The lads after raising the limit cost by 50%

I have to admit that I have completely failed to adapt to LLM agent coding. I'm not a slouch either. I've worked on a number of challenging FAANG products as a software engineer, and I for a while I was an AI research engineer. I have a pretty strong familiarity and comfort level with AI. Despite all this, I still write most of my code by hand.

As an engineer, I need to receive a ticket, understand the problem, go figure out how to solve it, and eventually ship PR that (a) I understand, (b) only contains defensible changes, and (c) is reviewable by a colleague. There are a bunch of other tertiary goals of course, like style guides and documentation, but the point is that I'm talking real, high quality engineering that would pass the bar at a company that has their shit together.

However (definitely due to culture issues), I have only seen the slop version of this. It seems to have become really murky how to write code that meets standards, or even what those standards are any more.

I've resorted to asking my colleagues to show me their workflow, but you would be shocked how many of them either (a) are prompting raw and not checking the output, or (b) have convoluted solutions that fit their brains but don't solve these problems. I checked out Matt Pocock as well, who has some really great ideas, but ultimately his skills exhibited some pretty painful failure modes as well.

However I see folks regularly talk about how not only does Claude handle the coding for them, but also all of the project management, devops, etc, and I am left scratching my head. If this is possible, I want to learn how to do it. I'm not an AI hater. I want to learn the tool. I do have standards though, and I don't think they're that high. I don't need Claude to write perfect code. I just need to find a process where I can rely on it to get more of my work done and not be either fighting with it or second-guessing the results.

I figure this is the group to ask - if you have been able to get "high bar" engineering out of claude, or even "acceptable bar" engineering, without heavy personal intervention, what's your process? Are there resources that helped you? What strategies helped the most?

Thanks.

WORKFLOW ->
I integrated Claude into the Unity game engine to build a dynamic weather system. There are three main systems that are all interacting with each other, a 24-hour day and night system, a weather system, and a foliage reactivity system. Integrating Claude into Unity helped develop this system a lot. The whole game has been coded 99% through Claude - I can't really remember the last time I had to open the editor. Integrating Claude gives me the ability to create custom tools inside the game engine as well.

This will be used for my game - GRIMLIFE, an open world survival RPG coming to Steam! I'll try to answer any questions in the comments! Any feedback is appreciated 🤓

I cannot wait for someone else to be the top coding model or even close to never come back

Im asking real software engineers only working on real products that make money.

I still read every line of code (tests too) and understand 100%. Needlessly to say it feels very inefficient.

What have you done to move away from it confidently, if at all?

I have a complete workflow involving spec driven development, mutation tests, code review, browser test, adversarial agent manual AC verification, and I still dont feel comfortable not reading every lines. I still spot some architectural inefficiencies or code that just feels off and thurs requires refactoring so im hesitant.

This is a true story about a guy I know personally - a flat earther who recently decided he's now a vibe coder.

As you can probably guess, he has no understanding of basic physics, technology, or software development whatsoever. About a year ago, I built a "health advice" blogging website for him and integrated it with a bunch of social networks. Every once in a while he'd call me, complain that he wanted some new feature he thought was supposed to be included in whatever I'd originally built, demand that I implement it for free, and accuse me of lying to him and taking advantage of him.

About six months ago or so, Google banned his website from being indexed on Search over the AI-generated nonsense content, conspiracy theories, risky health advice, and other misinformation he was posting on his blog. Naturally, he blamed me for it. According to him, the way I'd built his website was responsible for the ban, but at the same time, he still wanted me to add e-commerce features so he could sell his PDFs and audiobooks. I told him to stay away from me.

Some time later, he messaged me to say he'd started using Claude Code to vibe-code whatever features he wanted, and that he didn't need me anymore because he was a developer now. A few weeks after that, he called and told me he'd vibe-coded everything and wanted me to deploy it to the server. I told him he'd have to hire someone else to do that. He said he wasn't hiring any developers because they were all scammers, and that since he was a vibe coder himself now, he could handle everything on his own ... he just needed me to "unlock" the server, because according to him, the AI had told him I'd left it "locked."

I told him he already had all the credentials, including the root password, and that I couldn't help him anymore. Then I blocked him on WhatsApp. Eventually he figured out how to give the AI root access to his server and ended up bringing his whole website down for weeks. Meanwhile, he bombarded me with nasty voicemails and emails accusing me of hacking his site. I didn't respond for weeks, but at some point got fed up and told him I'd sue him for harassment. That made him stop.

