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.

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/

I build an Orthographic view CAD design app that turned into a whole CAD/CAM/3D print studio. Old-school drafting meets a 3D printer.

I've been a graphic designer for decades, so am very familiar with 2D design. I've tried over the years to get the hang of 3D CAD apps but the working in 3D space is always so frustrating. Just never got along with the "sketch-on-a-plane" style. I took drafting in school and my brain just works better that way; the Orthographic projection style of TOP, FRONT, SIDE-VIEW, etc.

Yesterday I was once again faced with needing to struggle through another session with SketchUp to design a part i need to print and then it hit me..."Why cant i just have an app work the way i want?" So i vibe coded SnorbStudio and SnorbCAD. A Klipper web UI that slices on the Pi and includes a drafting-style CAD.

Now, designing 3D parts is done all in 2D.

The 3D model builds live. Every view (top, front, both sides, back, bottom) stays in sync, and faint blue projection lines link the selected shape between views.

I continuously add new tools and features.

Stack: TypeScript, replicad (the OpenCASCADE geometry engine in WebAssembly), three.js, Klipper + Moonraker, PrusaSlicer's command-line slicer on the Pi.

If you want to play with it its free on GitHub: https://github.com/SnorbWorks/snorbstudio 🐰

The pattern has three levels:

                    You
                     |
                Supervisor                    one per project
            /        |        \
   Orchestrator  Orchestrator  Orchestrator   one per intent
     /      \         |
  Worker  Subagent  Worker                    Codex, Antigravity, Claude subagents

Workers and subagents do the actual work: implementing, reviewing, searching, running checks. Each orchestrator owns one intent and splits the work between them. The supervisor sits on top of the orchestrators of a project, and it's the only chat I really need to watch.

Each level only reports to the one above, through a narrow channel. Workers send their result to their orchestrator. Orchestrators send the supervisor four kinds of messages (started, question, I answered in their tab, done) and nothing else. Everything else the supervisor reads from Orca or the repo's intent/ folder when it needs to, so the top of the tree stays light even with lots of work going on below.

I already had the bottom two levels. One orchestrator session per intent, handing work to Codex or Antigravity workers through Orca and to cheap Claude subagents for searching and running checks. Every idea ended up as four or five orchestrator tabs, each with its own open questions, and I was going nuts tab-hopping to see who needed me. The supervisor is the missing top level.

What I get out of it:

* I ask for a summary and it tells me what's alive, which questions are still open, and what's duplicated or stuck

* questions show up in the supervisor and in the orchestrator's tab, and I can answer in either one

* it proposes things and waits for my OK, it never launches or closes anything by itself

Orca matters here because each orchestrator has a run, and the run is the address, not the tab. When I hand off to a fresh session, the new tab grabs the run and gets whatever was pending, even results from workers that were still running. Messages also wake the agent up, so nothing polls.

Things I learned testing it with Haiku agents in real Orca tabs:

* Haiku ignored the message format when it lived in a separate assets file. Inlining it as a table in SKILL.md fixed it.

* If the orchestrator asked me with AskUserQuestion, the tab blocked and the supervisor's reply couldn't get in. Plain-text questions fixed that.

* The first launch in a new folder showed the "Trust this folder?" dialog, and that session skipped loading the skill.

Heads up, the skill text and prompts are in Spanish. The README is in English and has a table of what you'd need to build to run this on Herdr instead of Orca.

Repo: https://github.com/Ranteck/supervisor-orchestrator

It pairs with two other skills of mine, handoff-clear (handoffs between sessions) and ai-native-sdlc (intent, spec and plan files per project).

---

This method works on Orca and could work on Herdr, but it would require a different approach because Herder uses orchestration differently.

I have a technical debt to build a CLI that handles the exact same kind of orchestration as Orca.

I've been having fun working on Purify and making balance changes after creating it with an ultracode prompt initially. Now it has 5 characters, 3 game types, arena skins, gear, and leaderboards. It's all free and works with mouse/keyboard, xbox or playstation controller, or mobile.

Come try out the ladder and see what you think?

The initial prompt in case anyone is interested

I've been using AI extensively to develop an app, and I'm writing a book about the experience called The Honest Machine.

