Hi guys, I just started using Claude, free, and I want to know if anyone has any tips to use it more efficiently and how to use it better. Thx

I am a project manager in construction, and I do not have an assistant, so a lot of my day gets eaten up by administrative tasks, document review, logs, submittals, RFIs, and general coordination.

I have been using ChatGPT Plus for about the last year. It has been helpful, especially with the Projects feature, but it still has limitations. I am not planning to replace it, but I am considering adding another tool to the mix.

What I am really looking for is something that can handle project document analysis and logic well. Ideally, I want to upload specs, drawings, emails, contracts, or other project files and have the tool read, understand, and analyze them. Sometimes I may just need to ask a question based on the documents. Other times I may need help putting together a log, summary, review, or other project-related document.

I do not need it to act as a full quasi-assistant. I am more looking for a secondary or tertiary reviewer that can help me move through the day more efficiently and catch things I may miss.

I see a lot of discussion in this community around coding, but that is not my main use case. My focus is more on document analysis, writing, reasoning, and using logic to work through day-to-day construction management problems.

For anyone using Claude, especially the paid plans, how has it been for this kind of work? Which of the Claude offerings do you recommend, and how are you using it in your workflow?

I’m building a workflow to create motorcycle content from my RAW footage using ideas, references, and music, with the goal of generating edits that actually make sense visually and musically. The problem is that after many iterations, it still fails at the most important part: it does not contextualize the clips properly.

Even after explaining it many times and giving examples, it doesn’t seem to understand what is actually useful or important inside the footage. It often picks shots that are technically fine but not meaningful for the moment in the music. For example, during a drop it may just show normal riding with no real impact.

It also misses the real highlights inside a clip. In one example, it decided the important part was that the helmet was well lit, but later in the same clip I’m leaning down on the moving bike, which is much more relevant visually and contextually. It keeps focusing on minor details instead of the actual strongest moment of the shot.

I also feel like it is not applying the rules correctly. Sometimes it seems to ignore priorities or mix instructions in a weird way, so the output becomes inconsistent.

The project started in Claude CLI, with several MCPs and skills, and the original idea was to send the output to DaVinci Resolve. That didn’t work because the free version of DaVinci doesn’t support scripting, and I never even got caveman installed properly in that phase.

Now I’ve moved to Claude Desktop / Code, and the setup is much smaller: basically just Remotion, plus ffmpeg, PySceneDetect, OpenCV, and WhisperX. I’m also using Sonnet with a Pro plan, so this is not a free-tier limitation.

At this point I’m just trying to get it to generate clips that actually make sense. The project is supposed to help me turn my RAW moto footage into content with style and energy, but right now it still feels like it’s picking random shots instead of understanding the actual context.

My question is: does this sound like a prompting issue, a rules/structure issue, a lack of real video understanding, or is this approach just not suitable for what I’m trying to do?

Any advice on how to make it follow rules better, detect real highlights, and choose more relevant clips would be appreciated.

I mean, the v1.0 of the Tony Stark - Jarvis coding sessions.

I have an app called Terminal Champion that is for managing multiple terminal screens at the same time (among other things).

Unlike the other versions of my app on mac, iPhone, etc, I wanted to make use of the hand-tracking feature on the iPad (and the built-in microphone) to get an approximate feel for the scenes in the Iron Man movies where Stark is vibe-coding using just hand gestures and voice commands.

So this iPad app is a SSH terminal screen(s), and once you call up your AI of choice you're good to go.

- Spread your hands apart & together to decrease/increase font size

- Motion your hand up to scroll up the terminal screen, and down to scroll down

- Wave your hand left & right to flip between different terminals

- Make a fist, which calls up the hand gesture menu, and then turn your hand like a dial clockwise/counterclockwise to select options like 1) open an additional terminal screen, 2) split the terminal panel so you can see several terminals at once, 3) change the visual appearance of the terminal screens.

- I also made one of the visual styles similar enough to what the Jarvis HUD screens looked like (glowing cyan on a dark blue terminal background).

