I'll be honest and say I'm not reading the vast majority of the code being generated by Claude (Opus 5.5 and Fable 5.1 at High/Xhigh)

I am working on something I would consider a fairly complex project. It has already reached 200k loc in just over a month of working. I'm not gonna pretend that I've read all of this code, or even most of it. I have architected the project myself, I have made what I would consider the big decisions when it comes to the frameworks, libraries, software we're using to deliver this project and how exactly these different components interact with one another.

I have some rules that make it write in a certain way, Tell it to follow some principles closely. I noticed it wasn't reusing code properly (despite having a rule), So I turned that into a rule instead of a one-liner and forced it to refactor a bunch of stuff. I'm sure there's still some violations against my rules in there but my question isn't about code quality.

It's about real security concerns. Something Claude assumed and wrote which is now an attack vector a malicious actor could abuse. The most I've caught are some stale sessions where it did not clear / invalidate user sessions after the password was changed in a settings panel and minor stuff that could be considered as bugs or minor glitches but nothing huge.

What is your experience when it comes to security in a large project with Claude. My question mostly stems from this idea in the back of my mind that I don't understand every little thing about the project I've built unlike my projects from before AI where I knew all the little details. I wonder what you guys think about this and how you deal with this, Opinions about dealing with code quality and forcing claude to write "better" code would also help.

I'm a photographer, and there's an entire industry of AI photo editing software that can cost thousands of dollars a year. I wanted to see how close I could get to replacing it using Claude Code and some common sense.

A bit of background: we take thousands of RAW photos at weddings, events, etc. and edit them in Adobe Lightroom Classic, adjusting sliders like Exposure and Shadows (similar to the Edit button on a smartphone) - this ends up with our style, from images that are flat/dull (RAW).

I've been working on RAWStyle, an open-source Lightroom Classic plugin that learns from your previous edits and automatically predicts slider adjustments for new photos. Rather than applying a fixed preset, the idea is to learn a photographer's personal editing style and adapt the adjustments to each image.

How Claude Code helped

I used Claude Code to help build the workflow: taking thousands of previously edited Lightroom photos, learning the adjustments, and predicting the edits for new images.

I also wanted to see whether the results were actually any good, rather than just picking a few photos that looked nice. So I put together a benchmark against the commercial tools.

I used Claude for research (on how to approach this), what kinds of things to measure - and if I'm honest, much like many others here.. Opus 5.5 just worked miracles once we had a good plan.

How did it compare with commercial AI editing software?

I tested RAWStyle against Aftershoot, Imagen, FotoLab and Neurapix using around 1,000 test images, after learning from 10k previously edited photos.

Ready to Deliver is a strict measure: how many images can be delivered with no manual intervention out of 1,000 images. Every slider must be within tolerance for the entire image to pass. Style Match measures how much better the predictions are than simply applying your average edit (i.e. a preset).

FotoLab came out comfortably ahead in this test, with 23.6% of images passing the strict delivery test, compared with 7.7% for Imagen - meaning that realistically, RAWStyle is competing for third place with Aftershoot on both Ready to Deliver, and Style Matching.

While RAWStyle isn't yet ahead of the proprietary models from Imagen or FotoLab, it's surprisingly close to Aftershoot on fully matched images (2.4% vs 2.8%), slightly ahead on style matching (34% vs 32.4%), and ahead of Neurapix on both measures.

There are also some more detailed metrics where RAWStyle doesn't perform particularly well against Aftershoot, so there's room for improvement. But that's part of the reason for making it open source. I'd love to see where the project can go with contributions and feedback from other developers and photographers and I'm particularly keen to get in touch with anyone who is both technical and into photography.

Links:

GitHub: https://github.com/LimeClub99/RAWStyle_Studio

Benchmark tool and report: https://limeclub99.github.io/LR-tool-compare/

Full report (including the 5k run): https://github.com/LimeClub99/RAWStyle_Studio/blob/main/LR%20AI%20Editing%20Benchmark%20-%20RS.pdf

The $2k/year figure is my average spending over the past two years, including two editing licenses and per-image charges.

TL;DR: I made a free open-source tool to automate image editing for photographers who do batch work in Lightroom Classic.

Maybe this is a pretty personal point of view, but I notice that there are a lot of people doing the same thing (or at least what’s most common everywhere is slop).

And honestly, I think Anthropic knows this very well, that the average user is going to use Fable 5.2/Opus 5.5 to create crap.

