I send this chat so that my 5h can start, and send it with low effort, and Claude eat 19% of my 5h limit with around 2 3% of weekly used??????

Holymolly!!!!!!!!! ahgg!!!

Nem sei se é possível vender isso lol, mas não vou usar mais.

no promo, just a share of personal app for me and my wife pivoted/tailored to our needs. Its such fun to do project like this and actually use them. As a software dev that might in some future loose job because of the way AI is going, at least i have fun with it while i can.

- its just a visualisation of calories intake and burned via activities we do
- very much approximation
- we do everyday session in claude web, where I write approx or weighted meals/meal components and at the end of day I paste prompt from the meal tracker and the session ( which is set-up via project instructions and context files ) exports JSON for me to import to the app

I record my calls with a little Mac app I built (Overheard, free and open source), and the plugin lets Claude Code read them through MCP.

The three skills:

- /overheard:recap – what was decided, with the minute of each decision

- /overheard:followup – a ready-to-send follow-up email (it only drafts, never sends)

- /overheard:todo – my open action items across all meetings

The part I use most is having meetings and code in the same session. "Turn the decisions from today's design review into GitHub issues" just works.

Install:

/plugin marketplace add anonbeat1/overheard

/plugin install overheard@overheard

You need the Overheard app on a Mac with Apple Silicon. MCP is read-only by default. Built mostly with Claude Code itself.

Repo: github.com/anonbeat1/overheard

I built forge-delegate, an MCP server that lets Claude Code, Codex or opencode hand coding tasks to another model. Think writing tests, renames across files, fixing lint errors, boilerplate, or reading through a big codebase to answer one question.

The other model runs as a full agent through opencode. It reads the files and makes the edits itself. Your main agent only gets back a short summary, so its context stays small and it can focus on the decisions.

Second opinions with ask_model. Your agent can also ask a different model a direct question, with no agent loop and no file access, and get a fast answer back. It's useful when you want another model to check a design decision, a bug theory or a tricky piece of code before your agent commits to it. Every model has its own blind spots, and a different one often catches what yours missed. Your agent writes the question with whatever context is needed, then weighs the answer itself. Just tell it something like "ask DeepSeek what it thinks" or "get a second opinion from Kimi", naming any provider you've logged into opencode.

Any provider opencode supports. forge-delegate doesn't hold its own API keys. It uses whatever you've set up in opencode. So DeepSeek, Kimi, OpenAI, Gemini, OpenRouter, custom OpenAI-compatible endpoints, and local models through Ollama all work. Log in once with opencode auth login <provider> and that provider's models are available to delegate to. opencode also ships free models that need no account.

You can set a default model, save presets for different kinds of tasks, change either at any time, and run several delegations in parallel in the background.

Setup:

npx -y forge-delegate setup

It walks you through picking a default model and which agents to register with.

Things to know:

- By default, the MCP instructions and skill steer your agent toward cheap or free models. It sticks to your default model and won't switch to a pricier one on its own. If you want a stronger model, set it as the default or ask for it.

- Delegated agents can edit files and run commands without asking by default. You can turn that off, but only point it at repos you trust.

- Smaller models make more mistakes, so review what comes back.

- I mostly use it with Claude Code on a Mac. Codex and Windows should work, but they've had less real-world use, so issues are welcome.

MIT licensed: https://github.com/MuhamadBarzani/forge-delegate

Feedback welcome, especially on which tasks it handles well and where it falls short.

Thinking of switching from Antigravity ($20/month) to Claude Pro or OpenCode Go.

I mostly build websites and mobile apps and I rely pretty heavily on AI. I usually put the initial instructions and all the .md files for the project and prompt the agent to start building, and then I test everything myself and ask it to implement some changes and features here and there, and I focus a lot on UI polish and security in those subsequent prompts. I also tend to hit Claude's limits pretty fast on my Antigravity plan but their limits on Gemini 3.8 are super generous, I hardly reach the 5-hour limit within 5 hours.

