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You.com AI

You.com AI

I built a free AEO diagnostic with Claude Code — every report has a "copy mega prompt" button that drops the fix back into Claude Code

Hey all! I just finished launching canaifind.com (free AEO/AI-search visibility scanner) end-to-end with Claude Code over about a week. It checks robots.txt, llms.txt, schema.org, and HTTP response headers for any domain, names the specific bug patterns (the GPTBot vs OAI-SearchBot fall-through is the most common one), and outputs a permanent shareable report URL. The feature I'm most happy with is the "Copy mega prompt" button on every report. It takes all the actionable findings and composes them into a single structured fix-prompt: diagnosis, recommendation, file changes, verification steps - formatted for direct paste into Claude Code (or Cursor, but designed for Claude Code). The loop-of-trust moment that made me write this post: After shipping, I ran canaifind on another site I own (sma200.trade). It flagged "Content Signals missing." Except, I'd added them three days earlier. As HTTP response headers, not robots.txt body. Lighthouse's SEO checker flags the body form as "Unknown directive" (-8 points), so I'd traded off the AEO signal for the SEO score. Pasted the megaprompt into Claude Code. The agent: Diagnosed the tradeoff I hadn't articulated to it (body vs header coverage, Lighthouse penalty, AI-crawler header awareness) Recommended publishing BOTH forms - accept the -8 SEO ding for the AEO win Shipped the fix to sma200.trade in 5 minutes Then I realized canaifind itself had the SAME gap.. it was only reading the body, not the header. So I shipped a fix to canaifind 30 minutes later. The fix-prompt template now explains the tradeoff so the next site that hits this case gets the same answer without re-discovering it. Diagnose downstream → fix downstream → fix upstream → all in an hour. The whole loop ran on Claude Code. The diagnostic itself is free, no signup, ~5s scan. canaifind.com if you want to try it on a domain you own. Would love to hear if if anyone else is utilizing tools to generate prompts, etc.. also if you see anything that I could do to touch up the site, please let me know! submitted by /u/printoninja [link] [comments]

Stop Claude Code from over-engineering: The 4 core rules every CLAUDE.md needs

If you are using Claude Code, the CLAUDE.md file is a powerful lever to shape its behavior and prevent it from making silent assumptions or writing verbose, speculative code. Derived from the popular andrej-karpathy-skills framework, here is a minimal instruction block you can paste directly into your root CLAUDE.md to keep Claude surgical and grounded: # Claude Code Behavior Rules ## 1. Think Before Coding - Never make assumptions about undocumented APIs or configurations. - Ask clarifying questions if a task's requirements are ambiguous. ## 2. Surgical Changes - Modify only the minimum necessary lines of code to achieve the goal. - Avoid refactoring adjacent or unrelated files unless explicitly asked. - Match existing style, even if you would write it differently. ## 3. Simplicity First - Do not write speculative helper functions or complex abstractions. - Prioritize simple, readable code over clever or DRY patterns. ## 4. Goal-Driven Execution - Establish clear test or verification criteria before writing any code. - Run local tests or build steps to verify your changes actually work before completion. Keeping these rules short is key to preventing prompt-drift. If you want to quickly generate and customize these rules for your specific stack, testing frameworks, and linting tools, I put together a simple compiler here: [Link] Would love to hear what rules or constraints you regularly use to keep your agents from drifting. submitted by /u/Ambitious_Voice_454 [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Working on a cgo-free CUDA binding in Go for ML stuff Week 3 - open source [P]

At our work we use CUDA in Rust since the company switched to it recently. Rust has pretty good Driver API bindings but it made me wonder why the hell we cant have something decent in Go without cgo. I mostly build ML tools in the last month and Go is my main language for pretty much everything. Problem is most Go CUDA projects still need cgo and the full toolkit at build time. That breaks cross compilation and makes Docker images huge which sucks when working on machine learning projects. So last month I started messing around with a proof of concept that loads libcuda.so at runtime using purego. No cgo at all. Biggest pain was thread affinity. CUDA keeps context per thread so goroutines switching around kept breaking things. I built a simple executor that locks an OS thread with runtime.LockOSThread and funnels all calls through a channel. Heres roughly what using it looks like right now: func run() error { cuda.Init() dev, _ := cuda.GetDevice(0) ctx, _ := dev.Primary() defer ctx.Close() a, _ := cuda.Alloc[float32](ctx, 1024) b, _ := cuda.Alloc[float32](ctx, 1024) c, _ := cuda.Alloc[float32](ctx, 1024) stream, _ := ctx.NewStream() start, _ := ctx.NewEvent() stop, _ := ctx.NewEvent() start.Record(stream) fn.LaunchOn(bg, stream, cfg, cuda.Arg(a), cuda.Arg(b), cuda.Arg(c), cuda.ArgValue(int32(1024)), ) stop.Record(stream) stop.Synchronize() duration, _ := start.Elapsed(stop) fmt.Printf("GPU time: %v\n", duration) return nil } On my 4070 Ti a 10M vector add showed CPU timer at like 160us but actual GPU event timing was 434us. That difference surprised me. The project is still super early and moves slow cuz i only code on weekends and im a total noob with CUDA. Slowly adding Graphs and multi gpu support. THIS IS SO early , so treat it more like a learning cuda repo, but im having fun learning cuda. Thought some of you might find it interesting too. repo is github.com/eitamring/gocudrv if you wanna take a look. Would be cool if anyone with 5xxx series cards wants to try it wink wink submitted by /u/Eitamr [link] [comments]