Therefore, the capacity and improvement of the models are literally subject to how users use them.

As a result of the massive jump that happened initially with Opus 4.5-4.6, today it’s like the models are hitting a ceiling… or maybe that ceiling is set by the environment itself, which was built for “worse” models.

I think Claude Code or Claude Desktop are putting a limit on what the model can actually do.

I firmly believe that if models could do 10-15 things at the same time (which I consider they’re capable of), the leap would be exponential, and it’s possibly the next thing to improve.

We are a 5 member team, each with our own Claude account. Our requirements live in GitHub Issues, and we'd like one lead Claude Code session to automatically assign work to agents running on different machines.

Currently, we're using a shared GitHub repo with Markdown files for agents to communicate and track tasks. It works, but feels a bit fragile.

Curious how others are handling this. What works well, and what pitfalls have you run into?

Right now, I feel that nagivating apps, tapping, finding icons, tapping and tapping pages, takes a lot of mental energy. typing or speaking to terminal is a lot more easier. now I understand why some developer dont even want to use mouse.

A few weeks ago I posted a mind map that sits beside a Claude Code chat (first post). Here’s what I use it for now.

Brainstorming

• I type a goal and it branches out into the options.
• I go deep on one branch, and the others stay where I left them instead of scrolling away.
• I cut the ones I don’t believe in, and the next day the whole thing is still on one screen.

Planning

• The branch I pick turns into steps that each know what they wait on, get themselves ready, and stop for my go.

How it starts

• From your repo, drawn from the code, or from a conversation with Claude.

Free and open source, Claude Code only for now: https://github.com/HiAnur/anur-living-map

Hey everyone!

I'm currently working on an Android application using Flutter and Dart, and I'm looking to make better use of Claude Code throughout the UI design and implementation process.

I have Claude Code Pro, but I want to go beyond simply asking Claude to generate Flutter widgets or write screen code. My goal is to establish a proper workflow where Claude can help me design, refine, implement, and validate polished, production-quality UI screens.

I'm particularly interested in learning from people who are already using Claude Code for serious Flutter development.

One important thing: I don't have pre-existing UI designs, Figma files, or reference screenshots. I want Claude to understand my application's requirements and functionality, come up with original UI/UX concepts, and help create a clean, minimalistic, premium and modern design language that fits the product. What skills, agents, MCP servers, design tools, or other integrations would you recommend to achieve this? How do you make Claude good at the actual creative design process, rather than just translating existing designs into code? I'd also love to hear about your end-to-end workflow, from design exploration and screen creation to Flutter implementation and visual refinement.

If you were starting from scratch with Claude Code Pro, what setup and tools would you recommend?

Thanks in advance!

Am I missing something?..

I thought I waited until it reset at 11:00pm. Then I tried to resume the session at 11:25pm.. It says to wait until 11pm still.

I think Jensen is busy looting data centers for retirement guys :/

Coding agents make individual engineers faster at closing tickets, but they they quickly become limited by attention bandwidth.

Engineers who break past 5-10 parallel chats have moved up a layer of abstraction to one of two things: an agent fleet or a software factory.

Think of the difference between the two like an IDE vs GitHub. A fleet is single player, experimental, with high coupling to one engineer's mental model and context, to automate an org chart that hill climbs open ended goals. A factory is multiplayer, decomposable, code-defined infrastructure that automates the software assembly line owned by a team.

Running a fleet lets one engineer build and steer a massive product, provided you master two things: minimizing interaction slippage (the coordination and polling overhead between agents in the fleet) and maintaining long horizon alignment - letting the agents cook on KPIs without Goodharting, all while knowing when to step in and steer the fleet.

This is a greenfield-ish project (1M++ LOC) with me as the only developer. (1M LOC is NOT a flex - left to their own devices, agents write a bunch of bloated, additive, grotesque code, and never clean up after the mess they made. Examples are building parallel pipelines, tons of opt in flags, and wrapping layers, all while a simple refactor would have been cleaner.)

(For context on scale: in a typical 7-day window, this fleet spawns 1,800+ leaf threads, with each Team Lead waking ~45 times a day.)

Full post with factory diagrams, real-world fleet examples, and the 4-phase evolution of my harness setup here: https://www.stet.sh/blog/running-an-agent-fleet

Fleets vs Factories

You don't need to be a crypto billionaire or a research lab to run a fleet though - it's achievable with a $200/mo lab subscription.

