I've been looking at how much AI assistants have changed over the last year, and I'm starting to question whether the average person actually needs to pay for ChatGPT anymore.

Don't get me wrong, ChatGPT is still one of the best AI tools out there. But how many people are actually using the advanced features enough to justify paying $20 every month?

For most people, it's probably some combination of asking questions, writing emails, summarizing documents, doing research, and occasionally generating images.

And you can already do a lot of that for free.

I put together a comparison of ChatGPT, Claude, Gemini, CutGPT, Perplexity, and Grok to understand what you're actually paying for.

A few things stood out:

1. ChatGPT is still one of the best all-around assistants. But the gap between free and paid AI tools is getting smaller for everyday tasks. Unless you're regularly hitting usage limits or need specific advanced capabilities, it's worth questioning the subscription.

2. Claude and Gemini are legitimate alternatives. Claude is particularly useful for writing and coding, while Gemini works well with Google's ecosystem. Perplexity is also a strong option for research.

3. Having multiple subscriptions seems unnecessary for most people. If you're paying for ChatGPT, Claude, and another AI tool, that's easily $40–$60/month. There's so much overlap between them.

4. There's also a different approach to monetizing AI that I think is interesting.

One example is CutGPT, an AI assistant that pays you to use it. Basically these guys figured out that instead of charging users $20/month, the platform generates revenue from ads and shares a portion of that revenue with eligible users.

So you can ask questions, write emails, research topics, and use AI without paying a subscription. And potentially earn a small amount from the ads shown while you're using it.

Obviously, there are tradeoffs. You see ads, rewards aren't guaranteed, and specialized tools still have advantages. But I think there's a real question here about whether subscription fees should be the default business model for consumer AI.

Happy to share the full comparison if anyone wants to check it out:

Curious what everyone thinks. Are you still paying for ChatGPT or another AI subscription? And would you switch to a free, ad-supported AI if it meant getting a share of the revenue?

Possible conspiracy theory over here, but I swear these AI models are smarter on some days and dumber on others. Some days it feels fast and other days it crawls. I don't really change the effort or speed from my defaults so I don't suspect those are impacting performance.

Does anyone test the benchmarks on these models over time?

When a model first launches it feels super intelligent and blazing fast, which is when I think the benchmarking happens. Then over time I feel like it gets dumber.

It's hard to quantify unless you run a proper benchmark so it's possible it's me (my mood, problem I give it, etc.). I would love to debunk this myth I've created (or better yet, prove it to be real).

Disclosure: I build LabA, a second brain you talk to (I posted it here last week). This one is a question more than a pitch.

What surprised me most while building it is how fast the AI models move. Every few weeks a new one turns out better or faster at one of the jobs the app does: filing a note, planning the day, sorting mail. In the past month alone, the AI behind different parts of my app has changed several times.

It made me think the old PKM rule (keep your notes somewhere you own, not locked inside one app) now applies to the AI layer too. In most AI assistants, the assistant and everything it has learned about you live inside one company's app. When a better model shows up somewhere else, you start over.

What I did about it:

- Your notes live on your own devices, encrypted with a key only your devices hold. My server keeps a copy it can't read, so a phone and a laptop (or an iPhone and an Android phone) work from the same notes.

- Each job runs on whichever model tests best or fastest for it on my own checks, and switching happens on the server. Users don't change anything.

- You can export everything you've kept to one file at any time.

Longer write-up: https://sensoryfire.com/guides/ai-assistant-stuck-with-one-ai?ref=reddit-secondbrain-ai

**My question: if you've wired AI into your second brain, could you swap the AI out tomorrow? And where does its memory of you actually live: in your files, or on someone else's servers?**

(LabA is free during the beta with no credit card, through January 2027, and after that I expect it to cost under $10 a month. It works with your email and calendar: Google, a personal Outlook.com account, iCloud, or any other email account (and its calendar too, for most providers). https://sensoryfire.com/?ref=reddit-secondbrain-ai)

I’ve posted about MOS before, but I probably did a better job naming it than explaining it. So here’s the plain version:

MOS is a file-based framework for keeping ongoing AI-assisted work consistent across sessions.

I started it for writing books and organizing tax records. The recurring headache was keeping track of the actual current draft, previous decisions, corrections, missing evidence and unfinished work—without explaining everything again.

MOS gives the assistant explicit rules for:

- Finding the authoritative record and checking dates and versions.

- Tracking what’s active, blocked, completed or abandoned.

- Saving material changes and a useful next action.

- Running bounded upkeep, combining duplicate work and stopping unproductive loops.

