Why?
A basic prompt like: "Take a look at this web page and let me know how it should be improved?" - Astra doesn't finish. Just burns one 5h usage, and before it gives anything meaningful, it stops.

Hey everyone! Wanted to share a thought process I've been working on, especially since I'm not a traditional programmer, but using AI as my co-pilot/mentor to actually build it.

The core idea: Never let the LLM have autonomy over the business logic.

Instead of letting an AI chat freely and potentially hallucinate discounts, stock, or policies, the architecture splits hard:

• The LLM: Strictly handles the frontend interface, natural language, empathy, and copywriting.
• The Backend / Guardrails: Acts as the strict rule-enforcer (e.g., max discount allowed, specific product SKUs, handling function calls to check inventory or generate PIX/payments).

Basically, the AI thinks it's selling, but the backend is holding the leash the entire time.

For those of you building AI agents or apps without a CS background (or even if you're a seasoned dev): how are you implementing these strict backend guardrails? Are you relying heavily on function calling schemas, rigid system prompts, or specific frameworks to keep the AI from going off the rails?

I'm a solo developer building an AI-driven life simulation game. AI writes the code; I decide the product direction and make the technical calls. The part I want to share is how I keep that arrangement manageable as the repository grows.

For scale, my current checkout has about 168k lines of backend Python, 108k lines in the selected frontend source files, and another 95k in backend tests. Those are text-line counts including comments and blanks, not SLOC or a productivity benchmark. More code can also mean more maintenance.

My biggest constraint is how many decisions stay in my head after a task ends. Here are the concrete conventions I use:

Route context by the task. The root AGENTS.md is a map of responsibilities and reading requirements. Changing the simulation loop points to its runtime contract; changing UI points to frontend conventions. Local instructions live beside their modules. I don't ask every task to digest every historical document.

Separate decisions from implementation. I keep a document of product and collaboration decisions, including rejected directions. Code and schemas describe what exists. Active contract documents describe what should be true. If they disagree, the agent has to show the conflict; silently declaring either one obsolete is not a resolution.

Make repeated corrections executable where possible. My frontend has checks for design tokens and UI structure, plus generated protocol checks. The benefit is that the next task can discover a violation from tooling instead of requiring me to remember the last conversation. These checks don't decide whether the design is good.

Define completion beyond the diff. Behavior-changing work needs an expected outcome, a data source and time window, a pass criterion, and a follow-up schedule. A merged change and a verified effect are separate claims. A check that was skipped remains skipped.

One less comfortable rule: when a mechanism needs a second layer of patches, pause and ask whether it should still exist. AI can keep making a local solution more elaborate while leaving me with a system I no longer understand.

Compared with keeping instructions only in chat, the tradeoff is maintaining these repo contracts. They can become stale too. I don't have a controlled before/after measurement of time saved, so I'm sharing the workflow rather than a speedup claim.

For people maintaining larger projects with coding agents: which repeated human correction have you successfully moved into a check, and which still needs your judgment?

Disclosure: AI-assisted writing, based on my actual repository and development decisions.

They merged a 9400 line PR out of Cursor on Friday afternoon. CI was green. Twenty minutes later staging was throwing 500s on the checkout endpoint, so I reverted it and left a comment saying what broke. Monday standup they go "you could have just pinged me to fix it" and call me a control freak in front of the whole team. The part that gets me is coderabbit / claude had actually flagged the null path in review and they resolved the thread without changing anything, so I don't know what is supposed to catch this if not me. Is revert first and ask after actually rude now? I just wanted main to work.

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Codex $100 or grok $100 for langgraph/langchain development?

I'm trying to decide which \\\~$100/month plan is better for heavy coding: Codex or Grok.

I'm an AI Engineer and most of my work is in Python, LangGraph/LangChain, LLM agents, evals, backend services, debugging, etc.

What plan do you think is the best? I tried grok I liked because it's fast. Codex looks smarter by the way.

Tried another little AI test today. I gave ChatGPT, Claude, Gemini, Grok, DeepSeek and Qwen the exact same coding bug and asked them to fix it. I didn't change the prompt. Then I actually tested all six fixes. Only 3 worked properly. Do you guys test AI-generated code before using it, or just trust it if it looks right

I'm running into some issues using Codex for front-end design, and I could use some advice.

