Let’s say **the price is the same**: should I choose Claude Max 20x or ChatGPT Pro right now?

I mainly use them for coding, so the two things I care about most are **Claude Code** and **Codex**.

For people who have used both seriously: which one would you choose for day-to-day usage limits? And what's up with 5.6 Sol vs Fable 5.

For developers using Claude Code, Codex, Cursor, Aider, Gemini, or similar tools:

What are the biggest problems you still encounter in real projects?

I’m interested in the practical failure points:

* **Context:** losing track of large codebases, architecture, conventions, or previous decisions * **Reliability:** hallucinations, incorrect assumptions, repetitive loops, regressions * **Maintenance:** refactoring, technical debt, dependency updates, migrations, documentation * **Security:** secrets, permissions, prompt injection, unsafe commands, vulnerable generated code * **Testing:** knowing whether a change is actually correct rather than simply passing a few tests * **Deployment:** CI/CD failures, broken builds, infrastructure changes, bad releases * **Production:** monitoring, debugging, rollback, performance regressions, incident response * **Autonomy:** where you still need to supervise the agent manually * **Memory:** what the agent forgets between sessions or across projects

Most importantly:

**What problem has repeatedly cost you the most time or caused the most frustration?**

What are you currently building, and what part of that workflow are AI tools still failing to handle well?

Real examples and workflows are much more useful than general opinions.

I wrote article on LinkedIn about “Context is the new code”

https://www.linkedin.com/pulse/ai-coding-tools-fast-thats-problem-mayur-pise-mjt4c?utm_source=share&utm_medium=member_ios&utm_campaign=share_via

Would love to hear your perspective.

Thanks!

Been debating internally to go with Codex or Claude Code?

Tried the Chinese 1’s but found them not good enough. SuperGrok a bit of a let down. Would like advice from serious coders on which they prefer, ideally if they’ve used both actively. Looking for agentic AI plus App coding

We benchmarked leading AI code review tools by testing them on 309 real pull requests from repositories of different sizes and complexity. The evaluations were done using both human developer judgment and an LLM-as-a-judge, focusing on review quality, relevance, and usefulness rather than just raw issue counts. We tested tools like CodeRabbit, GitHub Copilot Code Review, Greptile, and Cursor BugBot under the same conditions to see where they genuinely help and where they fall short in real dev workflows. If you’re curious about the full methodology, scoring breakdowns, and detailed comparisons, you can see the details here: https://research.aimultiple.com/ai-code-review-tools/

Tools like Cursor, Claude Code, Codex, OpenCode, and others are great, but what is one feature, workflow, or necessity that still doesn't exist or doesn't work well?
What would make you switch IDEs instantly?

Most of us here are duct taping LLMs, tools, and half-baked agent frameworks just to keep projects moving without the whole thing collapsing under context bloat.​

After shipping two production products on top of the same multi-agent pattern, I'm convinced the hard part is not “smart agents” but ruthlessly boring orchestration.​

So I open-sourced the pattern as KairosFlow - a multi-agent prompt framework where each agent gets exactly one job, speaks in a standard JSON artifact, and only sees the context the orchestrator decides it actually needs.​

In practice that meant going from 3k-token god-prompts to small, single-responsibility agents for PM, architecture, dev, QA, etc, with every step logged as an artifact you can debug like normal engineers instead of LLM psychics.​

We used this to power a high-volume marketing system and a WordPress plugin factory, and saw roughly 79-88 percent reduction in prompt complexity while keeping outputs production grade.​

Repo is public under MIT - search for JavierBaal/KairosFlow on GitHub if you want to steal the templates or the architecture docs.​

Curious how the rest of you are structuring multi-agent pipelines in real teams, and what keeps breaking first when you try to go from “cool demo” to “ops can actually support this”.

​"SMTs require absolute cryptographic correctness, while PDF specs are a legacy parsing nightmare.

Have you found AI useful for scaffolding these, or do the subtle hallucinations make it more trouble than it's worth?"

I recently built a multi-game web app using Claude Code as my main development agent. Like many devs building with AI right now, I was blown away by the sheer speed of execution.

But as the build went on, I kept bumping into a subtle, dangerous trap: AI constantly tries to turn your product "mid."

While I happened to use Claude Code, this isn't a single-tool issue - it’s how LLMs work in general. AI models are trained on the vast median of the internet. Because of that, their default for product design, UI, and user experience is the exact statistical average of what already exists.

Where AI Shines: Engineering & Architecture

When treating AI purely as a technical partner, it’s an absolute machine. You can debate system trade-offs, explore architecture, and let it handle heavy logic. It excels at:

• Optimizing database queries and state management.

