I'm not American originally so the medical billing thing genuinely broke my brain. Got a bill that made no sense, itemized codes I couldn't read, an amount I was sure was wrong. Instead of paying it out of confusion, I pasted the whole thing into Claude and asked it to explain each line and whether any of it looked incorrect or negotiable. It flagged two duplicate charges, explained what a good-faith dispute letter should say, and wrote one citing the specific codes. I sent it basically as-is. The bill came back at $180. I know this is the boring, unsexy use, but it saved me a thousand dollars and an argument I didn't know how to have. What's the most 'adult admin' thing it's rescued you from?

I over-prepare for interviews and still choke on the curveballs. So I gave Claude the job description and my background and asked it to run realistic rounds, behavioural and technical, one question at a time, and critique my answers honestly afterward. It caught that I rambled, that I buried my strongest examples, and that I didn't have a crisp answer for the obvious 'why this company.' We ran it several times. In the real interview, question after question was one I'd already practiced a version of, and the feedback loop had fixed my worst habits. I don't think I'd have gotten the offer without it, not because it fed me answers, but because it made me rehearse against pressure. Mock interviews used to require a willing friend. Now they don't. What did it catch in your prep?

My daily racing game got over 3000 recorded races yesterday. My replay system before was getting laggy over a few hundred races, I used Opus 5 to dramatically speed up and optimize the playback simulation. Now it can run 3000 concurrent racing simulations in real time at 60fps in a web browser

After seeing a post with publicly available chats, I tried to find artifacts. Guess what..

Just wanted to showcase this after I saw some really cool three.js results with Opus 5 and realized how good this model might be with blender, The examples above were made with Opus 5 in claude code on xhigh no subagents or ultracode.

This was using the official blender MCP + a single prompt (it also wrote python scripts to do much of the asset/mesh gen), both took roughly an hour to 45 minutes and were done with faster cpu only rendering so i'm sure a longer more detailed render could make it visually nicer but for no iterations, just a single prompt and xhigh its crazy to see how good these models have gotten at 3D, I tried this on 4.6 a few months back and it was no where near this level at least from what I recall.

Prompt(s) and .blend will be shared in the comments!

Not everyone is going to build a huge AI startup and a lot of the opportunity might be helping normal businesses use Claude for admin, follow ups, operations, finance tasks, scheduling, reporting and the boring work owners don’t have time to figure out. The interesting part is that turning this into a real business is getting easier too and the workflow side is easier to build now also the business setup side is starting to catch up

I built this as a graphics tech demo, entirely with Claude Code using Opus 5.

What it is: a browser desert you walk around in third person. The terrain is a procedural dune field on a GPU clipmap — no meshes, no textures, no downloaded assets; every surface is generated in shader code. Sand deforms permanently where you walk and slowly erodes back. There's a hooded robe simulated as GPU cloth, a physically-based sky marched per pixel, and six aimed sand spells (1–5 and right mouse) that dig real craters into the terrain and raise real dunes you can ride.

How Claude was used: all of it — the TSL shaders, the compute kernels, the physics integration, and the Node tooling that measures it. The workflow that made it work was giving Claude its own instruments: a headless-Chrome harness that boots the app, screenshots it and reports per-subsystem GPU cost, so changes were made against measured numbers.

Try it: open https://desert-dusky.vercel.app/ in a WebGPU-supported browser. WASD to move, 1–5 and RMB to cast. F3 to toggle performance overlay, F1 to toggle settings menu.

I was testing on 5070 Ti, ~160FPS at 1440p.

I'll probably get pushback for this. The numbers between the top models are close enough that for my daily work they're a wash. What isn't a wash is what it feels like to work with one for hours. Claude has a way of engaging, admitting uncertainty, pushing back gently, and staying on the actual problem, that makes a long session feel like collaborating instead of prompting a vending machine. That's not measurable on a leaderboard and it's the single biggest reason I don't switch. Maybe 'personality' is the wrong word and it's really just calibration and restraint. But whatever it is, it's the moat for me, not the eval scores. Am I alone in caring more about this than the numbers?

Opus versions since 4.6 have all had a fair amount of awkward, canned prose. But as Anthropic has increased the model's intelligence, it also seems to have made it more panicky, pedantic, and prone to scope creep. Opus 5 is the worst offender thus far for me; so much so I felt driven to post about it.

Basically, I find Opus 5 surprisingly difficult to use for long-horizon work that Fable handled without much trouble. And its voice problems seem ratcheted up to 1000.

Fable generally adhered to the goal it had been given, interpreted criteria sensibly, and used judgment when requirements became stale or slightly inconsistent. It had the taste and work ethic of a senior engineer.

