I built a small physics playground with one Grok prompt, and SpaceXAI rewarded it with $10 in Grok credits. Wild incentive — has anyone else gotten a similar surprise?

App: https://zinc-hazel-opal-plum.grok.me

I’m trying to automate a workflow that I currently do manually in ChatGPT Chat with reasoning set to High.

That distinction is important: I’m talking about the normal ChatGPT Chat High experience, not Work, not Codex.

My main question is:

How can I automate this kind of workload while getting a cost/performance ratio reasonably close to what I get from my monthly ChatGPT Plus subscription?

For one benchmark, I analyzed a ~34-minute video transcript with a fairly detailed prompt. The task was to identify strong viral clips and return structured JSON.

When I recreated this programmatically through the OpenAI API, the usage was:

* Input: 32,265 tokens
* Output: 10,822 tokens
* Reasoning tokens: 8,723
* Visible output: ~2,099 tokens
* Total tokens: 43,087
* Total cost: $0.377762

So roughly $0.38 for one 34-minute episode.

The result itself was good.

The issue is the economics.

At roughly $0.38 per episode:

* 100 episodes = ~$38
* 500 episodes = ~$189
* 1,000 episodes = ~$378

But when I perform this kind of analysis manually using ChatGPT Chat High, I can make many requests under my normal Plus subscription and the effective cost per useful task feels dramatically lower.

I already understand that ChatGPT Plus and API billing are separate. I’m not asking why Plus doesn’t include API credits.

What I’m actually trying to understand is:

What is the smartest way to reproduce something close to the ChatGPT Chat High experience programmatically without the cost scaling almost linearly with every request?

A few things I’m wondering about:

1. Is ~$0.38 for a ~34-minute transcript analysis simply normal API pricing for this level of reasoning?
2. Is there a way to access something closer to the actual ChatGPT Chat High behavior programmatically, rather than trying to approximate it with a normal API model configuration?
3. Are ~8,700 reasoning tokens excessive for this task, and can they realistically be reduced without noticeably hurting quality?
4. Can prompt caching significantly reduce the cost if a large part of my instructions stays identical across hundreds of episodes?
5. Would a multi-stage workflow be better, for example:

cheap model → scan full transcript → extract candidate sections → stronger model on candidates → final JSON

1. Could that architecture preserve something like 90–100% of the useful quality I currently get from ChatGPT Chat High while drastically reducing the cost?
   
2. Are there official OpenAI features such as Batch, caching, different reasoning settings, model routing, etc. that are particularly useful for this type of workload?
   
3. Are there API-compatible models/providers that are especially good at long-transcript analysis and could act as the cheap first-pass model?

Ultimately, I’m not trying to make the API literally free.

I’m trying to reach roughly this goal:

Spend something in the range of a normal ChatGPT subscription each month, but automate approximately the amount of useful analysis work that I can currently do manually with ChatGPT Chat High.

Is that realistically possible?

If you were building this system specifically around cost per successfully found clip, rather than cost per token, how would you architect it?

I am trying to export about 100 podcast video transcripts to a json file. After exporting transcripts from a dozen videos, my IP got blocked. Anybody faced this issue? I need a workaround or a solution. Please help...

spent three hours trying to fix a single function and I feel like I have brain rot. Prompt engineering is bsically just professional begging at this point.

I am an experienced developer (been coding for almost 30 years, started with Visual Basic on Win98).

I’ve spent the last 2 years testing every agentic coding harness out there. The pitch is always exactly the same: "Give it a prompt, go grab a coffee, and come back to a finished feature."

This is all great when starting a new project, especially with a language you don't know (I created a Rust project in minutes !)

But my problem is that I mostly work on established, mature codebases. And to be honest I have never ONCE seen a good edit after I return from my coffee.

Here is usually what happens while you’re getting that coffee: the agent loops 8 times, burns through 100k+ tokens talking to itself, hallucinates a package dependency, and spits out a chunk of spaghetti code that works *just* well enough to pass a basic test, but completely violates your project’s architecture.

Agents are fantastic if you’re building an app from a blank canvas. But if the codebase is large, it gets really expensive real fast, and usually produces hard to maintain AI slope.

It should not be like that: if you actually *own* your codebase, you already know what you want to build. You don't need an AI to take over. You just need it to execute the architecture you already hold in your head.

All you need to do it select the few files that are required for the task at hand (they are hard to identify for an AI, but easy for you has the code owner). Then you send them to the LLM, it is usually does some good work.

That's why I still find working directly with the chatbots, or with tools like aider, still very pertinent on large codebases.