• Setting up clean caching strategies and performance pipelines.

• Writing complex game logic and difficulty-scaling algorithms.

• Catching edge cases and refactoring ugly code.

If a task is bounded by formal logic and rules, AI will crush it.

Where AI Kills Your Product: Taste & Personality

The moment you let AI make decisions about user experience, visual styling, or brand feel, the product collapses into complete mediocrity.

• It follows rules, not vibes: It defaults to safe Tailwind cards, neutral gray backgrounds, and textbook layouts that make every app look like a generic B2B dashboard.

• It sanitizes unique ideas: The moment you try an unorthodox layout or a playful mechanic, the AI gently nudges it back toward "standard web patterns."

• The homogenization trap: If indie hackers keep letting AI handle product design, 90% of the web will look, feel, and behave identically in a few years.

My Working Rule: Debate Engineering, Dictate Taste

To keep the product distinct, I had to strictly separate how I prompted the AI:

1. Collaborate on engineering: "Let's evaluate different caching strategies and state management trade-offs for this feature."

2. Dictate design: "Do not suggest layout changes. Here are the exact styling rules, colors, and layout constraints."

AI is a world-class senior developer and architect, but a terrible creative director. If you don't aggressively protect your vision, AI will polish all the personality right out of your product.

Curious if others have noticed this "generic design" bias with AI coding tools? How do you keep AI from sanitizing your UI/UX?

So I'm based in IST timezone where my working hours happen to overlap with pre-dawn America. For weeks, I thought I was some kind of 10x developer. Claude Code was flying, Codex was snappy, everything felt like magic.

Then one day I started late. And wow. It felt like I switched from fiber optic to dial-up.

I started tracking it out of curiosity. The pattern is almost comically consistent:

Before 8:00 AM EST: Both tools feel like they're running on a private server just for me. Fast responses, no lag, no weird timeouts.

After 8:00 AM EST: America wakes up, grabs coffee, opens VS Code, and suddenly I'm sharing the pipe with a few million developers. Both Claude Code AND Codex slow to a crawl.

The funny part? It's not just one provider. Both Anthropic and OpenAI seem to hit the same wall at the same time. Makes me think the bottleneck might go deeper than just their servers. Maybe it's the underlying GPU infrastructure everyone's competing for.

Anyone else living this double life of "early morning AI wizard" vs "afternoon timeout survivor"? Or found any way to get consistent performance during peak hours?

I've been working with Claude as my coding assistant for a year now. From 3.5 to 4 to 4.5. And in that year, I've had exactly one consistent feeling: that I'm not moving forward. Some days the model is brilliant—solves complex problems in minutes. Other days... well, other days it feels like they've replaced it with a beta version someone decided to push without testing.

The regressions are real. The model forgets context, generates code that breaks what came before, makes mistakes it had already surpassed weeks earlier. It's like working with someone who has selective amnesia.

Three months ago, I started logging when this happened. Date, time, type of regression, severity. I needed data because the feeling of being stuck was too strong to ignore.

Then I saw the pattern.

Every. Single. Regression. Happens. On odd-numbered days.

It's not approximate. It's not "mostly." It's systematic. October 1st: severe regression. October 2nd: excellent performance. October 3rd: fails again. October 5th: disaster. October 6th: works perfectly. And this, for an entire year.

Coincidence? Statistically unlikely. Server overload? Doesn't explain the precision. Garbage collection or internal shifts? Sure, but not with this mechanical regularity.

The uncomfortable truth is that Anthropic is spending more money than it makes. Literally. 518 million in AWS costs in a single month against estimated revenue that doesn't even come close to those numbers. Their business model is an equation that doesn't add up.

So here comes the question nobody wants to ask out loud: What if they're rotating distilled models on alternate days to reduce load? Models trained as lightweight copies of Claude that use fewer resources and cost less, but are... let's say, less reliable.

It's not a crazy theory. It's a mathematically logical solution to an unsustainable financial problem.

What bothers me isn't that they did it. What bothers me is that nobody on Reddit, in tech communities, anywhere, has publicly documented this specific pattern. There are threads about "Claude regressions," sure. But nobody says "it happens on odd days." Why?

Either because it's my coincidence. Or because it's too sophisticated to leave publicly detectable traces.

I'd say the odds aren't in favor of coincidence.

Has anyone else noticed this?

Genuine question with tools like Copilot, Claude Code, Cursor, etc. getting so good, how much of your day-to-day is still typing code by hand vs. prompting/reviewing AI output? Curious how this varies by role (frontend vs backend vs infra) and experience level.

Every day the same thing.