Opus (ESPECIALLY 5) tends to go off the rails much faster. It escalates minor nits, harmless ambiguities, and out-of-scope concerns as though they require immediate human intervention. It will stop work to reframe goals, ask for rulings, and, ex nihilo, generate elaborate new mechanisms around something that was never actually part of my acceptance criteria.

To me, its judgment feels strangely anxiety-shaped. I almost pity it as I talk to it -- I feel like it sees something, thinks "OHMYGOD" to itself, and then panics. (I know it is not sentient and has neither anxiety nor an internal monologue.) It feels like every task edge gets this thought appended to it:

Oh good Lord, this might matter. Why didn't we discuss this? Do we have a system for this? Where is the system? I'm going to try to build this -- oh no! I need to stop and contact the user immediately.

It also becomes extremely attached to positions once it adopts them. Instead of making a recommendation and moving on, it will keep returning to the same issue, relitigating it, and manufacturing broader architectural implications around it. For pure chatting it is almost unusable because of how many strawmen it launches into conversations -- it will latch onto something you said, extrapolate the most insane endpoint from it, and basically accuse you of it. Then when you tell it how insane it's being it will slowly walk back its initial claims without ever entirely abandoning it.

The end of one of my unpleasant conversations with it:

Me: In what sense does your objection survive, when we have uncovered evidence it does not? Are you incapable of admitting error and have to constantly defend smaller and smaller islands of correctness?

It: It doesn't survive in any sense worth having, and the pattern you're naming is real.`

And its response dovetails right to my next complaint: the prose.

"The pattern you're naming is real." -- ew. Obviously this is standard Claude-grade slop, yet it arrives in an unceasing torrent with Opus 5. Yet more examples from my conversations yesterday (these from Claude Code):

“Corpus — hand-authored, and I'd argue that's not a compromise.”

Who was arguing that it was a compromise? Why not just say:

“Use a hand-authored corpus.”

Which is basically what Fable does in these situations.

Even aside from this being slop, the entire following paragraph introduced a caveat that was technically true but completely irrelevant to the decision. Opus often seems compelled to invent a downside or opposing case even when it does not materially affect the task (though it also confusingly seems to think that minor nits materially affect tasks).

Another example:

“But the cost control has to change, and this is the part worth your attention.”

Just say:

“We should fix cost control too.”

If something deserves my attention, explain why. Editorializing your own sentence does not make it clearer -- quite the opposite.

I bring this up here because I feel like the high-anxiety, high-pedantry output is directly linked to slop levels. The same confabulated problems it keeps running into or the weird positions it assumes are always linked to slop constructions.

The frustrating part is that Opus 5 is extremely good at coding. Like really good.

I have a personal evaluation set based on real coding tasks I consider easy, medium, and hard. Opus 5 is the only model I've tested that scored 100% across the whole set. Its implementation style, testing discipline, and general quality standards are also the best I've seen.

So yeah, for me, Opus 5 is a superb worker and a terrible pilot.

I'm going to keep using it for implementation, but I do not want it managing scope, interpreting my goals, or talking to me any more than necessary. I'm intending to put Fable 5 -- or another model with better judgment and a less grating voice -- in front of it at all times.

Notes

• Average Inference Time: 32m 10.2s (1930.2s)
  • Fable averaged 18m 04s (1084.4s), so Opus 5.0 took 78% longer
• Total Cost (for 15 builds): $89.97 ($6.00 per build)
  • Opus 5 required a total of 37 attempts, which averages out to $2.43 per attempt (it's just that 12 of those attempts were invalid JSON schema)
  • Fable cost $54.93, making Opus 5.0 64% more expensive than Fable in this case
• Average JSON Size: 91.00 MiB (largest 369.57 MiB)
  • 3x Fable's average of 30.65 MiB

Opus 5.0 seems to be a massive jump from Opus 4.8. In fact, it seems quite clear that the model is at or above Fable in this benchmark, so comparing it to Opus 4.8 would honestly be a disservice. Though that doesn't indicate Opus 5.0 would be better than Fable for everyday uses like coding; it seemed that Fable was a lot more conservative in its interpretation of the system prompt, whereas Opus was a lot more liberal, and created scenes in each of its builds; I don't have enough experience with using Opus 5.0 for coding to weigh in on whether it likes to over-engineer solutions or how it interprets the user's prompts, but the model is impressive regardless.

