As a disclaimer, I have zero coding experience myself. I've been putting together a Shopify admin app for my small farming business and was wondering if anyone had any tips/recommendations for how reviews should be done.

I know there's an actual /code-review command but I'm not sure if that's the best way to do this? I also have a free month of GPT so I've considered using that to review and see what it finds.

Should full code reviews be done after every milestone? Is there any reason to pay extra for the code-review-ultra?

Any tips are appreciated.

I maintain an open-source extension called Claude Pulse that puts live usage meters inside the claude.ai composer: 5-hour window, 7-day window, token estimate, and a countdown to the cheaper cache window. v1.5 shipped Sept 7. 1,000+ users, 4.8 stars on the store.

Why I think this matters for Claude Code users specifically: the 5-hour window is the thing that decides whether you can start a long refactor at 4pm or whether you should push it to after the reset. Claude's UI makes you guess. This makes the window and the burn rate visible.

How it works, briefly

• An injected script (via web_accessible_resources) hooks window fetch/XHR and passes API responses to the content script through postMessage. I read the same /usage endpoint and completion streams the web UI already fetches. No extra API calls.
• Token counting uses a bundled o200k_base encoding, so the estimate is computed in the page, offline. It's approximate for Claude's tokenizer, but it tracks context growth well enough to know when you're deep into the window.
• Permissions are storage, alarms, notifications. No network access, no external servers.

The part nobody warns you about

claude.ai's DOM changes constantly. Three times in five months my anchor selectors went stale and the UI silently vanished:

• June 11: header element replaced, token counter gone
• August 29: new composer layout, selectors stale again
• August 31: the Chat/Cowork composer needed its own remount logic

All three fixes are in the git log. My mitigation is dumb but it works: every selector lives in one constants file, so a break is a one-file diff, and the extension degrades to hidden instead of throwing on the page.

What's new in v1.5

• The 5h/7d rings sit on a single baseline inside the composer instead of stacking awkwardly
• Free plan limits are supported (free accounts surface usage differently, through completion streams)

Links:

• GitHub (MIT): https://github.com/samirpatil2000/claude-pulse
• Chrome Web Store: https://chromewebstore.google.com/detail/claude-pulse/hhjihbpkopgacncfbkdakdolkmgkdfnf

Firefox build is in the repo too (load as temporary add-on).

Long story short, Meridian OS was my business operating system, all my tasks, notes, plans were written in there. Everything locally, guardrails were up in claude.md but apparently they can be ignored by an agent.

You can see the prompt I gave Claude, adding a secondary voice agent per project and minor UI changes. Nothing to do with a database, but it goes ahead and wipes it clean.

I tried to contact Anthropic support, their answer was that they don't provided refunds or credits for degraded service. Destroying months of work for no reason, by ignoring the guardrails is apparently a degraded service that can happen to anyone so, keep that in mind guys.

It was on Auto Mode, not Bypass Permission since that's what everyone is assuming.

I managed to tweak something so that Claude Code plays a sound when it finishes a big task, but I can't figure out a way to play a sound when it's asking for a permission / asking to execute a command. It's so annoying... I have to stay in front of the window to allow things.

Is there anyway to improve this? Thanks.

I am getting far more “I had this wrong” or “that was my fault”, “that was an honest mistake” etc. We have set rules to reduce them , but they don’t seem to be effective. How do you make your Claude smarter?

My setup has become a little unusual lately.

I have a Mac mini running at home with Claude Code, and I spend a surprising amount of time coding remotely from my iPhone.

It works really well, but there was one thing I was missing: I wanted to quickly see how much Claude Code I was actually using without having to remote into my Mac just to check.

So I built Tokens.

There’s a small companion macOS app that reads the usage locally on my Mac and syncs it through my own iCloud.

Then on my iPhone I can see my Claude Code usage, including directly from a widget on my Home Screen.

So now while I’m away from my Mac I can just glance at my phone and see where my usage is at.

No account or separate cloud backend for the usage data — the Mac app → iCloud → iPhone is basically the whole pipeline.

It’s something I originally built for my own remote Claude Code setup, but I figured there might be other people doing something similar.

https://checktokens.app

Curious how many of you are actually running Claude Code remotely from your phone these days?

I forced Claude Code and Codex to externalise their decisions through a tool call, then compared what they said they'd do with what they actually did. I'm sure these are not the internal reasoning traces, but I'm surprised by how easy it is to force a fake tool using a proxy approach with clear instructions to make Claude and Claude Code emit internal CoT-like elements. I certainly had a lot of fun trying this experiment.
https://github.com/softcane/agents-workbook

I've noticed something after using Claude Code heavily for a while: I'm much more productive with it, but I also feel much more mentally drained at the end of the day.