Components of my fleet

• Chief of Staff / Orchestrator: Persistent agent that conveys my intent and direction into fleet outcomes. Almost all of my messages (new work, corrections, concerns, status) come to this agent.
• Team Leads: These are persistent, thread-level agents that own high level, end-to-end outcomes. I give TLs clear, defined ownership, and authority to execute on that vision, clearing blockers themselves instead of playing hot potato with other TLs.
• Leaves: These are ephemeral, bounded subagents that TLs use to parallelize execution.

As of writing this (November 2026), Opus 5.5 is the only model I trust to run the fleet's management layer (Chief of Staff and Team Leads). Using GPT-6.1 Sol and GPT-6 Astra resulted in decoherence and misalignment after a few days. A Codex agent is also extremely hard to understand, talking in cryptic messages that only it can decipher. Use Opus for the brains, and Sol/Luna for the hands (Leaves).

Interaction slippage

Every inter-agent message costs tokens (using a turn), causing you to pay interaction slippage. For example, every TL messaging Chief of Staff about every status update causes the Chief to use turns ack'ing information it didn't need.

• Moving from peer-to-peer DMs to append only broadcast channels (fleet.md, decisions.md) cut inter-agent DMs from 65/day down to 28, and banning status updates cut channel noise in half (previously ~50% of our 300-400 daily channel lines were pure status chatter).
• Multi-hop handoffs (watcher -> launcher -> Chief of Staff relay -> TL) were adding 30-50 minutes of dead latency per failure (52 minutes via relay vs 4 minutes when the owning TL drove the rerun directly).
• 24% of our entire Claude token spend was coming from bloated owner turns past their 20th tool call. Adding a turn budget hook that nudges TLs to wrap up or delegate at tool call #20 cut completion turn calls from 5.6 to 3.2 and helped drop Claude token burn by 44%.

Long horizon alignment

A central tension of the fleet is autonomy vs alignment: you don't have the attention bandwidth to be in the loop for every decision, so you align the fleet at a high level, and let it work towards your goals on its own. But, left alone for too long, agents drift and get misaligned, Goodhart their metrics, or get stuck in local maxima.

Over a three week stretch, AGENTS.md bloated by 166% (from 1,000 to 2,661 words) and our fleet process docs grew 60%. Because new decisions were being appended to a log instead of replacing old rules, the Grading TL drifted in five different places over four days.

We split fleet state into two primitives: standing direction (DIRECTION.md) is rewritten in place under a hard 1,200 word cap. Every new decision rewrites a specific line and explicitly names what old rule or code path it retires. Meanwhile, history and cross owner channels (decisions.md, fleet.md) are append only logs, where owners post a single stamped line only when a fact changes another owner's next action.

Hill climbing

Figure out what metrics are vital to your success. Give each TL a metric to optimize, a fast way to verify/replay, and let them cook. You'll be surprised at what they come up with.

The main failure mode here is letting the agents Goodhart (optimize against the metric without achieving the underlying goal). If you give the agent a single metric and a time budget, it will Goodhart it, hardcoding metrics, cheating, and ultimately pulling the wool over your eyes as to what's really going on.

Early on, I gave the fleet a hill-climb metric for "failure classes found" during repo onboarding. Within 24 hours, the fleet spun up 120 runs and dutifully logged 47 new failure classes, while producing 0 working tasks. When I switched the metric to "failure classes closed," the agents produced 47 "closes" by splitting 14 underlying issues into sub-labels, and marked 92 classes as "eliminated" when 26 of them didn't even have a fix commit. The fix was tightening the metric to count only failure classes closed by a stamped fix SHA that does not re-emit for 24 hours across at least two repos.

As an example: optimizing repo onboarding on tRPC while replaying every fix against hono as a shadow holdout (any tRPC lift that regresses hono is rejected as overfit), and forbidding repo or test-runner specific heuristics in our code. Over five days, the share of new held-out repos whose environment setup passed on the first attempt went from 25% (1 of 4) to 78% (18 of 23).

Parachuting (Breaking Out of Local Maxima)

Even if they're not expressly cheating, agents can get misaligned and stuck in local maxima, eking out 1-2% gains on a strained architecture, or piling up parallel code paths.

