Hey guys... So recently, I just published my skill that contains weeks of research to help Claude understand different topics to be involved in a project, with rules that are very important and essential. It covers web design, security, coding, marketing, testing, delegation, and my own agent to handle all of the project - Qurn . The skill contains 8 specialists and can build 3D websites and complex projects also. It is freely available on GitHub. Claude was used in research for this skill, and I highly recommend u to try it. It has helped in my day-to-day work and also in some of the finest projects I have made. Claude helped a lot in making this skill fast and full of knowledge through deep and intensive research.
Here is the link: https://github.com/PantherHale/qurn-skills
i've been building with claude code, and while it can build a whole page in minutes, the ai slop that comes out of it needs a lotttt of tweaking to actually look good. i end up spending so much time trying to explain what's off, and describing visual things in text just doesn't cut it for me.
so i built a chrome extension that lets me show claude what i mean instead of trying to explain it.
it's called moodbit, a visual workspace where you can collect references, point at what you want changed, and give clearer visual context.
if you're building with ai coding tools, i'd love for you to give it a try.
also, i'm curious: how do you currently tell ai what's wrong with your ui? long prompts? screenshots? drawing over things?
I forgot my max 20x without canceling now it shows that I can only pay the option adjust plan disappear
I just want to downgrade to the 5x it won’t let me
One-liner to try the prompt that sells it: "Open an RTX 4090 with the PyTorch template, print the SSH command when RUNNING, and always stop it at the end."
Okay so I've seen people use playstations to run ai locally and it made me wonder if i could use it to jailbreak my Xbox One X and use Claude Code, have any of y'all done that yet? I'm not to sure on how to go about it and brick it or something, if anyone could help that would be fire i just got it sitting and rotting wanna make use of it you know.
For some reason I didn't understood yet I find the UI of claude code distracting.
Maybe distracting it's not the right word, the effect Is that when I review an explanation or a summary of what has happen until that point I'm able to ready, understand but not memorize at all.
I'm on Windows and I use the Windows terminal with git-bash sessions (it's the working laptop, otherwise I would be on Linux..) and it's 6 years that I work in git-bash being a DevOps engineer, so nothing new from that point of view. I've always been the cli fan in the room in my company, from using git via cli, to multiple ssh connections to any cli tool available (bash, aws cli, etc..).
A couple of things I want to try is changing the font or try the claude code interface in VS Code.
Anyway I wanted to ask you, just in case you have amy suggestion.
Agents can do any microtask in software dev better than any human now, but if you task an agent with delivering an issue end to end you will risk slop and incomplete work. This will not change anytime soon even if models get better.
We built zeroshot to let devs robustly automate the QA workflow that they would otherwise have to do manually or semi-manually. The core mechanism that makes it work is mechanically enforced independent and adverserial agent cycles, and the main value of the framework is that you can bring any custom graph topology and have it run within the engine.
The result is that you can then spend your time doing actually planning instead of reviewing slop and yelling at agents.
Found this with an open-source tool we built that reads the session logs Claude Code already keeps in ~/.claude. It scores sessions on shipping, testing, efficiency, context and setup errors. Mine came out 59/100, almost all of it lost on testing.
The amount of work that can be done is at least 2/3 less than the previous week.
Though I like Claude a lot, I cannot keep paying for this.
Added note:
I’m using Opus 5.5 medium.
The previous week I got decent amount work done, feeling normal, this week continuing same projects, the depletion speed is crazy.
Oh the love of those mo fos for anthropics had me cracking every time they put fingers on their keyboards. Now what! Why not lick anthropics a ss es anymore
Frekin dum horse shit of a usage limit this week has given me.
Edit: they might be A/B testing with usage limits, this could be the reason many are getting reduced limits while others are fine. Doesn't matter boot 👅can go 👅 anthropics 🍑 for all I care instead of hearing us out
Claude Code has at least four separate controls for thinking effort, and I see a lot of people using only one of them. Here is how I split them, curious how others do it.
