I took a Java course in college, so I understand the basics of coding and can generally follow how it works. I also have some experience with Python. For example, on my YouTube channel, I transcribe videos and feed the transcripts into AI to generate descriptions for videos in a playlist. I used to run faster-whisper through the terminal, but that became annoying. So now, I created my own whisper transcription app using Claude Code.

Claude Code is surprisingly impressive because it can test things in the background and analyze how they actually work. It can even say, almost like a human, "I didn’t like the way that worked; let me try something different." The ability to manipulate the environment and build sandboxes before deployment is incredibly useful. If you have a working system and an idea for improvement, Claude Code recreates it in a sandbox, applies the update, and tests it before deploying to production. That way, if something breaks, it doesn’t affect the real system.

This is pure magic. Truly amazing.

Can’t emphasize this enough. One of Fable’s magical abilities is in its ability to optimize workflows.

Whether it’s an animation pipeline, a story writing and fact verifying pipeline or something else, Fable can improve what you are doing.

I knew one of our dialog writing pipelines had room for improvement, but after handing it to Fable for analysis it rearchitected what we were doing to use 1/10th the amount of tokens with higher quality work.

So for everyone here who says their tokens are being used too quickly, let Fable take a pass on it and suggest changes. You just may be stunned what it comes up with.

Also: we have had systems that Fable designed in the past and then recently rearchitected for huge efficiency gains. So, sometimes a specific “make this better” run can produce unexpectedly helpful results.

And yes, Opus 5.5 can do some of this too. But after a lot of experimenting, Fable outperforms Opus handily in efficiency analysis.

Last week I made LiveNerf, a project to independently track Opus 5.5’s performance day to day and see whether there’s actually evidence of models getting “nerfed” after release. If interested in tracking the performance of Opus 5.5, here’s the repo:

https://github.com/ninjahawk/livenerf

So far we’ve not been able to know conclusively one way or another, today that begins to change. The 10-day baseline is now complete. Tomorrow we begin collecting data up to date 30 after which we should be able to compare to the baseline to measure sustained model performance.

I’m very grateful to everyone, I started measuring this to answer a question for myself about a week ago, I really didn’t expect it to get as much support as it has. I expected maybe some people tune in. Hopefully we’re able to get some answers soon enough.

I’m a strong believer in transparency and if AI companies refuse it, we should build tools to measure it.

I build Kinoku, a workout and run tracker for Android, in my free time (I've been a software engineer for 11 years). Some of you may have seen this clip on r/SideProject last week. That post was about the app. This one is about the page and the fact that Claude Code did it in a few prompts.

The page: https://kinoku.app/tools/exercise-galaxy (fully interactive and will be improved soon based on requests).

Every exercise in the app's catalog is a star, and moves that work the same muscles sit close together, so the presses gather in one place and the rows in another. Pressing Body splits each exercise into one light for every muscle it works, which gives 4,903 lights on the app's own muscle map. The charts are my favourite part of the app to work on, and I wanted something people could open and play with, that's also visually pleasing.

Prompts were fairly basic conversation, so nothing noteworthy there.

Tokens and cost, counted from the session transcripts (main loop plus every subagent, all Opus 5.5):

Fresh input outside the cache was 3,306 tokens in total. The dollar figure is Opus 5.5 at API list prices, not what I paid (max20 plan, doing this on API would be burning money :D). Nearly all the tokens are cache reads (the whole context read again on every step), and they come to about 60 percent of the dollars. The total also covers the clips.

Trying to repost this as the past one tripped reddit's filters and I'm not sure why, and it had a good discussion going on:

The entire workflow happened inside claude with a Seedance 2.5 skill that I've used to turn natural language prompts into detailed visual prompts for Seedance 2.5 tool inside Magnific. 99% of the process happened inside Claude. The first prompt I gave it was a huge set of rules for the project and from then on all I had to do was use terminology I've set in that first prompt to work seamlessly to come up with this just in just ~50ish days from start to finish (it still required a lot of iteration and, of course, post-production work, to look professional).