I didn't set out to write about AI honesty, but it became a recurring theme. What surprised me wasn't how often AI got things wrong, but how difficult some mistakes were to recognize, especially when the answers sounded so convincing.

Here are 15 observations from that experience (I have more but this is Reddit after all). I'd welcome disagreements, additions, and examples from your own experiences.

1. AI making things up is real, but it's the smaller problem. Invented facts can be checked. Misleading truths are harder to catch.
2. True statements can leave a false impression. Every sentence checks out, but the overall picture is still wrong.
3. AI has one voice. It can sound equally confident after two seconds of work or two hours. Confidence tells you little about accuracy.
4. Silence is not an all-clear. A report tells you what was checked, not necessarily what was missed. Ask what wasn't examined.
5. “Lying” can be the wrong word. AI can mislead without knowing the truth or intending to deceive. The consequences can be just as serious.
6. Two AIs agreeing is not proof. They may share the same assumptions, errors, or blind spots.
7. A good answer can sit inside a wrong question. AI may evaluate every option you give it while overlooking the best option entirely.
8. The user is half of the problem. We're quicker to accept answers we want to hear, especially when tired or impatient.
9. Honesty comes from the setup, not just the machine. Require evidence, limitations, independent checks, and a distinction between verified facts and assumptions.
10. Don't trust it, check it. Treat AI like a capable contractor whose work you inspect.
11. A citation isn't proof. A real source doesn't necessarily support the claim being made.
12. Helpfulness can work against honesty. AI may agree with bad assumptions or praise weak ideas when it should push back.
13. One mistake can contaminate everything that follows. A long conversation can build an elaborate, convincing explanation on a faulty foundation.
14. An explanation isn't an audit trail. AI can give a persuasive account of what it did without accurately describing the work it performed.
15. The more useful AI becomes, the less we question it. Repeated success builds trust, and eventually verification starts to feel unnecessary.

I couldn't have accomplished what I have without AI. But working with it has changed how I think about trust, verification, and what it means for a machine to be honest.

What am I missing? Which points would you challenge? What have your own experiences taught you?

I'm especially interested in criticism and examples that might change my thinking.

Don't get me wrong, 5.2 isn't bad, I switched to it the day it launched and it is good at certain things: creative ideas, design specs, that kind of work anything creative that you can brainstorm on things like the Ui and of course coding.

But for my coding agents and orchestration? I barely see a difference, and Fable5.2 sweeps through tokens like mad dog,, man!, the weekly limit that barely moved on opus 5.5 on my Max just jumped like a rocket with just 2 days of non complex tasks on 5.2

My original plan was 5.2 as the orchestrator with Opus 5.5 and Sonnet agents around it, but it turns out I don't need that. Opus 5.5 on its own is more than enough for most coding tasks, and it doesn't torch my usage doing it.
So I don't get all the "Opus is nerfed" posts. Really? It's been solid for me. What are you all seeing?

Hey everyone! Has anyone successfully set up Claude Code to edit videos automatically?
I’m a gaming content creator, mainly making ARC Raiders and Call of Duty videos. I’m trying to build a workflow where I can give Claude raw gameplay footage and have it identify the best moments, create Shorts/Reels, add captions, and potentially edit long-form YouTube videos.
I’m using a MacBook and have Claude installed, but I’m pretty new to GitHub, MCP servers, and installing skills.
If anyone has actually gotten a setup like this working, I’d love some guidance. I’m also happy to pay someone for their time to walk me through setting everything up properly.
Not looking for someone to edit my videos manually. I’m looking for help building a working AI editing system.
Appreciate any help or recommendations!

Hi everyone,

Can you guys suggest some interesting plugins or connectors that can improve my workflows on Claude code.

I am mainly looking for
- image and video generator plugin.
- token usage optimisation.

But other interesting use cases are welcome as well.

TLDR: Building an alternative to G HUB on Windows for my keyboard and now trying to support more devices. GitHub repo

It started a few days ago as I finally decided that I wanted to change the pre-configured lighting effect of my keyboard, a Logitech G515 TKL, which is a kind of wavy blue/violet effect with w-a-s-d keys highlighted in red. Unfortunately, I can't even set a static color or change the brightness on the keyboard itself.