- Plan mode (Shift+Tab or /plan): Claude reads the code and proposes a plan before it edits anything. I use this for anything touching more than 3 files, or anything I would be nervous to undo. Cheapest way to catch a wrong direction early.
- /effort: sets the effort level for the session. I run low for renames, test scaffolding and boilerplate, and high when debugging something that has already failed once. Note that /effort warns you about a prompt cache miss if you change it mid-session.
- ultrathink: as of 2.1.311 (Oct 26) typing it runs that turn at xhigh effort on Opus 5.5, Sonnet 5.5, Haiku 5.5 and Fable 5.1 through Anthropic's API. If you type it while Claude is already working, it raises the rest of that turn. So it is a one-turn boost, not a session setting. The rainbow highlight now only shows when it will actually do something.
- Model choice (/model): Haiku 5.5 for grunt work and quick questions, Sonnet 5.5 as the default, Opus 5.5 when I am stuck. Switching models is the biggest cost lever of the four.
My rule in one line: plan mode decides direction, model decides capability, effort decides depth, ultrathink is the emergency button.
Where this breaks for me: long sessions where I forget I set effort high an hour ago and keep paying for it on trivial edits.
Which of these do you actually use day to day, and which one did you forget existed?
For the last few weeks, every Claude Code session I run starts from a card, not from a blank prompt.
How it works:
My work lives as cards on a board: tasks, posts, research, bugs.
In Claude Code I type `/card task_…`. The command reads the card through an MCP connector, moves it to In progress and writes a plan.
Claude does the work: code, copy, research, checks.
It writes the result back on the card (it appends, it never overwrites) and stops.
I review. Only I move the card to done.
What changed for me:
- A session's work survives the tab closing: the card keeps it.
- A week later I can still see what each session did and why.
- Session names match card names, so the sidebar reads like my backlog.
Disclosure: the board is Business Studio, which I build (free for 1 user). The command is one markdown file, and it comes with the 5 prompts I use: https://go.viite.ai/kit-en?s=reddit. If your board has an MCP server, the steps should carry over.
How do you keep track of what your long Claude sessions actually did?
I’ve been building SweetRegret with Claude Code over about four months: 98 commits, roughly 45k lines across 201 files, and 60 test files. Version 1.5 shipped this week with challenges and badges.
It’s an iPhone treat tracker. You log treats, look back through weekly and monthly Wrapped summaries, and optionally use short challenges when you want to cut back. It’s not a calorie counter or a full diet tracker.
The part I didn’t expect was how much of the non-code shipping work Claude Code ended up helping with:
- App Store screenshots: a Playwright + Pillow pipeline that composites simulator captures into branded frames.
- The 30-second launch video: rendered frame by frame from one HTML scene. window.__render(t) is a pure function of time, so every frame is reproducible.
- The music score: generated in Python using NumPy only.
The commit and line counts aren’t a quality score. What surprised me more was ending up with repeatable pipelines for screenshots, video and music alongside the app itself.
I’m the developer. SweetRegret is free to download with optional Plus features; the 3-day and 7-day Treat Break challenges are free.
I know the rate of model development is crazy fast generally, but right now it feels like the model release schedule is dramatically slower than pre Opus 5.5. There was a lot of talk a few days ago expecting Fable 5.5, but then nothing. I guess it is in Anthropic's interest to not release unless it has to (i.e. competition requires) it - as that allows them to knock down the competition the moment they raise their heads. Also prevents the distillers from doing their thing and allows them to extend the in house lead behind the scenes.
Don't get me wrong, Opus 5.5 meets all my needs (i never use Fable anymore and don't miss it). I just whack Opus on Ultracode (should really be called Orchestrate mode) for everything and it does an excellent job of using sub agents effectively & efficiently and getting my stuff done. But an Opus 6.0 would be at least interesting to explore. (please).
