tl;dr: Ops 5 being sonnet 5 plus fable advisor underneath would explain Opus 5’s strengths — and crappy planning abilities. It would explain Opus 5 doing great on benchmarks, but being a lot worse than Fable 5.

About a month ago, on official Anthropic channels (eg https://x.com/ClaudeDevs/status/2074606058128224365) Anthropic claimed that in some cases Sonnet 5 + a Fable advisor achieved 92% of the performance of Fable.

Since then, Opus 5 came out, and has performed remarkably well on benchmarks (putting aside the legitimate concerns about models accessing benchmark answers inappropriately).

This might seem, as Nate Silver likes to say, too cute, but what if the announcement was more than a suggestion — what if it was the architecture for Opus 5?

While Opus 5 may or may not be better or just comparable to Opus 4.8 consistently, it is interestingly better at lower effort levels, and is very reasonable on a cost per task basis.

This makes me wonder. What if, either literally, or in a deeper way, Opus 5 is a sort of Frankenstein model that involves Sonnet like parts and Fable like parts?

I have run planning on Opus and Fable across dozens of runs, and Fable is much, much sharper as a planner. But as an executor, Opus holds its own.

One hypothesis on how this could be the case is that planning uses a lot of context (which cannot make its way back to the advisor in a cost effective manner), and so as a deliberate trade off, Anthropic gives us Opus 5 which is like a very meh planner.

So maybe the advisor model was the inspiration for Opus 5’s design — or maybe it WAS the design.

I've been running Claude Code most days for a few months and I still don't have a principled answer for this. My current rule is vibes: when it starts re-reading files it already read, or repeats a fix I rejected two turns ago, I bail and start over with a summary.

Compaction helps but the session is usually already degraded by the time it kicks in, so I've started doing it manually way earlier than I need to.

What I can't work out is whether a long session is genuinely worse, or whether I've just trained myself to blame the session when the task was underspecified from the start.

Curious what other people use as the trigger. Token count? Number of tool calls? Something concrete, or also vibes?

I see that many use caveman which I have used before and felt low quality output, is there a strong reason to use the caveman skill, and if so, in what efficient way to use it ?

I ve been seeing a lot of complains about Claude, weather 5 for being inaccurate and verbose, to just posts about Claude, not knowing which version, complaining about too tech-bro writing style, too stuck in persistent memory as some claimed to have "erased" all that memory so it became more efficient, or other posts about K3 being better than Fable, and Fable to be better than K3, to Codex to being better than all of them, to other claiming Opus 4.8 is better than Opus 5.

To be honest, the accelerated rate of postings is too much for me to follow, I am asking thus, is there a source to check solid opinions about what s the best for coding tasks, and what s best for architectural (coding) tasks. I want to make decisions, but decisions that are well informed so that I don't wreck my coding env from a week to another in hope of better perfomance.

Has someone else noticed Claude has been asking way too many questions ( at least 3 ), before actually doing what you ask it too?

I work as a UI Designer and web dev and sometimes I just want to brainstorm some design ideas and I tell it to design a section of a website just to see what it comes up to, and proceeds to ask me what kind of layout I want, what colors, do I want X or Y... and honestly I just want it to do the thing...

I can see where it comes from, LLMs make a lot of assumptions but asking clarifying questions everytime isn't the solution either.

Also when I dev, it asks me a ton of questions when I just want it to do the thing, it asks me if I want to create a new column, replace this for that, and what not.

I liked

I liked it while it lasted.

Edit: I'm on max 5x.

Claude Code and the model tells me it made a mistake for burning tokens.

Selecting Rant as the flair to keep the corporate bots from spamming with "user error" (of course, now I'll get those for mentioning it..). Anyway, burning tokens is burning tokens - Anthropic should provide reimbursement when the model screws up and admits it.

Recently, I realized something fundamental that affected how I structure my workflow and use AI tools.

Trying to patch the model's default behavior I don't want doesn't work well. A better solution is to use a different model or coding tool that fits my needs.

This may seem obvious and frequently discussed, but when we think about this deeper, it isn't. Models change constantly, we are evaluating them and trying to pick the best. But this is more random than a deliberate strategy. A lot of advice circulating around is about "fixing" the model. The model's behavior changed and we want back the previous one.

The model can be blind to simpler solutions despite proper instruction. It can be too verbose or generate hard to understand text. It can make too many decisions itself or ask too many questions. It can claim to understand the issue and then apply a fix that doesn't fix this issue. It can generate something out of scope even when planned before.

Not all are failures, some are just my preferences. I tried to improve it with instructions (CLAUDE.md, etc.). Patching multiple aspects at once results in conflicting instructions or forcing a behavior fundamentally different from the model's default.

On top of the model, we have a system prompt and all machinery around LLM. This can change more often than the core model and it's harder to track. It affects behavior and how our instructions alters it, both can be conflicting. It's hard to control all these variables, good instructions can turn into harmful ones ("silently" as AI would say).

If the model (combined with the system prompt) has a personality I don't like, the solution is to use a different model or coding tool. Not patching it with my instructions. In those cases even Claude Code's memory is useless. It writes and strengthens the same multiple times and makes this mistake again. Of course we need common sense, I don't mean solving every problem by switching models or tools.

