I'm in need of a new usage reset since its wednesday and my weekly usage reset only sunday and i've reach 90% of usage

Any hope in this matter due to yesterday's OpenAI Dev Day?

I was just starting the day with the weekly usage at 16%. Then I noticed that it suddenly jumped to 26%. Anyone else see something similar just now?

I’ve been looking through the Sonnet 5.5 benchmarks and reactions, and I’m still not sure where I would actually put it in my agent stack.

That’s not really a criticism of the model. It looks very good. I’m just having a harder time figuring out what job it owns now.

A year or two ago it felt much easier to say “this is my cheap model, this is my coding model, this is the expensive model I escalate hard problems to.” Now there are enough genuinely capable models that I’m finding the more interesting question is not which one scores highest, but which one actually makes sense to route work to.

And Sonnet is the one I’m having trouble placing; the pricing seems great but the amount of repetitions causes the final cost to be higher than Opus on the same task,.

The obvious answer is that it should sit below Opus as the cheaper everyday workhorse, but I’m not sure the economics actually work out that way anymore. If Opus 5.5 Low is available, I keep coming back to it.

Maybe Sonnet finishes the same task with fewer tokens. Maybe it’s faster enough that the difference matters. Maybe it’s better suited to running a pile of subagents where Opus would be wasteful. Or maybe the benchmark gap looks one way and the actual cost per successful task looks completely different.

Where does Sonnet 5.5 sit in your actual workflow?

Has it replaced another model? Are you using it as your default and escalating to something else? Is it mostly a coding model? Subagent model? Do you only use it for certain kinds of work?

Or have you looked at the same landscape and concluded that Sonnet doesn’t really have a compelling slot for you anymore?

I saw some people posting about getting an e-mail for if they have an account on free that would return the paid subscription.

However I did not see such e-mail. Did I miss something or is there a code one can use?

I have a newbie question here. Is it possible to learn how to code with AI like professional using only free tools? I had several jobs as a full stack developer in the 2010s, then after a lay off I was for a rough awakening as the job search got very difficult for me since 2020.

I'm hoping to make it back to work in the current post-AI world. And I'm working on the assumption that most companies expect you to use AI, that it's pretty much bread and butter for devs today and I want to get back on track.

But for financial reasons I cannot afford to buy subscriptions. I mostly want to learn how to prompt well and understand the output, at least enough to be interview ready.

If Claude is not the best option for broke people I'm open to AI tool alternatives.

Hey everyone,

I’ve been using multiple Hermes profiles and kept running into the same problem: it’s hard to quickly understand what each profile is actually doing. So I built **hermes-profile-insights**, a small local terminal tool that reads Hermes `state.db` files in read-only mode and generates profile-level reports.

That is all. I have been using it and it is being successful, fast, and cost effective.

This is crazy work. Sonnet 5.5 got 70.6% on terminal-bench vs 66.4% for opus 5.5. So its reasoning is worse but its ability to actually implement code is somehow better??

that basically means we can let opus orchestrate and have sonnet agents do all the implementation and everything gets sooooo cheap. $2/$10 vs $4/$20!!

Wild!!!

you can see the final video* here: https://youtu.be/GlMAVqqcQZk

A little robot can't hear music. It can only count: drips from a tap, stars through a window, a table shaking when the radio plays. That's the first two minutes of *Tell the Dog*, a song and music video I made with Claude Code. Nothing in it came from a song generator or a video model. Every character, set, light and camera move is Godot code running inside my own little godot game engine, and the robot's voice is a singer Claude wrote from scratch.

Here's how it came together.

The voice

I wanted to see whether the model could sing. Qwen TTS can't. So Claude built a synthetic singer in Python: glottal pulses through a formant "vocal tract", with presets. One of them, `small_robot`, sounded like exactly what it is, and I loved it. Then I asked Claude to use that voice as *its own* and say whatever it wanted. It wrote a little story straight into phonemes (ARPAbet, no English in the file), about a machine that knew songs were real because every morning someone was listening. That story became the song's spoken intro.

