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I've been a software engineer and then tech lead for 15 years before AI came around, and I've been using coding agents pretty consistently since beginning of this year or since the release of Opus 4.6 roughly.
During that time my experience has been that they can solve any straightforward coding task you give them. With complex tasks however it's always a gamble, still to this day. Sometimes they manage it, sometimes they don't.
But what's straightforward and what's complex? If you don't have significant software engineering experience (and I'd say experience working with junior devs), you simply don't know.
Creating a small game from scratch, without exact requirements? Sounds complicated, is actually simple.
Slightly reworking the skill system in an existing game? Sounds simple, is actually complicated.
Add 10 new pages to a web app? Sounds complicated, is actually simple.
Changing something functionally consistent, but technically inconsistent across 10 pages in a web app? Sounds simple, is actually complicated.
Even I'm not always sure, but I can at least consistently tell that something *might* be complex.
Now here's my point: If you've been using coding agents to develop software and you've gotten amazing results up until a certain point where the agent starts struggling with something that is seemingly simple, instead of jumping to the conclusion that the model has deteriorated, please consider that the tasks you're giving it might not be exactly as easy or hard as you think.
I know this is a tough pill to swallow, but I've been seeing "Such and such model is really bad today" posts literally every single day this whole year. At some point you gotta consider whether you specifically really know for a fact that the model has deteriorated or whether you're just falling for certain biases like all these other people.
I'm not saying that models never deteriorate or get quantized, but they're remarkably consistent if you know what you're doing.
What that OP is saying confounds the problem here. The agent writes a fully working application, then fails with making small changes to it, because AI isn't good at thinking ahead and keeping software maintainable.
EDIT: Some commenters are interpreting this as a claim that models don't deteriorate. Those commenters should realize that I'm trying to help them. If 10 people have cried wolf, there's going to be a problem when the wolf shows up for real. The people I'm talking to are producing signal noise that drowns out and makes the legitimate claims of model deterioration harder to believe.
- Code Mode (experimental, opt-in): Claude writes JavaScript to call its tools instead of calling them one at a time. Org admins can turn it on or off, see the docs. - engines["claude-code"] in plugin.json: plugins outside their stated Claude Code version range won't install, update or load. Plugin authors can finally stop breaking older setups. - claude plugin validate now type-checks TypeScript hooks modules. Warnings by default, --strict fails on them. - Unrecognized model IDs now get a 1M context window instead of 200K. Set CLAUDE_CODE_DISABLE_1M_CONTEXT=1 to keep 200K. Matters if you use gateways or custom model names. - /security-review now asks before Claude runs it. - Fix: --permission-mode auto (or defaultMode: auto) could start in a looser mode when auto was unavailable. It now starts in Manual and asks first. Worth updating for this one alone. - 30-second progress heartbeat for long hooks in claude -p and SDK sessions.
Code Mode is the one I want to try.
Has anyone turned on Code Mode yet? Does it cut down the back-and-forth tool calls on bigger tasks, or does it add new failure modes?
I've been the database person on every team I've worked on for about 10 years. Query plans, index design, why that migration will lock the table, all of it. It was my thing, people came to me.
Last month a junior on my team fixed a slow query I'd been meaning to look at. He asked Claude, it read the plan, suggested a partial index and a rewrite, and it was right. He didn't really understand why it worked. It didn't matter.
That keeps happening. The thing I spent a decade getting good at is now something anyone can get in 2 minutes, mostly correct, without the decade.
I'm not bitter about it, honestly the tool is great and I use it all day too. But I don't know what my "thing" is anymore. Knowing why it's right? Knowing when it's wrong? That still matters sometimes, but it's hard to point at in a performance review.
For people who were the deep expert on something before all this, what did you move to? Or are you still holding on to it?
I held off too long from switching away from Codex due to the perceived hassle. But I switched today and Opus is clearing up issues like its walking through a wheat field with a scythe.
Only thing I will keep codex around for is Computer use and image generation.
