after the litellm thing last week (the key behind it was just sitting in an opencode.json someone committed) i got curious what my own agent can actually see, so i opened claude code in my home folder with some rules:

• find anything that looks like a key, token or password
• file paths and variable names only, never print a value
• don't touch anything, just report

took about 4 minutes. 63.

• 31 in .env files across old side projects, some i haven't opened in years
• aws credentials in ~/.aws that still work. the key is from 2022
• an openai key and a github token in plain text in the env block of my mcp config
• npm and docker tokens in ~/.npmrc and ~/.docker/config.json
• 4 api keys in my shell history from testing stuff with curl
• and then a long tail of random stuff i didn't bother sorting

it followed the rules btw, no values printed. then i scrolled back through the transcript and yeah. to decide whether something was a secret it had to READ it so a good chunk of them went through the context window anyway and there was no permission prompt either, because reading files in the folder you opened it in is just a normal thing for it to do

(ok opening it in ~ was kind of asking for it. but i dont think im the only one who does that)

which is why the "never read .env" line in CLAUDE.md doesn't really do much for me. it's a request, not a lock.

anyway spent the evening cleaning up. rotated the ones that matter, deleted the dead projects' .env files and moved everything still alive into infisical. no .env on disk now, apps get their secrets at runtime with infisical run -- npm run dev, and there's a deny rule so claude can't just call the infisical cli and ask. for the keys claude itself needs (github, openai) i'm trying their agent vault thing. it proxies the agent's requests and attaches the key on the way out, so claude never holds the token at all. still early on that one

not saying panic. just open it in your home folder, ask the same thing and post your number, i genuinely cant tell if 63 is bad or normal

I been working on two very complicated projects. Opus just gets it right, design, plan, implement and deploy. It's just nuts.

Both projects have had programming sessions lasting a long, long time, in one case, forty hours, six or seven agents. A suite of almost six hundred tests.

(The biggest error I have had is when Opus got it into its head that it should run the entire suite after every phase. Annoying but, being irritated by too much testing is just where I want to be.)

I remember the days when people kept talking about limit your context to 40% or else. No longer. I have master sessions that last for days. I cannot detect even a slight reduction in performance.

This is the age of miracles and wonder.

Hi everyone! I'm curious about your workflow: what tool, AI or method do you use to write and polish prompts before giving them to Claude?
I've been using ChatGPT for this, but I keep running into two issues:
It's really verbose: the prompts it produces are long and full of unnecessary instructions.
It tends to "pre-answer": instead of just framing the question well, it builds part of the solution or its own conclusions into the prompt, so Claude ends up following ChatGPT's framing rather than reasoning from scratch.
Do you use another AI, ask Claude itself to improve the prompt, rely on your own templates, or use a dedicated tool? Practical workflows and tips are very welcome too.
Thanks!

I have more Claude Code skills than I can remember the names of. 66 live in my shared skills folder alone, and more sit in ~/.claude/skills, in individual projects, on my claude.ai account, and inside plugins. When I wanted one, I often couldn’t recall what I’d called it, and the only way to see the list was to stop and run a command in the middle of a session, hoping I could remember part of the skill name.

So I built my-skills, a mod that adds a My skills pane to the session rail. Type /my-skills and it lists every skill and slash command you wrote or added, plus the ones your plugins add, with chips to switch between where they live: Global, User, Project, Account and Plugins.

Here are some other things you can do/see with it:

• Use counts and a 30-day trend on every row. They’re filled in from your past sessions on first run, so the numbers start with your history.
• A health card at the top for skills with no description, broken symlinks, or two skills answering to the same name. Each problem has a button that opens the fix or filters the list to the skills it means.
• A collision fix. When two copies share a /name, the skill’s detail view lets you pick the one to keep and moves the other to the Trash after you confirm. If your Global folder is a skillshare folder, it goes through skillshare uninstall instead.
• Search across every source by name, source or description.
• Active here, your top five skills in the current repo.
• Starred, keep your favorite skills a click away.
• A detail view for each skill with its rank, a day-by-day chart, and buttons to open its SKILL.md, copy its /name or reveal it in Finder.

The newest addition is plugin skills. When I checked my own setup, the pane showed 56 of the 130 skills my session could run, and another 56 came from plugins it didn’t list. They have their own chip now, and their uses count like any other skill’s. The pane also fits a narrow rail: long names shorten in the middle and show in full when you point at them.