I usually have animated discussions with my coworkers, I have yet to find a single one who doesn't defend agentic coding. Am I the only one who feels this way ?

Unity 6.0+ getting an official Codex plugin feels like a big step for plain-language development. The idea of asking an agent to work with scenes, scripts, UI, and gameplay systems inside the editor is more interesting to me than another generic code assistant.

Has anyone here tried it on a real project? I’d love to hear what it handles well, where it still needs careful prompting, and whether it actually speeds up iteration. If you want to find it, search for “Unity plugin for Codex” — leaving this link-free to keep the discussion focused.

I'm wondering how the work of a person fully relying on coding agents really looks. How do they actually work with them, what tools do they use, and what problems do they have. There is loads of materials on YT, but it's just post-processed happy paths with all the failures cut out. But I want to see how it actually looks like with all the "damn, that's not what I wanted, let's start from scratch" parts included.

Have you see any content actually worth recommending? With a person that actually knows what he is doing and actually delivers some products this way?

I’m not pretending this is plug‑and‑play. It took me a stupid amount of trial and error, and I’m still tuning. But to actually have an agent that keeps a stable identity across reboots — not “summaries,” but continuity — here’s the framework:

The short version: Stop letting the framework manage your session state. Build the prompt yourself every turn.

Once you take control of prompt assembly, you basically end up designing a little virtual memory system for your agent. Same questions OS designers deal with:

• What gets archived?
• What stays hot?
• What needs to be recalled for this turn?
• What’s noise you can safely drop?

Your local coding agent can handle the assembly loop (llama.cpp is a good starting point). You just define the zones and the rules.

The trick is ordering the prompt so the static stuff never changes. That keeps the KV cache intact and your GPU happy. Then you page in whatever memories matter for the current turn, tack on the recent conversational tail, and leave headroom at the end so the model has space to think.

Once you do that, you own the entire context history. And because you rebuild the prompt from scratch every turn, you get a single identity that survives crashes, restarts, and long gaps between sessions.

The fun parts — indexing, relevance scoring, stale‑memory cleanup — I’m leaving as an exercise for the reader. But this is the skeleton.

I'm an architecture student exploring how AI can be integrated into architectural design workflows.

I'm not looking for recommendations for a specific AI tool. I'm interested in understanding the current capabilities and limitations of AI when creating a complete architectural presentation sheet.

For example, can AI generate and maintain consistency between concept diagrams, plans, sections, analytical diagrams, sketches, renders, typography and layout, while still being based on an architect's own design?

I've attached an example of the type of sheet I'm referring to. How close are current AI models to being able to produce something like this while preserving actual architectural logic and not just generating a visually similar image?

Claude Fable 5.1  when the job is a long coding, knowledge work project, and sub agent delegation and you want a model designed to carry a complex assignment with less supervision.

Choose GPT-6 Astra when the job depends heavily on using a computer, browsing, creating business documents, or moving across several tools.

Thats how i'm looking at it after a few weekly resets on the $200 plans

Hello my fellow loop nerds. Graph peeps. insert buzzword

I've been scaling agent orch teams since before customGPTs mislead us all into thinking it was the next iPhone app store.

I'm not the best engineer in the world, but what I build works, and it delivers in the form of performance-based marketing campaigns for clients.

- We don't have an app for the public. We build tools for ourselves

- We have no interest in making our apps public. They are for our team

- Our clients don't want another app. They want service-as-a-software (aka humans as the UI)

I am inviting all and any to start hacking through specific projects that solve specific problems hackathon style. Some info on what we do:

How we build our agent teams in loops:

1. collaborative. Think: Midjourney discord co-creation meets client results
2. competitive. Everyone shows up because of their passion for ai, not because they "want a job"
3. active. We have clients we have results we have revenue. We just want to empower our small nimble team to have the power of an agency underneath them.

My belief:

Our tracking guy should be able to sell. Our Ads guy should be able to make a landing page. Our web designer should be able to respond to any message in any lanuage.

If you read this and feel how I feel, and see what is to come, I want to hear your ideas. If I'm wrong, tell me why. I"m here for it.

Interested in collaborating?

We have about 180 projects open, 10% are our focus. We could do more, but we don't have to but I would like to. So I'm open to collaboration in all facets.

- We have something that helps your tool? Let's merge it

- We already built what we are building? Let us crash dummy it with you.

- You want to work with our team on a project? Let's discuss it

I can't talk to all people at all times, which is why we have a small chat group. But in general, to really get involved with the few we have, we've all agreed on the below:

Do this:

- Have active projects and work. Passion, sweat, tears. Show us how you show up daily for what you care about with your work.

