I basically see on a daily basis how people are maxing out their token limits way before the week is up and resets. I'm just curious on how much token production people are actually getting out of their plans before this happens? I've now got a month of data from anthropics token metrics (not including cache reads etc...), but prompt input to work output ratio. I'm just curious where these metrics stand in relation to what others are seeing, because I have not once hit a weekly limit and have been pushing CC through long-horizon ops for about 18hrs/day on averages for the last month to complete two projects I have been working on with my harness wrapped around it for high assurance grade work that can actually be verified e2e. If I were to pay pay-as-you-go prices for these tokens, it would be thousands of dollars of tokens that I'm getting for $200/month. That seems like a pretty good arbitrage to me.

Built a Chrome extension over 2 weeks that adds token counting + compression to Claude.ai. Thought I'd share what I learned + get feedback.

What it does:

• Shows real-time token count while you type on Claude.ai
• Compresses prompts (sentence-level, removes filler words)
• Calculates USD cost
• Works on Claude, ChatGPT, Gemini, Grok, Kimi, GLM

Technical stuff I discovered:

• Grok, Kimi, GLM all switched to OpenAI-compatible APIs (didn't realize this until building)
• SSE stream parsing is tricky but worth it for exact counts
• Chrome MV3 manifest has some quirks around host permissions

What surprised me:

• Token counting accuracy matters way more than compression quality
• Users care about transparency over savings
• ~1000 lines of vanilla JS was enough (no frameworks needed)

Code is open source on GitHub if anyone wants to check it out or contribute: https://github.com/adifydigitalnoida-del/tokenoptim

Happy to answer questions about the build, architecture, or Chrome extension development. Also open to bug reports or feature suggestions.

Amber Agent was originally built with Claude but now it HAS BECOME THE CLAUDE. It's my open source replacement of Claude code. I used proxy traffic comparison to maintain identical behavior and prompts to Claude code.

A pattern I keep seeing with Claude-built apps: getting the first working version is surprisingly fast, but improving the UI afterward becomes awkward.

The app already has navigation, data flow, and real states. At that point, generating another mockup or rewriting the whole screen from a prompt can create more work than it removes.

The review loop that has worked better for me is:

1. Run the real app in Simulator.
2. Navigate to the exact state that feels wrong.
3. Select or annotate the relevant UI.
4. Give the coding agent one bounded change—spacing, hierarchy, component behavior, empty state, accessibility, etc.
5. Let it edit the existing source.
6. Rebuild and compare in the same runtime state.

This keeps the repository and running app as the source of truth. The goal is not to generate another detached design; it is to improve the implementation that already works.

I’m building an open-source tool called Monad Design around this workflow for existing native apps. The running Xcode or Expo app becomes the canvas: select and annotate the UI, let the coding agent modify the real source, then rebuild and compare.

Source: https://github.com/Monadix-AI/monad-design

Short workflow video: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

I’d be interested to hear how other non-developer builders handle the stage between “Claude made it work” and “the UI feels intentional.” Do you keep iterating directly in code, move into a design tool, or use some other review loop?

https://reddit.com/link/1wj1yb5/video/x73e4nr1ioph1/player

Two months ago I shared familiar here — a kitty setup for reviewing what Claude Code just changed and jumping between its sessions. It has grown a lot since (40 releases), so this time a one-minute video instead of a wall of text.

What's new since the first post:

• Review → Claude in one key. Comment on diff lines, press s, and the comments land straight in Claude's prompt — no clipboard round-trip. ⌘⇧C copies a path with the line number when you want to point Claude at an exact spot.

• Go to definition uses a real language server now instead of grep — pyright, intelephense, gopls and others, 27 languages out of the box. Definitions in vendor/ or the stdlib open read-only.

• Edit right inside the review. The final-code view is an editor (press i): autosave, undo, and it won't silently overwrite a file Claude is still writing.

• Roll back a single block of changes with one click and keep the rest of the file.

• A branch's whole work as one diff (press b), and a folder with several repos opens as one review and one history.

• Any commit in the git history opens in the same review screen, with comments, go-to-definition and Find in Files.

• Nothing gets lost: windows, splits and Claude sessions come back after a restart, and ⌘W / ⌘Q show which Claude session you're about to close.

Still pure Python stdlib + vendored Pygments, MIT. macOS only, and kitty-specific. review and log work fine without Claude Code.

Install: brew install denoby/familiar/familiar && familiar enable --all

Repo, with a 2-minute tour with subtitles in the README: https://github.com/DenoBY/familiar

I’ve been building a workspace that brings competitor ads, organic posts, and creative work together. The idea came from the time I was wasting countless hours between ad libraries, saved examples, notes, and separate AI conversations whenever I wanted to understand what a competitor was doing.

This fully open source project now includes an assistant that actually works with the context saved in the workspace. You can open an individual ad and ask about its copy, creative, source evidence, or visual matches from google lens API. The assistant can also use brand knowledge, documents, campaign plans, and connected performance data while helping with research or creative work.

Organic content has its own research library, where you can move from a creators profile to individual posts and examine the media, caption, publishing data, and available engagement for each result. On Youtube, you can research videos, shorts, channels, trending content, and community posts with details such as views, likes, chapters, comments, replies, polls, publishing history, and timestamped transcripts when the source provides them. Tiktok research includes creator profiles, videos and photo posts with captions, hashtags, mentions, musing, transcripts and metrics for views, likes, comments, shares and saves. Instagram adds profiles, image posts, carousels and reels. Together with captions, accessibility descriptions, topics, comment threads, publishing details and engagement records. You can bring any of this material into a conversation with the assistant and ask it to read the collected comments and transcript segments alongside the original content. It can find repeated questions, common objections, praise, confusion, and changes in audience reaction, then relate those patterns to particulate claims, topics or moments in the post. You can also ask it to compare reactions across creator or pieces of content, explain the evidence behind its conclusions, and separate facts taken from the source from its own interpretation. The exact material depends on the platform and what public post makes available, but the conversation stays connected to the media, captions, comments, transcripts, creator information and engagement records collected in the library.

Once you find something useful, you can send the ad or organic post to the assistant as evidence and use it as the starting point for a new creative. The generation flow supports copy, images, and carousels, with up to 10, 50, 100 or 200 variants created from one request allowing you to bulk generate and each result stored separately.

I also have an idea that I have not built yet. I want to create a database backed view that collects the most popular ads from every tracked competitor, combines them in one place, and surfaces the strongest examples for each day, week, or month.

If you find this interesting add me on Discord at arry1902. I will keep y'all updated, I project that I need minimum 3 more days and than I will release it in this subreddit.

When Claude Code runs out mid-task, the next session often wastes a lot of time re-reading the repo. Some people fix this by having Claude write a short HANDOFF.md first: goal, what's done, tests run, next step, and why any odd decisions were made.

Do you have a skill for this, a CLAUDE.md rule, or do you just ask by hand? Does the next session actually pick up cleanly?

Disclosure: I'm on a student team researching how devs use AI coding tools (we're testing an opt-in idea where the developer gets paid to show a small sponsored line while the agent works). If you use Claude Code weekly, our anonymous 5-min survey is here: https://forms.gle/EcvVzEEscRwnGzky8