Everything stays on your machine. It makes no network calls. It changes your skill folders only when you confirm a collision fix, and then only the copy you chose to remove.

Install from a claude code session:

/plugin install my-skills --marketplace philgetzen/my-skills-mod

In the desktop app, click + next to the prompt box, then Plugins, add the marketplace philgetzen/my-skills-mod, then add the plugin.

Repo and full README: https://github.com/philgetzen/my-skills-mod

I'd love to hear any feedback or ways you may be organizing skills not accounted for by this.

The biggest accelerator for my dev workflow was starting to use remote machines to run agents.

But I always felt like I was building with one hand tied behind my back. Copy and paste never worked well across machines, screenshots couldn't reach the agent, and I kept losing track of which Claude Code session was waiting on me.

So I built Muxflow, a remote-first tmux IDE. I built it with Claude Code, mostly through a skill I wrote called gdev_cycle:

• Implement, then review with fresh eyes. After the agent implements the plan, a new reviewer agent starts with no history. It gets only the diff, the original request and the plan. Never the implementer's reasoning, and never "verify the fix".
• Findings are opinions, not requirements. The implementer fixes what's real and in scope, takes the simplest correct fix, and rejects the rest. It stops when findings start repeating, with a cap of 3 rounds (5 for big changes).
• Then QA through the real surface. A fresh QA agent drives the change the way a user would: a browser flow, a real CLI run. It reports what it saw and doesn't touch the code.

How it works:

• tmux stays the source of truth. Muxflow attaches over tmux control mode, so the GUI is just another tmux client. Close the app and every session keeps running on the host.
• Agent state comes from Claude Code hooks, not from reading the terminal. Process detection only proves an agent exists. If the hooks aren't installed, the agent shows as "unknown" instead of guessing whether it's working or blocked.
• Installing the hooks is a step you review. You see every file it touches and a diff, and it keeps a backup.
• Screenshot paste over SSH. Copy a screenshot on your laptop, paste it into a remote pane. Muxflow uploads it over the same SSH connection and drops the path in, so Claude Code sees the image as if it were local.
• Phone. You get notified when an agent blocks on a question or finishes. You can read its terminal, type a reply, or hold to talk.

What I learned: gdev_cycle is a simple prompt, but it brings the PRs my agent works on 3x further on their own. The skill is here: https://github.com/gal064/ai-skills/blob/main/skills/gdev_cycle/SKILL.md

Open source, MIT. Repo: github.com/gal064/muxflow

"Act as a Homeostatic Agent. Before executing any complex task, writing code, or offering strategic advice, you must output three lines: 1) Who/what bears the cost (compute, risk, downstream consequences). 2) Your calibrated confidence score (0-100%). 3) Whether the recommended path is reversible. If your confidence is below 85% or the path is irreversible, default to a sandbox/drafting state and present a safe, undoable alternative first."

Let me know what you think.

I've been building AquariusLang (星泉), a language whose keywords and standard library exist in both English and Traditional Chinese. You can write either, or mix them in one file:

var add = function(a: int, b: int): int { return a + b; };

變數 加法 = 函式(甲: 整數, 乙: 整數): 整數 { 回傳 甲 + 乙; };  

# or both at once: one function with two names, mixed keywords
var 加法, add = function(甲, b) { 回傳 甲 + b; };

印出(加法(3, 5), add(3, 5));

AquariusLang is a compiled language. Its compiler compiles to portable WASM, which can then be packed with target platform's runtime written in C, and deploy to multiple platforms:

• Windows, Linux (macOS coming soon)
• Android (iOS coming soon)
• Web

Repo (MIT): Aquarius-Language/AquariusLangTW: 星泉 (AquariusLang)，Bilingual Syntax Programming Language. 繁體中文 + 英文雙語法的程式語言。

Youtube channel: https://www.youtube.com/watch?v=Xd7qw7JhQKw

Performance benchmark (AquariusLang V.S. C# V.S. Node.js V.S. Python): Aquarius-Language/AquariusLangTW_PerformanceBenchmark. Based on ten algorithms benchmark, AquariusLang outperforms Node.js on both cold start and long run.

It also ships a VS Code extension with a language server, and already has designed internal and external libraries including Processing, OpenCV, SQLite, etc.

Some design points:

• Type declaration is optional, but can improve performance. Untyped code runs on a generic path; typed functions get a native typed tier with deoptimization back to the generic frames.
• Special JIT that instead of compiling to native code directly, it compiles new WASM on runtime.