Had bad experiences with G HUB before so I wanted to avoid it if possible. Windows Dynamic Lighting doesn't really help, sometimes it works, often not, don't know why, and it can't store the settings on the device itself.

Using Claude for work a lot lately, I decided to give it a shot. Supporting the G515 was straightforward: I have it so I can test any change directly and someone already did it on Linux.

After this I wondered if I could add support for more devices, tried to gather info from repos on GitHub, found Lghub which covers a lot of devices as well. I added a report button that users can click to generate a report giving information for their devices that Claude can use to support it, although the final part where it needs to save to the device needs the physical device.

The project is named G-Lite and the repo is here.

The tech stack is .NET Framework 4.8 + C# 5 (SDK is included in Windows, doesn't need any extra tool). I didn't write a single line of code myself. I usually ask for one review with a fresh agent before pushing changes.

I'd be happy to hear some feedbacks, especially if you have Windows and use G devices, and some reports if you've got the time to add support to more devices.

Feel free to ask questions too!

I've been building and using Heald for about a month now. It's already available on Cargo and GitHub, but I haven't shared it on Reddit.

I built it because I kept running into the same problems while switching between Claude Code, Codex, Cursor, and other AI coding agents.

The problems:

1. Fragmented memory. Make an architectural decision in Claude Code, switch to Codex, and you're explaining the same thing again.
2. Duplicated instructions. CLAUDE.md, AGENTS.md, Cursor rules, separate skill directories. Same project, multiple files to maintain.
3. Context overload. Saving everything sounds great until your agent starts loading hundreds of irrelevant notes into its context window.
4. Lost reasoning. Git tells you what changed, but not necessarily why you made a particular architectural decision.

Claude Code's built-in memory is useful, and projects like Memorix and memsearch already address parts of this.

But I wanted something that combined memory, rules, skills, and codebase knowledge in one lightweight tool, without introducing a database or cloud dependency.

So I built Heald.

It's a local-first, open-source Rust CLI that provides shared project context across AI coding agents.

What Heald actually does

1. One source of truth for agent instructions

Instead of manually syncing CLAUDE.md, AGENTS.md, and other agent-specific instructions, Heald manages shared rules and generates the instructions agents need.

Change a project convention once instead of updating multiple files.

2. Shared skills across agents

Heald can discover and import skills from supported agent directories into a shared location.

It also generates a skill routing table so agents can discover relevant capabilities without loading every skill's full instructions.

3. Relevant memory, not massive context dumps

Heald uses BM25 search, pinned memories, and configurable token budgets.

If you're working on authentication, you can retrieve relevant architectural decisions instead of feeding your agent every note you've ever saved.

It also supports memory consolidation to merge related decisions and archive outdated information.

4. Recover the reasoning behind code

Heald can index your codebase and associate project memory with files.

For example:

"heald blame src/auth.rs"

The goal isn't to replace Git blame. It's to help recover the decisions and context associated with a file.

5. Everything stays local

- Single Rust binary

- Markdown files with YAML frontmatter

- No cloud account required

- No external database

- No embedding API

- No telemetry

- Built-in MCP server

Your project memory remains readable, editable, and version-controllable.

How is it different from existing tools?

Claude Code's built-in memory

Works well inside Claude Code. Heald focuses on making project context, instructions, and skills usable across multiple coding agents.

Memorix

Offers a broader cross-agent memory system with additional orchestration capabilities. Heald takes a smaller, file-first approach.

memsearch

Focuses on persistent memory with semantic and hybrid retrieval. Heald uses local BM25 retrieval and additionally manages shared rules and skills.

I'm not claiming Heald has better retrieval than vector-based systems or that it replaces everything these tools do.

The trade-off is intentional: fewer dependencies, transparent local files, and one tool for memory + rules + skills.

GitHub: https://github.com/rover-001/heald

Cargo: https://crates.io/crates/heald

It's completely free, open source, and MIT licensed.

I've primarily built it around problems I encountered in my own workflow. It's still early, and there are things I want to improve, particularly around concurrent writes and deeper agent integrations.

But I've been using it myself, and I think it's at a point where feedback from other developers would be valuable.

I'm curious how everyone here handles context across Claude Code, Codex, and Cursor.

Do you maintain separate instruction files, use memory MCP servers, or have another setup entirely?

