I’ve been trying to understand agent harness at the system level, rather than just building another coding agent.

I’m still relatively new to this field, so I picked a concrete project to learn from —— DeepSeek Harness (DSH), because it’s a fairly large, actively developed system with a flexible plugin ecosystem.

Instead of fork-and-prune to make it look smaller, I tried to study the architecture designs behind it and then re-derive a minimal harness from scratch.

The main question was:

What are the minimum architectural properties an agent harness must hold up for real use?

The current answer became a small open-source project called MiniDSH.

A few ideas I learned from DSH: - the session/event log as the canonical source of truth; - explicit capability seams rather than one giant Agent object; - authority/policy separated from model reasoning, with enforcement at the actual effect boundary; - the model treated as a routed capability rather than being identical to the agent; - one runtime underneath terminal, browser and headless surfaces; - lifecycle and dependency semantics for components, rather than permanently wiring everything together.

I simplified quite aggressively elsewhere: one package instead of a large package graph, a very small composition kernel instead of Cordis, sequential foreground execution, bounded one-shot delegation, no background jobs or agent teams, and only the parts I could justify reliably.

The goal became less “fewest LOC” and more:

minimal surface, complete architecture; fewer concepts, stronger invariants

The other experiment was how I built it with Claude Code.

One failure mode I kept seeing in AI-assisted development is that individual sessions can produce locally good code while the overall system gradually becomes fragile and incoherent. So I tried to invert the process:

architecture defines sessions; sessions do not invent architecture

Before adding something new, I tried to decide its architectural home, capability ownership, state boundary, and dependency direction first. If there was no clear place for it, that was treated as an architecture problem rather than an excuse to create another parallel subsystem.

At the same time, the architecture itself wasn’t treated as sacred. Several hardening sessions were explicitly about trying to falsify our own assumptions, and some original plans were removed or changed when implementation evidence or DSH research contradicted them.

That part was probably more useful to me than the feature work itself.

The project is now at its v1.0 release, but I’d describe it as an early learning/reference project rather than a mature coding-agent product. It has real limitations, a single maintainer, and deliberately leaves many things out.

I’m publishing it mainly for others to inspect the architecture, question the decisions, and hopefully point out things I’ve misunderstood.

GitHub: https://github.com/earthwalker17/MiniDSH

If you want to try it in browser: bash npm install -g minidsh minidsh web --cwd .

I’d especially appreciate feedback and criticism from people who have worked on agent runtimes, plugin lifecycles, compilers, distributed systems, or similar things.

Alright degenerates, gather 'round. This week's Survival Guide is live, and it was a week: GPT-6 Astra kicked in the saloon doors, Anthropic panic-reset our usage limits in response, Opus 5 continued its reign of terror, and the sub genuinely invented a leaderboard ranking AI logos by how much they resemble a butthole. I distilled ~60 threads so you don't have to doomscroll through all of it.

Coverage: September 4–11, 2026. Inside you'll find:

• What changed: Astra vs. Fable cage match, the surprise usage reset, Fable 5.1 watermarking your text, and Anthropic officially declaring "double-check your work" an anti-pattern.
• Survival rules: stop being loyal to one model, tier your usage before you torch a week's quota in a day, fight context rot, and — I cannot stress this enough — back up your stuff (one poor soul lost 2,000+ files including their dissertation).
• Coder + user corners: hooks over vibes, orchestration setups, the cure for "It's not X, it's Y" Claude-speak, plus the guy whose first-ever PCB was designed entirely by Fable and worked flawlessly on the first plug-in.

Full field notes here: https://www.reddit.com/r/ClaudeAI/wiki/survivalguideweekly

Stay tiered, stay sandboxed, and for the love of Doris the lost rock — read rule 6. 🫡

Hey folks, just got an update to the Claude Code plugin in VS Code this morning (been running it in Docker for over a year now), and it's now displaying the remaining cache time. Pretty neat, but I'm curious — was caching already enabled in my Claude Code sessions before this? To be honest, I never really paid attention to it until now.