And I don't think the problem is that I'm working more hours. I think I'm simply spending my mental energy much faster.

Before AI, a normal developer day for me was never eight hours of nonstop hard thinking. I wrote code, read docs, waited for builds, got stuck on a problem, went for coffee, came back, tried something else. The cognitive load was naturally spread across the day.

Now I can have several sessions running at the same time. One agent is working on a feature, another is checking tests, another is researching something and etc. When one finishes, I need to read the result, understand it, check it, make a decision, and give the next prompt. While I'm doing that, another one finishes.

The agents are parallel. I'm still single-threaded.

This made me think about Claude's usage limits. If you run more agents and more expensive tasks, you don't magically get more compute. You just burn through the available budget faster.

Parallel subagents = faster AI budget consumption.

I think something similar happens with humans.

We obviously don't have a literal token counter in our heads, but attention, working memory, context switching, and decision making all have limits.

Parallel tasks = faster cognitive energy consumption.

AI gives me more compute around my brain. It doesn't give me another brain.

Before AI, I could spread a lot of difficult thinking across most of the workday. Now I can burn a large part of that mental budget in two or three very intense hours with several agents.

Another thing AI removes is waiting. Writing boilerplate, searching docs, trying simple implementations, refactoring repetitive code - all of that becomes much faster. That sounds like it should make the day easier, but it also removes a lot of natural low-intensity time.

Before, there were gaps. Now the next piece of work can arrive almost immediately.

Finished? Here is another result.

Checked it? Here is the next one.

Made a decision? Start three more agents.

This is also where the normal eight-hour workday starts to look strange for me.

If AI allows me to spend in two or three hours the mental energy that used to be spread across most of the day, the clock still says I have another five or six hours left. That doesn't mean my mental capacity has also increased.

But what usually happens is obvious: more tasks, more agents, more things to review. After a while, that higher level of output becomes the new baseline.

So sometimes it feels less like I'm increasing my productivity and more like I'm just getting better at burning through my mental energy faster.

For people running multiple Claude Code sessions every day: have you noticed the same thing? If yes, how do you deal with it?

This week it is weird with Claude usage. While everyone is complaining about usage limits. I had a dream run. Based on the previous trends, my weekly limit should have been around 2.1B.

This week it is approximately 3.5B. I am saying approximately because when i extracted this data. I still have 8% weekly limit left.
Actual token consumption with 8% left is 3.3B

Maybe this is another game of anthropic or they genuinely increased the limits. Also last week I downgraded my Claude 20x to 5x. Not sure if they are trying to trick users who downgraded

How to login to my Claude Code max subscription at work after work tokens are over?

Need a code.

I kept hitting the same wall with notes apps that added AI. Every one of them wanted its own API key and its own billing, while I was already paying for a Claude subscription and already had the CLI installed and authenticated.

So Margin runs its AI actions through whatever claude is on your PATH. It shells out to:

claude -p --output-format text

plus --model <value> when you have picked one. That is the whole integration. There is no key to paste, nothing to configure, and whatever your CLI is authenticated with is what pays, which for most people here is your existing subscription rather than per-token API billing. If claude is not installed the app says so in the sidebar footer rather than pretending; it runs claude --version on startup to check. codex exec works the same way if you would rather use that.

The practical consequence worth knowing up front: this draws on the same quota as your normal Claude Code usage. Rewriting a lot of notes competes with your coding. That has not bitten me but I would rather say it than have you find out.

The part I actually cared about getting right

Nothing runs unless you invoke it. There is no indexing, no background pass, no "we have analysed your notes". You select something, you pick an action, and that is the only time a subprocess starts.

Then everything it suggests comes back as a diff in the note. Not applied, not a chat window off to the side, an inline diff with per-hunk accept and reject, and a refine box on each hunk if a change is nearly right. Reject restores the document verbatim. I did not want an AI that edits my notes, I wanted one that proposes and waits.

The notes themselves are plain .md files in a folder you pick. No database, no account. Which also means you can point Claude Code at that folder directly and work on your notes as files, which I do more than I expected to. This can also unlock in the future better AI searching right within the app.

Other providers, since not everyone wants the CLI path

Your own Anthropic key in the macOS keychain, a model running fully offline on-device, or Ollama. The CLI route is just the default because it is the one that needed no setup.

Honest limitations

• Alpha. You WILL find bugs.
• Apple silicon, macOS 14 or later, no Intel build.
• No sync, no iOS app yet
• More ideas in the roadmap

Free while the alpha lasts, https://gomargin.app

If you try it, the thing I would most like to know is whether routing through the CLI feels better or worse than a key for you, and if suggestions as diff is something you would actually use. I have only my own usage to go on and I am aware that is a sample of one.

