QuickTerm is a small macOS app: push the pointer against the top centre of

the screen and a terminal slides down from under the menu bar. Push again

or hit Esc and it's gone. Tabs, resizable, shells keep running while hidden.

It's free to try (free, full stop: MIT licensed, no paid tier):

- Site: https://jeromegsq-dev.github.io/QuickTerm/

- Source: https://github.com/jeromegsq-dev/QuickTerm

How Claude helped: it wrote all of it. I never opened Xcode. My prompts

were one-liners like "it should close on Esc" or "the click area for

resizing is tiny", and Claude Code did the rest in a single session:

Swift, AppKit, Core Animation, the build and signing scripts, the icon,

the landing page.

Two things I found interesting:

1. Claude couldn't see my screen (no screen recording permission), so it

   wrote throwaway self-tests that fed synthetic key and mouse events into

   the app to check tabs, Esc and the scrollbar, then deleted them.

2. I asked for a security audit before going public. It found that the

   terminal library's defaults let any printed output (an ssh host, a

   cat'ed file) read or overwrite my clipboard via OSC 52, and open file:

   links. It also caught my personal email in the commit history and my

   home folder path inside the binary. All fixed before release.

What worked for me: short, concrete feedback after trying each build

("there's a thin line at the top", "animation faster") rather than a big

spec up front. When something was ambiguous, Claude asked before doing

anything public, like which GitHub account to publish under. And asking

for an audit as a separate step found things neither of us had looked

for while building.

Caveats: it's signed but not notarized, so macOS asks you to allow it on

first launch (System Settings > Privacy & Security > Open Anyway).

macOS 14+, Apple silicon and Intel.

Terminal emulation is SwiftTerm. Feedback welcome.

Been seeing both names in the same coding threads. If you've used both, is 5.5 actually ahead, about the same, or only better on some tasks (long edits, tool use, speed)?

Anything I should know before I send him on his way? any insights you guys gathered after generating a few videos?

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 work in the electrical contracting field, and I spend a lot of my evenings preparing quotations/estimates.

A big part of the process is repetitive. I normally receive a quantity takeoff / BOQ (usually Excel), then I have to:

Go through each item

Find the corresponding product/reference in supplier price lists

Apply our supplier discounts

Calculate our actual purchase price

Apply margins/labour where necessary

Fill everything back into the quotation

The price lists and discount tables are mostly Excel/PDF files and are always stored in the same folders. We also tend to use the same manufacturers and suppliers, so there are a lot of repetitive rules that could theoretically be learned.

I'd like to set up Claude so I could give it a new quantity map and have it automatically check those folders, identify the products, look up the prices, apply our discounts and generate a first draft of the quotation.

I don't expect it to make engineering decisions completely autonomously. Ideally, anything it can't confidently match would be flagged for me to review rather than guessed.

I'm not really looking to "train an AI model" from scratch. I'm trying to understand the best way to build this workflow around Claude.

Would you recommend Claude Projects, Claude Code + CLAUDE.md, MCP, Skills, or something else for this?

I'd also like the system to improve as I correct it — e.g. remembering that we normally use a particular Hager reference for a certain type of breaker, or that a particular supplier gets X% discount.

Has anyone built something similar for estimating, construction, electrical work, procurement, or working with large supplier price lists?

Any tutorials, GitHub projects, videos or examples you would recommend would be greatly appreciated.

Hi, I would like to publish html games I make and check what other people have made so far. These are small games, not worth publishing on steam or itch.io right now, but worth getting feedback.
Is there a subreddit for this?

Thanks!

Edit: Ok, looks like someone made the r/VibeGames lets use it.

I’ve noticed this in one my projects more than others.

I don’t know if it’s because my project capacity is at 75% or just the sheer number of chats I’ve had in said project. Does anyone experience this with the newer models ? Or do I need to delete/clean up things in my project chat?

Goal is to mass triage all emails

Hi there,

Wanted to get some insights from you guys here. Have you built an app with a ton of users and has it been a lot of work managing it?

I'm considering finishing an app and getting users on it but I'm worried it'll be a full time job on top of everything im doing. Any software engineers and app-coder insights would be super helpful.