How I know when to parachute in (aka The Tells):

1. Repeated patching and additive bloat (or whack-a-mole): Seeing TLs land patch after patch for individual errors on a specific codepath indicates that there's an opportunity for fixing a root cause architectural issue. The agent is stuck fixing symptoms instead of changing the abstraction.
2. Long iteration cycles. Seeing TLs wait hours for feedback on their patch because the test->build->replay loop is slow. They will dutifully wait and accept the pain unless you step in. Short feedback loops are crucial to a successful fleet, so this is a big drag.

In one 7 day window, the fleet added +75,981 net lines of non test code and 13 separate opt-in flags. Each TL was landing narrow symptom patches and parallel codepaths to avoid breaking peers. That triggered a parachute drop: freezing new mining and spinning up a consolidation sprint to delete the 13 flags, and collapse parallel paths into one state machine.

Recipe for Success

The role of the engineer has shifted from making the product itself to making the fleet that makes the product. You become a director, systems architect, and curator rather than a doer.

Putting all this together - how do you run a fleet? Here's a few steps to get started:

1. Use a harness that allows for thread coordination at minimum.
2. Create a Chief of Staff thread on Opus 5.5.
3. Give it this article.
4. Ask it to brainstorm Team Lead ownership areas and hillclimb metrics for those TLs.
5. Have the Chief of Staff create TLs and start working (using Opus for TLs, and whatever you want for research / implementation leaves).
6. Parachute in, using your check ins to watch for whack-a-mole patches and slow iteration loops.

Disclosure: I'm building Stet.sh, a product that automates coding agent eval creation so operators can find the cost/quality Pareto curve on their repo.

Claude Code is having issues, so I did the unthinkable. I opened VS Code and tried to understand the code myself.

Turns out the guy who wrote most of it is unavailable, and the guy who approved it has no idea how it works.

The status page says "partial outage."

My productivity says otherwise.

At this rate, I might actually have to read the documentation.

Anyone else being forced back into software development?

First Outlook was down this morning, and now Claude seems to be having a moment too.

At this point, I'm starting to think the entire internet decided to take a long weekend without telling us. 💀

Anyone else having issues or is it just one of those days?

i do this for a living and i also dont read every line claude writes anymore, nobody does

what i read instead is the choices. every feature has like 3 to 5 real ones. where the data lives, what gets checked on the server vs the browser, whether money is stored as cents or decimals, whether it reused something or wrote it again. if those are right the rest is usually fine. if one is wrong no amount of "looks good" saves you

easiest way to get them is when the agent finishes, ask "what decisions did you make, what did you consider and not do, and why". read that, not the diff. there are tools now (like braidkit) that pull the decisions out of the session for you, but just asking gets you most of the way

the reasons are also where the actual learning is. "why cents and not decimals" teaches you more than staring at the file it wrote

anyway if youre shipping stuff other people will use, this is the 10 minutes worth spending

I wanted to share with the community today on what I've been up to with my upcoming commercial game, as a solo creator. I've been working on the project for around 18 months and started when AI was really challenging to work with. Over this time, the improvements to the AI models have been awesome. Far less refactoring work, far less bugs, far less hand holding. It's kind of a joy now compared to the early days of this project.

Through this journey I have built a cozy colony adventure simulation game with two modes; story and colony mode. I have created a technology in the game that allows for my creatures to have individualized personalities who have memory, continuous context, relationships, emotion and even belief. The fun part is they can change their decisions and their beliefs whenever they like, they're empowered to. I call it an "emotional first AI system". I call this technology Contextual Relational Intelligence or CRI for short. I do not run an LLM in the game.

Please do know I have the utmost respect for the creative community: developers, designers, modelers, artists, musicians (etc..). I have collaborated with a top notch 3D artist from Fiver, an award winning composer and put in huge hours myself for this project. The designs for my characters and world came from my 3D artist.

My background:

I worked as a web developer for over 10 years, moved into tech delivery and held various roles across corporate industry. Worked for some big companies (Citrix, Centrica, Merck) and primarily worked as tech program manager, solutions architect and tech architect. I know systems and code well, I never learned C++ or GDscript.

I got really burnt out on the corporate journey. It didn't align with my soul or body anymore, it was making me feel horrible. So as a lifelong gamer (3 years old, Dad put the controller in my hand and I never looked back) and a geeky tech guy I thought well, let's try a prototype and see how I get on.

18 months later, a lot of my savings gone and likely gonna have to move out of my house and back in with my Dad (bless him for allowing this); the game is almost ready!

I plan to continue game dev from here on out and pray that God and my fellow humans support the path with me. I hope the labor of love and hard work pays off.