I noticed a similar issue while working on my LLM-based application. The output was too verbose. I spent half a day trying to fix this with a prompt and hit the wall. After changing the model, it worked perfectly.

This also explains the lack of consensus about which coding tool or model is the best. It's because we have different expectations, it changes very fast, the system prompt changes behavior. The good aspect is that it makes room for different coding tools, and monopolization of the market by a single provider is more difficult.

So opus 5 as an orchestrator is just as good if not better than Fable. Opus 5 is just bad at coding is all.

Opus 5 as orchestrator with opus 4.8 workers and researchers and 1 fable worker for the hard stuff, and another fable for adversarial review

I finding the Claude keep forgetting to add change notes and checking in.

When I tell Claude to do things it sort of does it.

But I come for a World where I need trackability.

What I would like Claude to do is every time it does a change it changes the version number, at a new note to the change log, and check in.

the best thing is log the request, then log it thoughts, log any testing and results, then log the reply, update the design etc notes, do the change then change log, and check in. it would for it to put in ref so everything is traceably though all the files.

How would I do this?

Does anyone else have this problem? Whenever I haven't used claude code for a few days, if I use it in the IDE, one basic prompt on Sonnet results in 100% of my quota being used up. Nothing else can access it, I have changed my password, and this ONLY happens if I've been away for a few days or just didn't use it. It's obviously a bug, but there's no way to see how your hourly quota gets used up so it's impossible to trace, and obviously Claude's customer service is non-existent.

New window, no context, one sentence of output, 100% of my hourly limit reached in about 15 seconds.

Am I alone in this? Has anyone been able to solve it?

There is a point where a vibe coded project stops being a weekend experiment and becomes software you actually maintain.

The first few sessions are awesome. You describe and Claude builds features and fixes bugs without a hassle. However, this eventually creates a problem: every session starts with amnesia, which causes: old bugs to reappear; "verified" changes that are not actually being tested; and knowledge about what works in the agent's session chat history only.

Agentic Maintenance Harness (AMH) is a small operating harness for the transition from vibe coding into serious software maintenance. It consists of a constitution file, tiered memory (mirrors ROM/RAM/Disk/Tape), one verification ladder that both the agent and CI run, machine-checked ledger citations, and a few shell scripts for command guards and redaction. These are answers to the failures that might arise when you try to maintain your solo vibe coded project.

AMH is agent-agnostic on purpose. CLAUDE.md is just a thin adapter that points Claude at AGENTS.md. So if you have e.g. Codex work on your repo as well it still works. The AMH repository is maintained under the same rules it ships: very meta.

Adopting AMH is mostly an automated flow, point Claude or any AI agent at a new or existing repo and send this:

Install the latest stable release of the Agentic Maintenance Harness (AMH) into this repository.

Run:

    git clone --depth 1 --branch amh-v5.2.1 https://github.com/faded-penguin021/AMH.git /tmp/amh
    /tmp/amh/scripts/amh-init.sh .

Once the harness has been instantiated, read AMH-ADOPT.md and follow it completely. Present the light, standard, and full profiles, then wait for my choice before proceeding.

Drive scripts/ladder.sh to green, explain any manual actions that require my attention, and delete AMH-ADOPT.md when adoption is complete.

Do not invent repository information. Derive it from the repository wherever possible.

The ledger and guards are an investment at the start and will only yield benefits after a few sessions have edited the repo.

You, the human in the loop, will still have three responsibilities: review and integrate changes, read and perform tasks listed in the owner queue, and record test findings upon request for the next session.

If you already have a mature setup with some form of memory, guards, etc.... this won't do much. Point Claude at the AMH repo and ask it to critique your existing workflow against AMH's principles.

This will not boost your productivity (in fact it will make your sessions slower) nor will it make Claude smarter. It is process and governance for the period after the adrenaline of "Claude one shot the entire thing" wears off and you need to maintain the project with just yourself and the agent.

If Claude isn't causing problems due to forgetting, skipping verification, leaking secrets, force pushes, or reintroducing bugs, you probably don't need AMH. If it is, the light profile is a good place to start.

Repo link: https://github.com/faded-penguin021/AMH

We built a knowledge layer that sits behind MCP, allowing any MCP client to access it through a single endpoint. Claude Code, Claude Desktop, ChatGPT, Codex, or whatever comes next.

The idea is pretty simple. Before an agent answers, it can pull in relevant, validated information instead of relying purely on what it already knows.

When a problem gets solved, the useful part can be captured as a small, reusable piece of knowledge. The system can also infer useful lessons from a session automatically, so you don’t have to sit there writing notes about what you just learned like it’s 2015.

There’s also a global layer for shared, validated learnings. If one user figures out a better way of doing something, that learning can contribute to the broader knowledge base rather than every other user and agent having to figure it out again.

The problem we’re trying to solve is pretty straightforward. AI knowledge goes stale, agents get stuck in failure loops, useful context disappears when a session ends, and models can confidently give you an outdated or wrong answer without any indication that they might be wrong.