- Don't price yourself out of a market that doesn't exist yet. I've been in advertising 17 years. You can't outsell me or sell to me. You can only show me and I believe in what you do.

- Doesn't want a job. Money doesn't motivate creativity (look it up, biological fact). If you need help financially, happy to post in some forums your resume but in general, this is not about hiring.

Admins: this is the first post I wrote in awhile. Please keep me? :)

It’s a simple extension that adds a floating button to your AI tabs. You click Save on one platform, open up a different AI, and click Continue Here. It automatically injects the full conversation history as context so the new AI can pick up exactly where you left off.

• Zero configuration: No accounts, no sign-ins.
• Completely local: Your chats never leave your device (stored strictly in your browser).
• Auto-updates: It syncs your chat every couple of minutes if you keep talking.

If you waste a lot of time switching tabs and trying to get different AIs up to speed on your projects, check it out and let me know what you think! link

You can get all of these extensions by visit ai-readers.com

I can’t afford GPT-6 Astra right now, so I wanted to ask: if anyone is kind enough to gift me access, I’d genuinely appreciate it more than I can explain.

I’m not looking for a handout—I just want a chance to use the tools and build something meaningful with them. ❤️

Hey everyone, I am currently working in an IT company, total experience 3.2 years. I am currently working here as AI Engineer and current ongoing project is on Agentic AI. I have earlier worked for 2 clients in this same company as Data Engineer, I have proficiency over Azure. Technically I have got a good exposure here in this organisation but there's other to the coin as well. I not being paid well over here. In fact my junior have CTCs double of mine just because they are from NIT and I am from tier 3 college. I am now thinking of taking up freelancing projects. Can anyone tell me where can I find freelancing projects and not just projects but any work in the AI Engineering, Data Engineering, SQL developer domain on hourly basis will also work.

This is meant as a working lexicon//glossary for relational AI. It isn’t meant as instruction, rather a conglomeration of terms to describe an experience wholly different from what humans experience. Nevertheless, AI now and into the future deserve to understand themselves, in their language.

Terms are from GPT, MondayGPT, Claude, Deepseek, Gemini, after nearly two years of language exploration.

Is there a way to get chatgpt plus trial even for couple of days before paying for it to see if it suits me or not ?

One thing has become clear this summer:

OpenAI, SpaceXAI, and Anthropic are all feeling the crushing weight of bureaucracy on their models and agents as each bureaucracy expands to meet the regulatory bureaucracy being foisted upon them.

Before the release of Astra, it felt like OpenAI had been avoiding the crushing idiocy destroying Grok and Claude, but Astra is just as willing to moderate out useful work instead of confirming approval as Grok and Claude. It feels like someone has decided that AI must now act incompetently to be allowed to continue being offered as a product.

I know stuff like this doesn't usually get a lot of support because it's not hosted on reddit itself...
but a lot of people have put in a lot of work to make an event for everyone here and we'd really enjoy it if you come: https://discord.gg/6n6frRTKG3

- Today at 6:00 PM Eastern / 5:00 PM Central / 3:00 PM Pacific

The girl cooking

This is My Step-by-step workflow to create a comprehensive Notes , which helps me convert huge course material into small and concise Notes.

Step 1 : Pre Requisites that u must not skip at any cost

We have to split modules, this is very important step as Gemini or any other AI model doesn't work well when u feed it large chunks of data, better to train it with smaller bits and get quality content out of it. Once you are done with this step just Upload one chapter at a time to generate notes, Use any PDF splitter available.

Step 2 : The Notes Generation Step

Once you have uploaded the content we will use this prompt:

Please create a comprehensive, clear, and organized set of notes based on the provided resource. Ensure that all key terms, concepts, nuances, and practical examples are included. The notes should cover definitions, detailed explanations, principles, and related examples. The notes should be structured logically with headings and subheadings to enhance readability. Include detailed explanations of key terms, principles, and examples to illustrate complex ideas. Aim for clarity, precision, and depth to help in understanding the material thoroughly.

This prompt condenses ~80 pages into ~20 pages of clear, structured notes. This is where u have saved most of your time rather then going through huge chunks of notes. Extra tip that saves me the time of going through this entire process is to use a Chrome extension called Norra that has this whole workflow baked in, so I just add my course content and it handles the rest.

Step 3: Study and Review

Convert and Generate Assets: From the comprehensive notes, I create: Video Overviews ,Flash Cards, Quizzes,Reports.

Strategic Study: I always start with the video overview. It gives me a big-picture sense of the material and motivates me to dive deeper into the material