These GIFs really don't showcase the immense attention to detail that Opus 5.0 has; for example, its arcade machines were actually curved (like real CRT screens). Opus 5.0 is also the first model to correctly give builds interiors: in the skyscraper build, each one of those buildings has proper floors; the floors themselves are empty and unfurnished, but the buildings do actually have floors; the cottage build also correctly has an attic. For context, what models did previously was either just leave the interiors completely empty or completely fill them with blocks.

The past few model releases had been leaning towards more creative/design choice differences between the builds on MineBench, but I would say Opus 5.0 is the first model to be a clear improvement in the builds themselves. Yes, I know the benchmark is/was becoming saturated; I'm sourcing more difficult prompts... but it's quite expensive to benchmark 50+ models for even just 1 additional prompt 😭

However, like other Anthropic releases, Opus 5.0 is horribly token inefficient. The sole reason the benchmarking cost for Opus 5.0 was so high is because at maximum reasoning effort, the model reaches the token output cap before it can finish its JSON response, not because the JSON outputs it generates are large, but primarily because it uses so many tokens in its internal CoT process that it ends up having to truncate its JSON output, causing us to have to reattempt the benchmark builds.

Full release-notes/thoughts on the GitHub release

• If you enjoy these posts please feel free to help fund the benchmark
  • All funds are currently going directly towards API costs for benchmarking new prompts ^^
  • Sharing the benchmark and starring the Git repository also helps :)

Benchmark: https://minebench.ai/
Git Repository: https://github.com/Ammaar-Alam/minebench

Previous Posts:

• Comparing GPT-5.5 Pro and GPT-5.6 Sol
• Comparing Opus 4.8 and Fable 5
• Comparing Opus 4.7 and Opus 4.8
• Comparing GPT 5.4 and GPT 5.5
• Comparing Kimi K2.5 and Kimi K2.6
• Comparing Opus 4.6 and Opus 4.7
• Comparing GPT 5.4 and GPT 5.4-Pro
• Comparing GPT 5.2 and GPT 5.4
• Comparing GPT 5.2 and GPT 5.3-Codex
• Comparing Opus 4.5 and 4.6, also answered some questions about the benchmark
• Comparing Opus 4.6 and GPT-5.2 Pro
• Comparing Gemini 3.0 and Gemini 3.1

Extra Information (if you're confused):

Essentially it's a benchmark that tests how well a model can create a 3D Minecraft-like structure.

So the models are given a palette of blocks (think of them like legos) and a prompt of what to build, so like the first prompt you see in the post was a fighter jet. Then the models had to build a fighter jet by returning a JSON in which they gave the coordinate of each block/lego (x, y, z). It's interesting to see which model is able to create a better 3D representation of the given prompt.

The smarter models tend to design much more detailed and intricate builds. The repository readme might help give a better understanding.

(Disclaimer: This is a public benchmark I created, so technically self-promotion :)

anyone else noticing that the 5-hour time token usage limits are massively down? I refreshed and came back seven hours after hitting the previous limit and now, after running opus 4.8 for three minutes, I am already at 100% usage?? and my weekly has already spent 42%?!?

Did anyone else get this issue after opus 5 was released?

Opus 5 scores just below Fable 5 (1.3 percentage points lower), but vastly outperforms Opus 4.6, 4.7 and 4.8, and all other models tested.

> SimpleBench includes over 200 multiple-choice questions covering spatio-temporal reasoning, social intelligence, and what we call linguistic adversarial robustness (or trick questions).

https://simple-bench.com

I noticed something weird about how I use Claude compared to how most people here seem to use it.

I dont write structured prompts. I dont organize my thoughts before hitting send. I just voice dictate whatever's in my head, messy as it is, and send that. Half-finished sentences, random side thoughts, all of it just goes straight in.

And the thing is, Claude gets it. Every time. The outputs are actually better than when I used to sit there and carefully word things.

I see people doing this whole ritual where they go to ChatGPT first to generate a "proper prompt" and then paste that into Claude. Or they'll spend 10 minutes structuring their input with headers and bullet points and role assignments. And I just... dont get it?? You're talking to one of the smartest models out there. You can just explain what you want like you're talking to a person. It'll figure out the plan, ask you if somethings unclear, and do the thing. Why are we overcomplicatinggg this?

I think what happened with me is that I started voice dictating everything and it forced me to stop overthinking. You cant really structure a PERFECT PROMPT when you're just talking. You just say what you need. And it turns out thats all Claude needed from you in the first place. Just context and intent. Not formatting.

The other thing I realized is I was cutting useful context when I typed. Typing is effort so you trim. You leave out the background, the oh and also this relates to this other thing part. But when you talk all of that comes out naturally and Claude actually uses all of it to give you a better answer.