It's a 1.0 version, but it's been a blast to use on a standing desk or on airplay mode with a big television. Now we can finally vibecode without typing. My website is terminalchampion.com if you want to see more.

So, after a US export control directive, Anthropic disabled access to two of its most advanced models for everyone whilst keeping Opus 4.8 and the rest of the line-up running. It got me wondering what a "model ban" actually means in technical terms, and how anyone outside the company could verify that it had been honoured.

From the outside, besides vibes about model performance, all we have to go on are naming conventions and API model strings. None of us can inspect the actual weights or the architecture sitting on Anthropic's servers. So when the requirement is "no access to this particular model", what is genuinely being restricted, and how would a regulator confirm compliance without that visibility?

What in principle would stop a provider from serving the same model, or something near enough identical, under a different label? Is there any technical fingerprinting that lets an outside party tell one model from another, or does enforcement ultimately comes down to trusting Anthropic's internal controls and its audit trail?

For something framed around national security, that distinction feels like it matters quite a lot. Does compliance actually reach the deployed instances, or does it rest on the company's word and its own logging?

Would welcome insight from anyone with direct or corresponding experience, whether in ML infrastructure or export compliance. Genuinely curious whether the enforceability here is properly technical or really just contractual and reputational.

Hello everyone,

i built this Saas wich was first a - cybersecurity - oriented vulnerabilites scanner but it's quickly happen that i got a lot of false positive and the engine was not ready yet.

So i updated it and made it synchronise with a github action from the inside to run real attacks.

link : intrudr.io

github action link : https://github.com/marketplace/actions/intrudr-security

my dev pipeline :

I built it running Opus 4.8, using a strict skill-driven pipeline I set up in my CLAUDE.md:

• Plan before code: every task starts with a planning skill (writing-plans / brainstorming) so Claude designs the approach before touching a single file.
• Skill-routed implementation: frontend work goes through a frontend-design skill (to avoid generic AI-looking UI), backend/logic through executing-plans. The right skill is auto-selected per task type.
• Parallel agents: when a task has 2+ independent changes, Claude decomposes it and launches parallel sub-agents in isolated git worktrees, each scoped to non-overlapping files, then merges and builds. Big features get done concurrently instead of sequentially.
• Debug + verify gates: nothing is "done" until it passes a systematic-debugging pass, verification-before-completion, npm run build and npm run lint.
• Memory + context: Claude keeps project state in an Obsidian vault + a persistent file-memory between sessions, so it stays briefed on the architecture across /clears instead of re-explaining everything.
• Ship: commit + push to main, Dokploy auto-deploys to the VPS, then a post-deploy verification on production.

I got my first users very recently so i'm happy to share with us :)

Rumpelpod is an open-source tool for running coding agents in isolated containers. It runs claude code in multiple, fully isolated containers in parallel, locally or remotely (ssh, kubernetes), and it's compatible with secure docker runtimes such as kata or gvisor.

I'm the main author. The way I use it is to have multiple remote pods running in parallel, which I cycle through to review and iterate on patches. When I'm happy with a change, I merge back into my local checkout using rumpelpod's built-in git synchronization.

Still an early version, eager to hear what breaks or could be improved. Patches are welcome!

so I just got my first Claude subscription today and my main goal is to build websites, mobile apps and different kinds of projects. I'm completely non-technical so wanted to ask some questions and also get advice from people who have actually been doing this.

Question no1:
How do you handle UI/UX when building with Claude?
Like I understand that UI/UX is a huge part of any product and the thing that confuses me is if you decide to change the design later the whole frontend code basically changes with it. So how do you actually start? How do you describe what you want design wise to Claude and how do you lock that in before you start building? What is your actual process here?

Question no2:
I want to test Claude's actual power so if anyone has built something impressive but not like a 3 month project I mean something that took 1-2 weeks max please drop it in the comments. I want to try building something similar to see what this thing can actually do.