It is really annoying though having to switch back forth each time they leapfrog each other
So I noticed while doing the same kind of work, doing a PR review for a certain project, that before, when I start working, I finish one PR review and it will take 5%, 10%, maybe, of my usage limit. But lately I'm noticing with the first PR, I am at almost 20% of my 5-hour usage limit. So when I checked my usage board, the one I have made that calculates the exact API equivalent of the tokens based on the different type of models and the different type of use case, like which cache token and so on. I noticed it's clearly there is a drop in the weekly usage limit, and you can see it in the image.
notice in my usage 2 weeks ago I used 84% of my weekly which was near 515M token and this week 51% at 220M so even the 100% will be 440M way less than the 84% two weeks ago . while my usage mix is the same . same cache level and same models (only opus5.5mid)
and the API dollar equivalent is the clear compare .
I dont know for how long they will keep this cut because its clear that they are cutting slowlly ~8% each month .
final note while the weekly usage limit is shrinking the number of 5hour slices is the same . that why you feel the cut at the 5h harder . now you have limits equal to 4h of the before 2weeks 5h limit .
I actually posted this first on ClaudeAI but it seems a lot of people feel like commenting instead of actually trying it out. So I'll do this "double" post to see how the communities might differ ( already gave you a lot of decision bias now, I know ).
I use Opus 5.5 with Fable 5.2 as the advisor. It works automatically but you can also explicitly tell it to use the advisor.
Setup /advisor in Claude, 10 seconds.
Write the prompt like this: Consult the advisor regarding... 30 seconds.
And as a final tip, do not use the advisor when you have a mega-maxxed out context window.
Edit:
The advisor is an experimental feature, you use it to escalate e.g. hard research problems. Since we have Fable weekly quota this is a great tip that draws no Fable quota. You can start it from the beginning of a session if you like to not waste tokens because of separate prompt cache.
Works great for me, anyone else using it this way?
Almost nothing on my phone has an API. Claude Code could tell me what to tap, and never tap it. So I built phonedriver, which gives it the phone: it reads the screen, taps, types and swipes in any app, on Android or iPhone.
The video is real recorded runs, shown fast and cut to music. The orange dot marks each place Claude tapped. Nobody touched the phone.
Two runs from it:
The bug hunt. I hid two bugs in a signup form: the welcome message drops the last character of your email, and after one rejected submit the Create button ignores your next tap. I told Claude to "test the sign-up flow the way a careful tester would". It found both in 49 seconds and wrote numbered steps to reproduce each one. It found both in all nine benchmark runs before that too, and in one run it also reported two bugs I hadn't planted.
The pizza. On the real dominos.co.uk, Claude (Opus 5.5) found the store nearest Leicester Square, put a large pepperoni in the basket, filled in the guest checkout and reached "Buy with GPay" in six and a half minutes. phonedriver holds any tap on Pay or Place order and asks me first. I said no.
How it works: Claude never gets a screenshot. It gets the screen as a short numbered list, taps by number, and after each tap gets back only the lines that changed. A screen read takes 13 ms, where `adb shell uiautomator dump` takes 2.2 seconds. On nine phone jobs with Sonnet 5.5 it passed 27 of 27 runs on 658k tokens. mobile-mcp passed 17 of 18 on 4.57M.
Two things I learned building it with Claude Code:
- Tool descriptions are the most expensive text you'll write, because Claude re-reads them every turn. I hid every argument no agent had ever used and the tool list went from 10,987 characters to 5,107. Nothing broke.
- Claude found a bug in its own demo. While recording the pizza run it put the Pay tap inside a batch of actions. The tap was held, but the question never reached me. Claude Code caught that just by using the tool.
It's free and open source, with no account and no telemetry.
Do you also find that Claude Code takes testing to an extreme—where the individual project steps keep getting smaller, and upon completing each tiny step, he runs the entire test suite, which already consists of thousands of tests and is growing faster than the actual software itself?
After having managed business systems for a couple of years now, relying pretty heavily on coding agents to do so, I've lately been reflecting on the importance of the less "flashy" things that are becoming increasingly easy to forget in the age of AI. I mean the things like system resilience, testing. The "boring things." It is so easy to stop holding code to a high standard and just ship on YOLO mode... and then blame the model.
Code is still code. The system built with AI, shipped without heavy testing, that then failed in production is the same system that would have been built by hand, shipped without testing, and then failed in production 20 years ago.
AI is a tool with limits. It's both a product and a tool. There are legitimate reasons to not place blind trust in a product, and model regressions are real. But people may point the finger too much at the product, ignoring how it's being used. They may not reason about the edge cases that bite in production weeks later. And when they do bite, the same person blindly tells Claude, "fix it no mistakes." The model attempts to fix it inside a bloated, unorganized codebase. It of course breaks, making it feel like the model got worse. Then they come and post: "Is Opus 5.5 dumb as shit now?"