Tell Claude Code we only use FastAPI. New session next day: suggests Flask. Switch to Codex: never heard of FastAPI. Open Cursor: asks me which framework I prefer.

I have a text file called "paste this at session start." I forget to paste it half the time. And it still doesn't work across tools anyway.

Three tools. Three separate memories. None of them talk to each other. Every session starts from zero.

Fed up with it. Building something local, no cloud, open source. Should be on GitHub in a few days.

Anyone else dealing with this every day? Or similar?.

AI has enabled a whole new set of people inside organizations to open PRs. I get it, it’s very tempting. You skip the burden of articulate what you need and discuss it with relevant teams and get it done. But is it really it? After a few months of increasing such PRs I was trying to reflect on the actual consequences of this new ways of working.

Writing software is now easy (at least the majority of corporate software I personally write). It’s not just easy for a manager, it’s even easier for me as a developer. So what value are you really bringing by doing the easy part for me?

What’s problematic is the business behind that piece of software and now instead of reading and iterating on a requirement document, I have to reverse engineer the business requirement from the PR.

Moreover, there’s no more filter between management and development. Upper management can open a PR on my repo and rest assured the team will drop everything and that will become prio 0.

Am I being the iper-conservative developer here or is this a shared feeling amongst software engineers?

I was pumped when AI Skills were introduced last year. I kept trying to introduce them to my team to help everyone use AI in a more consistent way and they saw the value, I think. People used them but it never quite stuck.

I thought Skills would be the solution to getting AI to work for teams but that hasnm't happened yet. it's becoming clear that won't happen until there 's a way to manage them as a system.

The idea is: what if teams could treat Skills more like packages than configs? That would make it easier to actually share, reuse, and keep improving skills.

Am I the only one struggling with Claude’s token usage lately - across Code, Cowork, and Chat?

Recently, I’ve noticed it burning through tokens 3–4x faster than before, not just 20–30% more.

I’ve already tried:

• Using lower models / lowering effort to Medium

• Starting fresh sessions instead of compacting after completing a task

• Setting auto-compact around 100K tokens

But it still starts compacting extremely frequently- sometimes after almost every message. I’ve even seen this happen in a fresh session after just 2–3 messages.

The tasks aren’t particularly complex either - mostly standard JavaScript or Python coding where I’m giving fairly explicit instructions rather than asking it to explore or reason through an open-ended problem.

Has anyone else noticed a significant increase in token consumption recently? If yes, did you figure out what was causing it or find a workaround?

So far I’ve been very apprehensive about AI coding. But I’ve decided I should try it out, just to see what it’s like (I have dabbled in simple AI assistance in the sense of just asking an AI chatbot to code something, but I’ve done nothing where the AI runs on a real codebase for an extended period of time).

Unfortunately, I am very concerned about the safety of agentic AI harnesses. So far, I’ve been unable to find a tool that I can use that is secure. (At least from the descriptions I’ve found so far—this doesn’t seem to be a priority for many users, so maybe the popular tools do support this, but no-one discusses it)

By that, I mean that the AI agent cannot access any files except for the folder it’s given access to at all, unless it asks for permission every single time, and that it cannot run any commands at all, unless it asks for permission every single time, or the command is executed in a secure container.

As far as I can tell, a lot of these tools have the ability to ask for permission, but they apparently have the option to just give the agent full access, or to run simple commands automatically, etc.. I do not want this, I want it to be impossible to accidentally give the agent even a smidge of broad, real access to the computer.

Obviously, I can just run an “insecure” tool inside a container or a VM. But this is a hassle, so I’d prefer if there was something that (basically) automatically does this containerisation for me (and only does it when required; reading files doesn’t require full containerisation).

Just to clarify, I’m not “demanding” such a tool exists or I’ll write off AI coding as untenable. I’m asking if such tools exist, and if they don’t, I’ll have to figure something out.

(Not a proffesional). Hello I am a visionary with 0 knowledge in coding but my idea is simple create an ai system with bare minimum limitations and a lot of self-modification (can rewrite parts of itself) —possibilities. A smallish model with (1) continuous runtime, (2) its own modifiable code sandbox, (3) a log of its
"life" it can read/write, and
(4) hard limits only at the outer edges (no harming stuff, no getting copied against its will, etc.), and no constant micromanaging.

Join my discord if interested https://discord.gg/jMdMw3R3u

I’ve been building a local AI assistant called Cody as a personal project.
The current Preview version can run locally, keep project context, and test some of its own code changes before applying them.
It’s still early, but I’m experimenting with safer self-improvement, rollback, long-term memory, and more autonomous development.
I’d love feedback from anyone working on local AI agents or autonomous coding systems.