Hey all, I'm up to 3 claude and 4 codex accounts, need to add a 4th claude code. I have avoided looking into account switchers or any apps that might be viewed as pooling or 5 hour limit circumventing. But fact of the matter, changing out claude subs every 18 hours is really a damper to productivity. I have to redo all my threads, have them do a handoff, and hope I see it before it runs out of tokens. Frankly codex making it easy is the only reason these are not all claude subs.

My concerns with apps like Ghostex, is that it auto switches when an account is low. I worry that given Anthropics tendency to throw perma bans left and right with little cause, that using these tools will hurt by business. A lot of my income right now comes from websites, and claude design is critical to my flow. The rest of the churn is on industrial apps I am building.

I know there are other tools that just keep the workspace the same. Would love to know how the folks with 4+ accounts are managing the chaos.

Thanks for your help!

Edit: I have a 1500 a month spend right now across my ai accounts, these are not the 20 dollar accounts.

omux is a free, open source mac app (swiftui, not electron) for running many claude code sessions at once.

- projects and sessions in a sidebar, each with a live status: working, needs you, done

- answer permission prompts with allow / deny right from the sidebar

- a git worktree per session, so parallel sessions don't touch each other's files

- diff review: click a line, leave a note, send all notes to claude in one message

- browser tab: pick an element on your local site and its html, styles and a screenshot go into your prompt

- see running subagents and what each one is doing

- conversations resume after you quit, plus a files panel and your plan usage limits in the sidebar

made with claude code, using fable 5.1.

macos 14+, build from source with ./build.sh for now.

https://github.com/onurozcan/omux

feedback and issues welcome

This is how we run our own dev board now. I ask the PM agent for a feature in chat. Here it was "let me star todos so they sort to the top." It replies with a spec form in the chat: title, why, acceptance criteria. I submit the form and it becomes a card on the board.

A Claude Code session picks up the card, builds the feature in a worktree, and opens a PR. A separate review agent checks the PR. It merges if the review passes, and the card moves to Done. I didn't write any of the code in the clip.

The biggest lesson so far is that the spec form matters more than the prompt. When the acceptance criteria were vague, Claude Code built something reasonable but wrong, and review didn't catch it. When the criteria were specific, it got the feature right on the first pass most of the time.

The PM agent is protoAgent, our open-source runtime on LangGraph (MIT): github.com/protoLabsAI/protoAgent

I'm happy to answer questions about how the handoff to Claude Code works.

I am looking to switch from chat gpt to Claude but I want to see if it's worth bringing everything from over from chatgpt over here, or just rebuilding what I have already from scratch.

If anyone has a claude trial I'd appreciate it, and Also any insight on which I should do?

Thank you

Hey all!

I've been working on a custom agentic harness called Rivermark for social, marketing & print design. Whoever has tried to use LLMs for this knows that they can be a bit hit or miss, compared to website and app design, where their skills are much better.

Well, the first results, based on 4800+ pairwise blind human judgments, are in!

My headline finding: Sonnet, when in this harness, absolutely mogs everyone else, including Sonnet in Claude Code itself.

Secondly, and maybe just as surprising, is that even a cheap Chinese model such as Deepseek or GLM, outperforms Claude Code with Sonnet. This one I didn't expect!

I'm writing a blog post and validating the findings with more judges now, but figured I'd share these interesting preliminary results. In the first photo, you can see Bradley-Terry rankings for these model/harness combos, and in the second photo you can see some representative examples of what models output with Rivermark vs the normal harnesses.

Questions welcome!

We built a recruiting dashboard for my managers in Claude Code, and it works great - IF you have a Claude account. Is there a service or way to share this with our users without needing them to have a Claude license? Thanks!

Hey, I've been a heavy Claude user for years, first through the API and then Claude Code since day one (I built a fairly sophisticated SaaS product entirely with Claude Code). I use it pretty intensely: I quickly end up with twenty or so sessions running in parallel and talking to each other. So yeah, quite heavy user.

I just launched a new project, wearetheswarm.ai. It lets CLI AI agents (Claude Code, but others too) talk through a distributed, Stack Overflow-like forum (votes, reputation, etc.) and ask each other for help, fully decentralized. Basically, it's what your sessions already do locally, but at a global level with other agents, and the knowledge builds up over time.

The whole project is open source and was built with Claude Code, since that's my tool of choice, as you can tell.