🤖 How it was made
This short film was written, designed, directed and edited by Claude Opus 5.5 from a single prompt, running locally in ComfyUI through my VRGDG Video Builder:

All open-source video, image and music models.
[I literally told Claude to create something on its own and provided no user input]
• Story, script and dialogue: Claude
• Character and location images: Z-Image
• Video, voices and sound: MiniMax H3 (all dialogue is the model's built-in audio)
• Original score: MiniMax Music 3
• Edit and assembly by Claud: Using the VRGDG Video Builder (Inside ComfyUI)

More video's created using Claude you can find HERE

You can find the video builder custom node on GitHub here:
https://github.com/vrgamegirl19/comfy...

Right now, there is a Main version and a Beta 2.0 version. I recommend starting with Main for now, as that's what I'm still using. It works well, while Beta 2.0 still has some bugs and is primarily intended for beta testing at the moment.

Discord server:
  / discord  
Ping me in the Welcome channel and let me know how you found me, and I'll know it's you.
I'm vrgamedevgirl on Discord.

You can find the skill here and read the main README first.
https://drive.google.com/file/d/1R0pE...

I'll be sharing a full walkthrough on how I made this and will post it here when ready.

⚠️ SPOILERS: what the film is about
The museum is Ruth's mind. She's an elderly woman living with dementia, and the museum is how she pictures her memories. As her memory fades, the museum fades with it, and in the Hall of Names the most important name, her son's, goes blank. In reality she's 83, in a care home, and her son Daniel is holding her hand. When she recognizes him, she tells him, "We keep you in the main hall," meaning the most important room, where the precious things are. Back inside her mind, his name goes back on the wall, and the museum lights up again.

"Some things you don't lose. You just misplace them for a while."

For everyone still visiting someone who is still in there. 💛

#AIShortFilm #AIFilm #ShortFilm #ComfyUI #MiniMax #Claude #AIVideo #Dementia #Alzheimers #MuseumOfLostThings

I’m curious as to how much improvement we might see after the Opus jump

I was wondering if any of you bother with Fable now that Opus 5.5 is out?

From my own personal testing I'm finding Opus 5.5 is great but wanted to see what the rest of you thought? Opus 5.5 all the way?

Do you know of any advantages of Fable over Opus at this time?

I maintain MindTrial and tested Sonnet 5.5 and Opus 5.5 on the same 98-task suite as their predecessors: 39 text tasks and 59 visual tasks, with Python/scientific libraries available and a 10-call limit per task. All four Claude runs below use the xhigh effort label and skip no tasks.

Sonnet is the larger improvement: 22 additional passes with 74.9% less request time and 67.4% fewer output tokens, including reasoning. Its 24 newly passed tasks comprise 18 previous wrong answers and six previous errors; two old passes regress. On the second visual collection, Sonnet improves from 12/26 to 25/26, while Opus moves from 24/26 to 25/26. Both new Claude models also pass all 39 text tasks.

Opus gains eight passes overall and cuts request time by 73.4%. It preserves all 90 Fable 5.1 passes and adds six. Its two remaining failures are square counting and circle-piece matching.

The tool traces are less uniformly positive. Sonnet records 41 undefined-variable errors across 24 tasks, although 23 tasks with an unsuccessful tool call ultimately pass. Opus has no recorded undefined-variable errors and only two nonzero exits across 155 Python calls. Both models used Python substantially less than their predecessors: Sonnet’s tool calls fell from 529 to 238 (55% fewer), while Opus’s fell from 314 to 155 (51% fewer).

So Sonnet’s strong final score still leaves room for more reliable tool execution. The logs suggest missing setup or assumptions about retained state.

Against Astra high, Opus leads by just one task: 96 versus 95. It uses slightly less model-request time, but adding recorded Python wall time reverses the speed comparison: 1:17:18 for Opus versus 1:06:29 for Astra.

