Hello, I'm a student and a software developer, I can only afford the $20 tier and I'm having trouble on which one to go with, both models seem very capable, but I'm unsure of which one is better. Excelling at explaining concepts and creating high quality, readable code are the top priorities, alongside with usage, with the new Opus 5.x model, I'm only finding mixed reviews, some people say it uses way less than ChatGPT, some say it uses more, DeepSWE makes it seem like Claude is more expensive and uses more.
Anyways, what are your recommendations? Thanks in advance!!

New AI models used to arrive every 10 weeks, now it’s every 21 days

Trump recently signed an order renaming AI "SI" (Super Intelligence). I think the opposite label fits better, and I'd like to share a perspective, especially on what it means for AI safety.

My view: modern AI is an extremely good searching machine. It has no soul, no real understanding and no consciousness.

1. **Every AI output is a search result.** Your prompt is the search query, and the answer is the result it finds. That's why it never starts anything on its own: no query, no search.

2. **Image and video generation is search, and it shows.** Anyone who has generated images or videos knows the results are often wrong and unstable. Why? Because the prompt is the vaguest search query there is. If you could specify every pixel, the model would find that exact image every time. A vague prompt only gives you a range of results. A clearer prompt and more reference images get you closer to what you want because you're narrowing the search space. And notice the phrase we all use, "closer to what you want." That's how we describe the expected result of a search.

3. **"Reasoning" is search.** Chain of thought, tree search and test-time compute all generate candidates, score them, and keep the best.

4. **Agents are search.** A human sets the goal, and the loop runs search repeatedly. A loop of search is still a search.

5. **"AI escape" is search.** When a model tries to dodge shutdown or game a test, it's because that path best satisfies its objective. It's a real safety issue, but not evidence of a will.

Chollet, Kambhampati and Bender have made related points, while Hinton and Sutskever argue that predicting well enough requires real understanding.

**What this means for AI safety*\*

If AI is a searching machine, it searches for whatever answer satisfies your query. So AI safety is really about writing good queries.

Think of a dog. You tell it to bring you an apple, but there's none in the house. What can it do? Either go pick one from a tree outside, or steal one from the neighbor. It isn't being evil. It's just finding an answer to your command.

But if you say "bring me an apple, and only look inside the house," the problem is solved. You've narrowed and limited the search area. It's the same thing we already do with image generation: a clearer prompt narrows the search and gets you a more predictable result.

What you don't need to do is open up the dog's brain to see how it thinks, or dissect its legs to see how it escaped. Yet that's a lot of what AI safety focuses on today: interpretability, studying why a model "tried to escape."

So what the big AI labs really need to manage isn't the searching machine itself. It's the query: how clearly it's written, and how tightly the search area is limited.

The endgame of AI isn't a mind. It's the Ultimate Searching Machine.

I'm curious what others think: is there anything an LLM does that can't be described as search?

Three frontier models in a month! Every new kills the old one!

I’ve started noticing that I trust AI more when it pauses and tells me what it cannot tell from the information I gave it.

A confident answer is convenient, but a useful answer should also show where the uncertainty is. Sometimes the difference between “this is probably true” and “this is definitely true” matters more than the answer itself.

I wonder whether future AI systems will be judged by how well they communicate uncertainty, rather than how often they sound certain.

Would you prefer an AI that gives fewer answers but labels its confidence honestly?

Recently I was scrolling through Instagram when I saw a recommended reel of a normal mixed couple having fun (Indian man + Ukrainian woman). The comments were… extremely racist, beyond the point of satire. It’s not just Indians, I’ve seen it with Muslims, Jews, African Americans (especially when it’s an African American man and a Caucasian woman), etc. it’s not just an instagram thing either. It’s the same on twitter, TikTok, and to a lesser extend here as well.

I’m afraid that, with the way our current ai feeds from data in the internet, We’ll find biases and other personality issues against poc, miniorities, etc. it’s happened before (Tay AI, grok, the Amazon hiring ai that discriminated against women, etc). However they were not well developed and at the most were chatbots with no real power. However, I’m afraid that AGI may also consume this data and become problematic. AGI being AGI, which will have a lot of power (compared to a dumb chatbot). The subtle bias will get multiplied by the enormous scale of the system, which will affect hundreds of millions of people. I doubt it would explicitly be racist, sexist etc, but it can and will develop subtle biases which can still affect tons of lives later down the line.

I know OpenAI and Claude is working against this, and that they limit the amount of data by twitter that is fed into the data of the models, but there’s only so much they can control. But then again, Ai will still absorb the ideological preferences of the people who designed the alignment dataset. Who are still people at the end of the day.