PapersWithCode new features - week 1 [P]

Hi, Niels here from the open-source team at Hugging Face. It's been one week since I launched paperswithcode.co, a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. The reception has been great, and I'm excited to extend this over the next few months. This week, I've added the following features: - Support for multiple metrics for a given benchmark: leaderboards now support multiple metrics, see e.g., the Open ASR Leaderboard for automatic speech recognition, which supports both Word Error Rate (WER) and the Inverse Real-Time Factor (RTFx) metrics, or the Object Detection leaderboard, which now also reports frames-per-second (FPS) besides mean average precision (mAP) on COCO. https://preview.redd.it/owlxn0b5u23h1.png?width=2878&format=png&auto=webp&s=1dff2f8feab4f160f77c97ceeb5d90e82382e63c - Support for external papers: We do support submitting papers beyond Arxiv, such as a Github repo, a blog post, BiorXiv, and more. You can submit a paper at paperswithcode.co/submit. AI will automatically enrich it with task and method tags, the GitHub repo, evals, and more. See e.g. DeepSeek-v4 below, which is not on Arxiv: https://preview.redd.it/uogbt0fjw23h1.png?width=2928&format=png&auto=webp&s=8b81e48af69b8935ddeb569d882d866b3e9ba216 - Support for paper lineage: whenever a paper has a follow-up or predecessor, this will be displayed with a small banner above the abstract. See e.g. Mamba-3, DINOv2 and GLM-4.5. https://preview.redd.it/f6vgtd1du23h1.png?width=2228&format=png&auto=webp&s=f8627f7669405f1766eecfd3322e925e15b4806d - New methods: support for new methods based on popularity, including Gated DeltaNet, Kimi Delta Attention, Mamba-2, and more. Each method also lists all papers that cite it. Find all supported methods here. https://preview.redd.it/6pzagifvu23h1.png?width=2984&format=png&auto=webp&s=400efdc9677d1fbd369eedf684e622dd8c807973 - Support for screenshotting a leaderboard for easy sharing on social media: each benchmark now includes a "copy image" button both on the scatter plot and table, which can be shared on social media. Try it on ClawEval, for example. https://preview.redd.it/w7y7t7xnw23h1.png?width=2950&format=png&auto=webp&s=cb70ad91c6ba075e49b743d6e34f157d22266f04 - Added many more evals: we are adding evals gradually, starting with all models supported in the Transformers library. So far, we have about 3k evals! Find them at the bottom of each paper page, e.g. Qwen 3.6. https://preview.redd.it/zao056s9x23h1.png?width=2218&format=png&auto=webp&s=540d87f473be05cb6f9c0aca88afa74fd4373e15 Happy to hear more feature requests and feedback! I will also launch a channel on the Hugging Face Discord server for easier communication. You can also chime in on the GitHub thread here. Cheers, Niels submitted by /u/NielsRogge [link] [comments]

Here it is!

submitted by /u/LowCommittee5230 [link] [comments]

🚀 Skills for small businesses, officially released by Anthropic

Anthropic’s 31 small-business skills reportedly hit around 382,000 downloads on day one. And now someone has mapped the whole thing into a setup workflow that can apparently be deployed in ~10 minutes. This is actually a pretty interesting shift. Small businesses used to stitch together automations manually across: Zapier Notion CRM tools email workflows internal docs custom scripts Now AI companies are starting to package the whole thing into reusable skill packs: 🧠 workflow 📚 memory ⚙️ behavior 🔗 connectors 🤖 orchestration 📋 operating rules Basically: business operations as AI-readable skill files. The best part? You don’t necessarily need Claude to use them. At the core, these are still .md skill files describing workflows for AI agents. So even if you’re using Codex, Cursor, Gemini, or another coding agent, you can still study the structure, adapt the workflows, and plug the ideas into your own agent setup. This feels like the beginning of a new category: “AI business operating templates.” GitHub: https://github.com/anthropics/knowledge-work-plugins submitted by /u/davidnguyen191 [link] [comments]