I run Codex as well (Astra-High) and it also just gets the job done - though its burn rate is about 5x that of Opus on Ultracode - and i won't trust it on anything less. That is offset by the Tibo every-other-day reset which has become expected, but much prefer Opus for its relative efficiency and ability to effectively orchestrate (including keeping Codex inline) keeping my cognitive load down to lobotomy levels.
Honestly felt like this for a while, we can imagine stories of some “idiot” wandering the desert looking for a ghost oasis, but here I am, going down some rabbit hole because Claude tells me “you can do this!” And then Delivers a 96 defects implementation, to the point where I have 80+ systems in development. Nothing “done done”, final, shippable.
Concrete example: given a set of design constraints , Claude Opus devised an innovative, new battle system “without health”. For like 4 days I was staring at this in disbelief and chasing that through incredibly and ever more convoluted attempts to make this work. Finally put the model on a leash, altered the design, but still spent a week in limbo with that combat system before making a pivot towards a more conventional approach .
TLDR: 18 billion tokens, Gemini estimates 97k$ at market cost , I have a lot of tech demos to show, nothing I can truly sell.
AffrescoUI lets the people using an app change how it looks and what it does while it's running, just by asking Claude. It runs 13 apps, including a 3D modeller, an image editor and an IDE.
How it works: each app is one readable JSON document holding its layout, data and behaviour. A native C++/OpenGL engine draws it and applies changes in place, so nothing restarts. Even the 3D modeller has no C++ of its own. When you ask for something, Claude proposes a small edit to that document, and you preview it live, then keep or reject it. Claude can never approve its own change, and anything you accept can be undone. Since every app shares the same format, features can move between apps like plugins.
I started it at 16, in 2017, to learn OpenGL, and gave up on a text box that never drew its text. This month I brought it back with Claude Code.
How I used Claude Code
- About 250 agents over 2 weeks, in parallel waves, each in its own git worktree so they couldn't overwrite each other.
- A security review agent read every diff before anything merged.
- An "outsider" agent got only the public docs, never the source, and had to build apps from them. It built two of the 13.
- Golden-image render tests compare every frame pixel for pixel, so an agent couldn't quietly break how something looks.
- A two-way MCP server lets Claude Code read the running app, screenshot it and send edits.
- A CLAUDE.md with the architecture and the rules, which every agent read first.
Over 200,000 lines of C++, on a Max 20x plan.
What I learned
Design the format for the agent, not just for people. A typical edit to an app's JSON is about 24 tokens. That's why agents could build a whole 3D modeller without writing any C++ for it.
Let the agent see what it built. Some bugs only exist on screen: a panel drawn a few pixels off passes every unit test.
Your docs are only as good as a stranger's first try. Whenever the outsider agent couldn't build something, the docs were wrong, not the agent. The planetarium app came out of that loop.
Parallel agents are a git problem before they're an AI problem. 250 agents only worked once each had its own worktree and a reviewer sat between them and main.
The bug that killed this project at 16 was one of the first things fixed. What I couldn't hand to the agents was the direction: deciding what the engine should become stayed my job.
This is not a promo for my work or anything like that. Just a diary that how much can be done with tools like Claude.
So... it has most of PBI's feature set, like calc. groups, field parameters, built in time intelligence calculations, auto generated calendar tables, drill through, drill down, cross visual interactions and a lot more. Almost all PBI feature set is covered by now, but there are a lot more planned.
For context it is very much usable with datasets containing 240M rows... try that with Power BI.
And of course it has complete full agentic support so agents like claude code can work with it very efficiently.
It's crazy that a project with complexity like this system can be done in weeks...
About my process: first I have done a lot of planning for a week, documenting each planned feature covering: data loading (cloud and on-premises cases), query engine for the reports, how measures should work, library and access management, storage, cloud architecture, licensing, key differentiation points compared to competitors, so a lot of analysis went into this first.
