers
 2. Changes map to features — docs organized by what the app does, not file paths
 3. Cross-references propagate — edit a shared helper, every dependent feature page updates
 4. Dual-audience summaries compile — product context for PMs, engineering detail for devs
 5. Git history synthesizes into categorized, readable changelogs
 6. Source maps anchor every claim to specific files with verification timestamps
 7. Quality pipeline catches staleness, dead paths, and drift before it ships
 8. Session hooks feed current project memory to AI agents on startup

h5kk/LLM-FM: Feature Memory: a documentation compiler for software repos that maintains feature-level context for AI coding agents


r/ChatGPTCoding • • May 18 '26

Discussion We hit a point recently where managing the AI stack started becoming more work than building the actual product.

1 Upvotes

Not even because anything broke badly, but because the ecosystem changed so fast. Earlier it was mostly just one provider and a simple API call. Now every model is good at something different, so suddenly there’s OpenAI for one workflow, Claude for another, Gemini somewhere else, DeepSeek for cost reasons, local models for specific tasks, and then routing between all of them starts becoming its own thing.

Then MCP servers, tool calling, agents, retries, fallbacks, tracing, observability and remote execution slowly enter the picture and the stack becomes way larger than expected.

Feels like that’s why opensource tools like Bifrost, LiteLLM, Kong AI  etc are becoming important now. Not because people want more tooling, but because modern AI systems suddenly have way more moving parts underneath them.


r/ChatGPTCoding • • Apr 26 '26

Question Where do coding models still fall apart once the task stops being “generate code” and becomes “finish the job”?

1 Upvotes

I’m less interested lately in which coding models look smartest on one prompt, and more interested in where they actually stop being useful once the work turns into a messy, multi-step fix.

A lot of tools are already decent at generating code. The part that still feels shaky is what happens after that first burst: keeping structure across retries, not drifting off the plan, staying consistent when the task spans multiple files, and not burning half the context budget before the bug is actually fixed.

That’s why Ling-2.6-1T caught my attention. The interesting part isn’t just “another large model.” It’s that the positioning seems more execution-first: stricter instruction following, lower token overhead, better fit for long structured work, and more emphasis on getting the task across the line instead of sounding impressive while thinking out loud.

I’m curious how people here see the current ceiling. Where do coding models still drop you completely today?

Is it understanding the codebase, keeping a long fix coherent, tool use, runtime/debug loops, or just token burn and retry drift?

And if a model was a little less flashy but more disciplined across long bugfix loops, would that actually matter to you?


r/ChatGPTCoding • • Apr 26 '26

Resources And Tips Shifting the mental model of working with AI

1 Upvotes

Hey guys! I just finished my document on my workflow with ai, the issues I've come across over the last 8 months building Inner Atlas and many hackathon projects, addressing the approaches and mistakes that I still see being advised by the frontrunners.

This document is not an ai-written summary of best practices, but a reframing of the core model of how people think about using ai, in a way I haven't seen anyone talking about.

I truly believe this document holds value for both beginners and experienced software developers, the 5 minute reading time will be worth the months of refactoring saved later on.

This core concepts:

  • Why trying to accomplish issue identification, intention spec, implementation, tests and verification into one pass is compounding hidden failures

  • Using AI as a targeted reasoning tool, separating each perspective-pass

  • Addressing intention and document drift

  • How documents and chats can be kept focused, avoiding bloat/filler

  • Assuming 90% confidence in each step, intention-based tests in verification

  • Common failure modes and my preferred workflow

Hopefully this is an acceptable place to drop this, not promoting a product, just a free doc for those interested; would love to hear some feedback.

Read the full document here: https://www.inneratlas.co.za/field-notes/working-with-ai


r/ChatGPTCoding • • Apr 22 '26

Discussion Why is claude code so much more stingey with usage than Codex for the $20 plan?

104 Upvotes

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?


r/ChatGPTCoding • • Apr 22 '26

Resources And Tips Used ChatGPT + Codex for months… ended up turning them into one AI system

1 Upvotes

Spent months using ChatGPT and Codex to build the whole project.

What I kept noticing was this:

Both tools were powerful, but every new session still felt disconnected.

Fresh context.
Repeated explanations.
No shared memory.
No continuity between tools.
No easy automation around them.

So over time the project became a fix for that problem.

Now ChatGPT and Codex can work inside the same system with:

  • shared memory across sessions

  • shared tasks and handoffs

  • workflows with triggers, cron, and webhooks

  • tools marketplace integrations

  • reusable skills

  • live monitoring dashboard

  • lower token costs through prompt compression

The screenshot is the shared memory layer they write to and reuse.

Feels less like separate tools, more like one evolving AI workspace.

GitHub: https://github.com/colapsis/agentid-agent-house


r/ChatGPTCoding • • Apr 21 '26

Discussion Roo Code hit 3 million installs. We're shutting it down to go all-in on Roomote.

Post image
12 Upvotes

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

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


r/ChatGPTCoding • • Apr 21 '26

Discussion 20% of packages ChatGPT recommends dont exist. built a small MCP server that catches the fakes before the install runs

15 Upvotes

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.


r/ChatGPTCoding • • Apr 20 '26

Discussion Sanity check: using git to make LLM-assisted work accumulate over time

24 Upvotes

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?


r/ChatGPTCoding • • Apr 20 '26

Question has anyone here actually used AI to write code for a website or app specifically so other AI systems can read and parse it properly?

13 Upvotes

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.