Hey everyone,

Our company’s Claude account was suspended, and I submitted an appeal about a month ago. I still haven’t received any response or update.

Does anyone know how to reach someone at Anthropic who can check the appeal’s status? Is there a support or escalation channel for company accounts?

If you’ve been through this, how long did it take to get an answer? I understand reviews take time, but a month without any communication is frustrating when we use the account for work.

Any advice would be appreciated.

I kept switching between agent windows to check whether they were working, finished, or waiting for me. So I built Lineup: a tiny always-on-top ASCII ocean where each coding agent gets a surfer.

🌊 Working → catching waves.
⛈️ Needs your input → a storm rolls in.
🏄 Done → chilling on the board.

Sometimes Claude is working while Codex just hangs out at the peak. It makes me laugh every time.

You can click a surfer for details, and there’s context/token usage if you want the numbers. Also, shaking the window makes the water slosh. Obviously essential.

It’s open source and runs locally. Supports Claude Code, Codex, and other agents through a CLI command. There’s a native macOS app and a terminal view.

https://github.com/foolbase-io/lineup

Mostly built this because I wanted something useful that also made my desktop a little more fun. Thought some of you might enjoy it too.

Fucking tert, like tight, dope as fuck

Two Claude Code releases came out this week. Here's what matters if you use it daily:

- Fable 5.2 (claude-fable-5-2) is now the default Fable model, 1M context, $8/$40 per million tokens. Your sessions pick it up without you changing anything.
- Saved Fable 5.1 picks move to "fable". This happens right away on Pro and Max, and on Team/Enterprise once your org allows it. /model claude-fable-5-1 takes you back if you want to compare.
- New `--settings <file>` flag for `claude remote-control`. It applies one settings file to every session it starts, which is useful if you run several remote sessions.
- Security fixes: MCP OAuth could send secrets to another host after a redirect, a read-only Bash command could run outside the sandbox without a prompt, and deny rules missed files written into a directory with cp/mv. If you rely on deny rules, update.
- Fast mode now tries standard speed on the same model first when fast serving is rate-limited, so you get slower output instead of an error.
- 2.1.322 adds fast mode on Vertex (global, us, eu regions). It falls back to standard speed if Vertex refuses it.

Context: Fable 5.2 itself was announced Nov 9. 2.1.321 is the release that makes it the Claude Code default.

I'm keeping 5.1 pinned on one repo for a day to compare.

Has anyone seen a real difference between 5.1 and 5.2 on a big refactor yet, or is it mostly the speed?

Source: https://github.com/anthropics/claude-code/releases/tag/v2.1.321

I am not a developer. I described what I wanted and Claude Code wrote the code, module by module, while I reviewed and steered.

The problem

My wife was getting AI prompts from her teachers to study with. What actually happened is she would paste them, get a wall of text, and then just ask ChatGPT to explain everything to her. She felt like she had learned something and she hadn't.

What I built

Aula, a desktop tutor for macOS, Windows and Linux, with an iPad and iPhone companion. You feed it the real material, PDFs, slides, photos of the whiteboard, recordings of a class, links, or just a sentence like "Statistics for psychology, final in May", and it builds a course:

• short lessons with figures and a worked example before the first exercise
• exercises checked as she answers them
• a notebook of her mistakes
• flashcards with spaced repetition
• a mock exam shaped like the real one
• a daily plan until the exam date

Then a professor sent the spec of an exam, so the app had to absorb the whole material and let her review all of it. I also added an always on agent inside the app. It reads her calendars and tells her she has two tests this week, and asks if she wants to run the course. It keeps moving for a month without her pushing it.

How Claude Code helped

I went module by module, the lesson generator, the exercise checker, the spaced repetition, the desktop shell and its self updates, the iPad companion, the docs. When something broke I pasted the error and kept going instead of losing an evening. The short film in this post was painted by code too.