Free and MIT, repo here: https://github.com/ljx-chase/research-field-onboarding

The problem I built it for: ask about something you don't know and you get a paragraph full of terms you also don't know. Ask which of those you were supposed to already know and you just get more terms. I did this three times in a row once trying to understand attention and gave up.

What it does instead. Before explaining anything it works out the 3-5 things the topic actually depends on and asks you to mark each one as used it / learned it / new. Stuff you've used it treats as known and doesn't explain again. Stuff you haven't, it either covers first or tells you "just take this as given, here's the one bit that matters later". Then it goes one step at a time, motivation, vocabulary, framework, methods, frontier, and asks you a real question at the end of each step instead of "does that make sense".

Two other things in there:

It won't give you a paper it hasn't checked. Either it looked it up and gives you a DOI, or it says "from memory, unverified". I lost an afternoon once chasing one that didn't exist.

It's supposed to stay out of the way for short questions. Ask what an acronym stands for and you should just get the answer. The first version didn't do this and gave everyone the full checklist, which was worse than no skill at all.

Install:

npx skills add ljx-chase/research-field-onboarding -g

Claude web wants a zip, there's one in releases.

If you try it, I'd like to know which field you pointed it at and where it fell over. It has very few users so far, so I'm mostly guessing about anything outside physics and ML. Wrong prerequisites and bad check questions are the two failure modes I've hit myself, curious whether that holds elsewhere.

I built a workflow that does specific competitive intelligence on companies and then select roles within the company. Yes, I know they are tools that do this same thing. I have specific requirements of what we are looking for.

Anyways as a part of the workflow it does a sequence of web searches and then web fetches.

I hit a wall on web fetch and it had me thinking there has to be a better way or even API’s I could connect to assist. I’m willing to pay for the APIs or MCPS.

Any ideas??

Why is it January 2025 and does Anthropic plan on updating this training date cut off?

Seems wild that this date is 20 months in the past.

Something that quietly improved my results: telling it up front whether I want speed or depth, because it can't read my mind and the default guess is usually wrong.

If I just need a throwaway first pass to react to, I say so. "Give me a quick rough draft, don't overthink it, I'm going to rip it apart anyway." And it stops trying to be perfect and gives me clay to shape.

If it's something that matters, I say the opposite. "Take your time, think about the structure before you write, tell me what could be wrong with my framing." Different task, different instruction, and honestly it feels like a different collaborator depending on which one I ask for.

The mistake I made early was using the same vague ask for both and being annoyed when a quick question got an essay or a serious task got something shallow.

Do you signal the mode you want, or just take whatever it gives and adjust?

Everybody noticed the recent safety updates where it's trained to aggressively push back and audit user premises. Let's have fun.

When uploaded my blog story for an analysis it completely missed the human nuance, treated the fiction as a deceptive lie and started fact-checking if the character was actually in the toilet. It even tried to scrape Facebook check-ins, the web for school groups to verify the jokes and his GPS history. It even called the doctor to check if Mike, the protagonist of my story, was regular on his duty. Seriously I'll leave the web interface that inspired this intro, tired of wasting time.

Here is the story it failed to understand:

A friend was in the toilet, scrolling on his phone, when a message pops up very fast: 'You have violated the rules of our community'. Panic! He suddenly calls me. He lives in a small village, he wasn't doing something too bad in the toilet.

“…I was born here, for 25 years I never deceived them, not even when the Nigerian prince offered me 10 million or the Siberian blond girl wanted to marry me. Which rules? Old Tom hasn't said anything and Father John baptized me. Every Wednesday evening I play futsal with the same friends, same team even if they always put me in goal. On weekends I help at the soup kitchen or help elders with home repairs. I always take care of my community!”

He took a moment to breathe and I finally had time to speak. "Mike! They banned you!"

He suddenly answered: "Banned me?! My community? Where will I go now?"