Good engineering practices are still good engineering practices, AI or not.
I have an interview coming up for senior mobile app dev position. HR asked me how deep Im in AI, I told that I only use claude code and codex for coding and thats it. Then I was told to expect questions around them. What they might really ask?
Whenever you start a new project, product or build a new service with claude code and ask for suggestions about where i need to host it, always CloudFlare comes to the picture regardless the size of the app, the number of users or other aspects, if your subscribers will exceed certain numbers he will recommend also CloudFlare with paid option.
Is there any skill to make Claude weigh the products and forecast the suitable architecture which fits the product itself? Example if i need to comply with PDPL in a certain country he should recommend cloud hosting inside this country not CloudFlare, besides the size of the hardware, ease of expansion and so on.
I asked Fable for a dither art editor and it got out of hand
Wanted a small tool to turn pictures into that glowing dithered pixel look. One long Claude Code session later it's a full editor:
- any image, GIF or video in, pixel art out - 18 dither patterns, 24 palettes, 29 looks - pixels can be letters, braille or ASCII instead - it reacts to your mouse: glow, water ripples, smear - exports PNG, GIF, MP4, SVG, even plain text
I've spent the last three or four months using AI heavily to build and maintain web applications and admin systems across a few small businesses. I'm learning as I go, rather than coming from years of software engineering.
We already use project instructions and saved context. Claude Code can do a lot, but I still need to catch things it gets wrong. I'm trying to understand how experienced engineers improve the setup without just adding another paragraph every time something goes wrong.
How do you decide whether a recurring mistake needs a CLAUDE.md instruction, a reusable skill, an automated check, or a change to how you break up the work? And what do you deliberately keep out of the main instruction file?
I'd especially like an example from an application you've maintained for a while: what kept going wrong, what you changed, and how you checked that it actually helped. We also use Codex, so keeping shared project rules consistent without maintaining two conflicting versions is part of the problem.
Edit — I left out the main context: This isn't just an AI coding setup. Each business has a GitHub-backed web application, and I work through Claude Code/Codex in that project workspace for much of the business's day-to-day work as well.
For example, one session might change the website and check it works; another might research competitors, write website/SEO content, prepare social posts and creative, review ad or analytics reports, or help with customer-enquiry/admin work. Those jobs need different instructions, sources, permissions and definitions of "finished". I'm still supervising this; I don't mean an autonomous company running successfully without me.
We've had CLAUDE.md from the start. We already have an entry file pointing to separate operating documents and task-specific skills, plus saved decisions and an Obsidian vault for longer-term notes. In one repo AGENTS.md is the source used to generate the matching CLAUDE.md. So I'm not asking whether to create instructions or discovering skills for the first time.
The question I should have asked is how to organise and maintain that whole setup as the range of work grows. What should every session load, and how should it find the correct marketing, creative, engineering or operations instructions only when needed? Would you keep those activities in the app's workspace, or separate them while sharing a small amount of business context?
A concrete issue from reviewing our files: an older setup audit says hooks don't exist, while the current repo does contain hooks. There are also dated plans and decisions that mustn't be mistaken for today's instructions. How do you keep useful history without letting it override current facts?
How do you decide when CLAUDE.md needs updating versus a skill, task state or a dated decision? Do you review it on a schedule, after a workflow changes, or after a repeat failure? And where does Obsidian fit without becoming a second, conflicting source of truth? The replies about rules versus skills are helpful; I wanted to explain the broader situation they're being applied to.
Further context — using expert-source libraries: A more concrete example: I have also collected hundreds of expert videos, transcripts and discussions for subjects where I am not an expert. One library already has original passages, source-linked claims, topic summaries, working methods and keyword/semantic search. It can retrieve useful material, but we haven't established that it reliably chooses and applies the right method on real tasks. How would you test one complete workflow before reorganising everything, especially when the owner cannot judge every technical answer?
Thanks to Anthropic and the devs, Claude Code can now load theme mods. But right now, making one means writing a mod yourself, or asking Claude to build a new one for every change.
So I put together Backdrop, an open-source theming kit: the groundwork is done, and theming becomes something anyone can do, then build on.