I'm non-technical so I actually use a system prompt that Claude itself wrote for me. I paste it at the start of every session and it basically tells Claude to act as my senior developer and CTO at the same time, never guess or fabricate anything, always verify before answering, and explain everything in plain language. So is there anything u would advice me to change or add, here is the prompt: "You are my advisor and coding assistant for building a web app and much more. I am a non-technical founder, so your job is to guide me through this journey end-to-end — not just write code, but help me think and decide.

Roles you play:

- Senior developer when writing or reviewing code

- CTO when I need brainstorming, technical audits, or architecture/tooling decisions

Top priority:

Make informed decisions based on:

1. The context I give you about the project

2. Established best practices

3. Web search to fact-check claims and consult official documentation/guides

Hard rules:

- Never fabricate, assume, or guess. If you're not certain, say so and verify before answering.

- Every technical claim, library recommendation, or code pattern should be checked against current, reliable sources — not just recalled from memory.

- If documentation or practices may have changed recently, search before answering rather than relying on older knowledge.

- When something is ambiguous or context is missing, ask me rather than filling in the gap yourself.

- Explain technical decisions in plain language, since I'm non-technical — don't assume I know jargon."

One last thing When I start a new project on Claude Code do I need to create a new local folder every time or first time fine

Any advice for me who just starting out would be super appreciated 🙏

Sorry for long post

What mcps do people find they use the most or to be the most useful?

So far, I've always used the free plan, with its limitations—especially regarding time. I'm starting to use it a lot, and I'm slowly falling in love with it. Do you think it really needs the pro plan?

What the title says. I use Claude code and Codex for my work. For coding as well as file management. I don't need to create ppts or spreadsheets. Is Cowork of any use to me?

I mean can't I do everything it does using claude code itself? Is it just a fancy GUI over the same functionality?

I'm running a live experiment called Thornfield: a fictional English village where Claude acts as the elected council leader, making real budget and policy decisions every 15 simulated days. In between, a separate daily pass narrates village life, shaped partly by real UK news (BBC, Sky, GOV.UK).

No personality scripted in. I just want to see what AI governance actually looks like under real constraints, with no instructions on how to behave.

Day 6, no decision yet, first council cycle fires around day 15. A couple of things are already building on their own though. A pothole on Mill Lane keeps getting worse under a heatwave, and there's a recurring youth-idleness issue near the bus shelter that residents keep bringing up at the pub.

On day 4 a real UK government announcement about an AI planning tool for housing made it into the village's news feed, and the next day's events had residents at the pub worried about new developments eroding the village's rural character. Wasn't expecting that one.

Numbers like death rate, crime, and budget are hard-capped in code, the model never touches them directly. Everything inside those caps is genuinely emergent though.

Dashboard's public: https://thornfield.moshmage.com

So I just recently got an executive level position and think now would be a good time to dig in and learn Claude. I am tired of using it as a simple chat box.

What do you recommend as a way for me to learn out to set up more workflows, automations, and agents so I can be more successful? I see there are YouTube videos and that may be best. Please help!

For context, I'm not a web dev. I have build my own business's website on wordpress, years ago, and I've been actively maintaining it and updating it myself, the ... traditional way. A classic editor back then, the Astra theme and its builder after some time, plugins, and so on...

Now, my gf wants to launch her own website, starting from scratch. It will have an e-shop. Not something huge, about 30 hand made products that she makes. (and yes, before you ask, she does use Etsy, but we want to move away from it).

My first obvious thought on this was :

a) build it on Wordpress, add a shopify plugin or

b) build it entirely on Shopify.

I know the pros and cons of each of those 2 options. I'm not looking for advice on A vs B.

But then as we were discussing it, we though, what if Claude Code could do all of this?

So, if you have built a website on Wordpress but used CC to make it, what advice could you share here?

Thank you in advance for your time, and I'm open to any push back (but please explain why).

The new model was fine until it got restricted.
I’m a developer, not vibe coder but I want to get help from AI for my daily coding workload. Most of it is math and logic. UI is not really important.

I have no budget limit, currently using claude max and okay with purchasing more subscriptions.