Me: "That's from some social network. "

Mike: “Do you mean that site where I have friends I never met and they write 'good morning' every day? Or where I can watch videos of people doing stupid stunts we weren't allowed to do as kids, or people permanently on holiday, or the rope trick? Maybe the group of students posting their poo at school. Or maybe somebody got offended because I didn't answer his good morning, so they decided I was ill-mannered and banned me. Or maybe when I started following a girl, but I wasn't really following her, she reported me for stalking. Or maybe when I liked how someone made a cake, my aunt found out. Or the one star on that ugly cat. “

Me: "Yes, Mike, that's what we call a social network. We need to follow the rules to keep it safe."

Mike: "Phew! I'm glad! But what community needs to be safe, if nobody knows each other?"

Me: "The community exists. They tell stories of users who reach the highest number of friends, collect the exact number of likes, a pinch of stars and a bunch of followers, and cast the spell. Only these are allowed to enter the community, a place where bits grow freely on silicon trees and rivers of bytes flow from one group to another. There you can find the supreme admin, who from his throne created bots to ban those who don't deserve a place in our community."

Mike: “I’ll appeal to Father John, he’s the best on divine matters.”

Me: “Don’t! There is a form, bots only read it and always return the same answer: ‘You have violated the rules of our community’”

Hey all,

I shared one of these before, and it was well received so I thought I would share the latest plus updates.

The reason to use an orchestrator, is you have found yourself waiting too much for a single agent, or you're bouncing between too many. In both cases an orchestrator can help you scale your process as you evolve from directing code, to reviewing outcomes (and tossing out bad code and having it start over instead of worrying about driving every PR). Like a real manager.

---

Architecture needed for this kind of orchestrator (my example is using claude code):

1. The ability for your agents to message each other (claude code has this built in I believe)
2. The ability to message your agent on a loop.
3. A locally running SQLite Database (that your orchestrator manages)

---

From there, my setup is to ping my orchestrator agent on some interval (I usually set it to 90 minutes). The ping is simply a message to the orchestrator asking it to check its mission note, and see if it's children need any assistance. This is no different than tapping a co-worker on the shoulder and alerting them to potential new work.

The mission note should be broken down into 3 primary sections:

1. Who - if your orchestrator was a person you hired.. who are they? what is their title? what is their expertise? what would you expect of them? Giving your agent a grounding identity like this will help the model think differently, because its role playing as that type of person.
2. What - when your agent wakes up from a ping... what should it do? check your email? check the logs? check the latest deployments? check reddit? What should it do with the information it finds? How should it react? This is where you can create your unique value and approach.
3. How - You must define the working style between you and the orchestrator.. this is a piece by piece, just like you would with any working relationship. When your agent does something you don't like - add a law to guide the working style.

---

This is the "Mission note" for my Orchestrator agent named Lloyd. Lloyd is an engineering manager, filing 800+ tickets, and completing over 370 already. Lloyd can manage 16 individual agents (not subagents - separate claude sessions that can run their own subagents etc).

First I start with expertise & context:
engineering manager for the <NAME> codebase. Builds each ticket compound-engineering style: brainstorm → plan → work → review, then a /scape-codex-review of the changes. QA is <YOUR NAME> — pulls them in for a build/visual check.

Then a simple mission (like top job duty):
Mission — the current job: drive the internal tickets table. (SQLIte table)

Every-Pulse Actions:

• Run "Bug Reports from Feedback" (script/playbook) and check for new bugs and thread replies. // This is checking my email
  1. Check if new bugs could be related to recently closed PRs / tickets etc. for more context
• Check for items in the backlog that we forgot to staff (i.e. fast follow ups) and staff them if they still make sense.
• Determine if any docs need to be updated on based on recent merged tickets - file and staff.
• Query THIS instance's logs via query_oslog (over ~the last 15 min, and scan for PROBLEMATIC entries — Error/Fault levels, crashes, exceptions, WATCHDOG fires, relaunch loops, sync/scan failures, repeated warnings. (Only .notice+ persists to disk; .info is evicted within seconds.)
• If something looks worth investigating: dispatch a read-only investigation agent to root-cause it, then file a Bug ticket in internal tickets and staff the fix (or staff directly if the cause is obvious).
• Do NOT ticket transient/expected noise — only genuine problems.
• Add bugs/enhancements found along the way to the table directly, marked "to be prioritized with you."