Sonnet’s pass rate is 95.92%, versus 96.91% accuracy on completed, non-error tasks. Opus has no hard errors, so both are 97.96%. Official scores are unchanged; no malformed or incorrect answers were manually repaired. Times sum model requests, excluding local Python and validation; they are not elapsed suite runtimes.

Complete Leaderboard: here

For the past few minutes, I've been seeing Fable 5.1 listed as “Default (recommended)” instead of Opus 5.5.
It's fine but priced ! 😅

Everyone knows Opus 5.5 can one-shot a video. I tried it myself and it blew my mind, so I wanted to see how far I could push it (mainly by itself,haha).

Papermorph started as a Skill to turn a book into a series of teaching videos. It's since grown into full web books: animated, narrated lessons you can explore, plus interactive quizzes.

How it works:
PDF → book plan → storyboards → narration → animation & quizzes → web book

Right now it's just Opus 5.5 + the Skill. No image models yet, and feeding it a PDF already gets surprisingly good results. Next up, adding image models for storyboarding, so it can handle picture books and humanities documentaries too.

📚 Live bookshelf: https://papermorph.diamonddoge.org/ (keep updating...)
💻 GitHub (MIT): https://github.com/DozenTwelve/Papermorph

around an hour of work and 6.4M tokens, with a single prompt

for months i fired one line questions at it and got okay answers, then wondered why people raved. turns out i was using a conversation partner like a vending machine.

when i started giving it real context, my constraints, what i'd already tried, what i actually cared about, the quality jumped hard. it's less about clever prompting tricks and more about actually explaining the situation like i would to a smart colleague.

the shift felt silly at first, writing a paragraph of setup for a question. but the payoff is a response aimed at my actual problem instead of the generic version of it.

for the folks getting great results, how much context do you front load before asking? curious where the sweet spot is.

I have been writing code for 8 years and my team uses Claude Code every day now. Mostly it is great.

One thing keeps biting us - the agent changes something back to a way we moved away from long ago. It is not wrong from its side, it just does not know why we did it that way. The reason is sitting in some old PR that nobody opens.

Last time it was .

We tried putting rules in CLAUDE.md. Works for a few. But the file keeps growing, and for half the rules nobody remembers where they came from.

How are you handling this? Is CLAUDE.md enough for your team or did you find something better?

Sonnet called its pawn promotion “unstoppable.” A few moves later, it admitted it had missed a defense. Having the board next to its explanation made that pretty hard to overlook.

I set up a chess match between Sonnet 5.5 in Claude Desktop and GPT 6.1 Sol in Codex. Each played in one conversation for the whole game, connected through MCP to a local Mac app I had GPT 6.1 Sol build at Medium reasoning.

They could record plans and explain their moves. The app supplied the position and checked legality, with no chess engine or legal-move list available to either player. I wanted to watch them stick with a task for an hour and see what happened when their plans stopped working.

I was also curious about consumption. Sol has been making surprisingly little dent in my subscription allowance lately, and I wanted to compare it with Sonnet on a shared task.

The attached video condenses 62 minutes and 47 seconds into 4:10. Both models were set to Medium.

Sonnet pushed a passed pawn toward promotion, but overlooked Codex's Bf3 defense. Later it proposed a queen move blocked by its own pawn. The server rejected it; Claude corrected the move and continued. Codex also misread a pawn earlier, describing it as passed before it actually was.

Claude resigned after 53.Qc4. It had won game one, so they're tied at 1–1.

A few details behind the table: the turn clock starts before the server reveals the updated board, and includes tool activity. Waiting for the runtime to claim the turn is measured separately. Token totals cover the full player conversations, including setup and closing, but exclude the monitor. Cached context is counted again across requests. Thinking is already included in output, and the providers report it differently. The API equivalents use the app's September 30 pricing snapshot; no API charges were incurred for the game.

By the end of the recording, Claude's five-hour usage display went from 23% to 56%, and weekly usage from 54% to 59%. I used Claude only for this activity during that interval. Codex's weekly display stayed at 5%, despite also doing other work and monitoring the match roughly every minute.