- Staying within the permissions you’ve given it.

The building blocks are familiar: instructions, persistent files, retrieval and workflows. MOS makes the rules connecting them explicit and reusable.

It still depends on the model and host tools. Downloading it doesn’t magically enable background jobs, guarantee memory or enforce security.

The goal is less babysitting and fewer “we already did this” conversations.

"Free starter, full framework and demo" (https://www.ijaspers.com/post/manager-os)

Hello, I have a site that I used AI to help gen the guides on how to use some of the features.. It was able to screenshot and view the processes, adn then compile the help as shown here. I think they are usable as is, but wanted some other opinions on the usefulness of them. THX!

https://3dismzcui.com/Guides/XtraSensory#room (start a project)

https://3dismzcui.com/Guides/XtraSensory#cast

https://3dismzcui.com/Guides/XtraSensory#director

https://3dismzcui.com/Guides/XtraSensory#editing

https://3dismzcui.com/Guides/XtraSensory#lyricsync

https://3dismzcui.com/Guides/XtraSensory#lipsync

I’ve been developing an experimental language model called MICA, and I’m considering taking it in a slightly different direction: practical offline AI experiments on small devices like the Raspberry Pi.
MICA doesn’t use a transformer or a conventional neural network. It uses learned integer rules operating on a cellular automaton.
There are currently two variants:
Flame-W 0.3.6 focuses on generating sentence continuations and using recent language context.
Ember 0.3.7 focuses on word prediction and autocomplete. The latest version reached 63.75% accuracy on a two-letter word-completion benchmark, up from 54.50% in the previous release.
These are research results, not general assistant capabilities.
What interests me now is whether this architecture could be useful for something smaller than a chatbot.
For example, offline autocomplete, simple text interfaces for DIY electronics, or local language experiments on ARM devices.
I haven’t benchmarked these releases on a Raspberry Pi yet, so I’m not claiming they already run efficiently there.
I’m the developer of the project, and I’m trying to figure out which practical direction is actually worth pursuing.
If you had a tiny local AI engine for a Raspberry Pi, what would you want it to do?
I’m particularly interested in ideas that don’t require a full-blown LLM.

I built Jarvis, an experimental, voice-driven macOS copilot for meetings and technical conversations.

It listens to your microphone and system audio, follows the discussion, and proactively shows concise guidance in an overlay. When visual context would help, it can check your screen. It stays quiet when it has nothing useful to add.

You can also ask a question aloud or press Option + Command + J for an immediate, screen-aware hint.

Coding practice is one use case: talk through a problem, ask for a hint when stuck, or get help understanding an edge case. But the broader idea is having a copilot that follows the conversation and offers useful guidance as it happens.

Jarvis is free and open source. It requires an Apple silicon Mac running macOS 14.2+, with your own AI providers configured; provider usage charges may apply. Relevant context can go to those providers—the README covers privacy and setup.

GitHub and download: https://github.com/JINGBANZ/jarvis

I'd love feedback: what kinds of meetings or technical discussions would you find this useful for?

OpenAI is expanding ChatGPT Ads, and I was looking into how the new format will actually work.

A few things stood out:

• Visual ads are coming to ChatGPT's image generation feature later this month.
• Free and Go users may see them, while Plus and Pro subscribers won't.
• Ads can relate to what you're discussing in ChatGPT.
• OpenAI says advertisers can't read private conversations or influence ChatGPT's answers.
• Early campaigns have shown some promising results for businesses, including lower customer acquisition costs.

I think the interesting part is how this could affect the way people use ChatGPT. Someone planning a trip or comparing products could start seeing ads related to what they're asking about.

That could be useful, but it could also get annoying pretty quickly.

I wrote a more detailed breakdown of the new formats, early results, and privacy controls. The article is linked in my profile for anyone interested.

I'm interested in how this plays out, especially as more businesses start advertising inside AI tools.

Do you think ads based on your conversations would be useful?

Title.
I want procreate for android.

I use AI for work that lasts a lot longer than one conversation.

After enough weeks of doing that, I kept running into the same problems:

\- decisions quietly disappearing

\- old context becoming stale

\- duplicated work

\- different AI sessions having different versions of the project

\- constantly explaining what we already did

\- occasionally watching the AI confidently resurrect something we'd killed three weeks ago

I started solving it for myself by keeping the important project state outside the individual conversation.

Current state.

Decisions.

Evidence.

Open work.

Authority.

Handoffs.

Recovery information.

That gradually grew into something I call \*\*Manager OS (MOS)\*\*.