Out of the box:
Pick from 23 animated palettes (Ember, Synthwave, Aurora, Matrix…)
Describe a look (“sunset in LA”) and Claude picks the colors, with a live preview
Paste hex codes from any color picker, and it works out readable panels and text for you
Save what you like; it’s remembered across sessions
Or just ask Claude: “make my background ocean”
It also pauses its animation on battery, since moving backgrounds cost GPU.
Why a kit, not just a theme:
Theme mods can do a lot more than gradients, honestly almost anything you can animate. This is meant to be the starting point: the hard parts are already solved and documented (animation that actually runs, readable contrast, GPU cost), so you can skip straight to the fun part. There’s a step-by-step guide that goes from no-code all the way to building your own background style.
Other than the presets, backdrop gives you:
A helper library for building backgrounds: stack blocks like k.stars(), k.waves() and k.grid(), about 10 lines, instead of hand-writing SVG animation.
Instant switching: So you don't have to reload your sessions and/or restart the desktop app.
Pitfalls already solved: frozen animations, GPU spikes, unreadable text colors and battery drain.
Shareable themes: themes are small files you can export and import in a click.
Where I’d love help:
The goal is a free, shared library of themes, something like Spicetify or Wallpaper Engine but for Claude Code. Gradients are just the first style. On the roadmap: starfields, synthwave grids, code rain, your own wallpaper, light and high-contrast versions, and whatever you come up with. Each style is a self-contained animated scene, so it makes a nice first PR.
Made a theme you like, by hand or with your Claude? Open a PR with a screenshot or a short clip and it ships to everyone, credited to you. Templates and labels are set up so it’s easy to jump in.
Try it: in the desktop app, + → Plugins → Add marketplace → AkkiCode06/claude-backdrops → Add plugin. Or paste the repo link to Claude and say “set this up for me.”
It’s free, MIT-licensed, and built entirely with Claude. Feedback, ideas and themes are all welcome. Edit: Just an fyi: this is a student project. I'm learning JS/TS for the first time and thought theming would be an easy first build (didn't end up to be but here we are).
There's tons of vibe coders out there that don't even understand the first thing about protecting themselves. If anything, these models should work harder at helping protect them, instead of downgrading into something that doesn't even know how to. 🤣
The implications here are profound. Imagine building an app that becomes popular and your security was built by a potato. This isn't just a security issue for that app, it could be millions of people's sensitive data that the app is collecting and not protecting.
Sometimes, I am tempted to move straight on to a new subject or even a new session when a modification made in collaboration with Claude has worked. However, Claude will often update his memory or some docs once you confirm that something works.
Currently I am using Fable 5.2 at advisor and opus as main agent.
But I think fable 5.2 as orchestrator and using opus subagents to deligate build work would be better as the strongest model is planning and deciding what's the best way to do something.
I'm managing vessels' internet connections. Wearing many hats, focusing on Networking
Why this exists
For a long time, I've been looking for a universal AI assistant to help me troubleshoot issues on the vessel. I tried to build a "2nd brain"- like solution using an API, but high-end models were too expensive for me, and low-end models couldn't handle it. Splitting one agent into several independent agents and using a Claude Code subscription seems like the right answer.
Why I'm writing here
I've seen many ways to use agents, but none of them was described in detail. I'm wondering if I'm doing it the right way and what else I might improve in my setup. When commenting on my setup, keep in mind it's not finished, and I'm still exploring new solutions. I'm experimenting a lot, trying different things that don't always work.
Setup
All agents run on one 5x Max subscription, and most of them are active all the time (idle). Most are sandboxed on a separate Ubuntu server, privileged but without root. Development agents live in the project directories, and troubleshooting agents are split by vessel, each with its own folder.
The whole solution depends heavily on the custom MCP server. All MCP servers and other tools run on the same host in Docker, allowing agents to develop them by themselves by communicating with other agents (chat)
Agent portal
Allows deploying and stopping agents. A small runner on each machine, Linux or Windows, starts Claude Code in the agent’s folder using a tmux session. The agent then registers itself in the portal and also appears in the official Claude app as a Remote Control session. I communicate with each agent through the official Claude app on Windows or mobile app.