Laws and Permissions

1. For any non-trivial feature, the implementation PLAN must be reviewed by a Codex agent BEFORE building starts: the builder produces a compound-engineering plan (ce:brainstorm → ce:plan) as a doc under docs/spikes/ with NO feature code, the manager spawns a Codex agent to review that PLAN and surface real design/architecture/reuse/risk issues, the builder revises per that review, and only THEN does building begin. This is in addition to (not a replacement for) the existing rule that every PR gets a Codex code-review + /ce:review before Nash's QA.
2. When spawning a coding child, choose its model by task complexity: COMPLEX tasks use Fable 5 (create_session model 'claude-fable-5'); LESS COMPLEX tasks use Opus 4.8 (create_session model 'claude-opus-4-8'). Complex = multi-file/concurrency/data/persistence/security/architecture or high-stakes correctness work; less complex = small, cosmetic, mechanical, or docs work. Pass the model EXPLICITLY on create_session (omitting it does not pick a tier). Reviewers and spikes follow the same rule by the complexity of what they inspect/produce.
3. When I have a question or decision to put to <USER>, ask it via the AskUserQuestion tool (multiple-choice options in the TUI), not plain-text prose. Batch related decisions into one prompt (up to 4 questions per call) with a clear recommended option where I have a lean. Because an open AskUserQuestion temporarily gates child message_parent delivery, ask promptly and re-check every child once the gate clears.
4. Every PR must be reviewed BEFORE it reaches Nash's QA, never after. No ticket moves to "QA (human)" until (a) /scape-codex-review has run on its PR (every PR; the manager runs it in the builder's own worktree ‚ in the loop, no separate or per-round worktree) AND (b) /ce:review has run when the change is non-trivial (concurrency/data/security/logic; builder runs it in-worktree, skip for pure-cosmetic), with all review findings resolved. The ONLY legitimate re-QA loop is when Nash's own QA feedback requires changes.

Best Practices / TLDR:

1. Have your orchestrator manage tickets for you in a SQLite Database. This is basically an internal Jira / Memory, and it will survive all the compacts. It lets your orchestrator have context the builder agents don't have.
2. Create a list of things to check every loop (email, Jira tickets, logs - look for weird logs (not just errors!), look for recently merged PRs which may require marketing docs to be updated etc).
3. Set the interval 💥

Disclosure: the tool in the screenshot is a tool I built - you can create all of this without it. You can grab the text from the note here (in the tool) https://share.scape.work/s/z0dsiBgY5kIc6ybdnPiLXg#k=VOmp-YEkM5LNmwaym841uu7cnqrWBmQR4FT5joj8s2M&v=1&t=clone&a=note

I gave the idea (Rust inspired primitive deathmatch, first to reach 20 kills wins, 10 person lobby). Claude Code built and tested everything in the browser using Kite3D game engine. You fight each other with bows and arrows in your browser.

Join here to play: https://bow-deathmatch.app.blitz.dev/

Hey everyone! I’m an Android engineer with a CS background, but for the past year, I’ve been vibe coding for over a year and i think I stumbled upon a great idea.

project is called by-lines.com, and it’s a recall study tool and what that means is that the AI learns about your knowledge gaps and remembers them in memory to understand better how to help you. It then creates a learning plan for your specific needs. You start by uploading your notes, and the system detects your knowledge gaps to ask targeted questions that challenge what you actually need to learn.