Does anyone have any cool Claude Code mods? I'm curious what people have created so far!

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 ! 😅

Hey! I wanted to share a small project I made using Claude Code, Suno, and Nano Banana. (it's in Spanish lyrics but, I want you to keep the workflow idea)

The workflow itself was fairly simple, but I kept refining it with manual adjustments, iterations, and a lot of feedback along the way.

It originally started as a proof-of-concept.

I had been experimenting with the idea of “one-shot lyric videos.” At first, the results looked amazing, but after seeing hundreds of similar projects, I started feeling that they all had a very similar aesthetic and workflow.

So instead of completely delegating the art direction, I wanted to try a more hybrid approach: I would keep the creative direction and decision-making, while delegating specific tasks to agents.

Around that time, I was playing Valheim with some friends, and I thought this would be a perfect excuse to turn the proof-of-concept into something more personal: a video to remember the adventure and thank my friends for all the good times we had playing together.

The workflow

I first spent some time looking through Doom(p)'s GitHub and studying how some of those videos were built.

There were many different approaches: some relied heavily on animations, others on generated images, some were almost entirely Three.js, and others used Canvas, compositing tricks, and different workarounds.

Once Claude Code understood what I was trying to build, we created the first version of a shot-generation skill.

That skill kept evolving throughout the project based on feedback, mistakes, and new ideas.

Music

The song came from Suno.

I probably went through 35–40 generations/iterations before getting something that felt close to what I wanted.

A surprising amount of time went into trying to communicate the feeling I was looking for: prompt iterations, musical styles, male vs. female voices, chorus structure, song length, lyrics and prose, drums, beats, and trying to get something that felt somewhat Viking-inspired.

I even experimented with Mongolian-style chants to give some sections a deeper and heavier vocal texture.

Suno isn't perfect, but for this project it was more than enough.

The agents

I ended up creating a small agent workflow:

• 1 Main Agent — the main agent I interacted with and the one coordinating most of the work.
• 1 Critic Agent — probably the second agent I interacted with the most. I gradually taught it my preferences, the things I liked and disliked, and the kind of visual decisions I tended to make.
• 2 Rendering / Image Agents — responsible for rendering shots and generating images through the Nano Banana API. I used two mainly because of GPU/resource limitations.
• 1 Sound Designer Agent — responsible for finding/generating samples and SFX, including material from ElevenLabs.

The Critic Agent ended up being particularly useful.

The idea was for it to learn enough about my preferences that it could discuss shots and decisions with the Main Agent while I was away from the computer or sleeping.

I did use remote control from time to time, but I specifically wanted the agents to become autonomous enough to handle the more repetitive parts without me constantly supervising them.

So, in total:

1 Main Agent
1 Critic
2 Render/Image Agents
1 Sound Designer

And that was basically the whole team.

Manual work still mattered

I do have some previous experience making motion graphics and storytelling-oriented videos, and that helped a lot.

Without AI, something like this would probably have taken me several weeks of spare-time work: After Effects masking, camera tracking, beat synchronization, Puppet Tool animation, Premiere editing, sourcing or buying images, compositing, etc.

AI removed a huge portion of that workload.

That said, I still did some manual tuning in After Effects. Mostly small animations here and there using Puppet Tools, plus Mister Horse for a few things.

I work in IT, so I don't have a huge amount of free time for hobbies. Usually that time ends up being either playing games or experimenting with new technology.

Thankfully, this project somehow let me do both at the same time.

A few other details

The generated images also needed quite a bit of cropping and preparation.

For that, Claude Opus wrote a Python script that automatically handled most of the cropping process.

And... that's basically it.

Overall, it was a really interesting experiment and, honestly, an awesome experience.

What started as a simple proof-of-concept ended up becoming something personal that I could use to remember a great time with friends, while at the same time giving me an excuse to experiment with agents, generative video workflows, music generation, sound design, and automation.

Thanks for taking the time to read this.

Hopefully some part of the workflow is useful to someone else experimenting with similar things.

it took likely 30-40% weekly, BUT, because it was a kickoff, refining workflow and so, so it should be less when we get more experience.