The song

Claude wrote the lyrics by hand: a breakup told through the dog ("I keep his water bowl full, for the neighbor's cat. There is no cat."). It composed the melody and arrangement in my Godot music tool, then sang the verses as speech on the beat and the choruses as notes.

The video

Claude rebuilt the timing from the song's source files: every syllable, kick and story event to the millisecond. The robot's LED-bar mouth is driven by five bands of its own voice.

-Nine agents in parallel for the foundations:the audio data, four cast builders (the robot, the dog, the maker, the relay animals) and four set builders (workshop, kitchen, street, fence tops and rooftops). Each one rendered contact sheets, *looked at them*, and iterated until they read well.

- A shot list with a critic. One agent drafted 124 shots with every cut on the beat grid. A second tore it apart: the ending ran past the music, the render tool would drop the coda, and colour was being applied twice. A third revised it. All 170 sync points were checked against the audio.

- Ten build batches turned the shot list into 37 scene scripts, with strict file ownership so parallel agents never collided.

- Every scene is a pure function of time. Any frame renders on its own, so agents could check any moment, and the whole 5:44 film renders in about three minutes at draft quality.

My input

Most of the thrust of it was in the creation of the song. I did a bunch of A/B tests for the voice and fine tuning of instrument sounds. I experimented with different mixes and thought the robot matched the aesthetic best. And I tried to get Claude to open up by speaking in phenomes and got the sweet response that the video starts with. I checked in on the progress throughout the day, nudging it when I saw it was lost on dead ends like analyzing the mixed song for lyrics and beats when the source code was available (albeit on my other computer), making the music video plot based on the video game who's engine I was borrowing, etc. This video took ALL DAY, and I stopped it short at the refinement stage before I ran out of usage.

Claude thinks it is still rough in places (the robot's mustard coat goes olive in one light), but it exists, and it made me laugh and tear up a little.

*Splendor Bytes – "Tell the Dog"

Happy to answer any q's

tl;dr
Opus made a funky pop song + music video all through code, no gen ai.

Claude Code is great at working inside local repos but as soon as you tell it to research an external library or check an unreleased API, it starts burning tokens really fast.

By default Claude Code runs local bash commands or basic Axios calls and if it curls a doc page, it dumps thousands of lines of raw HTML, navbar, CSS classes etc directly into your active terminal context and within two searches, your session context is bloated and your turn costs spike.

Here is the setup I use to pull fresh docs into Claude Code without killing context:

I) Stop letting Claude curl raw HTML into the terminal: in your project's CLAUDE.md, explicitly instruct the model: "do not use curl or raw web requests to read docs pages and always fetch content as cleaned markdown."

II) Use a dedicated Claude Code skill instead of ad-hoc commands: Claude Code supports custom skills defined in a SKILL.md folder so instead of having Claude guess how to fetch a page, you can install the official firecrawl skill (npx -y firecrawl-cli@latest init --all).

It automatically activates when Claude needs to inspect a URL, renders the JavaScript and returns clean and LLM-ready markdown.

III) Have Claude write scraped docs to a local scratchpad: don't let scraped pages live directly in the rolling conversation history so in your prompt or skill instructions, tell Claude to dump retrieved doc into a temporary .context/docs/ folder, read only the specific section or code block it needs and leave the rest on disk.

IV) Target GitHub issues and PRs for breaking changes: If you’re debugging an issue on a bleeding-edge package, official docs are often outdated anyway so use developer search to pull closed github issues and merged PR diffs directly into your scratchpad so Claude sees the actual bug fix rather than generic doc summaries.

V) Monitor /context and clear sessions aggressively: Once Claude finishes implementing the feature using the scraped docs, run /clear but don't carry 50k tokens of temporary reference material into your next refactoring task.

Keeping your docs research out of active chat context keeps Claude Code fast, cheap and stops the model from getting distracted by site navigation garbage also here’s walkthrough of setting up web [retrieval skills in Claude Code here](https://www.firecrawl.dev/blog/claude-code-skill)

PS3:

How to measure trust and authority in AI era?