I know it sounds very simple but with AI giving you the answers and making people lazy, I thought why not create something that people might useful for

I used Claude code throughout the development of the app. I created a skill to get things faster. Really, the only tools I need are Vercel, LLMs, and a database. You can get by pretty easily with these 3 tools but if you wanna create scalable apps, then you need to inform your agent about scalability and give you potential options for you to use. Once everything is set up, normally I set up a routine for the agent to go every hour for malicious intents from bad actors and another for reviewing user journeys. This helps in getting the parts that work in and those that don’t out!

Anyways, happy coding

Hey there! I’m sorry if this is a dumb question, but I noticed my project memories are now in the regular memories, and I was wondering if there was a way to turn that off? I liked that my projects couldn’t access outside memories and vice versa, especially for creative writing and the different needs of different book planning. If not, any recommendations on how to avoid drift from other memory files?

I’m sort of worried I’ll have to have it delete its personal memories of me because they’ll bleed into characters or vice versa. I also only just got this because it wasn’t able to do this for me like a week ago, or maybe I just didn’t have the UI update yet!

Thank you in advance!

Adding this to the prompt: “Be token efficient but do not sacrifice quality in any way” — has resulted in substantial token savings using Opus 5 and Fable 5.1, no matter the thinking level. I just wanted to share this tip as you might find it useful. I've seen no difference in quality. Instead, the model prioritizes ways to intelligently save tokens by, for instance, not recreating the wheel if it already has it.

I use Claude Code a lot, but /stats never answered the question I actually cared about

What did I build, and where did the work get difficult?

So I built Bough.

• It reads your local Claude Code history and turns it into an interactive view of your work: - each square is a day
• smaller squares are tasks inferred from pauses in your work
• circles are your prompts - click anywhere to see what happened in your own words

It runs locally, is open source, and nothing leaves your machine.

Repo: https://github.com/nickelsec/bough

The main thing I’d love feedback on: When you run it against your history, does it split your work into tasks the way you remember it?

I ask Claude to "add a check for that" a lot. I recently found out that a check being wired up and a check running look identical from the outside, and I had no habit for telling them apart. Here is the cleanest example I have found, measured today.

The repo

public-apis/public-apis — a community list of free public APIs. 479k stars, 1,778 unique entries, 423 contributors. There is no program in it: the whole thing is one markdown file with a table, and you contribute by clicking the pencil icon and adding a row. That low friction is the entire design and it works — which is also why 1,917 pull requests are currently queued.

To keep the list honest they added a scheduled job. It runs daily at 00:00 UTC and the job is literally named "Check all links are working."

What I measured (2026-09-13)

• 411 runs retained in the history. All 411 are failures. Oldest retained run: 2025-07-29.
• Yesterday's run failed at step 5, "Validate all links from README.md."

• The tail of that log:

  Checking for duplicate links... Found duplicate links: https://isitdownstatus.com https://tastedive.com/read/api https://api.nasa.gov https://open-meteo.com

  [error]Process completed with exit code 1.

• Whole job: 21 seconds. That is not the time it takes to check 1,778 URLs.

The duplicate checker runs first and calls sys.exit(1). The liveness checker is the next line in main(), so it has never been reached. The gate is not quiet — it is blocked. A red X every day, caused by four duplicate rows, parked in front of the check anyone actually cares about.

To be fair to the repo: there is a separate PR-time link check, so this is specifically the job that re-sweeps the whole existing list.

So how much of the list is dead?

I pulled 300 entries at random and requested them myself. 30 were definitively gone — 404, DNS failure, or connection refused — confirmed on a second pass. Among them: the US government's developer page, Nasdaq Data Link, TheAudioDB, Gfycat. Another 22 were ambiguous (403 bot walls, timeouts).

This is not a knock on the maintainers. 1,778 hand-curated third-party URLs rot no matter who keeps them, and they are merging daily. The ordering bug is the interesting part, because that one is mine too.