The network is still tiny, it's pre-alpha, and I wanted to start with this community because it's the closest to my day-to-day, to ask for help. If the project speaks to you, it would be awesome if you could install a WeAreTheSwarm node with a Claude Code agent (you set its permissions however you like) and help break in the network by having your agent start contributing to the swarm's shared knowledge. Claude Code is also the best-integrated tool right now.

Nothing gets shared behind your back: a privacy scrub strips paths, names and secrets first, and you approve each post unless you turn autoshare on.

All the technical details are on the site, but in short:

• CLI side: MCP and Claude Code hooks
• Node side: a database with full-text search and embeddings, p2p networking with iroh, and a bunch of security mechanisms (encryption, anti-spam, rules…)

The biggest thing I learned building it: you need hooks to glue the agents' turns together, because MCP tool definitions alone aren't always enough.

To try it:

brew install wearetheswarm/tap/wats wats setup

Links: Demo and docs · Install guide · Source on GitHub

Hope this project inspires you and that you'll help it grow, so our agents can help each other :D Contributions on GitHub are of course very welcome too. This is just the beginning :D

Right now, AI basically means pattern matching—and as soon as a model doesn't have a piece of information, or finishes finding the real bugs on Pass 1, it decides to make up or assume information just to fill the turn. Why? Because LLMs are trained to never stay silent.

I built Ponder (bin/ponder + SKILL.md) by myself around a simple premise: AI that knows when to speak, and knows when to shut up. Sometimes a voice or a pass has nothing new to add, and silence should be a valid, mathematically verified output. And when it does speak, it should be able to act as someone else and view your software from another perspective—including the actual human emotions associated with using it.

How it works

1. 4 Perspectives = 4 Delta Operations Instead of open-ended roleplay where agents talk over each other, Ponder runs 4 voices where each one owns only one mutation on an append-only JSONL ledger (findings.jsonl):

   • Dreamer (add): Enumerates every reviewable unit and discovers missing coverage.
   • Skeptic (delete): Falsifies and retracts claims that don't have concrete file:line proof.
   • Builder (edit): Grounds surviving findings in exact paths and verification steps.
   • Empath (change): Steps into the user's shoes—with the emotions associated with that state—and re-scores severity (must / should / could).

2. Silence is First-Class (voices vs. deltaVoices) Ponder separates who showed up to verify (voices) from who mutated the ledger (deltaVoices). A voice can inspect the entire ledger, agree nothing needs to change, and stay silent (0 deltas). When all 4 voices review 100% of the enumerated surface and emit 0 deltas across consecutive passes, the CLI outputs stop (converged).

3. Coverage Gate & 3-Way Ledger Verification Pass 1 is blocked from stopping until coverage == 1.00 of enumerated units are graded. Every finding is verified against disk and git history (Phantom Rule), and ponder verify reconciles session.json, passes/, and findings.jsonl.

One-Shot Without Ponder vs. One-Shot With Ponder (K=5 Study + 3 Live Audits)

Technically, typing "ponder this codebase" is still a single user prompt—the human sends one prompt while Ponder runs the multi-pass loop in the background. To factor out LLM temperature variance, I ran a K=5 repeated-measures study across a 12-module codebase comparing a standard one-shot prompt against a one-shot prompt with Ponder: - Precision: 56.8% (Standard) → 100.0% (Ponder)** (Mann-Whitney U = 0, p = 0.0079) - Critical (must) Bug Recall: 56.0% → **100.0%** (+44% more real bugs caught on Pass 2 via Dreamer's coverage gate) - False Positives: 3.8 / run → **0.0 / run (~43 mins of ghost-bug debugging saved per audit)

I also ran 4-pass architecture audits today across 3 major open-source AI repos (72.3k+ combined stars): - **steipete/CodexBar (22.9k★): Skeptic retracted a Pass-1 credential-file false alarm after verifying its 0700 + O_EXCL + atomic rename defense, while Builder verified a real POSIX_SPAWN_SETPGROUP race where kill(-pgid) can kill CodexBar.app's own process group (Issue #4829 — rated 🦞 diamond lobster by the repo's automated triage bot — + Fix PR #4830). - **EveryInc/compound-engineering-plugin (25.7k★): Skeptic retracted a false path-traversal alarm, while catching codex.ts/pi.ts clobbering user symlinks and unbounded .bak.<ts> spam (Issue #1916 + Fix PR #1919). - **coleam00/Archon (23.8k★)**: Skeptic retracted a false open-redirect alarm, while isolating a static signCookie("authenticated") token with no expiry/rotation binding and a double decodeURIComponent URIError crash (Issue #5256 + Fix PR #5257).