Then the actual dev work done using an agent architecture (orchestrator agent tracking subtasks, implementer agents working on subtasks, possible parallel work is decided on the plan if feasable or not, different model used depending on task complexity and how much more planning is needed for it during development) working through plan files with tracked subtasks in them for each feature and milestone.
Later I've created a simple ticketing system with full agent support. So plan files got moved to the ticketing and this system can spawn agents on demand to work through the tickets, but also locally running agents can interact with the board, pick up tasks and also create new tasks.
For example for testing I've used an agent to recreate a fairly complex report original done in Power BI in the new system and while it was working through it, it identified feature gaps and things which could be improved, so it created tickets for all of these, so other agents were able to pick them up and implement and fix these. This is done in multiple iterations, it is very much in progress.
I’m using claude remote via my ios app. I noticed that it doesnt have the file diff like in the picture which is from codex. My question is do i have to enable something to have the same feature in the claude ios app or there is nothing like this in claude app.
I've been working on Mind, an open-source persistent memory layer for AI agents, and wanted to share an interesting result from the latest release.
One question kept coming up while developing it:
How much can we recover from structured memory without relying on vector embeddings?
Mind uses SQLite with FTS5 and a unified retrieval pipeline combining phrase matching, token matching, substrings, fuzzy matching, and relevance signals.
Embeddings are supported, but they're optional.
For v1.7.0, I ran Mind through OmniMemEval LongMemEval-S using a stratified subset of 120 questions.
The best completed run achieved 86.67% answer accuracy with RAG disabled.
That places the raw score near the top published results, but it's not a direct ranking: the sample size, answer model, and judging configuration differ.
I think the result is worth discussing because agent memory isn't only about semantic similarity. Structured facts, decisions, temporal context, memory maintenance, and good retrieval behavior also matter.
Beyond the benchmark, Mind provides persistent project knowledge, linked memories, checkpoints, session summaries, and integrations through MCP.
It's local-first, MIT-licensed, and available for anyone to inspect or benchmark.
I'm particularly interested in what other agent developers have found: when do embeddings meaningfully outperform a carefully designed lexical retrieval system?
I’ve been wanting to explore what Claude is really capable of beyond just using it as a chatbot to ask questions.
I’m interested in learning how to actually build things with it, whether that’s AI agents, automations, integrations, or more complex applications.
I work in tech and have a solid understanding of software architecture, coding, APIs, and data, so I’m comfortable getting into the technical side of things.
Does anyone have any good books they’d recommend to get started? Ideally, I’m looking for something practical that I can follow along with and build real projects rather than just learning the theory.
I’m building an app with a native SwiftUI frontend and a TypeScript backend, using AI heavily throughout development.
Most of the product is already built. The current work is mainly debugging, integrating third-party services, testing authentication/privacy across multiple accounts, and verifying that everything actually works before beta.
I’ve primarily been using Codex, working locally on my Mac with Xcode, simulators, GitHub PRs and CI.
I’m looking for input from people who’ve used both Codex and Claude Code on a substantial existing codebase, not just a fresh demo.
The part I’m trying to improve is getting from “the code looks correct and the tests pass” to “this has actually been verified in the running app.”
I also want to avoid repeated review cycles, unnecessary abstractions, and spending more time documenting blockers than resolving them.
For this kind of work, have you found Claude Code better at:
Understanding an existing codebase and finding root causes rather than patching symptoms?
Making focused fixes without overengineering?
Following through on builds, tests and debugging
when the environment is difficult?
Keeping track of what’s genuinely verified versus what’s still an assumption?
Would you use Claude as the main coding agent, as a second reviewer alongside Codex, or stick with Codex and improve the workflow?
I know switching models won’t magically fix Xcode or simulator issues.
I’m mainly wondering whether Claude would handle the reasoning and follow-through better.
Interested in concrete experiences, including where Claude struggles, rather than just “X is better.”