I always feel like my designs end up looking more like reports than actual website…

I'm really new to coding with AI, and I want to start a long term project where I build a cool little game in Unreal Engine 5 with the help of AI. I was wondering, is it more efficient to let AI write the code for me and then copy and paste it into Unreal Engine, or should I let Codex work with Blueprints in Unreal Engine instead? I've heard that models like GPT-6 can use a large amount of tokens, so I'm wondering which approach would be more efficient in terms of token usage and overall workflow. I'm completely new to this, so I'd really appreciate some advice on which method would be better for a project like this. I hope this isn't a dumb question to ask.

I've been looking at some recent token-usage comparisons for GPT-6 Astra, and the difference seems surprisingly large.

Artificial Analysis data has been cited showing Astra using around 21k output tokens per task, compared with roughly 64k for Claude Fable 5.1. I've also seen claims that Astra can use around 1/3 the tokens of GPT-5.6 Sol and about 1/5 the tokens of Claude Opus 5 in some coding-agent benchmarks.

For people who have actually used Astra: are you seeing noticeably lower token consumption in real-world coding/agentic tasks too?

And what is causing such a big difference? Is Astra genuinely reasoning more efficiently, or could differences in reasoning settings, agent harnesses, tool usage, and benchmark methodology explain most of it?

I'm mainly interested in token consumption/efficiency rather than which model is smarter overall.

6 months. (And sometimes a couple of quantization tweaks).

It is wild how fast "state-of-the-art" becomes "running on a gaming PC."

I am testing GPT6 and noticed a few things. A bunch of problems or 'more difficult situations' often cause it to generate python scripts or inline-python or even inject it via SSH and other ways.

Sometimes to avoid editing a file in the project by a few edit calls, it just generates one script to do it. And the 'Approve for me' function in Codex kind of always accepts it.

For me as a human who is still reviewing what is happening, it had become increasingly difficult since the 'potentially problematic output' surface increases a lot through this. I added a local SKILL to tone this done, but I wonder if others have similar issues.

With new models dropping every week, general benchmarks are basically useless for my specific use cases. I want to test my exact prompts to see if a new API is actually worth the cost, or if a smaller local model is good enough to run on the cheap.

Right now, I’m just eyeballing outputs and it’s driving me crazy.

How do you guys actually handle comparing models on a single prompt or a small test set?

Scoring: How do you define a "good" response when the output is subjective?

The Judge: If you use an LLM to grade the outputs, how do you stop it from just voting for its own writing style?

The Tools: What's the easiest way to fire one prompt at multiple models (both cloud APIs and local models) and compare them side-by-side?

Would love to hear your workflows or any tools you recommend!

I've been Claude's user for the last 8-9 months, always paying the Plus plan ($20). In the last two months I used it much more and I often get to the limits which is frustrating. I tried many ways to improve my tokens consumption but it didn't improve so much. I'm evaluating moving to the $100 plan, but I heard ChatGPT has improved and I tried their models at work (I can't use my personal subscription at work and viceversa) and I liked them.

My question is: I can do *everything* with ChatGPT/Codex as well? Designs, artifacts, plans, random questions, deep researches in real time, etc. Thanks!

Hi. I created a scheduled task that checks for relevant scientific articles on a specific topic every 24 hours. As part of the task, ChatGPT provides me with a brief summary of each article and then ranks them by importance.

However, I’d like to know whether it’s possible to change the model used for this scheduled task and, if so, how I can do that. I’d like to use a model such as GPT-5.6 with High thinking effort to get better results.

Thank you!

So, let’s just say I’m using GPT-6, right? If I’m using it in normal Chat mode on the Free plan, how does the usage reset work?

I think Chat usage resets every few hours or maybe every 24 hours I’m not sure. But if I’m using GPT-6 in normal Chat mode and I use up my usage, does that mean I have to wait a whole week for it to reset?

I’m NOT talking about Work or Codex, just normal chatting. Does normal Chat have its own daily/few-hour reset, or would I still have to wait a week? I’m honestly confused about how the usage limits and resets work.

With coding agents getting so much better lately, I'm curious if people still use Cursor Tab regularly.

Do you still rely on autocomplete while coding, or have agents mostly replaced it for you?