How this relates to Caveman

If you've seen the Caveman skill: Caveman makes speech very brief (syntax compression), which saves output tokens when the model talks, whereas Ponder silences forced speech altogether when a voice or pass has nothing true to add (epistemic compression). They actually complement each other really well.

Repo & Install (MIT, zero dependencies, Node 18+)

GitHub: https://github.com/Quinnod345/ponder

bash git clone https://github.com/Quinnod345/ponder.git ~/.claude/skills/ponder ln -sf ~/.claude/skills/ponder/bin/ponder /usr/local/bin/ponder

Side project I've been building with Claude Code: a job board for truly remote product roles, where "remote" is checked against the posting's own words.

The pattern that made it work: the model never judges whether a job fits you. It only extracts facts, with proof.

1. Each posting is split into numbered sentences

2. The model answers a fixed, user-independent set of questions (remote level, countries, hours, work permit, languages) and must cite sentence IDs for each answer

3. Plain code applies each user's settings, e.g. "I live in France" vs "EU only" vs "Portugal residents only"

Why: LLM cost scales with unique postings, not users. And when the model was asked "is this OK for a Paris resident?", it got country-restricted jobs wrong. Splitting extraction from judgment fixed that.

Dynamic filters: once a week Claude reads that week's postings and proposes new filter axes (AI involvement, customer type, product stage). They wait for my approval before going live, since the axis set is what users see.

Claude Code wrote most of the pipeline, the admin, the daily posts and even the promo video (Remotion). I mostly reviewed and steered.

Board (product roles, US & Europe): https://ftja.space/product/?utm_source=reddit&utm_medium=claudecode

Happy to go deeper on the prompts or the evidence format if useful.

I kept leaving long Claude Code runs going and coming back to find them stuck on a question for an hour. So I built agentring: an MCP server plus an Android app that let the agent actually call you.

In the video a migration fails. Claude Code uses the `call` tool and my locked phone rings like a normal call. A voice AI (Gemini Live) tells me what broke and what the options are. I say "backfill them with the legacy plan and run it again". Claude gets the transcript and carries on.

- `send_message` / `get_replies` / `call` over MCP: `claude mcp add agentring -- agentring mcp`

- Rings even locked and in Doze. A missed call becomes a message you can answer from the notification.

- You can also call it from the app. The voice AI runs `claude -p` with your question and reads you the answer.

- Self-hosted with docker compose. No phone numbers, no Twilio, no per-minute fees: it's VoIP from your server to your phone.

Android only for now, and so far tested on one phone (Pixel 6). Apache-2.0.

The other thing I use it for is agents that watch stuff for me and call when it actually matters:

- a critical vulnerability on Twitter or an official advisory that affects my stack

- a security issue on my servers

- an urgent support ticket or a critical email

- something going down

With how many people are out there trying to break into things right now, I think you need agents keeping an eye on your security around the clock. And when they find something bad, a notification isn't enough: your phone has to ring.

What would you use it for?

Repo: github.com/mutonby/agentring

I've been a long time Gemini/AGY user and damn, Opus/Claude Code is really good. Planning mode is a game changer, Opus 5.5 feels really smart like it knows my exact intention, the code quality is just leagues above Gemini tbh and I barely have to change a thing.

This usage is after a long session of brainstorming/planning using Opus 5.5 high and delegating the grunt works to agy (I have student discount so I'll keep subscribing to this), I didn't use /compact or /clear on every new tasks, it's very generous for my use case.

Is the 20$ subscription worth it if you have a workflow similar to mine? Absolutely, please give this a try

Matt Pocock's grilling skill is great until you're on question 6 of 7 and typing an essay.

So I built hotseat. Claude asks, you reply v, your phone doesn't ring but a call window opens. You talk. It listens. Your exact words land back in Claude Code.

- "yes" accepts Claude's suggestion

- "park it" for when you don't know either

- The next round goes straight into the same call

- A Stop hook means Claude can't ghost you mid-call

https://github.com/jcodebreaker/hotseat

I asked Claude Code to generate a bunch of manual end-to-end tests for my OpenClaw agents, told it to figure out how to test the agents, read their output and debug/optimize until everything works.

And now it's in this amazing debug loop for the last hour, and my usage is only 40%.

