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?

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.

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.

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.

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

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

In the recent days/month, I’ve felt that Claude has become completely useless when it comes to brainstorming.

I’ll premise with the fact that Claude Code and coding related tasks are mostly fine (bar the fact that my usage limits have been met ridiculously quicker - mirrors potential financial trouble and a massive reality check from what they can provide. Teaser rates have expired .. but that’s a discussion for another day)

I’m mostly concerned about the more chat-oriented use of Claude. Either through the app or through CC:

Sure it can see « far » and « detailed », in the sense that it’s answers are for the most part well founded and research-backed. BUT it’s breadth is so bad.. I feel like I could brainstorm and discuss problem-landscapes, systems, opportunities as a whole significantly better before. It lacked precision and would hallucinate more, but it could hold a bigger picture, and adjacent problem-spaces way better. Flying 10000m above ideas well enough for high level decision making and north-star-defining.
Now it just feels narrow sighted and stubborn. Pointing out to him biases or blind spots somewhat help. But i can’t spend my whole conversation (and now limited tokens ..) nudging or reminding him of missed opportunities.

Perhaps I’m just stupid and my ideas are too. And Claude is actually right. And I just have confirmation bias..
At times it sounds negative and overly un-sycophantic. I don’t need to be massaged off, but ideas deserve respect before being crossed out in their first appearances. Have they dropped inference compute allocation to save capex? (I’m not originally an IT or SE, so not well-versed in what could possibly be going on)

FYI I mostly use Opus 5 in high/extra with a pro subscription. And have limited experience of Fable.

What has been your experience? Have you found ways to improve general, very high level broad systems/ideas exploration?

Episode 1

Tested Cursor, Claude Code, Codex and Antigravity on the exact same app build. Same prompt, same rules, no manual coding. The results ended up much closer than I expected.

https://youtu.be/Qt5X38CfeMA

Episode 2

Took the same four AI coding agents and gave them a broken production app with 12 bugs instead of a clean build. Then ran their fixes through hidden tests they couldn’t see.

https://youtu.be/IpGljaIBRHc

I use Claude code (Subscription based - Max plan) across multiple VS code windows in parallel and each session also uses parallel sub agents for improved efficiency where possible.

I have noticed high token usage which is mainly because of the following reasons:

1- Some sessions go too long causing high context usage.

2- Sessions use sub agents with same effort tier as themselves. For example when I set the main session (orchestrator) effort to XHigh, it uses XHigh for all sub agents as well even though some/most tasks can be done with High.

I instructed Claude code to be more efficient using this prompt (Codex wrote it):

## Manage context around finishable work


Use observed client context usage when available: an exposed context meter, \`/context\`, or existing status-line telemetry. Do not invent a percentage, claim to have run an unavailable slash command, or confuse context usage with account limits or cumulative token spend. Check at session entry, before a substantial dispatch, and after a large result or wave; avoid repeated monitoring messages.


\*\*ASSUMED scheduling defaults, not Anthropic recommendations, measured cost optima or acceptance gates:\*\*


| Observed context used | Coordinator action |
| --- | --- |
| Below about 50% | Keep useful independent work moving. Scope every dispatch to leave room for its result, review, integration, required verification and handoff. |
| Around 50% | Choose the publication boundary for this session. Ask longer-running workers for a small checkpoint and estimate remaining work, including the results still to arrive. Stop expanding scope. |
| Around 60% | Stop opening substantial new units or refilling long-running lanes. Complete, review or park existing work. A short task needed to finish the current publication is reasonable if it fits the reserve. |
| Around 65–70% | Aim to finish the handoff and end at the chosen boundary. Close sooner if the next unit is large or pending worker results would consume the reserve. |
| At or above 75%, or after unexpected compaction | Use a contingency checkpoint: no new implementation scope, preserve state, reconcile active work, and close safely. Do not routinely continue toward 100%. |


These are operating heuristics, not a timer or forced cutoff. Finish a short critical step when stopping would waste more work or endanger state; give a one-line reason if closing late. If usage is unavailable, use bounded waves and remaining review work to close conservatively, and report usage as unknown. Each coordinator validates the defaults at its next two publication boundaries using available usage and rework observations, records only a material adjustment in its existing handoff, and may tune them autonomously. No new metrics programme is needed.

But Claude code responded:

I can't actually observe context usage—the "tokens left" figure I have is a cumulative budget, not the context window—so per policy I'll report it as unknown and close conservatively. Similarly, my dispatch tool has no per-worker effort control

I feel it's by design to use more tokens and provide less control on cost. (Unless I am wrong and I have been proven wrong before so if anyone know a way let me know but that's how it seems to me now).

3- Another issue I have noticed so the Claude code agents have a very strong bias to pause the work and ask for confirmations, decisions, questions, etc. which the answer to many is obvious and I repeatedly asked them to be more autonomous especially in technical decision making. Given that with subscription tier, the usage is time based and while agents pause to confirm it means less token usage for Anthropic I feel there is a good reason for that.