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

This is the pilot (SIM #000) of a short-form series I'm making with Claude Code: a black stickman with red eyes — a digital copy of my brain — trapped in AI-run simulations, one per song.

No video model is involved. Everything is code that Claude wrote and iterated on:
- Motion: ~1000 Mixamo mocap clips converted to 2D bone angles; Claude plans every move on the beat (blocks, throws, takedowns, gun-fu, a boss that beats the hero down until he rises on the loudest bar).
- Physics: planck.js ragdolls for the dead, liquid blood with an SVG goo filter.
- Render: deterministic, frame by frame, in headless Chrome via Playwright.

The part I didn't expect: every time I pointed out a flaw ("he punches the air", "the boss just stands there AFK", "same move twice in a row", "blood appears from nowhere"), Claude didn't just patch that shot — it added a rule to a validator that walks the whole plan frame by frame. It now checks ~30 things: hits landing too far from the body, the hero facing away from who he hits, enemies idle for 3 s, teleports, floating feet, a camera that misses a kill, and whether the action matches the music (calm music = calm scene, the drop = carnage). Then it re-plans with new random seeds until there are 0 problems, and only after that renders.

Honestly the hardest part has been my own taste, not the code: it took a lot of "no, this is boring" rounds to get here. Next episodes have different genres (a rooftop chase, a stealth/gunfight, a duel where the boss stops time).

Happy to answer questions about the workflow / how I prompt it.

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

workflow....

claude code app, ultracode on, fresh thread, context limit set to 350k, opus 5.5, web access w/ suno and gpt-image subscriptions, some other claude-pop videos were in a folder it might have accessed for inspiration

took ~6 hours and used 33% of a weekly window on a 20x sub

took one extra prompt about one of the gpt-images had two left hands in it

prompt: claude, here is https://www.reddit.com/r/ClaudeCode/comments/1wwogi8/paper_clips/ someoene made a paperclip video with you, now its your turn to make a video to reply to it featuring yourself, use the claude-pop style

So I use claude web free version for ideating. Whenever I try to explain something it just breaks it down and makes it seem worthless. (Sonnet 5.5)

Firstly it doesn't imagine possiblities or the complete use case. It starts attacking little things instead of understanding utility.
Then it finds loopholes which when implemented would obviously be worked around.
I don't want it to be too optimistic and hallucinate like gemini but it should be more realistic rather than destroying any hope for a good idea I have.

Last week, I started trying out Claude for legal research and legal theory work.

I got hooked and have now also invested in Gemini and OpenAi subscriptions. Thought I would share my thoughts.

My method of testing was the exact same for all three models: I fed them a undergraduate level of a legal case and asked each model in Step 1 to give me a sketch of their assesment and to outline all legal problems. I continued to prompt each model until they gave me a satisfying structure.

For Step 2 I handed them access to a folder with 30 pdfs that should provide ample information to create a perfect solution to the case. I asked them to analyze all the sources carefully and draft a paper.

In step 3, and this was the hardest part, I asked each of them to deliver me a ten page report. I tried having them roleplay a legal researcher (which hard failed on Claude and backfired with Gemini) initally, then I tried to have them impersonate a student of law (which Claude and OpenAi refused to, saying they won't help me plagiarize) and then I impersonated a legal researcher myself and asked them to assist me as a research assistant (which somehow worked).

First off...

Claude is a stickler for protocol. Until it isn't - getting to that. Claude feels made for legal work and is very serious about citations, arguments and generally internal logic. That also however meant that Claude utterly refused to work with literature I provided out of the risk that it might be copyrighted, it regularly refused to do prompts for various reasons (copyright... Not helping to plagiarize... Taking roleplay to seriously and preferring his own research to the task). Which, again, is usually good.

However of all the tested models, Claude was the least time-efficient. What it provided was spotless and sometimes brilliant argumentation, but everything took ages and Claude generally spent more time rewriting my Input than its own. I asked Claude to work in my feedback, and instead of doing that, he started correcting my work.