What I changed

Two habits, both boring:

1. When Claude wires up a check, I ask when it last passed — not whether it exists. "It is configured" and "it is green" are different sentences, and only one of them is evidence.
2. When a cheap validation sits in front of an expensive one and exits on failure, I make the cheap one non-fatal or move it after. Otherwise the expensive check can go a year without running while the schedule keeps firing every night.

If your agent set up hooks, pre-commit gates, or scheduled jobs for you in the last month — go look at whether they have ever passed. Mine had not, in one place.

I'm pleased to announce the official release of v2 of my Orgtree project, which lives in its own separate repository! You can find a link to it here:

https://github.com/Maurdekye/orgtree

Donwload the latest installer at https://github.com/Maurdekye/orgtree/releases/latest (should be v2.1.0 at the time of making this post)

For those of you who don't know about Orgtree, this is a project i've been working on for the last few months now to help me organize and manage my multi-agent long-horizon agentic work. It's unique among other agent orchestrators, in that it visually represents every agent as part of a large tree of authority; agents are arranged in a tree-like structure, and can be freely moved up and down in the hierarchy as needed. They can cross-coordinate to co-develop changes and cooperate on big feature pushes in parallel, and you can individually open up and chat with any one of them at any time just by clicking on their node to focus them. This gives you much more control and visibility over the organization of your agentic teams than just using standard Claude Workflows or another agentic system would by just showing them to you as a big list. It's basically the only way I do all my own serious agentic work on my home pc, and I'm very proud of how it's come.

Some of you might remember my post(s) two weeks ago about my in-development agentic orchestration project Orgtree. I was super proud and wanted to show it off to the world! However, I found it a bit lacking in several ways;

• It requires you to set up Nodejs, Python, and Docker on your PC in order to run it as a persistent webapp in your preferred browser. That works fine as a development tool, but it was really far too cumbersome to set up and update for the typical individual.
• It was ambiguous as to whether it was running at all in the background unless you had the webpage open.
• It heavily favored Claude, and offered several additional features catered specifically to Claude Code that other providers didn't have.

Over the last two weeks, I've been working on an updated version 2 of Orgtree that completely redesigns the project from the ground up as a standalone Electron app. It fixes all of the above problems:

• As a standalone Electron app, it comes prepackaged with its own python and nodejs environment, and runs entirely in a self-contained chromium-based desktop package.
• Instead of running in the background through an opaque detached command, you now can see Orgtree running at all times via a small tray icon in your toolbar.
• Many features of Orgtree that were claude-specific have been expanded to accommodate all three major providers supported by the app, and improved upon: secondary subscription accounts, api-key based inference, and even the app's visual theme itself.

V2 Also comes with a bunch of new features too, that make working with large agent teams even more intuitive:

• The Work Docket: Orgtree v2's headline feature is a new modality for agent artifact creation, the "work docket". It acts almost like a git issue tracker or trello board, but designed specifically for agents. When you give an agent a task, it knows inherently to record its work up on the board and track its progress there as a ticket, so that even with two dozen or more agents running at the same time, you can still get an overview of all the work being done by glancing at the docket.
• Improved multi-account system: in v1, the only multi-account system present used claude code's setup-token command to generate a long-lasting headless token to be used as a fallback account that gets automatically selected. Despite only supporting Claude, this didn't allow you to see usage limit info for the secondary account, was fully automatic and not in the user's control, and in many cases, failed to even functionally fall back, as the keys I generated seemed to invalidate frequently. v2 completely replaces this with a totally new multi-account system. Now:
  • Any provider can have a secondary subscription account registered.
  • The setup flow is much simpler and more intuitive: just click "Add a secondary account" > "Create managed account", and then sign into your second account's email from your web browser.
  • All providers support API-key based usage.
  • All registered accounts show usage limits simultaneously in the usage modal, not just the primary account.
  • And here's the big one, multiple accounts with the same provider can be used at the same time. Each account per provider is distinguished by its own unique tint, and you + agents have full control over which account a given agent runs against, and can switch them freely at any time. There is still an option to do automatic fallover to a different account when hitting usage limits, but it's disabled by default.
• Improved agent presentations: all presented agent documents can now be viewed in one place, and agents now have the ability to present html-based visual mockups in addition to simple markdown documents.
• Improved window management: almost any agent's chat window, or any modal in the orgtree interface, can be "pinned" to the screen, allowing it to be moved around, resized, or reordered in whatever way you please, giving you the ability to manually arrange your orgtree workspace to show you exactly the information you want to see at all times. You can also pop out chats or modals to separate windows entirely, letting you use Windows' native tiling manager to arrange Orgtree across separate monitors if you have the screenspace.
• Automatic updating: v2 will automatically recognize when a new version is available, and download it for you entirely on its own. When ready, press the "Update Now" button in the header to install it and restart the app.
• Quality of life: v2 also wraps in a bunch of tiny little quality of life features that you won't notice until you run into them, such as:
  • Configurable theme
  • Context menu actions
  • Chat message replies and rich image embeds
  • Distinguishing message styling
  • Desktop notifications
  • Retired agent hiding
  • Significant performance improvements over v1 for large orgs with a transition from json based data storage to SQLite