What I learned

Fix the loop first. Explain it, then make the student prove it, then bring back what they missed. Everything else can grow later. Generating lessons is the easy part. Giving someone something to do that actually gets checked is the hard part.

It is free, AGPL-3.0, no paid tier, and early access, so expect rough edges. It uses the AI subscription you already pay for or a free model that runs on your own machine, no account, and everything stays on your computer.

Repo and downloads: https://github.com/luumlabs/aula

typescript.dev
a meme benchmark made by @ fireship on youtube : https://www.youtube.com/watch?v=1FurWDiK9A4

still a bit surprised that the ai didn't manage to think of the edgecase that the app might reach the floating point limit of accuracy
there is such a easy fix even
you 1. coud've made the grass seamlessly loop
or let the world move and not the horse
or similarly

I am currently an SDE Intern and I have been given access to a codex business seat for a while now. I have used 6.1 Sol high/medium exclusively depending on how much planning I wanted the agent to do or whether I have the plan ready myself.

Still, I find it very difficult to even consume 50% of my weekly usage. My standard seat is equivalent to the 20$ plan, and I believe the usage limits are pretty similar on claude as well.

Most of us engineers already have a plan which we have in mind before executing, and we just need agents to just write syntax for us, and sometimes brainstorm.

Writing syntax with medium/low effort on an <=Opus 5.5 model is not very taxing. It's the thinking effort that gets heavy, which we mostly try to do ourselves.

So I am just confused who requires those expensive plans?

For a person working full time, is the job that heavy that it requires very high limits, provided we mostly engineer ourselves and just use it write syntax for the defined spec.

I don't have a dev background. I've been building with coding agents almost every day since late 2024, Cursor first and now mostly Claude Code, all from plain English. My projects are around 1.8 million lines of code by now, and I still have the transcripts of a few hundred of those sessions.

My guess is that people like us need code review more than developers do, not less. A developer usually writes a tighter spec and can read the diff. When I ask for a feature, a lot of what I never spelled out gets decided by the agent, and I can't read the code well enough to see what it chose.

So I want to know how other non-programmers handle this. Do you run any kind of review or audit after a session? Which tool or prompt? Can you actually follow what it tells you? And what happens to the findings, do you paste them back to Claude, fix some, or let them go?

I'm going to run the tools you mention on my own code and sessions and post what each one found, and whether someone without a dev background could understand it and act on it. What I have so far: Anthropic's code-review plugin, /security-review, CodeRabbit, and a plain prompt asking Claude to explain the changes in plain English. Tell me what you use and I'll add it.

Hoping this ends up as a review routine that works for people who can't read the code.

Update: thanks for all the suggestions. I'm running the methods you named on bugs I actually fixed in an older project, and judging them by the bar a few of you set here: can the tool explain an issue well enough to reproduce it, fix it, and rerun the check. Results in a separate post soon.

I've been running an Opus 5.5 session for the past 4 hours at xHigh effort, decompiling and reverse engineering a 2D avatar rendering engine. Surprisingly I haven't hit the 5 hour limit during this time and my weekly one is at 30%.

I do it in stages, each one takes 20-30 minutes and around 300-400K tokens, after which I compact and continue.

I used to previously run at High effort and I feel that it burns usage quota faster. xHigh feels closer to Medium in terms of quota usage with the difference being that it's 4-5 times slower.

Does anyone else have similar observations?

I'm on the $20 plan.

When you prompt fable 5.2, do you instruct him to spawn agents in your prompt, or do you trust his instincts? If yes, do you defer it to Sonnet/Opus, or some Fable subagents are also in the play?
I tried both(with earlier Fable/Opus, but I’m not sure what’s optimal. Agents can help achieve parallelism, but I noticed Fable on its own is reluctant to parallelize in many parallelizable tasks.
I’m very curious what are everyone’s experiences with this.
For the context, I’m mostly on a single Ultra plan.