I built a Chrome extension to navigate long ChatGPT conversations more easily

Hey everyone, I built a small Chrome/Chromium extension called ChronoChat to solve a problem I kept running into: long ChatGPT conversations becoming almost impossible to navigate. ChronoChat adds a sidebar to ChatGPT that turns the current conversation into a searchable map. You can: jump directly to specific messages filter by user or assistant turns search inside the current conversation use keyboard shortcuts for faster navigation export the full conversation as JSON, CSV, Markdown or PDF The extension runs locally in the browser. There is no backend, no analytics, and no remote runtime assets. I made it because I often use ChatGPT for research, coding, planning, and long iterative work, and scrolling through huge threads gets painful fast. GitHub repo: https://github.com/sickn33/chronochat Would love feedback, especially from people who use ChatGPT for long workflows. What would make this more useful for you? submitted by /u/Fickle_Guitar7417 [link] [comments]

I made a Claude Code plugin that draws matplotlib figures in that soft-pastel "alignment research blog" style

You know the look — the figures in Anthropic's research posts. Bold sans-serif titles, scatter points under a smoothed trend line with a shaded band, those bars with the slightly rounded tops, little ↓better badges in the corner. I kept wanting my own plots to look like that and kept rebuilding the same matplotlib boilerplate, so I packaged it into a Claude Code skill. It's called nice-figures. Once it's installed, you just describe the plot you want and Claude picks it up automatically: "training-curve plot of these RL scores with a smoothed trend and shaded band, research-blog style" "grouped bar chart comparing three models across four evals, with the rounded bar tops" Bring your own CSV/arrays and it maps them onto the closest chart; describe a figure with no data and it generates a clearly-marked synthetic placeholder. Under the hood it's one skill plus a small style helper (matplotlib + numpy, no other deps) and 16 chart recipes — training curves, grouped bars, ROC, heatmaps, scaling-law scatter, forest plots, Pareto fronts, etc. White background by default so the output is paper/conference-ready, with an opt-in cream background for the blog look. Install: /plugin marketplace add Mapika/nice-figures /plugin install nice-figures@nice-figures Repo (MIT, example images in the README): https://github.com/Mapika/nice-figures Built it for my own use, figured others might want it. Happy to take feedback or recipe requests. submitted by /u/Mapikaa [link] [comments]

Claude code - Cultivate your context window to get the max out of your tokens

Many times during the start of the session or when you have cleared or compacted the session, claude tends to read the entire codebase resulting in context window bloating. if your repo is large and/or if you are working with multiple repos it means your context window will have a lot of stuff which are not really relevant for the feature work that you are doing rn. Instead of claude having to read the entire codebase you have a map of your repos at different granularity and guide claude using claude.md file to read the map. this helps claude get the context better without the context window bloating. if you are working on typescript/javascript based repos you can check what i built here in this repo: https://github.com/justinjamesmathew/tokenmax-mcp the idea is to have three tiers of structural context loaded at three different times. The Registry is a small directory of every repo that is registered, with a short paragraph for each covering what it does, what stack it uses, where it lives, and when it was last indexed. It loads automatically into every Claude Code session via ~/.claude/CLAUDE.md, so Claude knows what exists from the moment a session starts. Per-repo codemaps are the second layer. Codemaps cover architecture, conventions, public APIs, and file purposes for one specific repo. These only load when the current task actually touches that repo. this compresses the input tokens 33x as measured by 1 of my active projects. Just-in-time tools are the third layer. When Claude needs precise information like exact lines or the current source, the tools fetch it on demand from the live file. There's a CLI version (codemap find, codemap read) and an MCP version with the same capabilities exposed in-session. Super curious to learn your thoughts. please let me know what you guys think about this. submitted by /u/LifeEducational [link] [comments]

I built a Cybersecurity MCP Server that gives Claude real-time recon capabilities

Claude has zero native security tooling by default, so I built a local MCP server that adds: - WHOIS lookup - DNS enumeration (with subdomain brute-forcing) - Nmap port scanning with service detection - SSL/TLS certificate inspection - Technology stack fingerprinting - Full recon mode (all 5 tools in parallel) You just tell Claude "analyze google.com" and it runs everything automatically. Built with Python + FastMCP. Runs locally so your data never leaves your machine. GitHub: https://github.com/gaoharimran29-glitch/Cybersecurity-MCP-Server Happy to answer questions about the MCP setup — it was trickier than expected on Windows. submitted by /u/Cold-Article-4502 [link] [comments]

Your AI agent is one tool call away from doing something you didn’t authorize. Here’s the fix.