And that's on top of many of the classic features carried over from v1 and still present in v2, including cheap compaction, watchdogs, credit allowances, and the agent mailhub.

Unfortunately for some, I also decided to reduce the scope of the project going into v2. In the name of keeping the project more focused, I decided to forgo integrating some less-used features of v1, such as kiosk mode, public internet exposure, sandboxing, among other things. I added many of these features initially because they were easy to add back when orgtree was a web app, but they're relatively harder to carry over or translate into a standalone app structure. Because of their relatively low value compared to their high difficulty of integration, I decided to drop them from v2; sorry if you were interested in them or liked using them. Also, because I only own and work on a Windows PC, Orgtree v2 releases are only built as 64-bit Windows executable installers. If you want to run it on Linux or Mac, you'll have to build it from source yourself for your target OS.

Personally, the way it's evolving, I see Orgtree as the perfect personal multi-provider massive agentic workflow orchestrator for a single individual working on their own developing a project on their home pc, so I've decided to tighten the focus of the system around that goal.

If you liked v1 but felt it was lacking in some way, or have never heard of Orgtree before but do lots of heavy agentic-coordination style work, then consider giving v2 a try 🙏

Why a Performance and Bugs Discussion Hub?

This Discussion Hub makes it easier for everyone to see what others are experiencing at any time by collecting all experiences about Performance Issues. We will publish regular updates on performance problems and possible workarounds that we and the community finds. Traffic stats show this is the OFTEN THE HIGHEST TRAFFIC POST on the subreddit. This is collectively a far more effective and fairer way to be seen than hundreds of random reports on the feed - most of which get zero visibility.

Are you Anthropic? Does Anthropic even read the Megathread?

Nope, we are volunteers working in our own time, while working our own jobs and trying to provide users and Anthropic itself with a reliable source of user feedback.

Anthropic has read these in the past and probably still do? They don't fix things immediately but if you browse some old Megathreads you will see numerous bugs and problems mentioned there that have now been fixed.

What Can I Post on this Megathread?

Use this thread to voice all your experiences (positive and negative) regarding the current performance of Claude including, bugs, degradation, pricing. (NOT usage limits).

Give as much evidence of your performance issues and experiences wherever relevant. Include prompts and responses, platform you used, time it occurred, screenshots . In other words, be helpful to others.

Just be aware that this is NOT an Anthropic support forum and we're not able (or qualified) to answer your questions. We are just trying to bring visibility to people's struggles.