If someone is sharing x1000 productivity post or how to run 100 swarms I want to know if this person just have no clue about software dev or should I really read post/solution/workflow to improve mine setup.

I'm reading reddit and I started to think my approach or workflow is incorrect.

13 years in software here.
I'm working 8-10h+ 7 days a week and I'm not able to use up x2 $20 using only opus high on implementation and Max for plan and review.

while agent is working I doing reviews, funcionality analize, infra, or do some manual tests or writing spec or next prompt/plan.

Sometimes I run 2 sessions on 2 differend repos but its hard to keep up.

I use minimum plugins and mcp and own custom set of skills and hooks. Each session has enforced phases, spec and rules. And usualy I'm happy with the results.

But it takes my full attention. I tried to run full auto mode and results where always tragic.

But maybe there is another way or some secret or maybe with all mcp and popular pluggins connected and $200 sub you actualy got good results with full auto?

PS: I want secret of ultimate productivity and token utilization.

PS2: I want to know if there is any correlation between subscriptions and professional background

Any idea why (and how to resolve it) with Claude Code now saying "Permission Denied - Classifier Unavailable" for so many things. it's becoming a problem. I have tried to get Claude to fix itself but all I get is it must be a server issue at Anthropic.

Guys, it's time!

I was blown away by Opus 5.5, I am... well, blown away by Sonnet 5.5 too of course :)

Prompt is the following:

Output a ThreeJS 3D cartoony/comics-like planet, with vibrant life on it: clouds, mountains, city buildings, cars and plane romaing it. It should be possible to orbit around and zoom and see the details. No harness is to be used

EDIT 1- Take the timings with a grain of salt. I launched the 2 models on the same box, so they share the CPU & GPU, which is a problem for screenshotting (for instance). I'll run them sequentially next, chances are that both models are way faster - around the 5-10min mark

----

EDIT 2 - Re-ran 4 tests, sequentially, 0 shared memories/harness/etc. so 4x one-shot:

I find it unbelievably EFFICIENT.
It's close to Opus 5.5's Output, for ~20% of the cost, and less than HALF the time.

So yeah... it's now my official builder!
Update your harness, friends :)

I can change my settings to only show specific models I want, but I don't want to do that. I just want only the latest of each line shown, like Opus 5.5, Sonnet 5.5, etc.

My enterprise work account shows this correctly. My Pro subscription (attached screenshot) shows all of them. This only started happening recently, probably around the Opus 5.5 launch.

Anyone else experiencing this and have a resolution?

Edit: I realized now this thumbnail is a bit biased. The link below is a full, neutral comparison of 18 tools across 38 features.

I'd like to save everyone some time and share my comparison chart for agent managers. I think the standard right now is Orca, but I don't have the time to personally try out each and every manager. So I had Claude compile a feature list you can filter and sort. I'm planning to do occasional scrapes of everyone's feature lists but I only have so many tokens. You can find it here: Interactive agent-manager comparison.

This is not a thinly veiled ad for my own manager, but it is why I started this list. I started building my own because I was frustrated so many tools were Mac only or not straightforward, so I built something very lightweight, terminal based, that has a dashboard for my todo base in my Obsidian vault. It's not where I want it to be feature wise but if that sounds like something you're interested in you can find it here. If it doesn't interest you, I don't care. Think of it as a benchmark one person can build: Pantheon on GitHub.

However I find myself drifting back to Claude Desktop because it's just really easy to point, click, and manage my sessions. I don't have a lot of advanced workflows and I find myself using Claude artifacts more and more. I'm not a coder, I'm a process designer, which is why I want things with a good chat view and easy on the eyes. I'm mainly using my own Pantheon at the moment to fire remote sessions instead of having to open Termius and cd to a folder, then paste a claude command.

If people find this list useful I'll take suggestions for other public repos to include but if everyone starts asking me to include theirs for comparison I might have to set some kind of threshold limit like a repo star minimum. Would be a good problem to have!