The attack doesn’t come from your users. It comes from your agent’s environment, the emails it reads, the webpages it visits, the documents it retrieves, the database rows it queries. Every piece of external content your agent processes is a potential instruction source. And your agent has no way to tell the difference between data it was sent to process and commands it should follow. This is not theoretical. It is happening in production systems right now. Once you give an agent tools, email access, browser access, API calls, memory writes, the stakes change completely. A poisoned document doesn’t just return bad text. It tells your agent what to do next. And your agent does it. We tested this. Arc Gate blocked 100% of agentic tool poisoning attacks across 54 scenarios from ETH Zurich’s AgentDojo benchmark. 99% on 200 blind test cases from University of Illinois InjecAgent. 0% false positives on legitimate workflows. Arc Sentry caught a USENIX 2025 multi-turn jailbreak at Turn 3. LLM Guard caught 0 out of 8 turns on the same attack. The difference is architecture. Text classifiers read what the prompt says. Arc Gate enforces where instructions are allowed to come from. Arc Sentry reads what the model’s internal state does, before generate() is even called. If your agent touches the real world, you need a runtime governance layer. Finance agent demo — no signup: https://web-production-6e47f.up.railway.app/finance-demo Arc Gate — hosted proxy, one URL change: https://github.com/9hannahnine-jpg/arc-gate — $29/month Arc Sentry — self-hosted models: https://github.com/9hannahnine-jpg/arc-sentry — pip install arc-sentry submitted by /u/Turbulent-Tap6723 [link] [comments]

Cache miss in Claude Code costs 12.5× more than a hit. Here are 5 things you do mid session that quietly trigger it

Two numbers from Anthropic's prompt caching docs that explain most of your token bill: "5-minute cache write tokens are 1.25 times the base input tokens price." (source) "Cache read tokens are 0.1 times the base input tokens price." (source) That's the math: cache miss = 12.5× more expensive than cache hit for the same prefix. On a 50,000-token Claude Code session prefix (system + tools + CLAUDE.md + early turns), the difference per turn is real money — and most users bust their cache without noticing. Anthropic publishes the exact invalidation table. Cache is built in this order: tools → system → messages. Changes at any level invalidate that level and everything after it. So not all cache busts are equal — some flush only the recent messages, others flush the entire prefix back to your tool definitions. Here are the 5 actions in Claude Code that trigger this, ordered from "nukes everything" to "trims the tail": 1. Install or remove an MCP server mid-session — busts everything Anthropic: "Modifying tool definitions (names, descriptions, parameters) invalidates the entire cache." MCP servers register tool definitions. Adding claude mcp add or running /mcp during an active session changes the tools block at the top of every cached request. Everything downstream — system, CLAUDE.md, full conversation — gets re-written at 1.25× cost. Fix: install all your MCPs at session start. If you need a new one mid-task, finish the current task, /clear, then add. 2. Switch model with /model — cache namespace changes entirely Caches are per-model. Switching from Sonnet to Opus mid-session doesn't migrate the cache; the prefix is processed fresh on the next turn. There's no warning in the UI. Fix: pick the model at session start. Use Opus for planning, Sonnet for execution — but split them into separate sessions, not one session you keep flipping. 3. Edit CLAUDE.md while a session is open — busts system + messages CLAUDE.md content is delivered as part of the system prompt area. Anthropic's invalidation rule: any system-level change invalidates the system cache and everything in the messages cache that built on it. Edit a single line in CLAUDE.md, save, send the next message → prefix below your CLAUDE.md gets re-written. Fix: edit CLAUDE.md between sessions, not during one. If you must edit mid-session, /clear first so you don't pay to re-write a long conversation. 4. Toggle fast mode (Shift+Tab) — busts system + messages Anthropic lists "speed setting" as a system-cache invalidator: "Switching between speed: 'fast' and standard speed invalidates system and message caches." Every Shift+Tab toggle re-writes the cached prefix. Fix: pick one speed at session start and stay there. If you toggle 3 times across a session, you've paid the cache-write premium 3 times. 5. Paste an image mid-conversation — busts messages only The lightest of the five. Per the invalidation table: "Adding/removing images anywhere in the prompt affects message blocks." Tools and system stay cached, but the entire messages prefix is processed fresh. Fix: this one is often worth it (screenshots are high-signal). Just know that "let me drop a quick screenshot" isn't free — you're paying ~10% of your input bill to add it. The general rule Anthropic's exact phrasing: "Cache hits require 100% identical prompt segments, including all text and images up to and including the block marked with cache control." 100% identical. Not "mostly the same." One character changes in your CLAUDE.md, you pay 12.5× to process the next turn. This is why every Anthropic doc tells you to lock your configuration at session start. Sources Prompt caching — Anthropic API docs (every quoted number is from this page) How Claude remembers your project — Anthropic Claude Code docs Best practices for Claude Code — Anthropic submitted by /u/lawnguyen123 [link] [comments]