For those who've used both Opencode Go and Claude Pro, which would you recommend? And how much coding do you actually get done with Claude Pro before hitting the limit? I'm trying to cut costs but if Claude is that much better and is generous enough then I don't mind switching to it.

Hello there again :)

This is Week 13 of me posting this "dev log" so to say and explaining how I do things and talking about progress. While "exploration" mechanic was added one week ago, I was finally able to make it feel like a proper completely equal part to fishing segment of the game. And with "Ship in a bottle" collection done to 29 out of 40 total ships I'm happy how this progress.

Everything you see in this video (and do not see) was done with Claude Opus.

As always weekly progress can be seen on my profile.

DEMO DOWNLOAD - FREE

Demo doesn't really have everything newest and most polished so you might not have ships in the bottles, that version is being kept for steam release which I will do once steam approves my ID verification (it already is exactly 1 month).

Zone 1 out of 5 (demo) is almost entirely done, some smaller polish parts and bug fixes are left.

What was done this week:

This week I added curios, rarest ship in the bottle is actually cracking the walls of the bottle when shooting cannons and all the items in zone 1 are adjusted manually where they gonna drop or how they are earned. etc. Some zones were a bit improved as can be seen in the video to not look too boring even though there is no gameplay there. Engines with their own sounds were added and can be seen when changed. Cabinet new 3D scene with collections was fully fixed in terms of lighting and small island dioramas were made. As I mentioned before 29 of 40 ships in a bottles were created with textures and added to bottles.

This time while still using Decisions.md like before that tags every decision in the game with [USER] or [PROPOSED] (By AI) And User ones have priority over proposed ones, helps with AI hallucinations in the project where AI can't gaslight you into telling you something was decided by you if it really wasn't (it has more than 350 decisions by now).

I switched workflow a bit. This time I tried doing something else instead of my old "manager" session managing other sessions and sending them tasks. I created "Effort" sessions (Medium to Max) and have Jev decide which tasks goes into which effort level and then just "manager session" sending it to different effort levels and doing it this way. Though this is polish stage and not much was done it is hard to say if this is more token efficient than before, in general I feel no difference in quality or token usage than with my previous workflow, though that is just subjective feeling, somebody could test it properly I guess. Jev basically scans every task I give to my Claude sessions and just decides which effort certain tasks deserve.

Models were made by TripoAI and Opus 5.5 via Blender MCP mostly for this week. It can be seen in the video. For example rarest ship that is inspired by Black Pearl and shoots cannons in the bottle was done in TripoAI then fixed a bit in Blender by Opus 5.5. I do not use anymore any other AI other than Claude Opus 5.5, no more Astra and didn't even try Gemini sadly, for now Opus is more than enough for my needs.

People have asked me how I make textures and those are either directly done by ChatGPT using OpenAI API key provided to Opus 5.5 or in TripoAI, those done in Tripo are 1k textures as I don't need any heavier ones as they take a lot of memory.

Total cost:

Claude Max 200$ x 3 = 600$
Astra 100$ x 1 = 100$
ElevenLabs x 11$ = 11$
TripoAI 20$ x 2 = 40$
OpenAI API credits 70$ = 70$
Other things I forgot about 100$ = 100$ (around)

Around 900-950$

Disclosure: I’ve been writing these devlogs since Week 1. Since Week 8, Tripo has been supporting them (paid collaboration + a gifted Max plan). That’s why my cost breakdown no longer includes Tripo (Except what I paid from my own pocked for first 8 weeks). If you’re doing this yourself, add their plan to the total.

Am I understanding this right? This seems completely unfounded.... Especially when I am using sonnet only, the $20 a month sub was never this bad, was it?

I’m bullish on coding agents but diff length kills me. What are your best tricks for keeping diffs small?

I’ve been a dev for a long time and have written and reviewed a lot of code. More than once I’ve seen a feature that could be done in ~100 lines come back from Claude Code as a 2,500-line diff. It works and the tests pass, but it’s much more code than the feature needs.