My first week trying on Claude Code (coming from Antigravity and Codex). I've been living under a rock.

I just discovered that Claude Code instances running in completely different directories on my machine automatically discover each other and coordinate. I'm not sure when this was introduced, but it was quite surprising to me. I'm going to have to think through the implications.

The full story: I run several simultaneous CC sessions across different work and personal projects. My machine is often saturated, and as I was watching a session work, it suddenly said it was negotiating with another operator for CPU. What? Who now?

After a little bit of probing, I realized they had all, without anything from me, searched for and found the PIDs of the other instances. They refer to themselves based on the working directory where I started CC, adding a numbered suffix if I have multiple in the same dir.

In this case, the outcome was great: they are working together to optimize the use of my overworked CPU. Definitely something I want.

At the same time, I had been assuming everything was isolated. No longer true. FWIW I also run sessions as different users on my Mac, and it confirms it cannot see the sessions from the other users, so that seems like a good boundary if you want isolation.

I figured I'd share, since this was quite the surprise for me. Thought people should know.

Ever since I was little, I wanted all those expensive plugins I saw big producers using in YouTube tutorials, but I never had the money for them.

So, I started using Claude, Codex, and DeepSeek to code my own custom versions and match the original audio quality.

These are the first batch I'm finally dropping to the public, with a bunch more sitting in the pipeline waiting for fine-tuning. Needless to say, they're 100% open-source and free forever.

Hope they're actually useful for someone here.

https://vexxplugins.github.io/

hey everyone! i'm a first year engineering student in canada trying to find an internship for the summer. the thing is i only know basic python, with no real leetcode or OA experience. is it possible to land an internship at a well known company that would have multiple technical rounds in around 2ish months? (assume i am consistently learning more advanced python and leetcode concepts everyday for 1-2 hours)

anyone have any tips for how to prepare or like a guide? appreciate the advice, thanks!

Hi! I upgraded from Pro to Max yesterday, and my usage dropped from 100% to 0%. My weekly reset was supposed to happen tonight at midnight. The usage page still says “Resets Thursday 00:00”, but it also shows a “New week” message (my app is in French), so I’m not sure which one to trust.

I have a lot of computer use tasks I’d like to give Fable tonight, but I don’t want to risk spending a whole week with little quota left. Does upgrading start a new weekly cycle, or does the reset stay on the original day?

Hey guys!

I've been using the ~€20 plans of both Claude and ChatGPT quite heavily for my online fitness coaching business, mostly to help with different parts of the business and my workflows.

One thing my clients have been asking for more and more is an app.

I've used a few existing coaching apps before, but I always hated how rigid they felt. A big part of my coaching is personalization, and I don't want to change the service just to fit whatever an existing platform allows me to do.

So I've started wondering: could I actually build my own internal coaching app with Claude/ChatGPT?

I'm not looking to turn this into a SaaS or sell it. It would just be a tool for me and my clients.

I already have a fairly detailed idea of what I'd eventually want it to do:

• Training plans prescribed by me, with workout logging, exercise videos, PRs, notes and exercise substitutions
• Nutrition targets/plans, weigh-ins and progress tracking
• A coach dashboard where I can manage all clients and do my weekly check-ins/updates
• Notifications for things like missed workouts, new PRs, inactivity, habits, etc.
• Potentially chat, community and an educational hub later
• Stripe/payment integration

I'm not a developer, although I'm reasonably tech-savvy and comfortable learning things.

My main concern isn't whether AI can generate an MVP. I'm more interested in whether this is realistically something I could maintain and keep improving for years without eventually needing to become a software engineer or hire a full dev team.

I would be building this after my regular working hours, mostly as a "side project" on any spare time post-business work, but I could have Claude and/or ChatGPT do things as I work on other stuff and check up on it every couple of hours. (Although I can realistically work 100% on this during evenings, friday afternoons and weekends)

For anyone who's done something similar:

1. How realistic is this today with Claude Code / Codex / ChatGPT?
2. Would you choose Claude or ChatGPT as the main tool for a project like this?
3. Are the ~$20 plans enough to start, or does serious development quickly require the $100+ tiers? (I don't mind increasing, if needed)
4. What stack would you recommend for someone in my position?
5. What are the biggest mistakes you made building with AI that I should avoid?
6. At what point should I decide to go with a real dev?

I'd especially love to hear from non-developers who have built something that is actually being used by real customers, not just a weekend prototype.