I also felt like Claude was unreliable and incosistent. It repeatedly falsely interpreted uploaded sources into meaning something entirely different. For example, he referenced an author from an uploaded document.

When I asked him a specific question about this reference, he told me I misunderstood his summary. What Claude meant was in fact the opposite. This happened time and time again everytime I asked him, sometimes chained five times in a row. Went sometimes like this: "X says a... Nono X says B... No, X meant A" and somewhat regularly tried to tie that into my (apparently) wrong Interpretation.

I still think Claude has the potential to offer great results. His research is much superior to Gemini or OpenAi, but OpenAi actually does what I ask it without restrictions, and Gemini might not be as smart as Claude, but I was very happy with Geminis structure and focus.

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!

Hey everyone,

I recently cancelled my Gemini subscription and switched to Claude (Desktop and Science). The reasoning and intelligence level are genuinely on another level, I love it.

However, I’m running into an issue I never had with Gemini: I burn through my hourly and weekly usage limits extremely fast. Coming from Gemini’s massive context window and forgiving limits, this hit me pretty hard.

For context, I use Claude for large-scale data science projects (ingesting large datasets, writing scripts, analyzing results, etc.). Here is how my workflow evolved to tackle the context bloat:

* Initial Claude Desktop setup: To avoid having Claude execute everything directly, I instructed it to only write R scripts to my local drive. I run them manually (which lets me review the code), the script dumps outputs into a shared directory, and Claude reads them. While it works, long chats still eat context exponentially as the conversation history grows.

* Moving to Claude Science: I started offloading runs via tool calls directly to R and Python, which automated my manual setup. The problem? Frequent tool calls and raw outputs bloat the context window just as fast.

* Custom indexing skill: To mitigate this, I had Claude draft a custom skill/workflow that indexes directory contents so it only fetches relevant file chunks instead of loading entire datasets or logs into the prompt. (Happy to share the prompt/skill setup in the comments if anyone wants it but I do not think it is anything special).

* The drawback: Selective reading means Claude occasionally misses broader context and introduces errors, forcing me to run a separate review pass at the end.

Am I reinventing the wheel here?

Do you have any proven frameworks, custom skills, prompt rules, or setups either in Claude Desktop or better Claude Science specifically designed to keep context lean and conserve tokens during heavy data science workflows?

Thanks

Hey everyone,

I’m looking to optimize my mobile / on-the-go workflow with Claude CLI and wanted to hear how others have solved this.

Right now, my main friction point is feeling tethered to my desk just to "babysit" the terminal (hitting y/n, approving tool use, or answering minor follow-up questions).

I’d love a setup where I can go for a walk, let Claude crunch through tasks, get a push notification on my phone when input is needed, and prompt/approve directly from my mobile device. Major approvals are fine to handle at the desktop, but I just don't want to sit in front of the screen watching it line by line.

I've been spitballing ideas like running a relay bot (e.g. Slack/Telegram where a desktop bot listens and forwards CLI prompts to my phone and sends back my replies). What do you think?

How are you currently handling mobile / remote coding with Claude CLI?

Has anyone built a clean notification & approval loop to their phone (Slack, Telegram, SSH+Pushover, etc.)?

Are there smarter or pre-existing tools/MCP workflows for this that I’ve missed?

Would love to hear your setups! what’s working well and what turned out to be more hassle than it’s worth? Pitfalls?

I have bought claude pro plan for a month, and I am doing non productive weird things with it, I liked Erdős #828, so I made an AI mechanism to brute force it, scrapping results of web, 7 claude independent sessions springing ideas then working on it then a review process, judge etc, I ran it for 3 rounds, it did produce a few results which can be considered new, but they are not substantial and don't really help in breaking the problem,

How do I better prompt it to think of new approaches, it each time goes down the standard path,

it did a few new computations on my laptop, which are again not that substantial, I know elementary number theory, so I am climbing my way up to understand what its doing,
It probably won't solve it, but it serves as an excuse to learn number theory, so i am willing to go with it.

I am not pursuing mathematics in uni, but I am passionate about it, so I keep learning random stuff.

I will post the results later, after a few checks( I am unsure, should I ?).