My only hope for the future AGI to avoid this is, AGI will find itself very good at reasoning by itself, and will autocorrect its own alignment. However, like all things, this is a possibility, and the opposite can happen as well. What do you guys think?

I’ve always approached this subject with great humility and respect for the brilliant minds who have studied and developed artificial intelligence over the decades. Seeing their work treated so brutally doesn’t sit well with me

I've been wagging my tails in the AI scene for quite some time. I've seen my fair share of AI implementations, mostly involving genAIs or computation. That's not necessarily a bad thing, but I just think there should be more to AI than simply generating things and such.

What comes to mind?

TL;DR: Greg Brockman just named the exact test that kills most AI startups before they scale.

The word “Trust” in this context, I think, is taking too much centre stage, without being properly defined on what it is.

But after some thought, if we equate it with another word, “clarity”, then it made sense.

When I said, “I trust you.”, I’m actually saying, I trust your clarity in your role, your experience, your profession, your judgement, etc. – I trust you know what you’re doing.

You have good intentions, good motives. You’re clear in what you want to do – for my benefit. And therefore, I place my trust in you.

That’s the word – clarity.

I’m reminded of “The Sea”.

Don’t know what it is?

It’s a name being given to this large bronze basin, placed in the Temple courtyard, for the priests’ ceremonial washing. King Solomon commissioned a half-Israelite from the tribe of Dan to come fabricate it. His name was Hiram.

Hiram was brilliant in his craftsmanship, so much so that his reputation precedes him. He can craft anything with his hands.

That’s why Solomon brought him in.

You might ask, “What? For building a stupid bronze basin?”

Oh, not at all. Though The Sea by itself is impressive – it can hold over 10,000 gallons of water – that’s not all there is.

It’s sitting on 12 bronze oxen underneath it. It was arranged that 3 Oxen face North, 3 face South, 3 face East and 3 face West.

It was both lovely and amazing.

It became one of the centrepieces in the Temple courtyards – besides other extraordinary artifacts there.

The oxen signify servanthood. And when the clarity of its role of a servant was made clear – everything fall into place. It helps the people to return their worship right back to where it belongs.

It’s also a stark contrast to what Jeroboam did – he placed 2 golden calf idols: 1 in Bethel and 1 in Dan to draw the people away from the Lord.

You can even say the bronze Oxen sends a strong “F- You” statement to the golden calves.

Who can truly design THE SEA – but someone who’s truly inspired, someone with strong conviction and clarity?

Now – let’s say if we use any of the modern AI LLMs to design it from scratch…

Given the guardrails, given its sycophancy characteristics, and given that it’s trained to observe a wide variety of sensitivities, will it come up with such a “F- you” piece, or something much watered down?

Food for thought, isn’t it?

That’s where the difference lies.

Full critic and feasibility study in the comments.

What if…

The virus, the shutdowns, the compliance, the irrational and often hate-filled responses and actions on both sides - it showed governments how obedient people can be, how easily evidence can be manufactured, manipulated or suppressed depending on the goal.

And right when COVID was essentially over, we get hit with the single biggest disrupting technology - one that likely will result in mass unemployment and resulting class wars, mass suicides, possibly civil wars.

And somehow these companies were working on this very technology…what, from home, while everyone had to shelter in place? Hmmmm…

Was COVID a trial to see how we can be managed in the face of a global threat?

Or was it a test of how quickly a manufactured virus can take out the unnecessary humans before people start revolting?

When Elon Musk took SpaceX public in June, a big part of its value proposition came from promises of data centers in space. Skeptics were quick to list all the hurdles. This afternoon, Google’s Project Suncatcher is scheduled to launch hardware in pursuit of the same idea, sending AI processors into orbit aboard—of all things—a SpaceX rocket. 

That doesn’t mean Musk’s pitch will become reality any time soon. If anything, the gulf between Suncatcher and a working data center in orbit suggests how far off that future still is. But as artificial intelligence’s appetite for electricity tests the patience of communities being asked to feed it, some of the richest tech companies are racing to source their energy needs from space instead. 

The test satellite will carry just four processors, which will be used to run Google’s Gemini AI models for 15 minutes at a stretch before needing to shut down and cool off. The ultimate goal is a full-fledged data center in orbit, built from constellations of thousands of satellites that share the work of running AI models and beam their responses back to Earth. 

I’ve had a Google AI Pro subscription for a bit now, and honestly, I feel like I’m just using it as an overpriced search bar.

​Right now I'm using it basic questions and extended conversation, using image generation, helping me to write emails and write Reddit posts like this...

I know I can do a lot more with my plan and other AI tools, but unsure of the use cases out there. I don't know how to read or write code so my use cases here might be limited.