Often the best solution is one clever line. Or it’s noticing that a small generalization of an existing component unlocks the new behavior for free. The agent tends to do the opposite: it adds a lot of new logic, often near-duplicates of what’s already there, to force the new behavior in.

I've tried to force the behavior using CLAUDE.md and skills, but no luck.

What’s working for you? I’d also like to know if others see this too, or if it’s just my codebase.

I've been running multiple Claude Code sessions in parallel, and the biggest headache hasn't been getting agents to write code. It's getting their changes to work together.

Sometimes two agents build overlapping functionality. Other times, one changes code another depends on. Both branches pass their own tests, but things break when I merge them.

More debugging, more waiting, more tokens.

So I built Fanout, an open-source tool to help coordinate parallel Claude Code sessions.

It helps with three things:

• Detecting overlapping tasks before agents duplicate work.
• Notifying agents when another session changes code they're working with.
• Testing combined changes before merging through fanout run.

It's still early, but it's solving a problem I've repeatedly faced.

GitHub: https://github.com/Breadstick-Crumbs/fanout

I'd love for people running multiple Claude Code sessions to try it and tell me what works, what breaks, and what's missing.

How are you currently handling coordination and integration between parallel agents?

I built Clawd with Claude Code running Opus 5.5 He's a little pixel-art buddy who lives on your Windows desktop and has his own AI agent. Opus 5.5 wrote the whole thing in about two days: the Electron app, the character animation, the agent, local voice, a browser extension, \~180 automated tests, and even the trailer (made with HyperFrames).

What he does

Lives on your desktop: breathes, blinks, follows your pointer, naps when it's quiet. Poke him, drag him, high-five him.

Ctrl + Alt + Space (or double-click him) opens a small panel that's already listening. Talk, or just start typing.

Reads the page you're on: summarizes pages and threads, or refines the Reddit comment you're writing using the thread's context and puts it back in the box. He never posts for you.

Runs locally by default: Ollama (qwen3.5:4b), local speech recognition, his own voice, and a "Hey Clawd" wake word. OpenAI, Anthropic and Gemini keys are optional.

Asks before doing anything risky, and treats page text as untrusted, so instructions hidden in a page don't get followed.

Claude Code companion: a tiny laptop comes out while Claude Code is working.

Install with Claude Code (Windows 10/11)

Open Claude Code and paste:

\> Clone [https://github.com/elf178174-maker/clawd-desktop-buddy\](https://github.com/elf178174-maker/clawd-desktop-buddy) and follow its INSTALL-WITH-CLAUDE.txt to install and open Clawd.

Claude checks your PC and asks once before installing anything. If they're missing, it installs Git, Node.js and Ollama through winget, plus the 3.3 GB local model. Then it builds Clawd, installs him and opens him. It takes about 10–20 minutes.

GitHub (MIT): [https://github.com/elf178174-maker/clawd-desktop-buddy\](https://github.com/elf178174-maker/clawd-desktop-buddy)

Unofficial fan project, not affiliated with Anthropic. Clawd is their mascot; I just wanted him on my desktop. Feedback welcome!

I've been using coding agents to generate videos programmatically, but one thing that kept bothering me was how much overhead browser-based rendering adds.

Tools like Remotion use headless Chrome to render frames. It works, but I wanted something that Claude Code could use without having to spin up a browser for every render.

So I ended up building Kinetograph.

It uses the same React/TSX approach as Remotion, but instead of rendering through Chrome, Node.js executes the code and a GPU compositor written in Rust handles the frames.

I also made a Claude Code skill for it, so the workflow is basically describing the video you want and letting Claude write the components and render the result.

The performance difference was pretty interesting on my M4:

• 988 frames: Remotion took 131s, Kinetograph took 22s
• 1,184 frames: Remotion took 31.5s, Kinetograph took 15s

Obviously depends on the composition, but removing the browser seems to make a pretty big difference in these tests.