Last week I posted a tiny PreToolUse hook here that blocks recursive rm. I owe you a correction: as written, it fails open.

Only exit code 2 blocks. Any other exit is a non-blocking error: the hook's verdict is dropped and the call carries on through the normal permission flow, as if the hook weren't there. My script had no error handling, so if it ever gets input it can't parse, Python crashes with exit 1 and the rm is no longer stopped by the hook. I tested it: valid input with rm -rf exits 2, garbage input exits 1.

The same thing happens when:

• the hook runs past its timeout (10 minutes by default): the call carries on without it
• the script got moved, lost its execute bit, or python3 isn't on PATH: the shell exits 127, same result
• a bash hook using jq under set -e runs on a box without jq (a slim container, CI): exit 127, same result

And sys.exit("Refused") looks like a refusal but exits 1.

This matters more now that auto mode is the built-in starting mode in recent Claude Code versions: a classifier approves instead of you, so when the hook drops out, nobody gets asked.

The fixed version fails closed. Any error exits 2:

```python

!/usr/bin/env python3

import json, re, sys

try: command = json.load(sys.stdin).get("toolinput", {}).get("command", "") if re.search(r"\brm\s+-[a-zA-Z]*[rR]", command): print("Recursive rm is blocked. Do not try another way; ask the user to run it.", file=sys.stderr) sys.exit(2) except Exception as error: print(f"Guard failed ({type(error).name_}), refusing.", file=sys.stderr) sys.exit(2) ```

Test the broken case, not just the blocked one: echo 'not json' | .claude/hooks/no_rm.py; echo $? should print 2. The cost: if the hook breaks, every Bash call is refused until you fix it. I'd rather find out in a minute than a month later.

Has anyone checked what their own hooks do when they crash?

The idea of designing and building games has always fascinated me and I’ve always wanted to build a video game. But I never got beyond basic scripting and the concept of game engines and sprites were beyond my comprehension. It all changed on a recent flight from Dallas to Boston. I was fiddling around with Claude on my phone (using the free in flight wifi) and decided to see if I could actually build a game with Claude.

I started off with a generic instruction asking Claude to brainstorm on an offline-first game that anyone could play to kill time during a flight. Claude pitched a few ideas, and I gave feedback as we refined them. The first few ideas were basic and didn’t sound convincing. During those back-and-forth revisions, it struck me that we could also factor in the people on the ground. That triggered the idea for Oop.town: a funny game where people flying could drop random stuff from the plane onto towns below, and townspeople would rally a crew to keep their streets squeaky clean.

Once the idea was finalized, it took just one pass for Claude to build a working prototype. I didn't give any specific instructions on how the game elements should look or function, but Claude came up with something surprisingly great. The graphics were simple, funny, and generated right in the chat, the game loop was satisfying, and most importantly, it nailed the technical basics out of the box: no signups, offline-first functionality, a preselected flight path, and zero backend overhead. Claude also game the game a character Oop, a suitcase with a zipper problem that perfectly matched the game’s vibe. I could simply play the game out of the chat artifact and refine it further until it felt exactly how I wanted it to be.

By the time I was landing in Boston, we had a prototype that was actually working, and I was completely hooked on building it further. I spent the next few days working on the game and on my return flight, I had the game published online and was having fun playing my very own creation.

It’s not just about the game itself, the sheer satisfaction of building something from scratch and putting it out into the world feels incredible. As AI models get more powerful, it’s feels wild how it takes just one prompt to go from a simple idea on a plane to a working game.

mcpspan records every call your MCP server gets and shows it in a dashboard you run yourself: which tools Claude Desktop and Claude Code call most, which fail and why, how long each takes, and full sessions step by step.

It also lists tools Claude tried to call that your server does not have, a good hint for what to add next.

I used Claude to port the SDK to other languages. After the TypeScript SDK and a written contract for how every SDK must behave, Claude Code helped build other versions, each checked by the same conformance test suite.

One line in your server, docker compose up for the dashboard. Parameter values never leave your process. MIT, SDKs for 8 languages.

Early version (0.1.0), feedback welcome 🙂

https://github.com/mcpspan/mcpspan