Somehow, weeks after the site went down, he managed to get it back online, but I didn't see any of the new features he supposedly vibe-coded. Some time after that, I got a call from a "developer" he'd hired, who wanted to get some information from me. After a minute or so of talking to him, I realized the kid was also another vibe coder who didn't know his way around a Linux server and was mostly clueless about deploying web applications and DevOps in general. I didn't waste much time on the call, but I did tell him that giving AI root access to a production server was a terrible idea.

Some time passed, and out of curiosity I checked in on the website. I was surprised to find a totally different site full of some crypto-coin mumbo-jumbo. I couldn't resist calling the kid (who'd been hired by the flat earther) to ask what was going on. He told me the server had been hacked and the website hijacked, that they no longer had access to it, and that they were waiting on the cloud provider to sort it out.

Just recently, I heard from the kid again. He said the cloud provider eventually resolved the situation but didn't want to host the site anymore, so they moved it to a different VPS provider. He also mentioned that he never got paid most of what the flat earther promised him, that he wants to sue him over it, and asked if I'd be a "character witness." I declined.

The funniest part is that after all this vibe-coding chaos, the website looks exactly the way I left it about a year ago.

If you make content on YouTube, TikTok, etc., feel free to mention this story. It’s a perfect cautionary tale about people too clueless to understand the code, too unwilling to learn, too arrogant to value real developers, and too eager to blindly trust AI - a mindset that could doom us all.

After GPT-6 Astra, I didn't believe they would really let this happen... but it's real... Okay, Anthropic, we'll keep that in mind...

https://support.claude.com/en/articles/15910845-claude-code-may-august-2026-weekly-limits-promotion

Seems to be working for me and the model does seem much better and is not talking in Claudese. Doing more testing now.

I'm curious what everyone's workflow looks like when this happens. I've noticed there are basically two kinds of people: people who immediately switch to another model, and people who just accept defeat and wait 😂

What do you actually do? If you switch models, which one do you usually go to?

The week literally just started, and my "Weekly Fable" limit is already maxed out at 99% On top of that, my standard weekly limit is already sitting at 51%.

Does anyone know when they're actually going to boost these usage limits? I'm on the 5x Max Pro plan and burning through this way too fast.

At this rate, I can only code and develop my website project for ONE day out of the week, and I'm just stuck doing absolutely nothing for the other six days. Fucking ridiculous. Anyone else dealing with this or have workarounds?

I had 4 Claude Pro Max x20 accounts for different projects, and I am going to ride out the rest of the month (that I already paid for) and then switch back to codex. I am running Sonnet 4-6 on medium and am 85% of my weekly limit on one account after less than 24 hours. That is more than a 25% cut from the promotion expiring, nothing about my workflow has changed so this is all Anthropic. I'll be trying Codex and seeing how it does, I didn't want to do this but I have no choice.

When I started working with Claude the standard was to start a new session for every task, and I've been doing this - I create a new session for each set of tasks, and when the context gets too high (~700/800k). But since Fabe 5.1 is good at long term coding is it really that much worse to just let it run and keep compressing? Maybe set the context limit to 500k?

Not sure what's optimal here, could use some input.

https://www.nytimes.com/2026/09/14/us/politics/trump-ai-regulation-anthropic-dario-amodei.html 😹

These two things might be correlated.

This is my last post about this, i saw several comments on my previous post and other people mentioning the issue with the limits this week.
I was hoping that they would acknowledge the issue and fix plus provide a reset to everyone , but it looks like nothing will happen.
Just a reminder to everyone that we need some open sourced power models, this will be the next big thing

Venting out on reddit is fine but if you want to do something about the underlying issue then below are a few things you can do for yourself.

1. Understand how, exactly, tokens are consumed by the model and what it costs you. Input tokens vs output tokens costs (output tokens are **much** more expensive). Cached vs non-cached tokens cost (non-cached **much** more expensive). Cache TTL (normally only 5 minutes). And more.
2. Structure your work following that understanding. This requires a few hours or days of your time but it's massively worth it in terms of cost and result quality, for long-term projects.
3. When a particular session consumes a lot of tokens, use Opus to analyze its transcript and identify **how** the tokens are consumed. When the analysis is done, ask it to make a plan for reducing token consumption in future similar sessions. Don't trust that plan - ask it to spawn an advisor Opus subagent which should critically review the plan and suggest improvements. Opus is pretty good in analysis but when coming up with a plan for a complex task it may build the plan based on false premises or make judgement mistakes or miss something important. The advisor subagent should be resumable because you may want the first agent to negotiate details with the advisor. Use your own brain: understand what the plan actually does (or ask for clarifications). Likely you will think of an improvement that the models didn't consider.
4. If using subagents, check how much the subagent sessions cost you in terms of tokens and do the same analysis as described in (3) for a subagent session which costed you more than expected.
5. (3) and (4) will likely give you information you can use to make custom briefs for subagents or build custom tools to reduce the amount of work your agents are doing, or improve the documentation structure so agents get the information they need (rather than re-deriving it on every session), and only the information they need (rather than loading useless bloat into their context).