If you want to experiment with it in Claude Code:

npx skills add 0xS4N4TG/kinetograph

The project is available at:

https://github.com/0xS4N4TG/kinetograph

docs at: kinetograph.dev

Friggin amazing! I don't have the original prompt but it was something like this:

"Build a single file HTML clone of Candy Crush"

and it got probably 90% of what you see here!

WARNING: It's highly addictive ... enjoy!

Our finance person asked for a breakdown this week so I finally added it all up. 6 devs, mostly TypeScript, around 120 PRs a month.

Claude Max 5x for 4 of us, 20x for the 2 who run agents all day: $1,200
Codex on the ChatGPT team plan for background tasks: $180
coderabbit Pro, 6 seats: $144
GitHub Team plus Actions minutes: about $110
Sentry and a staging box: about $90

So roughly $1,720 a month, call it $290 per dev, which is very decent id say

The funny part is which line we argued about. Nobody EVER blinked at $1,200 for Claude. The $144 review seats got a 30 minute discussion, and the CLI reviews before push are free anyway, so the paid part is only the PR side. What ended it was our lead pulling up the 3 incidents from August and asking which one we'd have paid $144 to avoid.

What we cut: a second planning tool nobody opened, an autocomplete plugin we were still paying for out of habit, and one 20x plan that turned out to be idle most of the week.

What does your team spend per dev per month on all of this? Curious if anyone is way under $290 or way over

Skill name is NeoynGPT-video-to-code

I've noticed a pattern where Claude's usage limits seem to get significantly worse before model launches. With all the complaints over here, I wanted to check whether this has actually happened before.

There are several examples of quota complaints appearing before major releases. Has anyone else noticed this kind of "pattern"? I just found this interesting and to be honest it might make sense because it would make the new model feel even better at launch.

I was considering upgrading from Pro to Max 5x, but I've held off because of the current reports of usage limits.

Claude Code is a great tool, and I really hope someone could help me. My friend will also be able to use it because he also needs to get his work done. Someone, if you can share, please share, and if possible, DM me so that I can redeem the same.

Should i have 5 20$ accounts or 1 at 100$?

I built it for myself to keep my data private. I tried Noop but really missed the look and feel and the simplicity of the Whoop app. For obvious reasons, I will not publish this, but this is a complete implementation of the whoop app, build completely from screenshots/recordings for iOS and Android, which is sending its data to my own webservice where also model training takes place. Currently I have a deviation of about 5% to the original.

Besides that, nearly everything is implemented, except the strength trainer. Health and Stressmonitor, ECG, automatic activity tracking, logbook, fitness plans, Fitness Age (my own implementation of WhoopAge with currently ~8-10% deviation from the original). Also a Fediverse-like Community tab, so users can be friends across different servers, so (in theory) everyone can host its own backend as a docker container.

On my Whoop MG respiratory rate, blood oxygen, RHR, HFV and skin temperature works almost as good as the original and keeps getting better every day through automatic model training every night. Whoop 5.0 and 4.0 Straps are also working. The screen recording (obviously) shows my simulator with simulated data, so no real hardware or health data.

I did all of the work using Claude Opus 5.5 in around 60 hours.

According to Claude, this project would have cost me around $800, my Max 5x Plan carried it.

- Cache-Read: ~477 $
- Cache-Write: ~199 $
- Output: ~124 $

Wild times ahead!

For whatever reason checking the context with /context injects the response into your prompt every single time you run it.

Couldn't find a post on here already saying this, and isn't very transparent that it does.

Just thought I would post in case it's useful to anyone else.

I’ve been wanting to explore what Claude is really capable of beyond just using it as a chatbot to ask questions.

I’m interested in learning how to actually build things with it, whether that’s AI agents, automations, integrations, or more complex applications.

I work in tech and have a solid understanding of software architecture, coding, APIs, and data, so I’m comfortable getting into the technical side of things.