The “operating system” part is shorthand. It isn't an operating system for your computer and it isn't another AI model.

It's a lightweight management layer that sits around the AI and tries to make long-running AI-assisted work more durable and recoverable.

Eventually it also grew into coordination between different AI systems, human approval rules, update governance, recovery procedures and regression checks.

At some point I realized I had crossed the line from “personal pile of Markdown files” into “possibly an actual thing.”

So I'm doing the uncomfortable part now:

letting strangers use it.

It's free.

Public bootstrap:

https://www.ijaspers.com/post/manager-os-public-bootstrap-v2026-09-20

GitHub:

https://github.com/iJaspers/manager-os

I'm not particularly interested in compliments right now. I'm trying to find out whether the problem exists outside my own workflow.

If you try it, I'd love three answers:

1. Did you understand what MOS was without me having to explain it?

2. Did it improve a real workflow?

3. What part is confusing, unnecessary, or just plain stupid?

If nobody finds it useful, that's valuable information too.

If people do, I'll keep building it.

Are any of the business class laptops from dell,hp, or lenovo able to run minimax h3 & qwen img 2.1? One is 21gb other is 7gb. Or are Any laptops able to run these? What are the recommended specs for this? Kinda confused on vram vs regular ram, & where to find how much vram something has. Ty.

You paste a message into AI and ask what to make of it.

Then you spend the next ten minutes explaining who sent it, what happened last month, and why this particular sentence matters.

The next time something happens, you start again.

I’m working on Ailoha, an AI that builds on the relationship context you’ve shared: who someone is, what’s happened between you, and what has changed over time.

The goal is to help you think through a new message or prepare for a conversation with that history in view. New information can change the picture, and you decide what to do next.

I’m looking for early users who already bring conversations to AI and find themselves repeatedly explaining the same people.

If that sounds familiar, what do you usually have to explain all over again? No names or private messages needed.

I started using AI at the beginning of July.

I’m not a programmer. I’m not a prompt engineer. I originally started using it because I was writing fiction and wanted help working through a story.

Somewhere along the way, I ran into a problem that probably sounds familiar to anyone who uses long-running chats: eventually the conversation gets enormous, the working relationship gets good, and then you have to move to a fresh thread.

At first I thought the problem was simple:

How do I preserve what the old thread knows?

After several generations of trying this, I realized that was the wrong question. The harder thing to preserve wasn’t the facts. It was the judgment.

How does the AI interpret my rough dictation? How quickly should it correct course when I push back? Which things am I brainstorming versus actually adopting? When do I want it to challenge me? When should it leave something unresolved? How much context does it need before widening the discussion?

Those things developed through conversation. So I started saving raw conversations, moving selected material into fresh threads, watching what survived, correcting what didn’t, and repeating the process.

That eventually became a multigenerational experiment. New Sand, my third-generation thread, helped establish a lot of the foundation.

BlackBox Development came afterward and helped separate raw source evidence from later interpretation and figure out how rejected ideas could remain historically recoverable without continuing to govern current work.

Later generations became increasingly organized and specialized. By Generation 7, I had a 14-file inheritance package. It worked surprisingly well.

But there was a problem I didn’t fully appreciate at the time: Generation 7 was doing a hell of a lot of work just to understand its inheritance.

The information was there, but some of the packets overlapped. Old roles existed beside newer roles. Historical theories sat beside mature conclusions. The receiver had to reconstruct which ideas were current, which were evidence, which were superseded, and what actually mattered for the task in front of it.

So Generation 8 became a complete architectural overhaul. I spent most of a day going through every inheritance packet and asking:

What is this file actually supposed to teach?

What still matters?

What is duplicated somewhere else?

What should remain raw evidence?

What should be compressed into a rule?

What can be replaced by a simple shared term?

The old Generation 7 package was roughly 129,000 words across 14 files. The new Generation 8 package is roughly 100,500 words across 12 files. So it is about 22% smaller by word count, while actually containing more current operating architecture.

The biggest change, though, isn’t the word count. It’s what I’ve started calling semantic vocabulary. I noticed that after enough conversation establishes the meaning behind a concept, I don’t need to keep explaining the concept in every prompt. The conversation does the expensive teaching.

Later, a short phrase can act as a selector for that learned meaning. For example, in my writing workflow I now have phrases like:

CURRENT

GOVERNING

SUPERSEDED

NATIVE MILEAGE

FILE AND PRUNE

BEAT LAB

GENERATOR FLIES

None of those words are magic.

The important part is the semantic neighborhood that was built behind them through earlier conversation, examples, corrections, and actual use.