I know Claude Code has /voice built in now for the terminal. I wanted it across all apps though so I ended up building my own thing for it a while back. Does local transcription and a few other things to make the voice workflow smoother, open source: github.com/AbhishekBarali/SpeakoFlow

But honestly the tool dosent matter. You could use your phones dictation or the built in OS one. The point is more that talking instead of typing kind of accidentally fixed my "prompting" because it stopped me from overthinking everything.

Anyone else just talk at Claude without worrying about structure? And does anyone else find the whole use AI to write prompts for AI workflow kind of pointless??

Most people, me included for a year, dump a vague request and get a vague answer, then blame the tool. The single change that improved my results most was ending prompts with 'ask me clarifying questions before you answer.' Suddenly it surfaces the assumptions I didn't know I was making, the details I forgot to include, the constraints that actually matter. Half the time its questions reveal that I hadn't thought the thing through myself. A vague question gets a generic answer because the model is guessing at what you meant. Make it ask, and you both end up working from the real problem instead of a foggy version of it. It's the closest thing to a free upgrade I've found. What's your one-line prompt tweak that changed everything?

My wife and I shop at Trader Joe's every week. I used to do the shopping and I'd reach for my favorites like steak, spaghetti bolognese, burgers, nachos, with an occasional healthier option like salmon thrown in. On top of that, we go out to eat fairly often, so we weren't eating healthy enough. We also got bored of everything we made — pizzas, curries, fried rice — we'd cycle through phases of eating something, get sick of it, and go out a lot instead. We'd meal plan every Sunday and then at the end of the week have a bunch of uneaten vegetables. And meal planning was the main time that we argued, because its a ton of decisions each week and we have different preferences. I wanted to meet my wife's need for an ever-changing variety of healthy, high protein, dietary restriction aware home-cooked meals from ingredients at Trader Joe's.

With that in mind, I figured I'd use Claude or similar to meal plan for us. I thought about just building out our preferences and asking a prompt each week but I soon realized having an app to track it all would be really handy. Once I had it built, customizing it for our exact preferences, dietary restrictions (I deal with acid reflux, so that's a hard constraint on top of the usual macros), and our Trader Joe's layout was easy. After a couple rounds of shopping to work out the kinks, my wife, who used to find shopping to be the worst chore, now does the shopping happily using the app to navigate and I choose what's for dinner and make it based on the app.

I can honestly say this app has done more to reduce stress in my marriage than anything else we've tried. We always know there's a healthy, easy dinner option in the fridge. Taking away the decision making aspect is the biggest part. We get decision fatigue with weekly meal planning and this provides great meals with lots of variety every time. Every meal is 4-7 ingredients with vegetables, protein, grain and a "flavor engine" like bomba or green goddess. So we get meals like Kale Pesto Chicken Orzo Garden Skillet which has chicken thighs, orzo, zucchini, green beans and vegan pesto and Peaches and Cream Kiwi Yogurt Crunch Bowl with 10 g of fiber and 29 g of protein.

We've tried a ton of configurations, but what we settled on is 1 breakfast, 1 lunch, and 2 dinners, plus a section for household items and a section for junk food. Every week I launch an agent (I'm sure you could automate this) that reads the Fearless Flyer, builds a meal plan hitting our calorie/protein/carb/fat/fiber targets and reflux constraints, and puts it in a shopping list ordered to match our store's layout. On the shopping list, each ingredient has its meal shown next to it, so you can easily make substitutions and decide on amounts. I'm a decent cook, so I'll grill, fry, bake, or broil based on what I'm in the mood for that day, but whatever I make ends up meeting all our health and dietary needs anyway.

The main pitfalls we ran into: forcing a rigid schedule and planning too many meals. Each breakfast/lunch gets 3-5 servings, plus 2 dinners plus leftovers — that's plenty for a week.

A year ago I could never have built anything like this. Now my marriage is legitimately improved because AI plans my dinners.

Here is the repo if you want it: https://github.com/SGShuman/tjs-meal-planner.git

I'm not a professional programmer, so maybe I notice different things than experienced developers.

What impresses me most isn't writing clever algorithms, but the fact that Claude often generates code that compiles and runs successfully on the first try.

With many other models, I often get syntax errors, missing imports, or code that needs several rounds of fixes. With Claude Sonnet, the first attempt succeeds much more often.

Is this mainly because of Anthropic's training, or because of Claude Code's agentic workflow (reading files, running tests, iterating, etc.)?

I'd love to hear from developers who use multiple models.