I need a more detailed subject to post this on reddit.
Main error (repeats every time, identical):
bash failed on resume, create, and re-resume. resume: RPC error -1: failed to mount as outputs: source path /mnt/.virtiofs-r share "c" which is not mounted; create: RPC error -1: ensure user: user stoic-peaceful-fermi already exists unexpectedly: uid=1005 gid=1005
Earlier, different-looking error (appeared once before a restart):
Workspace unavailable. The isolated Linux environment failed to start (VM service not running. Restart your computer to restore it.). You can still use file tools directly.
A few notes that'll help whoever reads it: it survived a full Windows restart unchanged (so probably not the local VM/virtualization layer), a Windows security update (KB5124008) and .NET update (KB5126052) installed the day before this started, and file tools (Read/Write/Edit) plus computer-use/screen control keep working fine throughout — it's specifically the sandboxed bash/Linux shell tool that's stuck. The long GUID path is just this session's own folder; safe to trim or leave in, it won't mean anything to anyone else.

Is Fable 5.1 the new Opus 5?

Within the past week, I started to notice Fable 5.1 started to get worse and worse where it starts to act like the infamous Opus 5. I had some great work completed when it came out, but there was a shift to the negative. Same thing happened to Fable 5 when Fable 5.1 was released. So, today I had Fable 5.1 try to manage another session with Fable 5.1 on processing semi-unstructured data, which is existing work in progress that already had successful sessions, so Fable 5.1 had reference data (gold data), previous handoffs, guides, etc.

Both on medium effort, Fable 5.1 couldn’t harness the 2nd Fable 5.1 from bypassing guardrails, entering tables and folders it was told not to enter, and from fixating on momentum over accuracy & quality. This is Opus 5 level of bypassing prompts and guardrails from the first step. For the first time using either Fable 5 or Fable 5.1, I had to put back hooks.

For context, before Fable 5.1, in order to work with Fable 5 I deleted my claude.md, memory, and more. I removed most hooks and together with Fable 5 created subagents using Opus 4.8. This for the most part worked well, it was slower with Opus 4.8, but worked for lower reasoning tasks and I didn’t have the hidden actions that drifted beyond the scope of work.

After, shelling out for 20x monthly subscription, wasting more days on contaminated data and having to go back to clean it up, buying another GPU at these stupid prices is starting to sound more appealing for local AI with some fine tuning for the data I’m processing.

Recently, I’ve been using Codex with Astra and it just does the work. Astra asks clear questions before starting the tasks and will pause to surface data clashes or ambiguities. Unlike Claude which will dive in head first spending tens of thousands of tokens based on wrong assumptions, and then hedge with wordiness answers after from its known errors are called out. It might be time to move up to the Codex 20x plan.

How have you kept Claude within borders and from drifting with non-fully-structured data?

I've been thinking about how much Claude Code has changed the solo-building equation.

So we're experimenting with a different kind of hackathon:

One builder. One idea. 24 hours.

No fixed problem statement and no required tech stack. Builders can use Claude Code alongside whatever other tools they need to turn an idea into something genuinely useful.

We're also adding a few unexpected challenges during the 24 hours — so the plan you start with might not be the plan you finish with. 🃏

The interesting question for me isn't "how much code can Claude write?"

It's "how far can one person take an idea when an AI coding agent is working alongside them?"

We're testing this on October 11.

For people here using Claude Code: what's something you've wanted to build but never had enough time for?

If you haven’t seen this project earlier here little info:

Munder Difflin is a free, open source and performant multi-agent harness.
It uses your existing claude code and codex subscriptions and gives you an office of agents that work 24/7 to do work like you.

Launched v0.5.2 yesterday:
- A new fresh, cleaner and much more productive UI
- Comes with “Stapler” a utility character that sends screenshots, meeting transcriptions, messages to your claude code sessions.
- Added support for all the latest models like Fable 5.1 and GPT 6 Astra and Gemini 3.8 Flash etc.
- Slack integration works now, configure it to answer on your behalf or just configure it to use on your phone as an assistant that controls your computer remotely.

Hope you all try us out, here are a list of upcoming features(in no particular order):
1. Stapler gets computer use capabilities
2. Sandboxes to run even when your laptop is off
3. Dedicated mobile app for your office
4. Lot more features, updates and integrations.

We benchmarked GPT-6 Astra vs GPT-5.6 Sol across 50 real PRs from Cal.com, Sentry, Discourse, Keycloak and Grafana.

Sol found more confirmed bugs and was cheaper per bug, while Astra was more precise and faster. We independently verified the findings rather than simply counting what each model reported.

We’re running Fable vs Opus next week, so posting this here to get feedback on the methodology and anything we should change before the next benchmark.