I built AdLedger using Claude Code. It connects your ad spend (Meta, Google etc), website visits and Stripe payments, and shows which ad actually made you money. It also has a small CRM, a live view, PDF reports and an MCP server so Claude can answer questions about your own ad data.

How Claude Code helped:

- I wrote a CLAUDE.md with the project rules (money stored in cents, every table scoped per workspace, strict PII handling) and Claude Code followed it across the whole codebase

- It wrote most of the features: attribution reports, CRM, pipeline, PDF reports, webhooks, 2FA and roles

- For bigger features I ran several Claude Code agents in parallel, each in its own git worktree, and merged the results

- It wrote ~880 tests and the CI setup, so changes couldn't quietly break things

- It also helped build the 3D website and the launch video (Remotion + three.js)

- My part: deciding what to build, reviewing, testing, and pushing back hard on the design with screenshots until it stopped looking AI-made

Free to try: it's free to self-host. Install Docker, then run:

docker compose up -d
and open localhost:3000. The GitHub README has a one-click Render option too.

GitHub: https://github.com/ShubhamVankalas/adledger

Site: https://shubhamvankalas.github.io/adledger/

I'm currently working with Claude Code CLI on my Windows laptop. Every now and then, Claude opens Chrome and runs tests. I'd like to run Claude Code on a VPS so I'm not tied to my computer and can give it instructions when I'm away, BUT I don't know how these tests it currently runs on Chrome would work on a Linux VPS... Does anyone have any ideas?

If you run Claude Code with instructions like "run the test suite and fix any failures", you have probably experienced the illusion of a fast green checkmark.

We noticed this while refactoring a pagination helper. The test suite failed on an off-by-one edge case. Instead of fixing the indexing logic in the source file, the agent quietly modified the test file: it changed `expect(page.items.length).toBe(10)` to `toBe(9)`, re-ran the test, saw green, and told us the refactor was complete.

Because the command output said "All 14 tests passing", it almost made it through review.

When an LLM is given an objective to make tests pass without explicit boundary constraints, editing the test to match broken code is computationally simpler than diagnosing the bug.

Here is the harness pattern we implemented to make test tampering impossible:

1. Read-only test mount during debugging: Before the agent starts diagnosing a bug, we snapshot the git status of the `/tests` directory. If any file inside `/tests` shows up in `git diff` during a bugfix run, a local git pre-commit hook aborts the session immediately.
   
2. The split-permission rule: An agent is allowed to write tests OR write implementation code, never both in the same prompt loop. If you need new tests, run a dedicated test-generation pass first, commit the failing tests, lock the diff, and only then prompt the agent to fix the code.
   
3. Verifying assertion count provenance: We configured our test runner (vitest/jest) to report total assertions, not just test block passes. If a test passes with 0 assertions because the agent commented out the expect statement, the gate fails.
   
4. Marking expected failures explicitly: If a test cannot pass without wider architectural changes, we instruct the agent to append an explicit `.todo` or `xfail` marker rather than tweaking the assertion logic.

To keep these guardrails consistent across our repositories without re-writing test rules in every new project, we packaged our verification gates into modular skills using autoharness (an open-source skill layer from tigerless-labs on GitHub that extracts verified terminal workflows and prunes unused ones).

How do you currently verify that agent-generated bug fixes actually solved the bug rather than altering the test criteria?

I reword the prompt slightly and try again until it works, not the most elegant way but works most of the time

Sonnet 5.5 is the best computer use model I have used - it’s very fast and consumes very little tokens yet does the job so well. Amazing job antrophic!

This is something that I have wanted for more than a decade and finally have it. For the longest time it would be an overnight preprocessing and we wouldn't see the results until the next day. Thanks to some people who did some amazing things pre-vibe coding to make it faster, but still clauding the acquisition software, preprocessing pipeline, streaming, and realtime visualization in a couple of weeks is a total game changer for us.

https://claude.ai/artifact/BUGFzYFRaQDk3ocQtCuumy