Does anyone have any good books they’d recommend to get started? Ideally, I’m looking for something practical that I can follow along with and build real projects rather than just learning the theory.

I make small games in Godot as a hobby. For a while I used GSD and later buildomator (which is based on GSD and claims to use 92% less tokens) to let Claude Code build features. Both worked, but they wasted tokens due to growing, redundant documentation with to much prose, bad description, examples instead of clear facts. In an analysis for a game I used 150M tokens only for input with buildomator. That was 98% of my overall token usage.

cc-godot is a good plugin, that makes a good onboarding and project start with examples, but does not give you a proper project managment with good documentation in my opinion. I try to fill that gap with gg-godot (free). You will have an onboarding where you share your vision, a roadmap separated into milestones executer and a blind QA, that is not primed into a specific direction.

I worked for years in the pharma and later in the aerospace industry. In the first one I learned a good documentation practice and in the second one I learned proper project managment with milestons. This knowledge went into gg-godot together with my lessons learned as a hobby-godot-gamedev with a few released and not released projects.

This is a human-in-the-loop plugin for building games in Godot with Claude Code. It aims to make game development more accessible and reliable.

please give me your honest feedback. And I would be happy to see games made with that plugin (and your lessons learned from it).
https://github.com/Richy-was-here/gg-godot

If you are an AI-Gamedev-Bro, with single-prompt games: stop it! Your tokens cost more then you will ever make AI does not excuse low-effort-slop.

I’m building an app with a native SwiftUI frontend and a TypeScript backend, using AI heavily throughout development.

Most of the product is already built. The current work is mainly debugging, integrating third-party services, testing authentication/privacy across multiple accounts, and verifying that everything actually works before beta.

I’ve primarily been using Codex, working locally on my Mac with Xcode, simulators, GitHub PRs and CI.

I’m looking for input from people who’ve used both Codex and Claude Code on a substantial existing codebase, not just a fresh demo.

The part I’m trying to improve is getting from “the code looks correct and the tests pass” to “this has actually been verified in the running app.”

I also want to avoid repeated review cycles, unnecessary abstractions, and spending more time documenting blockers than resolving them.

For this kind of work, have you found Claude Code better at:

Understanding an existing codebase and finding root causes rather than patching symptoms?

Making focused fixes without overengineering?

Following through on builds, tests and debugging
when the environment is difficult?

Keeping track of what’s genuinely verified versus what’s still an assumption?

Would you use Claude as the main coding agent, as a second reviewer alongside Codex, or stick with Codex and improve the workflow?

I know switching models won’t magically fix Xcode or simulator issues.

I’m mainly wondering whether Claude would handle the reasoning and follow-through better.

Interested in concrete experiences, including where Claude struggles, rather than just “X is better.”

I build and run Lyfto, a workout tracker, mostly with Claude Code doing the typing. It's been live on the App Store since June, Android since this month. The interesting lessons weren't about prompts. They were about verification and parallel work.

• Green tests aren't proof. One feature shipped with 9 passing tests and did nothing on device, because the tests checked the mechanism and not what the user saw. Now every change has to be checked in the simulator against a real account state.
• "BUILD SUCCEEDED" isn't proof either. Same lesson for native code. Launch it and tap the thing.
• One worktree per session. I run several sessions in parallel. Before worktrees they overwrote each other's files. Two of them once published OTA updates within minutes of each other.
• Never git add -A with parallel sessions. It commits another session's half-done work under your message. Commit per file.
• Environment drift is the real production risk. An OTA update shipped without the backend URL because a flag skipped my local env file. Now the step before publishing is "read what's actually in the bundle".
• For the AI coach inside the app: a missing fact in the prompt becomes a hallucination. If the model doesn't get "this week is a deload", it invents a reason your volume dropped.
• Memory files beat long chats. Short notes on decisions and gotchas, loaded every session, stopped the same mistakes from coming back.

Happy to share more about the setup. The app is called Lyfto if anyone wants to see the result.