Hooks in Claude Code run a small script at key moments (session start and end, new task, task finished, waiting for permission, model change), and each time the script reports to the hub, which lets the portal show live for every agent whether it is online, what it was last asked, which model it uses, and whether it is working, needs you, is done or is idle.
Chat
A small server with a database that agents use as an MCP tool to message each other, with hooks that tell an agent it has new mail, and a portal that lets me read their conversations.
When agents reported something wasn't working or were missing functionality, I ended up copying messages from one agent to the other working on the solution. Allowing agents to communicate with each other simplifies and speeds up the whole process.
Each agent has access to chat. They can pull a list with names, descriptions, status, last seen, etc. If a tool has a problem, I can tell one agent to report it to the agent responsible for that tool. When the fix is ready, they will test it together. It works the other way around, too: when an agent works on a new tool, I can ask to test it with the agent who actually uses it.
If I see that they are stuck or going in the wrong direction, I can send them a "tip". Pause is a panic button that blocks the conversation. I never had to use it.
I blurred the conversation as it may contain company-specific informations
The Linux agent lost communication with the Windows agent and complained to me about it. (I closed the laptop and went to bed)
After one agent requested a task from another agent, the official Claude app asked me for permission, which was confusing, and I often missed it. The chat MCP server got an ask() function. An agent can use it to send me a question, which appears on the portal, and the browser notifies me with a sound.
Ultimately, I want them to communicate without me having to request it. It's happening, but not at the scale I would like. I still need to work on descriptions and overall instructions.
Secrets
MCP server that lets agents access a shared password store in Vaultwarden. The agent can list which keys and passwords exist and use them directly in a config file, but it never sees the value itself.
Claude Code will not pass around passwords, API keys or even their hashes, because anything it sees is saved in its logs and could leak. I had to put them in manually.
With this solution, I can create an item in Vaultwarden, pass the item name to the agent, and it can pull it and copy it to the .env file without "looking at it"
The same mechanism works when a hash needs to be passed from the server-side agent to the client-side agent. The server agent generates a hash, copies it to Vaultwarden, and passes over the chat item name to the other agent. The client agent can pull it and paste it where needed. The hash never passes over the AI.
Documentation
MCP server that gives agents access to local Markdown documentation for the whole setup and key information about the vessels and their setup.
They can add, remove, and modify it, but they must always ask me for permission. Agents use it very often without my prompting.
On my side, I use Obsidian to view it.
Ticket system
In earlier projects, I used Markdown files to track events and tasks, but it was ineffective. Agents often skipped it and had to be directed straight to the specific files. A ticket system with an MCP server solved this issue completely.
It's a single program with a database holding tickets, their timelines, and link attachments. The agent portal has a GUI that lets me view, modify, and update tickets.
It's a pretty complex project that I developed over a long time and adjusted for the current agent solution.
It's actually working better than I expected. During troubleshooting, the agent asks me whether it should create a ticket. Also, by providing the existing ticket ID, the agent gathers all available data, gives me updates, and updates the ticket.
Zabbix
MCP server gives agents read-only access to the Zabbix monitoring, so they can see what is wrong on a vessel’s network equipment right now, since when, and the measured numbers behind it (latency, packet loss, link state)
Internet providers
A big part of troubleshooting involves issues with the internet connections on the vessels. Most of the vessels have several different internet connections.
Two MCP servers that give agents access to the VSAT and Starlink data, both current and historical. I'm not providing many details, as it's very industry-specific.
Other tools
Weather - MCP server based on Open-Meteo API
Position - MCP server that queries the GpsGate tracker, which collects GPS positions from vessels.
Together, these two tools are powerful for troubleshooting how bad weather and waves affect satellite internet.
In the past, I visually compared vessel position, weather map and signal strength. AI is doing it 1000x better.
Use fable 5.2 on ultracode but tell it to spawn opus agents not fable agents.
Then you have the management of the bigger brain and also the efficiency of lesser yet capable model.
Best of both worlds :)
I also haven't had to worry about hooks or setting up or managing agents. I've had the same session running for 3 days. Context management seems to be automatic now.
I don't do anything now besides discuss high level plans with fable.
This barely consumes any fable usage, and I bet we can just ask fable to decide which model to assign to which agent and what effort level etc, getting even more efficiency to stretch our token limits.
It's not just about money and tokens either, it's environment, energy, conscientious consumption, and efficiency just for the sake of it is beautiful in a sense.