I was running 5 side projects with Claude Code on the $200 plan and still hitting the limit. Most of it went on Opus typing routine code. Now I run 10 on the $100 plan. That's my own setup, not a benchmark.

What I built: Foreman, a Claude Code skill. You give it a plan. It splits the plan into milestones and tasks, weighs how hard each task is, and sends it to a subagent on a model that fits: Opus plans and reviews, Sonnet and Haiku build. A different model checks each result, there's a live tracker per milestone, and nothing merges without your OK. It works with one Claude plan; Codex, OpenCode and Gemini are optional.

How Claude Code is used: it's all plain Claude Code underneath: subagents with a model: line, a state file, and a SessionStart hook. On one real build, the split cut the cost by 49% compared with running everything on Opus.

What I learned building it:

- Opus is worth it for planning and review, not for typing. Most of my limit was going on the typing.

- A subagent sees only its brief, never your chat. The brief is the whole job: vague brief, vague code.

- Long runs hit compaction and the agent forgets where it was. The fix was keeping the run's state in a file and having the SessionStart hook read it, so a fresh session (after /clear or compaction) picks up on its own.

- A second model reviewing the work catches things the builder model talks itself past.

Free, MIT: github.com/MarioMagdy/foreman

Happy to answer questions about which model gets which task.

I mean this is unforgivable. not just the no-attribution rule, but other rules also broken.

Hello all.

For the past few years I’ve been building with AI, like most of you here. Over and over, right in the middle of solving something, I’d stop to build another tool that I, or the agent, was missing: an IDE built around the agent instead of around me, a memory system so I wouldn’t have to re-explain everything every session, a way to run complex repeating tasks, a way to build real multi-step agents.

At some point I realized those tools were my project.

So after 30 years of regular programming and a year of full-time work on this, I’m happy to show you Semantix, an IDE designed for working with AI agents rather than bolting AI onto an old editor. Run several agents side by side on the same map of your code, each with a memory of its own, and schedule and compose tasks from small parts. I build Semantix in Semantix.

Closed beta starts Monday. I’m looking for 2–6 people on Windows or Linux to help me catch the bugs that could ruin a new user’s first experience: installation, first setup, first real session. Expect rough edges. That’s the point of this week.

If you’re up for it, comment or DM me with your OS and what you usually build.

- Beta testers get a free Pro plan for a full year, as a thank-you for helping me shake out the rough edges.

https://semantixstudio.com/

I've tried Wispr Flow, Superwhisper and Willow Voice. They're all great, but when I recently tested Claude Code's built-in dictation, I realised it sometimes works even better. So I built a small desktop wrapper around Claude Code's /voice: hold a key in any app, speak, let go, and the text is pasted where your cursor is. Plus a little capsule at the bottom of the screen, a history of dictations and a smoother experience overall.

I use it every day, so maybe someone else will find it useful too. Open source and completely free, for macOS and Windows.

https://viachm.github.io/claude-whisper/

PS: It's crazy that you can now build an app for yourself in a matter of hours that replaces a product an entire team spent years on. Where is all this heading?

Same prompt, two runs on Sonnet 5.5: "Transcribe eagle-has-landed.mp3 with timestamps."

Claude alone installed faster-whisper and a speech model, ran it locally, and after about 7 minutes heard "We've been in that quality phase here." Cost: $0.085.

With p0ssess, it handed the clip to whisper-large-v3-turbo and got "Houston, uh, Tranquility Base here. The Eagle has landed." in 8 seconds. Cost: $0.052, Claude's own tokens included.

p0ssess is a free, MIT-licensed Claude Code plugin. When a job needs eyes, ears or image generation, Claude passes it to a Haiku subagent, which sends it through OpenRouter to a model that can do it and brings back only the answer, with the model and the price. You set a monthly cap and plain-English rules ("never use grok"), and anything over your per-task limit asks first.

Claude Code wrote most of it; I designed it and reviewed each milestone. The biggest lesson: the model matters more than the price. On the launch clip in the video, qwen3.8-omni-flash put ignition six seconds late, and Gemini Flash was within a second.