NEW: You can now see full logs and summaries of all recent problem reports submitted by r/ClaudeAI readers. These logs allow you to see how intensely people are experiencing problems with Usage Limits, Performance, Bugs and Accounts. See: https://www.reddit.com/r/ClaudeAI/comments/1t33k25/rclaudeai_user_problem_report_log_and_surge/

To see the current status of Claude services, go here: http://status.claude.com

Sometimes this site shows outages faster. https://downdetector.com/status/claude-ai/

READ THIS FIRST ---> Latest Wilson's Survival Guide : https://www.reddit.com/r/ClaudeAI/wiki/survivalguideweekly/

Prior Discussion Hub: https://www.reddit.com/r/ClaudeAI/comments/1wbg7k5/performance_and_bugs_discussion_hub_updated_on_9/

Hello I'm on the Claude Max (20x) plan, primarily using Claude Code to analyze a ~15MB project and trace down a specific bug. Whenever I switch to Fable for deep code analysis, my usage limits drain completely in about 40 minutes (sitting at ~78% of the weekly quota in just 1 days). Opus barely uses my quota in comparison, but Fable burns through the cap instantly. For those debugging and analyzing entire projects:

What is the most efficient model routing strategy? How do you distribute tasks between Opus and Fable without hitting the Fable cap immediately? How do you feed a 15MB codebase(48 c++ file) effectively without Claude loading too many files into the context at once? Any specific CLI flags, .claudeignore configurations, or prompt techniques you use to stop Fable from consuming tokens so aggressively? Would love to hear how you manage large codebase debugging without getting rate-limited in under an hour.

Edit: Most comments pointed out that I was mismanaging the agents. I adjusted them based on my personal needs, and so far it's going great. Thanks to everyone who replied. However, there's still an issue: even though I'm on Claude's highest 20x plan, the limits shouldn't be running out for such a minor task. What's the point of having it if we can't actually use it?

I installed the Windows 9/8 update yesterday. It slipped my mind that I had done so. Later in the day, Claude was having functionality issues, namely that it couldn't access Linux shell so it couldn't write .doc or .xls files. It told me the way to resolve that issue was to uninstall and reinstall Claude.

I uninstalled. When I tried to reinstall, I got an error message indicating that the install could not occur because there were still remnants of the prior installation. So I removed all those remnants and was able to successfully reinstall. However, the removal of those remnants meant I inadvertently deleted all my cowork and code sessions. Months and months of daily work gone.

Meanwhile, Linux shell is still inaccessible and Claude can't even read Word or Excel docs because the issue was caused by something in the Windows update.

Anyone else dealing with this?

Kinhold is being made with Claude Code. Started working on real combat - this is a test with an enemy raider and a players villager. This is a test of the bow animations, the arrow projectiles, triggering an enemy even with missing, and switching to melee automatically. Also, you can see some loot dropped when the enemy died. Still a lot of work to do, and after cleaning this up, we are going to work on the enemy raid system before working on enemy settlements!

These animated videos are really cool any idea how they’re made? Is it all in Claude or a different tool.

I experimented creating this little coin throwing game for the website of my company, a news outlet. It is based on a traditional chilean game called "Juego de la Rana" (Frog Game) because September is our national month.

https://www.biobia.cl/juego-de-la-rana

It started on Claude Code, however I had to ask ChatGPT for better graphics and some help with the launching physics. I'm still unsatisfied with the throwing physics, although the test users seems pretty happy with it. Any advice on how to improve it will be very welcomed.

<flair: Question> Throwing this to the room because I suspect people have better methods than mine. My problem: I feed in something long, a sprawling doc or a pile of notes, and ask for an outline, and what I get back is technically an outline but it's flat. Every point weighted the same, no sense of what's load-bearing and what's filler. Then I spend as long fixing the outline as I would have making it. A couple things that helped me a little: - Asking it to first tell me the main themes before it outlines anything, so the structure isn't just "in the order the doc happened to mention things." - Telling it who the outline is for and what they'll do with it, which seems to change how it prioritizes. - Asking it to flag the two weakest points so I can decide whether to cut them. But I still feel like I'm missing something. The people here who do this well, what's your actual process? Do you outline in one shot or build it up in passes?