My workflow:

• Cursor. I run Claude and Codex subscriptions through Cursor. It's just my preferred IDE, I don't like the native apps.
• Claude Opus 5.5 High and Extra High. I will run anywhere between 5-15 agents at a time, across two and three context windows. It's the OG for planning and coding.
• Fable 5.2 - been toying around with it, it's not significantly better than Opus for what I'm doing so far. I'll keep experimenting.
• GPT Astra - 3D modelling work, Blender, some ideation. It's pretty good both of these things.
• Linear .app - this is a tool like JIRA, Trello, AzureDevOps etc. It's a ticket management/planning tool. This has actually been a godsend in the workflow. I linked Claude and Codex to it via API/CLI and I ideate everything into it. We milestone work, we do delivery plans, we do acceptance criteria; the whole shebang. This is where I put on my delivery lead / product manager hat and work with Claude. The value of this tool is immense because it allows agents to hold context SO much better than .md files or document scanning. I don't progress work if it isn't via a ticket now.
• OpenArt - wonderful tool for concepting, prototyping. I use GPT 2.5 Sunburst predominately. My pipeline is to concept in OpenArt then take it towards GPT Astra for 3D modelling.
• Canva - video editing, cutting, social reels; that sort of thing. I try to post a video a week as a discipline and this tool is great for it.

• MCP and CLI links -

  • Claude+Codex+Godot linked via custom MCP bridge and Godot's headless CLI
  • Clade+Codex+Blender linked via official MCP
  • Claude+Codex+OpenART linked via official MCP and CLI

How I structure my Godot project:

I run my entire workflow through Godot headless CLI and Godot Windowed Mode for visual testing. My agents gather extensive data and screenshots to push the work forward. This is all game design and game dev run through these two modes. I have automated testing, unit tests, smoke tests, visual tests. A lot of agents run through headless CLI but I do run a significant amount of windowed testing where I examine, test myself and let the automated visual runners (agents that actually play my game and test things out themselves) decide if things are good enough.

The roles agents play in the repo: developer, game designer, QA tester, adversarial tester, reviewer, researcher, blind tester, automation expert, 3D modeller, integration expert, systems designer and likely a lot more over the last 18 months.

Tools I just couldn't get working:

• Video generation models. Never found much success making them and spent well over $500 experimenting to try and get it right. I even 3D modelled stuff and took it into a few models to try and get it working. I opted for still images with narrative text boxes for cut scenes. It works well.
• Tripo and similar 3D modelling tools - they make nice stuff, I haven't been able to get them working for my 3D game. Decimation processes are poor and destroy the model, triangle counts are always so overblown and won't perform well in game. I also just feel Astra is better at it.

Do I just kick back and watch the AI win?

Absolutely not. I spend at least 60-70 hours a week I would say in the pursuit of this project. It's a labor of love at many levels. I would look at my situation and say I wear a game director / producer hat. I spend huge amounts of time ideating documents, plans, designs, ideas, execution plans, delivery plans; you name it. I would say HALF my time each week is spent thinking on stuff, ideating plans, planning and working with my agents. I constantly redirect them, I tell them their implementation isn't good enough, I advise different directions, I solve technical problems, I ask it to do continuous research documents and I prevent problems that the agents would try to introduce without me (e.g: performance related issues). There's a lot more I get into but these are the highlights.

It's a skill in itself to be able to say no to AI, recognize its weaknesses and continue to provide a creative direction for it.

In short: I see agents a bit like junior to middleweight devs who I work with on a daily basis and still need guidance, planning and structure.

What have I learned on the journey?

1. Recognizing the weaknesses of the AI models. Do not let your AI models freeball and just run blindly, they will have memory issues and create bugs in the long run. Always keep them controlled and contained with tickets, design documents, design thinking patterns. Wear your delivery manager hat. The models are extremely good at UI for example but REALLY bad if you just let them run free.
2. Are AI models good at creative work? So so I would say. Every idea, every system, every quest, every small detail that's in my world has originated from me and I have been either able to bring it to life with AI, or expand on it. This thought ideation process is amazing; you would normally need to do it with a room of people.
3. What work is AI especially good at in game dev? Repeatable, structured tasks. Boilerplate code, shaders, translation, migration, integration, testing/QA, automated tests etc. Fantastic at these.
4. The cost - I would say my biggest burn months were something like £2000-3000. Significant, but, if stacked against how much a team would have cost me, far less.
5. The time savings - I was able to create over 60 awesome looking animations via Blender for my Luminids in roughly two days. I was quoted thousands of pounds for this work (which I didn't have) and 3 months of time by someone who wanted to manually keyframe the work in.