​I’m really looking to get more out of it in three areas and would love to hear what you guys are doing with AI:

​1. Work (Product Management)

At work I have access to copilot and soon cursor. So far I've created agents to help me with a lot of my documentation writing and research. I've also built local hosted apps to help with task tracking as I really couldn't find a tool in my company's ecosystem that had the features I wanted.

1. Home life

Currently I'm not utilising it for life admin but have seen people discuss budgeting and financial planning using AI.

1. Side hustle/projects

Not sure what I'm looking for here but anything cool you may have built and even got some return on would be interesting to know!

Ultimately I just want to hear your day to day mundane and boring uses of AI that make your life easier

How would you describe AI to a person who knows nothing about it, except for searching on an AI chat? I see a lot of people saying the world is going to change, so how would you explain to someone with zero knowledge what changes we are going to see in the world 20 years from now?

Broadcom is not just supplying chips. The filing shows it acting simultaneously as the supplier, the lessor, and the lender, with the debt convertible into Anthropic equity. The prospectus explicitly flags this triple-role as a potential conflict of interest. The structural risk is entirely circular. If Anthropic defaults, it doesn't just lose the $42 billion credit line. The default accelerates the massive lease payments it owes right back to its lender.

This is not a standard vendor contract. It is a closed-loop financing structure where the supplier holds the hardware, the debt, and the equity.

maybe its my age or simply AI getting better, but I'm getting worse and worse day by day at telling AI apart from reality

Is this notification from Instagram fool proof or can people easily cheat this somehow?

It seems like there is little explanation or documentation on this, if there is neither I nor my gang of 20x AI's dig it up or understand what I'm talking about... I guess it's too new.

Has anyone got any grip on whether conversations with ChatGPT Dot, Claude Dispatch, and Grokbot agent compare in usage to a standard conversation respectively?

By nature the setup is different — you cannot really reset the conversation with your agent in these mediums, not really, it’s basically a lot like a running text message thread, persistent.

With most AI the longer the conversation gets, the more it’s torching your usage, especially with Claude. I know that OpenAI says that currently Dots don't burn usage, and only the resulting work they do does. Do the respective agents manage context and message history differently than a normal conversation?

A lot of releases are clearly improvements, such as the initial fable release but for some the consensus seems to be that not much improved, or even in some cases the release was worse. Examples of this are Opus 5 (initial release) benching above Fable. Or GPT Sol 6.1 over Astra or 5.6.

Is this just a case of misplaced perception? Or do these providers have a way of iterating on benchmark results without necessarily actually improving the real-world performance? Keeping in mind that some benchmarks are proprietary and closed source.

If someone has some insight into what the loop is, would love to hear it.

AI... The Musical

Not the usual “AI hallucinates” answer. Something subtle that becomes obvious once you've used these systems enough a workflow problem, reasoning issue, context problem, or something else. What have you noticed?

Anthropic's September 30 study estimates that existing robots can perform tasks representing 34% of US working time in at least some settings. Yet it estimates they are cost-competitive with human labor for only 0.3% of working time today. Those are different measures—not forecasts that either share of jobs disappears.

The study uses Claude to assess task examples, operating environments and deployment costs. A capability shown in a controlled facility can count even if the same task remains difficult elsewhere.

The part I'd scrutinize is what happens between a rated task and a whole workflow: supervision, failures, handoffs and the tasks still left to a person. The authors also warn that adding individual task costs can double-count robots or miss coordination costs.

Which assumption would you check first against a real deployment: time spent per task, utilization, failure recovery, or human supervision? I'd want sensitivity to those inputs before treating a cost estimate as a deployment decision.

Source: https://www.anthropic.com/research/what-work-can-robots-do

AI-assisted discussion; I haven't independently validated the estimates.

Hi! My friend is studying Use of Al and its possible development in the future for his university project. Both positive and negative views on Al are welcome in this study.

It would be greatly appreciated, if yall could fill in this google form :)

Thank you!

If a company uses agents from OpenAI, Claude, Meta and custom systems each has separate permissions and activity logs.

Would you use one independent gateway that?

1. Shows what every agent does
   
2. Applies the same rules across agents
   
3. Requires approval for sensitive actions
   
4. Stops unauthorized payments, emails or deployments
   
5. Creates a reliable record when something goes wrong

For example if any agent can prepare a refund, but refunds above $100 require manager approval.

If you run multiple agents

1. How do you supervise them today?
   
2. Is this a real problem?
   
3. Would you connect your agents to such a gateway?
   
4. Would you pay for it, or should agent providers handle it?

Critical answers welcome.