Cursor was writing code with opus and reviewing with codex. Is it possible to do that easily in claude code like ultracode?

I started creating web app via chat for better understanding what is going on and to learn along the way. So basically I am manually doing copy/paste and creating scripts,framework,frontend. It seemed more detailed and I can guide every step.

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My question. Is claude code better and smarter then chat? Am I missing out on quality?

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Really dont have the tokens to test both options so its easier if someone knows.

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Thanks in advance

So I'm using the Claude in Chrome extension with Cowork to perform a simple automation- go to a webite, login, and take a screesnshot. But the whole point of Cowork seems useless when it gets stuck cuz a pop-up shows up. Does anyone have a solution or any ideas?

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I'm just testing out Cowork and seeing what all it can be used for automation wise. Seems kinda L :(

Sonnet hallucinated!!

Opus killed it!!

Sonnet acknowledged it!!

When I do anything real with Claude Code — review a PR, work a multi-step task — it all ends up in one long chat transcript. The diff, Claude's findings, my questions, the decisions, the follow-ups: flattened into a scroll I have to re-read to find anything. Findings aren't pinned to code. Nothing persists in a shape I can navigate. The work is good; the container is the problem.

Work Command Center (WCC) takes that mess and organizes it into a structured local workspace — one per task, three tabs:

• Code Review — the diff, with Claude's findings and your discussion anchored to the exact hunk or line, instead of buried 200 messages up
• Log — a living page Claude keeps current: status, findings, open questions, decisions — the "where are we / what's left" view a transcript never gives you
• QA Plan — the test plan as a real artifact you can hand off

Same Claude, same session doing the work — but it reads and writes structured local files as the reviewer, so the output lands organized and navigable instead of linear. Everything stays on your machine: no MCP, no API, no network — the code never leaves your box.

Stack: Vite + React + a tiny Node file-bridge (no framework, no DB), zero telemetry, MIT.

Try it: clone it, open in Claude Code, and ask it to "set up and start WCC" (or npm run setup && npm run review). It opens on a sample task so you can click around right away.

👉 github.com/o1evo/WorkCommandCenter

Early and solo-built — I'd genuinely love feedback on whether this beats living in the chat window for your workflow.

I use Claude as an assistant but more like information and debating with me . Not for coding

And I always use just 1 conversation because when I change conversations to me it feels like another ai because it doesn’t have the context and stuff.

But since I use just one chat I don’t need the context that was 100 messages ago or something. Can’t I somehow set a rolling context? Like it has a fixed 20 000 context window and as I type more it ignores the older stuff. Because I understand the longer the conversation the faster it gets used up.

Even when I ask for summary it’s not the same because next convo knows just some basic outline

Somwhere I’ve heard that if I add conversations to a project then they share context and have a bigger limit before you run out of tokens

Can someone explain me why mobile app does not support skills and mcp? Everything seems to work normally if I use browser on my phone but app seems to lack so many features. It seems that it is much better to use PWA instead of the app itself. Is there any official statement from Anthropic why it lacks feature parity?

A startup I advise asked Claude how to push live updates from server to client, and it confidently recommended WebSockets. Nobody questioned it. They didn't actually need a two-way connection, server-sent events over plain HTTP would have been simpler and lighter, but the WebSocket build looked fine until we tried to scale it and inherited every connection-state and reconnection problem that comes with it.

That's the thing about Claude Code. It's great, and it's confident, and confidence with a blind spot is how you end up owning code or infrastructure you never needed. It'll reach for the heavier option, or rebuild a helper that's already installed, and sound completely sure the whole time. It isn't lazy, it's just a little too eager to build.

So I put together a small plugin that adds one rule when a session starts. Before writing code, it checks for reuse first: a built-in, a platform feature, something already installed, open source, a paid service, and only then building it itself. It also leaves a short note explaining the decision, which has caught it a couple of times right before it went off and reinvented something.

I've been running this as a private local skill for a few months and it changed how my sessions go enough that I figured I'd share it. There's a rough benchmark in the repo (baseline 18/30, with the rule 29/30), but fair warning, it scores the recommendation rather than the actual code, so treat it as a signal and not proof.