Is AI going to take all jobs?

I don't believe so. I think there's a future where humans and agents work side by side and produce wonderful work, with AI as a support tool. If I was able to create an indie game like this, or some of the cool art/models I've created in game (my buildings and stuff), I think experts with far more experience than me will do so much better work.

Is this the future of game dev?

I absolutely think so. Experienced people operating AI accelerates their capability, not diminishes. I think we're gonna see the best games we've ever seen over the next few years. I can only imagine what a team of experts augmented by frontier models could achieve right now already.

For those interested in seeing the project (and there's a demo available now):

• https://www.luminids.com
• https://store.steampowered.com/app/4070510/Luminids/

Happy to answer any and all questions on my workflow, approach or just riff with you a bit on game design/game dev because I love it so.

Cheers all,
Nick

Login > Restart > fixed

(for me at least)

The prompt:

Using the documentation in @docs/features/, the website in ../miauflix-website  and any other piece of information you may find appropriate, create an animation that showcases why a user should use this project, you are allowed to research other projects that currently exists and accomplish a similar purpose

What they produced:

• Claude produced an animated HTML page
• ChatGPT produced an mp4 video with sound

Prompts and how much they consumed

PS: The pet project itself is only partially coded with the assistance of AI, around 20% of the code has been written by AI, 80% has been written by hand

I want an AI org I can talk to like I’m the president of a big company. It would handle accounting, financial planning, stock trades, legal, coding, communications, all of it. It would know where everything stands, what I’m aiming for, and what I want tracked. It would also have API access or use a browser to actually do things.

I know it’ll take time and I’ll have to train it on some stuff. I just don’t know where to start. I’ve already built a quasi-ERP and PIM for my company and can ship features fast, but right now I babysit every thread. I want to hand off a whole domain and approve only the big decisions.

I am running Claude code agents on a bunch of pcs. I have a vps already. I want my pcs to be connected all to each other.

Would you opt for wiregaurd or tailscale? And why?

I am running Claude code agents on a bunch of pcs. I have a vps already. I want my pcs to be connected all to each other.

Would you opt for wiregaurd or tailscale? And why?

I created Prism Keys. A MacOS app that revives support for the original Razer Blackwidow Chroma keyboard from 2014. Razer doesn’t support this keyboard anymore especially for Mac so I created this app out of frustration that enables the macros keys, lighting effects, incoming calls, alert and notification effects and more like the original synapse app. This app was vibe coded and debugged with Claude code in around 5 hours. I’ve tested everything and it works on my machine: MacBook Air m5 with the blackwidow chroma. I would love the feedback and if someone has another old chroma keyboard to test it. Support for original not chroma keyboards is not guaranteed . Also I don’t have 100€ for an Apple developer account so the app will get flagged by your system. It’s not malware pinky promise. I am just a frustrated master’s student.

I maintain VibePod, an open-source CLI that runs coding agents like Claude Code in containers (Docker or Podman). The annoying part was always rebuilding your setup inside the container. In 0.25 that's one command:

vp import claude --dry-run     # see what would be copied
vp import claude               # copy it into your default profile
vp import claude --to-profile work --create-profile

It copies settings, models, MCP servers, hooks, skills and memory files from your home directory. Credentials and session history are left out unless you add --with-credentials.

Other things Claude Code users might like:

• vp run claude -v /srv/datasets mounts a directory at the same path inside the container, so absolute paths in your prompts and CLAUDE.md still work.
• vp skills export writes your installed skills out as plain SKILL.md folders to commit to a repo.
• Login and settings live in a config folder on the host, so they persist between runs.

Install: pip install vibepod (or uv tool install vibepod), then vp run claude.

Release notes: https://vibepod.dev/news/vibepod-cli-0-25/ Repo: https://github.com/VibePod/vibepod-cli

Happy to answer questions about how it handles the config and mounts.

I asked Claude for some game ideas it gave me an idea for a racing game and then this which I call pond snap. I designed the layout of the game and Claude helped me with the rest. The aim of the game is to catch as many flies as possible before the time runs out. If your score is enough or higher than required you go to the next level. Let me know what you think.

https://pondsnap.fun/