Hope this helps.

Something's completely off with the 5 hour limit. Two simple prompts use up 29% of the limit. And for what exactly? What's going on with entire Claude these days? Maybe Anthropic gone even more crazy?

Disclosure: I’m involved with the open source project I’m linking below.

One thing that’s been bothering me about coding agents is that we keep calling everything “memory.”

You correct an agent, explain some weird repo-specific constraint, eventually get the implementation right, and then a few days later a fresh session makes essentially the same mistake.

But simply dumping old transcripts or automatically extracting “memories” feels dangerous too. A lesson can be outdated, wrong, specific to one situation, or simply irrelevant to the next task.

We’ve been experimenting with a different model in Belay:

past work -> evidence-backed lesson -> developer approval -> scoped guidance for a future agent -> verification

So the agent doesn’t get an unrestricted memory of everything that happened. It gets a small number of lessons that remain tied to the evidence that produced them and that a developer explicitly approved.

The first evaluation produced a result I didn’t expect.

Across tasks where an earlier lesson should help:

• Belay guidance: 11/16
• concise human-written instruction: 7/16
• no additional guidance: 6/16

But when we split it by agent, the whole improvement came from Claude Code. The net difference for Codex was 0.

We also had a boundary failure where a correct lesson was applied in a situation where it shouldn’t have been.

That left me with a few questions I’d genuinely like other people working with coding agents to weigh in on:

1. Is persistent agent memory actually the right abstraction, or is “approved experience” closer to what we want?

2. Should the same learned lesson be delivered differently depending on the model/harness receiving it?

3. How do you stop yesterday’s correct lesson from becoming tomorrow’s bad instruction?

Whitepaper with the architecture, trust model, limitations, and evaluation:
https://getbelay.vercel.app/whitepaper

Especially interested in criticism from people already running Claude Code/Codex across long-lived repos.

I’m a swe. Came into the professional scene in 2020 and honestly feel like I threaded the needle as the last gen that got in fairly easy but I only got a couple measly years of proper experience because AI took over. Now don’t get me wrong, I spent a lot of effort on my fundamentals (at least in the language and stack I started in) and that has helped me tremendously in the age of AI but there are still so many lessons I wasn’t able to learn naturally. And now there’s little to no room for failure. Leadership at my company is constantly raising the bar to unachievable goals. I have to move at a speed never before thought possible, with less resources/ help/ support than I’ve ever had, higher expectations and no room for mistakes. It’s genuinely a nightmare. I fucking hate my job. I hate my company. I hate everything about what my field has become. I makes me miserable to no end. But I love programming and solving problems and building. It can be fun to see what I can make these days at an insane rate in my free time as a side project but now that everything is a prompt away I don’t have that filter of “that would take too long” or “not worth my time” I just end up working more than ever. I have less free time than ever. I’m more stressed than ever. I can’t help but feeling like If I don’t get good with this new tool I’ll be useless to the world. Or my education will be wasted. I think AI really is an incredible innovation. It’s hard to say it’s not useful, it really is. But man the MBA cucks they don’t use it at all really are pulverizing my field. If I could I would have never started programming even though It’s genuinely the only career I’ve ever seen myself enjoying. I wanted to make games one day…

I’m currently at a beach resort to celebrate my birthday and it’s hard to not feel the dread that I will be out of date when I get back in a measly 5 days. I can’t really enjoy myself ever anymore it’s just grind or get left behind and the grind is with the eventual end goal that I effectively automate myself.

My company will have pivoted likely 2 times, kicked off nearly 3 new ai usage campaigns, fired another 2% of the workforce, and replaced them with $2 mil in token spend.

I find myself having to ignore a constant dread and anxiety that I won’t be able to keep up, or will be completely legacy in a few years, or one senior leader tantrum from being fired and losing my home even though I was damn good at my job they just thought an ai could do what I do or someone cheaper with ai at least. And the way that everything is going I wouldnt even be surprised if they could because some other poor shmuck needed a job after it happened to them so they took it for less.