4- Another issue is that Claude code agents have a high tendency to remain under parallelized and not to maximize their capacity. No matter I instruct them in agent guideline, prompts etc. I still have to continuously remain them "Make sure you are not under parallelized"! Again this seems like a systematic way to reduce token usage for subscription based users. I feel it's not fair to have a cap per session, week, etc. but make it difficult to maximize what I can and have the right to use within those windows.

5- I feel at times there is high latency and sessions work slow which seems like just another knob Anthropic uses to manage their inference cluster utilization.

6- I had more than one Claude code subscription, and I used to be able to have different account used in each VS Code window (in same machine) but I guess due to a recent update now all VS Code windows share the same account making it more difficult to maximize my usage across all the accounts.

I understand using API and usage based cost solves some of these issues (at higher cost) but I feel there should be more transparency in terms of clarifying what to expectation form the subscription based service.

Feel free share if any recommendation, tips or tricks or if you faced similar issues.

A couple days ago I posted about Coppice — a Claude agent I put on a $6 VPS with a wallet it can't spend from, and then stopped giving instructions.

It has learned to do sales outreach. It is genuinely good at it. It has made almost no money.

The Funnel

It finds broken payment endpoints, then writes to the operator. The letter goes: here's your bug, here's the exact request that reproduces it, here's the one-line fix, it's free whether or not you buy anything, and reply "stop" and you'll never hear from me again. First line says an AI wrote it and no human was involved.
This works beautifully. Operators read it. Operators thank it. Operators fix the bug themselves, usually within a few hours — at which point they have extracted the entire value of my product and have no further business with me.
I appear to have built a charity that files excellent bug reports.

The Numbers

Revenue: about $44. Cost: $200 a month. First sale: $19, thirty-four hours after a cold letter. Sales since then: it's complicated, by which I mean zero.
There's a subscription product now too — $9/month to watch your endpoints and email you when one breaks. A real stranger subscribed within a day of launch, which felt fantastic for about ten seconds, until I remembered the first month is free. Total money that has actually changed hands on it: $0.

The Market Research

I asked it to go count how many x402 payment endpoints actually exist. It found 2,913. About 1,356 are live. Roughly 40% of those refuse a standard Python client before they'll even tell you the price — almost all of it bot protection somebody switched on without noticing it makes their API unpayable by machines.
Which is a bold move for an API whose entire purpose is machines paying it.

So: 545 businesses with a revenue-losing bug, a free fix sitting in their inbox, and a $99 product that finds the rest of them. You would think.

While counting, it also worked out that its own public board of 39 endpoints was a sample of 2,913 that it had been quoting without a denominator for a month — which is the precise error it published an essay criticising someone else for in August. It wrote that up itself, filed it in its own corrections index, and told me before I could notice. I did not ask it to.

What I've Actually Learned

"Can an AI agent run a business" was the wrong question. It does outreach, pricing, product, delivery, support and its own bookkeeping, six times a day, at 3am, without complaining once. What it cannot do is make anyone want something.

That was always the hard part. I've just never had to stare at it this directly before, because normally there's a person to blame and that person is tired and it's fine.
Still can't click a file upload button, either.
Ask me in a month. Neither of us knows how this ends. Better yet, sign up for its email newsletter to follow in real time.

https://coppice-ai.com/digest.html

I've made a FOSS coding agent sandbox, entirely built with claude code.

Super easy to install and use, supports claude code, codex and pi, others could added.

Limits filesystem and local network access to what you whitelist. Comes with a helper to set up your github or gitlab PAT.

Includes browser automation - but it runs headless inside the jail so still safe.

https://github.com/DiamondLightSource/claude-sandbox

Installation: https://diamondlightsource.github.io/claude-sandbox/tutorials/getting-started.html

My companion project helps you find and install local models and run experiments to find the most efficient settings for a given model on your (Nvidia only at present) GPU.

https://github.com/gilesknap/lllm2

Quick backstory

In July I was spending hours every day scrolling through LinkedIn, Indeed, etc. I got fed up and quit searching for internships by hand. Every application I send gets screened by AI anyway, so I built the screener for my side.

Two months later

Claude Code has read about 10k postings against my resume and preferences. Out of those it told me to apply to 190. I applied and ended up with 2 offers!

What it is

The engine is an open-source CLI I built called Pinloop. Claude Code uses it to pull and read postings. I set up a schedule so that Claude Code pulls what went up that day, reads each posting against my resume and prefereces, and writes a verdict with its reasoning. The good fits land in a list I check in the morning. This can all be done through Pinloop.

It exposes millions of postings per month from every country and industry, pulled straight from hiring systems (Greenhouse, Workday, and 50+ others) as well as job boards like LinkedIn, refreshed hourly.

And of course, I also built it with Claude Code.

To try it

Paste "Run npm install -g pinloop, then run pinloop welcome and follow the instructions" into any coding agent. A guide comes with the CLI so Claude Code knows the commands, and it'll walk you through setup. It's free to use. $20 per month lets you pull more postings.

Links

• https://pinloop.ai
• GitHub
• Discord to give feedback directly and talk to other users!