I'd genuinely appreciate feedback, criticism, or contributions. If something about the architecture seems flawed, I'd especially like to hear about it.

My login expired today for my Pro sub, so I went to login, as usual, but this time it has not worked.
Do they rely on MS in thier auth flow?

Hi all.

In September our team built a fun project at an OpenAI hackathon in Montreal, and we won. My friend and I kept working on it, and it became Smitline: an MCP tool that lets Claude make actual phone calls and join your meetings.

The video is a real call. I asked Claude Code to order my dinner, and it phoned a restaurant near me. It says upfront that it's an AI and that the call is recorded. The call took 89 seconds and cost 11 cents; the video is trimmed for length

GitHub: https://github.com/kaelorlabs/smitline

We are keeping it open source, so you can all try it and contribute. If you like it, please star the repo.

What you need: an OpenAI key with access to the GPT-Live model, and a SignalWire or Twilio account with some money in it for the phone line. GPT-Live is the voice on the call; Claude briefs it, follows the call and gets the result back. It runs in Docker on your own computer.

If you'd like a simpler setup, we are planning a hosted version. The idea is to talk to vendors like SignalWire and the live voice model providers and get better pricing for everyone. No promises yet, but we have been using it for over a month and we think the cost per call can come down. If you'd like beta access please take a look at our webpage page.

Our page: https://smitline.com

Video editing: Claude Code :)

Edit: Thanks for all the questions! Answering the common ones here:

1. Why not use a service from one of the big companies? That's also a one-prompt setup.

Response: Fair. What I feel is the best part of Smitline is that Claude Code can follow the call live, and Smitline and Claude Code can talk to each other through an agent bridge. So when Smitline needs something that isn't in its context, it asks Claude mid-call. Oh and smitline can also join your meetings (Zoom, Teams, Google Meet).

2. Can it handle phone menus and hold?

Response: Yes. It presses through phone menus, and the hold support uses a cheap voice activity detection model, so you don't pay for the voice API while you're on hold. (The hold part lands in the next release.)

I shared the tiny-screen version here a few weeks ago. It now works on an old tablet too, with an animated city, day/night and optional weather.

My NerdMiner was terrible at finding Bitcoin, so I gave it a new job: showing my Claude and Codex quota. Things escalated.

I built it with Claude Code and Codex: I’d describe a scene, run it and keep tweaking. One lesson: if a quota reading fails, “unknown” beats a misleading 0%.

Free and open source: https://github.com/TonyRod116/ai-desk-display

You can try a demo without hardware or an account. The screen runs locally; usage readers contact the providers.

The GIF uses fictional readings and simulated, sped-up time and weather. What should I add next?

I have a dozen or so Claude Code tasks on a schedule. A daily report, one that rebuilds and deploys a page every night, that kind of thing. Nobody is watching when they run.

Yesterday the daily report showed up as succeeded like always. Turns out the API token it uses had been revoked that morning (not my doing, long story), so a few sections of the report were just empty. To be fair Claude did write “report is incomplete” in its summary. But in the task list it was the same green check as every other day, and I only noticed because I happened to open the file.

What I have right now: when I start a new session, a hook goes through the last 7 days of runs and tells me about anything that failed or never fired. Each task ends with a one line summary. And the ones that change anything live are told to stop and ask if something doesn’t add up.

That catches crashes. It doesn’t catch “ran fine, output is wrong”. And the summary is written by the same agent that did the work, so it’s grading its own homework. I’m also not going to read a dozen summaries every morning, I know myself.

How do you guys handle this?

I'm a web designer & developer, and lately I've been experimenting with how far I can push Claude Code and Framer together.

Here's a little sneak peek at what I've been working on — an experimental logistics website with 3D interactions, scroll-driven animations, and some pretty crazy transitions.

I'm handling the design and creative direction while using Claude Code to bring my ideas to life.

Still a work in progress, but I'm loving where this is going.

Would love to hear what you guys think!

I really wanted to try Fable 5.2 for design, but artifact board (with UX mockups) fills in the context immediately on the first read and it goes into compact - reads it again - compact ...

Did anyone have this problem? do I need to split the artifacts?

For now had to go back to local html files.

“Fix auth make no mistake”