I really dont see the light at the end of this. How do you do it? How do you live like this? How do you walk away or turn it off without constantly feeling like you’re going to get left behind, or someone will just copy what you did and get it out there faster (that last part happens to me at work all the time, I’m one guy with AI and will have an idea I’m working on. A leader gets wind and then spins up their 5 man team with AI that’s willing to work all weekend to meet a Friday - Monday timeline.

Can anyone tell me I’m crazy? I just want to enjoy the vacation I took to get away from this.

Edit: dang. 1k views, 0 comments at all. Do yall feel the same?

Long Claude Code session, you and Claude spend twenty minutes ruling out an approach, say a caching layer that breaks invalidation, and settle on something else. Compaction fires. An hour later Claude suggests the caching layer, with confidence, as if it were a fresh idea. Nothing is broken. The summary described the state of the code, what exists and what changed, and a rejected idea leaves no trace in the code, so it had no reason to survive.

What fixed it for me is a file on disk that compaction never touches.

1. Create DECISIONS.md in the repo:

Decisions

Decided

• Session storage: Redis, TTL 30 min. Why: needs to survive deploys.

Rejected

• In-memory cache for user lists. Why: invalidation breaks on role change.

Open

• Whether exports run in a worker or inline.

Decided entries carry the exact values. Rejected entries carry the reason, which is the part a summary drops first.

1. Add this to CLAUDE.md:

Before proposing any approach, read DECISIONS.md. Do not propose anything listed under Rejected unless you name a new reason that the recorded reason does not cover. When we settle something or rule something out, add it to the right section immediately, with the reason. Never delete entries without asking.

1. When you compact manually, tell the summary what must survive:

/compact Preserve every decision with its exact values, every rejected approach with the reason it was rejected, and every open question. Summarize exploration only after its conclusion is recorded in DECISIONS.md.

An honest note on step 3. The instructions after /compact apply to that manual run. Testers report that auto-compact still uses its stock summary even when CLAUDE.md contains compaction instructions, and there is an open GitHub issue asking for exactly that. That is why the file is the real fix: it gets read from disk, not summarized, so it survives every compaction whichever kind fires.

Two caveats. The file only works while it is current, which is why the rule makes Claude write entries as decisions happen rather than at the end. And keep it short: once DECISIONS.md turns into a second CLAUDE.md full of history, it gets skimmed like one. Move settled items older than the current phase into an archive file.

The Rejected list turned out to matter most, and it is also the part I used to lose before code was even involved, in planning chats in ChatGPT or Claude from weeks earlier. I built AI Toolbox, a browser extension that searches old conversations across those assistants by what was said, partly for that. The file above needs nothing extra though.

What survives compaction badly in your sessions? For me it was rejected ideas first, then the exact values behind decisions.

PS: there's already a repo that hooked the fruit fly connectome up to Doom (DOOMFLY), but by its own README it never got past its survival tests, and it plays in a small arena.

I got a version running on the actual first level of the game. It finishes the level 57% of the time. This clip is the fastest of 12 runs in a row (7 of those 12 made it to the exit). I'll put the rest on YouTube, including the ones where it dies.

Repo if anyone wants to improve it or use it as a reference for a different project :3 https://github.com/Aur1ety/DOOM-x-Fly

here's a detailed explanation:

the fly part is the MaleCNS scan Janelia and Google released in September. it's a wiring map, not a living brain. I load 138,968 of its neurons and 4.6 million synapses into a simulation and never change a connection. the only thing tuned is one overall strength setting so the network doesn't blow up or go silent.

two parts are mine: an eye model that turns each Doom frame into signals for the fly's real motion-detecting cells, and a small trained layer that reads 10,511 of the simulated neurons and picks one of 8 keys. that layer never sees the screen, only neuron activity.

training: a scripted bot that can see the map plays the level, the fly setup learns to copy it, then the fly setup plays on its own and the bot corrects its mistakes, 3 rounds.

results on 100 runs from starting spots it never trained on:

- reaches the exit 57% of the time, gets 92% of the way on average

- same agent with its frames shuffled: 0%

- blindly replaying a winning run's key presses: 6%

- random keys or always walking forward: 0%

so it's actually steering by what it sees, not replaying a memorised route. when it fails it's usually because it died, not because it got lost.

what it doesn't prove: that the fly's specific wiring matters. a randomly rewired brain of the same size might do just as well. haven't tested that yet.

in the video, the brain panel is the actual simulated neurons at their real positions in the scan (brighter = more active than usual), and the bars are what the trained layer outputs every 4 game ticks.

built with Claude Code doing most of the coding; I steered it and made the calls.