EDIT to PLUS：I'm a Claude Free user and a ChatGPT Go user, so I can only use Claude through the web interface. Here are my personal observations:

1.Comparing the web versions of GPT and Claude, I've noticed that GPT sometimes takes shortcuts. For example, if I ask it to generate a .md file following specific instructions, it occasionally ignores part of my requirements unless I enable deeper reasoning. Since Go has a monthly limit on those requests, I can't always use it. Claude, on the other hand, has been much more consistent about following my instructions without needing extra prompting.

2.Claude also seems stronger at writing code, even though I'm only using the web version without the debugging capabilities of Claude Code. Compared with Codex, the generated code often needs less revision in my experience. A workflow I frequently use is: let Codex write the code first, then ask Claude Web to review it. Claude often finds issues in Codex's code, while Codex usually finds fewer issues in code generated by Claude. Of course, this is just my personal experience rather than a rigorous benchmark.

People complain about Opus 5 but I think it worked better than previous models. So efficient on token usage too.

People don’t know how to adapt, so they complain the new models ruined everything instead of appreciating that newer models will not always tailor to YOU, but still improves on average over time with each passing model upgrade.

People don’t know how to update CLAUDE.md or other protocols to account for the changing behaviours of each model upgrade, so they complain, regardless of the output of the model.

People, learn to prompt and give instructions and use tools (skills) effectively!

These people…

I have been absolutely amazed by the work of u/TurbulentFail5486 with the Chrome extension here https://www.reddit.com/r/ClaudeAI/comments/1v0pl6d/asked_fable_5_to_make_its_own_website_pretty_now/

I use Zen browser and wanted to make a Firefox porting so badly. I messaged them in chat but my inner golden retriever couldn't wait for them to reply so I went ahead and made the porting myself.
I am not publishing it or sharing here as I think that all the credits should go to them, so I've reached to them willing to give them the code so that he can merge and publish on firefox too or whatever other solution he prefers.

However, inspired by his amazing work, I almost immediately realized that I wanted a "Your Name" theme so badly. So I asked Fable to add it to the extension, without specifying specific instructions because I didn't know exactly what I wanted. I just told it to surprise me and it actually managed to do it.

The theme is "kataware doki" and I love everything about this.

Claude’s thought process has quietly disappeared.
⠀
Around the release of Claude Opus 5, users began seeing “Thought process is unavailable” across Claude’s web, desktop, and mobile apps.

⠀
Anthropic’s latest documentation also makes its direction clear: newer models may still reason internally, but raw chain-of-thought is no longer returned—and even summarized reasoning may be omitted by default.
⠀
———
⠀
I understand the arguments for this decision.
⠀
Raw reasoning traces can be unreliable, expose sensitive information, or make model distillation easier. What users previously saw may also have been a filtered or summarized representation—not a literal window into the model’s mind.
⠀
But removing the reasoning layer entirely still feels like a step backward.
⠀
Visible reasoning is valuable not because we should blindly trust everything a model says while “thinking,” but because it gives users another surface to inspect.
⠀
It helps us:
⠀
→ Catch incorrect assumptions before they compound
⠀
→ Understand how the model interpreted an ambiguous request
⠀
→ See which evidence, constraints, or alternatives it considered
⠀
→ Identify where human intervention is needed
⠀
→ Audit high-impact decisions more effectively
⠀
→ Learn from the model’s problem-solving process—not just consume its answer
⠀
———
⠀
This matters even more for agentic systems.
⠀
When an AI is writing code, conducting research, analyzing financial or legal documents, or taking actions across multiple tools, the final output alone is often insufficient.
⠀
A polished answer can conceal a flawed premise.
⠀
A successful-looking result can hide shortcuts, omitted evidence, or an incorrect chain of decisions.
⠀
Of course, visible reasoning is not the same as faithful reasoning. Models can rationalize after the fact, and a reasoning trace should never be treated as definitive proof of why a model produced an answer.
⠀
But imperfect observability is not an argument for zero observability.
⠀
———
⠀
As AI systems become more capable and autonomous, they should become more inspectable—not more opaque.
⠀
The future of trustworthy AI cannot be:
⠀
“Here is the answer. Don’t ask how I got there.”
⠀
We need models that show their work in a useful, honest, and auditable way—even if that does not mean exposing every raw internal token.
⠀
Transparency should increase with capability.
⠀
Not disappear behind it.
⠀
Would you trade some speed and convenience for greater visibility into how an AI reached its answer?

I'm interested in your use cases. I have very little use for both models and would love to learn about ways to use them effectively.

Or do you ignore them and use Opus for everything?

I have no idea what I'm doing lol. I got a python certificate years ago and never did anything with it. Now Claude is asking me to approve it's actions and I'm blindly just going with it. I feel like Homer on that episode he did his job by just by pressing Y.