I made a Claude Skill - CodeLedger. This is just to reduce code read redundancy, and save tokens.

I've been using Claude Code for some time and it does eat up a lot of my tokens. I wanted to find a way that Claude saves the context of the files it touches, and in my future prompts, if it sees that it needs to modify a specific file based on the context, it doesn't go around reading the entire codebase again. So I've made a pretty simple skill. Assuming you're running this skill for the first time - Claude works normally, documents every file it reads, then builds the index after the task is done. Once there's a database to use - Claude reads the index first, identifies the relevant node files, and works with full context without touching files it doesn't need. Here's the skill - https://github.com/kindaRai/CodeLedger/tree/master ; I would love to know what you guys think about it, or if there are other skills which does it better! submitted by /u/BurningCharcoal [link] [comments]

Built a free self-hosted web terminal interface for Claude Code CLI

https://github.com/HalfLucid/Claude-Code-Cli-WebTerminal I like using claude code CLI from my phone sometimes but I had issues with the method I was previously using (tailscale + termius) and decided to make something that works better for me. Sorry Windows only at the moment but feel free to fork/copy do whatever you want. I just wanted to share what I made in case someone else would like to use it too. Built this using claude code just specifying what I wanted If you do like it or have any feedback for things I should add let me know. Screenshots are in the github page. Would love to hear what you think. -- Browser-based terminal over WebSocket with persistent, multi-tab sessions. Built for running Claude Code from any device — including mobile. ASP.NET Core minimal API backend + xterm.js frontend. Connects your browser to a real PTY (pseudo-terminal) on the host machine. Features Persistent sessions — PTY stays alive through disconnects, screen sleep, network loss. Reconnect and pick up where you left off. Multi-tab — run multiple shells or Claude Code instances side by side with a tabbed interface. Claude Code integration — launch Claude Code directly into any configured project directory. Open new or resume existing sessions. Mobile-friendly — touch-optimized button overlay with configurable keys (Enter, arrows, Ctrl combos, Esc, Tab, etc.) and paginated layout. Native text input — uses a virtual text entry layer that preserves your device's autocomplete, swipe typing, dictation, and IME support. Edits are transparently bridged to the PTY, so the full mobile keyboard experience works naturally in the terminal. Session ring buffer — 256KB buffer replays recent output on reconnect so you never lose context. Basic auth — credentials set on first run, encrypted with Windows DPAPI. Startup toggle — optional Windows startup registration from the main screen. Configurable buttons — reorder built-in buttons, switch Claude model/effort, and create custom buttons that send any text to the terminal. Custom buttons can trigger slash commands (e.g. /review), full prompts (e.g. summarize all changes, commit, and create a pull request), or any terminal input. Usage PowerShell — click "PowerShell" on the main screen to open a shell tab Claude Code — add a project (name + directory), then use "Open Claude" or "Resume Claude" Tabs — use the + button to open more sessions, click tabs to switch Mobile — tap the arrow button on the right edge to expand the button overlay for touch-friendly input Remote access — access from other devices on your network at http:// :7681 (works great with Tailscale) Custom Buttons The button overlay on the right side is fully configurable via the Buttons settings on the main screen. Reorder — move any built-in button up or down to change its position Model / Effort — built-in popout buttons to switch Claude's model (opus, sonnet, haiku) or effort level Custom buttons — add your own buttons with a label and a command string Custom button commands are sent directly to the terminal as text input, so they work with anything the active shell or CLI accepts. Examples: Label Command What it does Review /review Triggers Claude Code's review skill Compact /compact Compresses Claude Code context Commit summarize all changes, commit, and create a pull request Full natural language prompt sent to Claude Code Status git status Runs a git command in a PowerShell tab submitted by /u/halflucids [link] [comments]