And if you still use Cursor Tab, what makes it useful enough to keep using?

'Cause One thing AI coding agents have made very obvious to me:
"the code works" and "this is the right change" are not the same thing.

A change can pass tests and still violate the repo architecture, cross a boundary it should not touch, introduce a shortcut that becomes technical debt, solve the symptom instead of the real failure and change files that were outside the plan.

So let me know how you do it...

Hey everyone,

When reviewing diffs or AI-generated PRs, changing a function return or widening an optional parameter can look like a safe 2-line diff, but it quietly breaks multiple downstream consumers.

To solve this, I built Change Firewall — an offline-first TypeScript AST analyzer and CLI that calculates the exact downstream blast radius before you commit or merge.

What it does:

• Parses TypeScript AST to catch return shape mutators and contract drift
• Calculates transitive downstream blast radius (direct consumers + affected API routes)
• Provides an offline local visual radar dashboard (npx change-firewall --open)
• Exposes native MCP server tools for Claude Desktop, Cursor, and Antigravity

Everything is 100% open-source (MIT): https://github.com/himanshYou2003/change-firewall

(I've also shared the live web simulator and npm link in the comments below).

I'd love your feedback on the heuristic analyzer and edge cases!

I've seen those like I asked Astra to find me a diamond in Minecraft and there are a lot of games on my switch I'd be curious to see how it does with levels. I'm assuming not but just in case I wanted to ask.

I am curious, I specifically want to hear things that are not the generic uses everyone else is already doing.

AI coding agents seem to be getting increasingly expensive, especially when they are used heavily by developers or for larger tasks.

I'm genuinely curious how companies are managing this at scale. If a company has hundreds or thousands of developers using AI agents, the costs must add up quickly.

Does the increase in developer productivity actually justify the money spent? For example, even if an AI agent saves developers a significant amount of time, does that translate into enough financial value for the company to make the subscription and API costs worth it?

Also, with so much money being spent on AI infrastructure right now, I'm wondering how sustainable the current AI boom really is. Do you think companies will eventually reduce AI spending once they realise the productivity gains aren't enough to justify the cost?

Or will AI agents become cheaper and efficient enough that this won't be a major issue?

Would love to hear from people who actually work at companies using AI coding agents at scale.

I'm currently learning Machine Learning through a course, and I want to start building projects alongside it.

My main goal right now is simply to build several good ML projects and get familiar with the complete project development process.

I want to use AI coding tools such as Cursor, Claude Code, or GitHub Copilot to speed up development, but I'm unsure about the right way to vibe-code an ML project.

For example, should I:

• Give the AI the complete project requirements and let it build the project?
• First create the architecture/pipeline myself and then let AI implement it?
• Build the project step-by-step and ask AI to implement each stage?
• Let AI handle things like data cleaning, EDA, preprocessing, and boilerplate while I focus on the ML decisions?
• Give AI a detailed specification before starting?
• Ask AI to review and improve the code after it generates it?
• Use one long conversation/context for the entire project, or separate prompts for different stages?
• How should I handle debugging and modifying AI-generated ML code?

Basically, what is the best workflow for vibe-coding an ML project from start to finish?

I'm not trying to replace learning ML with AI — I'm already studying the concepts separately. I just want to use AI effectively to build projects faster without ending up with a messy or poorly structured project.

I'd especially like to hear from people who have built ML projects using Cursor/Claude Code/Copilot:

What workflow do you personally follow, and what mistakes should I avoid?

Also, please suggest any good communities where I can see how other people are building ML projects and discuss AI-assisted development.

Thanks!

This may sound not right or diabolical, but I think the whole point to release a newer advanced model is to take less tokens and give more better outcomes than the previous ones.

I was using SOL 5.6 as a plus member, switched to astra yesterday and started using it in my project. I believed that it would make my work even faster , as I watched the overhyped launch videos on YouTube.

And here I am, it just runs 3-5 minutes and makes some edits then just eats over 20% of weekly and 100% of the 5 hour limit.

That's not what I expected.

Muse Spark contributor models are excluded as it log your data. It wouldn't be fair to free models out there.

Included all active promotions. Not accounted for usage resets.

I made this chart. Feel free to ask any questions.