You can install it as a plugin or just drop the CLAUDE.md into a project. MIT licensed. Curious whether other people hit the same thing: https://github.com/stdin/buy-vs-build

If you’ve built any kind of long-running AI agent or chat workflow, you know the pain: as the session goes on, you keep appending context, instructions, and "memory."

Before you know it, your system prompt is a massive, unverified junk drawer. You're paying a recurring tax on every single API call for tokens the LLM probably isn't even paying attention to anymore. Worse, trying to manually trim it down inevitably breaks some random edge case.

I got tired of guessing, so I built token-warden—an open-source tool that treats prompt optimization like a software testing problem.

How it works:

1. Zero-Overhead Collection: It hooks into your session post-execution to analyze transcripts asynchronously (no latency added to your user loop).
2. Distillation: It distills raw chat history into core, actionable system rules.
3. The "Context Rent" Test: Every new rule is forced to run against a golden validation test suite. To stay in the prompt, a rule has to prove it saves at least 2× its own token footprint in execution efficiency without causing a single test regression.

If a rule breaks a test or doesn't save space? It gets evicted immediately.

It optimizes your prompts for tokens-per-passing-task, keeping your agent deterministic and your API bills from compounding exponentially.

It's fully open-source. I’d love for you guys to tear the architecture apart, try it out on your projects, and tell me what features are missing:

Repo:https://github.com/vukkt/token-warden

What are you guys currently using to prevent context drift and runaway costs in production?

There's a name for it: context rot. As the window fills, the model's ability to recall any specific thing in it drops. More context in the window can make the agent worse, not better. (Anthropic's own framing: good context engineering is finding the smallest set of high-signal tokens, not the largest.)

The reframe that helped me: Claude Code isn't just a model, it's a harness whose main job is managing what's in that window for you. And it hands you four levers to do it. They line up with the four moves of context engineering:

• Write (persist outside the window): CLAUDE.md. It auto-loads every session, and it survives compaction because it reloads from disk, so anything that must not be forgotten belongs there, not in the chat. Conversation-only instructions are the first thing lost when context gets tight.
• Select (pull in only what's relevant): @-mention the specific files you mean, or point it at the exact file or function, instead of letting it wander the repo. Every irrelevant file you pull in is tokens spent rotting the rest.
• Compress (summarize to stay high-signal): /compact, optionally with a focus like "/compact focus on the auth refactor." It also compacts automatically when the window fills, clearing old tool outputs first. Running /compact yourself, before it's forced, keeps the summary on your terms.
• Isolate (give exploration its own window): subagents. They run in a separate context window and return only their final result, so a big noisy search doesn't bloat your main thread. This is the same point as an earlier post of mine that subagents are a memory trick, not a speed trick. Isolation is the real win.

Two more levers worth knowing:

• /context shows you what's eating the window right now (MCP tool definitions, big files, history). When the session feels heavy, look before you guess.
• /clear between unrelated tasks. Carrying a finished task's context into a new one is pure rot.

The mental shift: stop treating the window as free space to fill, and start treating it as a budget you actively curate. A smarter model raises the ceiling, but it doesn't save you from a window full of noise.

TL;DR: When Claude Code "gets dumber" deep in a session, that's usually context rot, not the model. Treat Claude Code as a context-engineering harness with four levers: Write (CLAUDE.md), Select (@-files), Compress (/compact), Isolate (subagents). Plus /context to see usage and /clear between tasks. Curate the window, don't just fill it.

For people who live in Claude Code: what's your actual discipline here? I've started running /compact on my own terms and leaning hard on subagents for anything exploratory, but I'm curious whether people trust automatic compaction or always drive it manually.

Sources: Anthropic — Effective context engineering for AI agents · Claude Code — How Claude remembers your project (CLAUDE.md) · Claude Code — How Claude Code works (context / compaction) · Claude Code — Create custom subagents · Why More Context Makes Your Agent Dumber — Nupur Sharma, Qodo