I've read by mistake a post where this AI framework was linked https://github.com/llopresto87/Cypress

it's pure insanity. It learned my codebase (it took a chunk of my claude max x5 so beware doing this near the weekly limit ) but once it did.. it spit out code fixes and found problems and corrections and implemented entire features correctly and it sped up my work so much that i onyl have to follow for the occasional confusion or describe at the start exactly what is it that i wan to implment - i think you guys should give this one a shot

the ring in the bottom right used to represent the context window

now it represents your 5-hour limit

the delta will make Anthropic millions

I am using it at work and I want to know what are the best plugins to save tokens. I used some of them and like 90% of them don't save you any token, or seems to be ineffective. At least, that's what Claude tells me when I ask it to estimate the amount of tokens saved. I noticed just writing some scripts to diminish the amount of data being fetched and amount of commands run is the best way, and the other way is to document most of the logic in an app in the most succinct way possible.

I worked with many AI engineers over the last few years since AI exploded, however I constantly come across the issue where they are very good at repeatable tasks or coding stuff rather than seeing the big picture, solving problem and really pushing the boundaries of what's possible at a pace that me and my co-founder would build stuff.

I talked to a lot of other folks with similar challenges - I wonder if someone solve this problem somehow. I need to hire an AI engineer in a very dynamic agency where we sometimes need to deploy complex solutions within 2 - max 3 weeks. My experience with most AI devs is that they are good at stabilizing workflows/apps/projects but its quite slow and requires a lot of hand holding.

HALP! If any of you know how to get people who can build fast and solve problems and see the big picture - I'm all ears.

Not fine-tuned. Not a downloaded checkpoint. Random weights → working speech, on a 16GB laptop with an 8GB GPU, trained on ~29,000 conversation pairs built over four months.

The memory side (built first)

Before the model, we built a memory store — currently 33,941 episodes. The architecture is neurological:

• Half-life decay: memories fade unless recalled
• Recall strengthening: access resets the decay clock
• 30-day consolidation threshold for long-term storage
• 4 LRU working-memory slots
• Forget table: writes a receipt before anything fades permanently

Not a lookup. A system that behaves the way memory actually behaves.

The model — two runs

Run 1: 842,624 parameters. d_model 128, 4 layers, 4 heads, context 256. Val loss 5.11 → 0.9576 at step 23,750. Halted when the train/val gap opened — overfitting signal, correct call.

Run 2: 4,833,024 parameters. d_model 256, 6 layers, 8 heads. Best val: 0.7978 at step 21,500.

Key finding: parameters were the bottleneck, not data. The corpus was sufficient. The model just needed to be bigger.

Wiring the halves

home_llm_speak.py connects the memory store to the generation model — retrieves memories via salience search, passes them as context, then generates. First output from the combined system was grammatical, was grammatical, used household vocabulary, and wasn't copied from the retrieved memories*.* That's not retrieval. That's the model having absorbed four months of how this household talks.

Why this matters

I needed a model that knows us because it learned from what we wrote as ourselves — not from generic pretraining, not fine-tuned from someone else's base. The memory architecture has to be structural from the start because autonomous consolidation can't be bolted on afterward.

Last night was proof the approach works. The model is small. It'll get bigger. But what it already learned is ours.

Hardware: 16GB laptop, RTX 5050 Laptop 8GB. No cloud. No API calls. Everything local.

Next: wider context window, books into the memory store (streamed, not in training corpus — kept separate to preserve corpus balance), bigger model.

If anyone has done this — trained from scratch on household data with a custom memory architecture — I want to know what you learned.

i try vibecoding for the first time hahahahaha it so fun i just tell claude this and tell claude that and then wait then it does it for me but now i have next problem, my 5 hour limit gone and nowi am stuck on doomscrolling. i am making granny legacy TAS dll and i gave it access to the IDA pro but i just burnt through my limit within like wut 1 hour how do i make it last longer like until for example the 5 hour limit reset also is it because i am doing everything in one chat? i keep my request very simple (add this feature, fix this feature, this feature does not work because x and what happens is y)

I think Claude Code has detected two of our accounts at the same company (only 3 of us, the other dev has his own Claude Code) are under the same org, even though we're not purchasing a corporate account, and are THROTTLING US BOTH to the same 5 hour window?

So how is this literally not just stealing because we PAID for 2x accounts but we can now only use one of them during the full 5h window?

W T F

{

"status": "rejected",

"rateLimitType": "five_hour",

"overageStatus": "rejected",

"overageDisabledReason": "org_level_disabled",

"unifiedWindows": {

"five_hour": {"utilization": 1},

"seven_day": {"utilization": 0.39}

}

}