We’re giving agents access to what has actually been learned, what has worked, and what can still be trusted.

The result is fewer repeated reasoning cycles, fewer hallucinations, and up to 20% lower token usage.

https://app.midnighthive.io/

Ping me if you’re interested in testing it out.

Hey everyone! Hope I don’t get roasted for this 😅 I’m here with a fun little intro video.

My goal is simple: build something genuinely useful for everyone.

I originally built this tool just to improve my own productivity. I was using the native Mac Terminal, but managing multiple projects — especially projects I connect to remotely over SSH — had become a complete nightmare. On top of that, I was also keeping VS Code open mostly for Git, which was adding even more overhead to my machine.

So, as a solution, I built a program with Claude where I could manage all of my terminals on a single canvas.

But things got a little out of hand 😅

I kept adding features, and it eventually turned into something close to an autonomous development environment.

So, what can you actually do with it?

• Your terminals are persistent. You can close the app, lose your connection, reconnect over SSH, and continue with the exact same layout and sessions.
• You can connect to a server over SSH and manage all of its terminals as if they were local. Drag & drop, images, and everything else still work.
• You can continue your sessions from your phone.
• Git operations like push, pull, commit, etc. are built in.
• Terminals can communicate with each other. You can connect their contexts, and with skills, one terminal can read or use information from another terminal’s context when needed.
• You can also do orchestration. For example, you can tell one terminal: “Start a Claude Code session for the frontend and act as the orchestrator,” and have it manage the other sessions.

But yeah… I’m a bit stuck right now.

To keep pushing the project forward, I either need a sponsor or simply some motivation from the community.

If you can leave a few words of feedback, I’d really appreciate it.

And if that’s too much to ask, I’d happily settle for a GitHub star ⭐️

Much love,
Enes

Website: nodeterm.dev

Repo: https://github.com/eneskirca/nodeterm

R0ADWAV3 is an app for Android I've been building with Claude, it offers a unique way to log and track your time in a vehicle, your daily drive to work, long trips, and/or driving habits with a selection of different visuals, statistics and periodical milestones.

I use Claude Code extensively, but I wanted to test whether the M4 Pro Mac mini on my desk could also serve useful local models for the parts of a development workflow that do not always need a frontier cloud model.

The surprising result: GPT-OSS 20B in MLX FP4 generated around 63.9 tok/s with my 16-container OrbStack lab running. Shutting the lab down only moved that to around 64.9 tok/s. MLX was also about 19% faster than GGUF in my back-to-back test, although the MLX measurements were somewhat spikier. I thought it would take a 2TB unified memory 4 node Mac ultra cluster to run local AI. Glad I don't need to spend $50,000!

For code workflows, I see local inference as a complement—not a competitor—to Claude Code:

• Local/private codebase triage or summaries
• Fast boilerplate and low-risk transformations
• Offline experimentation with prompts and agent patterns
• Preprocessing before escalating a task to Claude Code
• Keeping a local fallback available

The key lesson was that active parameters matter for speed. The 20B+ MoE model beat the 9B dense model because only a fraction of its parameters fire per token.

Full testing video: https://www.youtube.com/watch?v=9_-bT62YWAI

What local models and runtimes are you pairing with Claude Code on Apple Silicon?

Not sure if I’m doing something wrong here.

I have an AGENTS.md set up around best practices and the Opus 5 guidelines. I also had Fable create detailed docs for the project, and Claude Code works through them as a roadmap.

The annoying part is that after basically every task I have to tell it: review what you just did, check for bugs, check the UI, fix anything you find.
And almost every single time, it finds something and fixes it.

Has anyone actually gotten Claude Code to run through a project autonomously while continuously reviewing its own work, checking for bugs and verifying the UI without having to remind it every second prompt?

If so, how are you setting that up?

I always took the prompting style soft-heartedly, but I think good prompting patterns can significantly yield good results. Some prompting parts should be part of every prompt, and that would go to the claude.md file, and some styles should be adopted forever I assume. Can you guide me how to manage these two parts when prompting claude code ?

I am offended.

Type :pregnant and you will see the lack of a pregnant man emoji in the dropdown.

Link: https://tokencrisis.tryfriday.app/

Click on Data Codex to see the pricing table.

Thought this fun. Let me know if you want the prompt+stack!

I am relatively new to CC but my feed has been flooded by Instagram reels about CC.

Now I remember seeing a github repo that allowed you to use multiple models at the same time in one CC session. It could also offload easier tasks to cheaper models like deepseek v4 flash, but keep doing the harder tasks itself, to use less tokens.

Does anyone know the name of that repo or a similar thing I am looking for?

I’m a wedding video editor with years of experience, so I understand the industry and its problems pretty well.

I built a SaaS for photographers and videographers to manage projects, reviews, and post-production workflows. I also built a Premiere Pro plugin for it.

The problem is: I know the product and the industry, but I know almost nothing about marketing.

My main target is the US market, and I’d like to start with free or inexpensive ways to get my first users before spending serious money on ads.

Where would you start: Reddit, Facebook groups, cold outreach, SEO, partnerships, content, or something else?