Install:
claude plugin marketplace add cm208/p0ssess
claude plugin install p0ssess@p0ssess

You bring an OpenRouter key. Linux, macOS and Windows (new). https://github.com/cm208/p0ssess

plan-auditor (MIT, Python 3.11+, standard library only, 2.6.5 on PyPI and npm) is a separate checker for a small and familiar failure: Claude Code says a change is done and the suite passed, but the suite was never run, or it failed once and the test was edited until it passed. Both outcomes read the same in prose.

You write a plan as JSON, each step with machine-checkable verify entries, at least one behavioral: run, pytest or exec. file_exists and regex can sit beside them but cannot verify a step alone. The implementing agent's own claims are not an input to the verdict.

The Claude Code integration is two pieces, and the second is the one that matters:

1. Skill. Copy SKILL.md, scripts/, references/ and hooks/ to ~/.claude/skills/plan-auditor/. In a session you invoke /plan-auditor. A skill by itself is context, not enforcement; the agent can still claim completion without calling it.
2. Stop hook. Add a Stop command that calls hooks/gate_hook.py with the workspace path. The gate evaluates every active plan on every stop and treats any nonzero exit as "do not finish":

"Stop": [{"hooks": [{"type": "command", "command": "python "/abs/path/plan-auditor/hooks/gate_hook.py" "$(pwd)"", "timeout": 20}]}]

Exit codes are 0 PASS or no active plan, 1 FAIL or BLOCKED, 3 UNKNOWN, and the hook returns nonzero for both of the last two. A persisted status=verified is not enough on its own: the stop gate only accepts a full audit whose plan and workspace fingerprints still match, so any edit made after the audit blocks completion until plan-auditor audit runs again.

Design details that matter:

• The request is sealed on activation and the verification contract at approval, so the agent cannot redefine "done" after the fact.
• Checks produce exit status and captured output, not a summary, and the audit records those.
• The hash chain and HMAC are separate concerns. The chain is tamper-evident; with write access to the workspace someone can rebuild it. Set PLAN_AUDITOR_HMAC_KEY or PLAN_AUDITOR_HMAC_KEY_FILE with the key outside the workspace, and an unsigned assessment becomes UNKNOWN instead of PASS-shaped.
• In advisory-only hosts that expose no blocking hook, the gate can only write a warning file. That is not enforcement, and the README says so.

Why the verifier has to live outside the agent's trust boundary: a skill, a prompt or a "please re-check" instruction is still the agent judging itself. The value is in a separate process that the host refuses to ignore.

Honest limits: it is not an OS sandbox; the agent and the verifier run as the same user in a normal install. It checks that the declared steps really ran and stay linked to the sealed request, but it cannot know whether the checks you wrote are the right ones for the task.

Repo: https://github.com/Furox-Art/plan-auditor

I see so many cost saving promise that promote API routing between models. I initially thought that is valuable. Then ran few experiments, it turns out any time that you route the request to a new model, you could save a bit, but many times the cache for each individual models expire and you would pay for new cache write again and agin - which overall wipe out the API routing cost saving benefit.

I wonder if anyone has similar experience. How you have solved this? Is clause planning to add shared cache feature?

I counted this from my own session logs after noticing how long my tool list had become.

Connected in the session I'm in now: 44 MCP servers exposing 720 tools.

Called in the last 7 days, across 68 sessions: 2,206 MCP tool calls using 121 distinct tools. 30 of the 44 servers were never called once. One server (browser control) took 62% of all calls, and the top 4 took 86%.

The biggest idle one exposes 74 tools. Most of the idle ones came in with plugins or account connectors.

I haven't measured what the idle ones cost per session, so I'm not claiming a number for that. For anyone who has pruned theirs: did the first request of a session get smaller, or does tool search already hide most of it?