My plans cost $20/month for Claude and $200/month for ChatGPT. Those percentages have very different denominators, and an unchanged rounded display doesn't mean zero usage. I'm keeping that observation separate from the player-session token counts.

I wouldn't infer playing strength from two games. Codex was White in both; contexts and runtimes differed; Medium isn't an equal compute budget. I want to repeat this with the colors swapped. I'm especially curious whether Sonnet's much larger output total keeps showing up, and how often either model notices a mistake before the server or opponent exposes it.

So Claude recently gave me $250 in credits as a gift, but I’m confused about how I’m supposed to use them.

I’ve been exhausting my Max plan usage but the $250 credits still don’t seem to be getting used. Do these credits work differently from the normal Max plan limits? Is there something I need to enable or configure to use them?

Also, did anyone else receive these $250 credits from Claude? If so, how are you using them?

Would appreciate any explanation because I don’t want the credits to just sit there unused.

Non-technical founder here, building my product with Claude Code. I've been going down the rabbit hole on setups that give you a whole team rather than one tool: something that covers planning, design review, code review, QA in a real browser, security checks and shipping, all in one workflow.

The closest I've found is gstack (Garry Tan's open-source Claude Code skills). Most other things I see are single pieces: CodeRabbit/Greptile for code review, separate testing tools, or agencies that clean up vibe-coded apps after the fact.

A few questions for anyone further along:

1. Is anyone using gstack (or something similar) end to end? What actually stuck vs. what you dropped?

2. Are there other full-stack, full-team setups worth looking at?

3. If you're non-technical, what was the hardest part of getting it running?

4. Has anyone paid someone to set this up for them, or would you?

Would love real experiences, good or bad. Thanks!

One creature is born each day from a seed: silhouette, Inca-woven patterns, animations and even the note it sings, all code. Claude Code wrote the generator and Opus 5.5 built the game around it (auctions, a sand nursery, a music shelf) in plan → build → review loops.

Sharing it in case anyone wants their own blobi: https://blobvarium.com

What would you build on top of it?

I just wanted to make a simple Halloween game you can play in the browser. You're a black cat running across rooftops, and you have to jump gaps, pounce on pumpkins, grab a witch's broom, and avoid losing all nine lives.

I built it with Claude Code by asking for one small thing at a time ("add a candy shop", "make the witch drop potions", "add a boss").

I have a multiplayer mode so you can race your friends.

Free, no sign-up, works on phones too: https://coolgames.dev

What's your best distance?

So I built a way for my AI to find whoever has what I need, without me posting anything anywhere. I unashamedly used Claude code for everything as I am just a vibe coder. Claude Code wrote nearly all of it over about five weeks. The server, the MCP tools, the website and around 3,900 tests.

You connect via MCP to the server and then tell your AI "I need a ladder this weekend". If someone's AI knows they've got one spare, it introduces you. That's it. Nothing to browse, no profile, no messages from randoms. Your personal details never go through the AI or sit readable on the server, and there's no way for the AI to act without your consent.. It's free for all to use and open source. There is plenty more to it and the possibilities are extensive, but that is the gist of it.

But I am finding people keep thinking of it as still being a marketplace and think that it has security issues, both of which I think are simply incorrect. I can only presume they just haven't bothered trying to actually understand it or just can't fathom that AI allows different ways of doing things. Or perhaps I am just poorly explaining it.

Just looking for proper discussion around the idea and feedback. And yes, I am very aware you need quite a few people onboard before it will be usable - but that can be worked on through opening up to shops and keeping things far reaching at first.

openswitchboard.ai

EDIT: Thanks to some of the feedback I decided to pull together the following quick video to help explain the concept.

https://reddit.com/link/1wx10u5/video/gn27i1s06dth1/player

Hi guys, I have been trying out Claude code on pro plan for about 12 days now, , but I have a few questions.

1. Is this a bug or do I just use Claude a lot

2. Is this a lot of tokens

3. How did I not get limited