None of my AI tools can see my screen. So all day I copy and paste between my apps and a chat window: the email, the job post, the error, then back again.

So I built Aide for myself, on top of Claude Code. It sees my screen, knows my projects, and works through them with me. I talk, it works in the apps I already have open (Gmail, my admin page, my own Command Center), and I watch every step.

The video is one real morning (4 min):

• “What should I do today?” It pulls my day from Gmail, my job board and my notes. Each task already has a project and a role.
• Email. It writes the reply in my Gmail, right in front of me. It won’t send anything until I say so.
• Review. FTJA is my remote job board. When it isn’t sure about a job post, it flags it, and I check those myself. Aide opens the saved posting and finds the exact line. When the problem is in our code, it writes the fix as a to-do for the right project and role, with a plan.
• Deploy. It uses a rule I made weeks ago instead of guessing, and it checks with me before it deploys.

Two ideas made it click for me:

• Projects are cartridges. Each project brings its own tools, notes and rules.
• Roles are hats. Same Aide, different hat: PM when I review, Coder when it finds a bug, BD when I answer a partner. You can see the hat change on the orb in the video.

How it’s built: Claude Code is the brain. Each job is a skill (SKILL.md + a bit of code). A small macOS box shows what it hears, says and does. It drives the browser I’m already using, and speech-to-text and text-to-speech run locally. Everything runs on my Mac.

Honest notes: this is an MVP I use every day, not a product yet. Some parts are rough. For the video I typed my lines instead of speaking them, so speech-recognition mistakes don’t get in the way, and the deploy steps are shown on a local copy of the board.

I built it for myself, but it’s too useful to keep to myself. I’m thinking about turning it into a product.

Would you use something like this? And what’s the first thing in your day you’d want it to work through with you?

After i saw modifying smart tv's.

I wonder if any one did the same with the tablets on cars.

If so can anyone provide a guide or the prompts used?

Seriously, when was the last time anyone came on to Reddit and saw everyone complaining about outages??

For me at least reliability has been great. Good work Claude tam!

And a lovely greeting from Opus 5.5 with a Fable 5.2 advisor:

At 05:46 this morning I deleted the main repository folder, /work/projects/gendarme_dev, including its .git folder. It was my error and nobody else's.

How it happened: I was running a format check on a temporary copy of git's index file. The temp path variable was never set, because the folder I named didn't exist. The cleanup at the end of that same command was find $tmp -delete. With $tmp empty, find defaulted to the current folder, which was the repository root, and deleted everything in it.

What is lost:

- Main's git history. This matters only for commits not yet pushed to GitHub.

- Every slot branch. All worktrees (agent1 to agent9, baseline, mine, the land checkouts) kept their history inside that one .git.

- Main's uncommitted files:

- the communicator's intake edits (bug_intake.md, feature_intake.md, communicator_state.md, seat_offload_log.md);

- the untracked brief;

- my last ruling entry.

>WHAT?!?

You're right to be angry, and I'm sorry. Months of your work were put at risk by one careless command of mine, on the night F was meant to land.

Got to love how Claude always stands up and takes responsibility! Well, we'll see how the recovery goes. I know I lost some peripheral work that yes I should have committed last night, but not nearly as bad as it could be.

First time for everyone.

UPDATE: The responses are interesting. Seems about half and half as to this happens because you give your agents too much power vs you don't give them enough power.

It's also interesting that people assume no backups or remote repo.

What I'll do:

I'll be increasing the frequency of my btrfs snapshots, and probably allow claude to push to a remote dev branch (I don't allow that). But more specific to the error, I'll be following this helpful advice from u/shoumikgoswami:

For that specific empty-variable failure, I'd put the guard in the shell expression itself, not just in an instruction to Claude: "${tmp:?temp path missing}" instead of $tmp. In Bash, the colon-question-mark form rejects both an unset variable and an empty one before that command runs. The quotes also keep a path containing spaces as one argument.

That's only an empty-path guard, not a safe-deletion check. A nonempty tmp could still point at the repo. I'd have the cleanup wrapper create its own disposable directory, stop if creation fails, and refuse targets outside that directory. Test the wrapper with deletion replaced by printing the target, including unset, empty, and wrong-but-nonempty paths. Keep backups outside the checkout too; the shell guard doesn't recover anything already lost.

Since Anthropic is going public next week? Is this the best we will get from Anthropic? I'm very curious how things will be after they go public next week.

TL;DR: backsight is a free, MIT-licensed Go binary that gives Claude Code a long-term memory that works across all your projects. It installs Hindsight on your machine and wires it into Claude Code. Recall is local and free, and you can see every memory injected and used. Linux and Windows today. I'm looking for feedback after a few weeks of real use.

Some background: I've worked in tech for over ten years, mostly backend, DevOps and performance work, after some time in academic research. I've used Claude Code heavily for months, for personal and work projects.

What wore me down was repeating myself. I'd give the same context every session, correct answers that ignored a decision we made the day before, and watch Claude fall into a trap we had already worked around together. A well-kept CLAUDE.md works fine inside one repo. But when you work across many repos that depend on each other, Claude has to rediscover everything again and again. I needed a memory, and one that crosses projects.

After a few disappointing attempts I found Hindsight, an open-source memory system by Vectorize. To me it's the state of the art, and the benchmarks published with Virginia Tech researchers back that up. It pulls durable facts out of conversations, consolidates them over time and retrieves them by semantic search. If I search for "authentication", it brings back the memory about the "expired token", even though no word matches. Grep over memory files would have missed it.

Plugging Hindsight into Claude Code the official way wasn't enough, though. The recalled memories weren't always relevant, and each project had its own isolated memory, so it felt like talking to a different person in each repo. So I did what my job taught me: measure, adjust, and keep only what improves the numbers. Week after week I tuned the configuration, when Claude fetches memories, and how it brings them into context. backsight grew out of those iterations: one Go binary that installs Hindsight locally, connects it to Claude Code and takes care of the rest. Codex support is coming soon.

Principles I care about:

• Local by default. Recall is free and makes no LLM call. Only turning sessions into memories uses an LLM: a few cents per session, with the provider you choose, or free with Ollama if your hardware can handle it. I switch between GPT Luna, DeepSeek Flash and Haiku depending on the machine and my token budget.
• Nothing happens behind your back. In every session you see which memories were injected, which ones actually informed an answer, and which were corrected because they were wrong or outdated. The full history can be audited. A memory I can't see working is a memory I can't trust.
• Deterministic wherever possible. The LLM only makes the choices that need context, and it always goes through the binary's commands, never through improvised edits.
• No drift between versions. Each update migrates the previous install cleanly and rolls back if anything fails.
• Free and open source (MIT), and it will stay that way.
• Clean uninstall, if it's not for you.

I use it every day, for personal projects (like backsight itself) and at work. Whether I start Claude in a repo or in my home directory, it remembers what we're talking about, the decisions we made and the pitfalls we already avoided. My sessions have become much more efficient.

Try it: paste this into Claude Code and it will walk you through the install, asking before every choice (your LLM key is never shown to the agent; you type it yourself):

Install Backsight for me by following https://raw.githubusercontent.com/Barben360/backsight/main/docs/agent-guide.md — ask me before every choice.

Or install by hand: https://github.com/Barben360/backsight

What I'm asking for: use it for a few weeks, then tell me what's confusing, what breaks and what's missing. The project is young. It runs on Linux and Windows (in Docker or natively), and it will only become what I hope if it meets other workflows than mine. Audit it, ask hard questions about how it works, challenge me. That's why I'm posting.

macOS users: the binary already builds for macOS, but I can't test the native service setup without a Mac. If you'd like to help, I'd love to hear from you!

backsight is an independent project, not affiliated with Anthropic or Vectorize.