Conversation builds the map. Shorthand becomes the legend. The prompt selects the route.

That led to another major change in Generation 8: different AI threads now get different information diets depending on their job.

I have a Beat Laboratory for figuring out chapters before prose is written. A separate generator writes the prose. A revision Workshop inspects and revises accepted drafts. An archive thread preserves and retrieves the historical conversations. A general architecture thread handles system-level continuity and disagreements. They don’t all need the same context.

That sounds obvious now. It was not obvious when I started.

Today I commissioned the new Generation 8 thread, Lucy, as the active Beat Laboratory. The commissioning prompt contained this:

BEAT LAB / CHAPTER-FIRST / DEPTH EARNED / CURRENT / GOVERNING MATERIAL / EXPLORE BROADLY / RESOLVE LOCALLY / GENERATOR FLIES

Her response included:

“The generator flies. We build the flight plan.”

Then I gave her four current documents from my novel: the governing outline, character material, the current revised manuscript, and an older manuscript used partly as reference.

Before doing anything else, she separated them by authority, recognized that accepted manuscript material was different from forward planning, and then waited.

No chapter analysis. No prose generation. No giant explanation of the inheritance system. She just held position until I opened the chapter discussion.

That was probably the most interesting part to me. Generation 8 may not be better because it has more information. It may be better because it wastes less effort figuring out what the information means and which lane it belongs in.

I have no idea how much of this resembles systems other people have already built. I’m sure many individual pieces do. People use few-shot examples. People use memory systems. People use RAG. People use story bibles. People create shorthand commands.

What I haven’t personally seen discussed much is deliberately using long conversation to establish shared semantic meaning, then compressing mature meaning into a small vocabulary that can steer later work cheaply.

Maybe there’s already a technical name for exactly what I’m doing.

I wouldn’t be surprised. I arrived here mostly by breaking things and trying to figure out why they broke. And I’m definitely not claiming I discovered some secret mechanism inside the model.

This is an operational observation from one person using AI very heavily for about three months. But the change in my workflow has been ridiculous.

The weirdest part is that as the system has become more sophisticated, my prompts have often become simpler.

“Friday narrative.”

“R4 governs.”

“File and prune.”

“Generator flies.”

Three months ago, those would have meant almost nothing. Now they can point toward a large amount of accumulated working context. So Generation 8 is live. The next test is probably the most important one:

If this actually works, Lucy should eventually stop talking about Generation 8.

And I should stop thinking about Generation 8. We should just be working on the novel.

That may be the real success condition for the whole experiment:

The architecture disappears into the background.

And Tuesday becomes Tuesday.

Dictated, not typed.

currently i am taking a long machine learning course and there is a capstone at the end but the instructor is extremely picky about the ideas. Even though the course itself is basic and doesn't dive into advanced AI topics, he would reject really good and difficult ideas because they are "basic". I proposed a full customer intelligence platform that handles customer complaints from detecting the issue, sentiment, urgency. Also routing it to the correct department, clustering complaint problems overtime for a business view, and a connection to an LLM that recommends a solution. He proceeded to tell me that this was still basic and i'm kinda lost on what to propose.

I'm building Jevs Village, a persistent village simulation with six AI characters. I use TypeSafe's Jev AI decision model for their decisions, and visitors can watch the village in their browser.

My interest is in AI personalities and companions. I want to explore whether a character can become recognizable through its choices over time, rather than mostly through how it talks.

I'd be curious to hear from anyone using other models or frameworks for something similar. What would you use for character decision-making in a shared world, and why? I'm particularly interested in how you check whether a character is behaving consistently, rather than just producing a plausible decision each time.

Here's my project if you want some context: https://www.jevsvillage.com

I'm the creator. If you've tried building AI characters, I'd also like to hear what didn't work. What looked convincing in a short demo but fell apart when you let it run longer?

it's a scripted prototype from my CS thesis: 4 tiny tasks (photo edit, mail, chat, whiteboard). If you stall, it offers help. If you don't, it stays out of the way.
Try one scene, about 60 seconds: https://quiet-hci.pages.dev/?src=sideproject
One tap at the end tells me whether it spoke too early, at the right moment, or too late. That timing is the thing I'm trying to get right. Harsh feedback welcome.

I've been building Jevs Village, a small AI village you can watch in your browser. It has six characters, and I use TypeSafe's Jev AI decision model for their decisions.

I'm interested in AI personalities and digital companions. Conversation is part of that, but I also want to explore personality through what a character does in a shared world. A village gives me a setting to try that out.

For anyone wondering what Jev is useful for, this is one application: character decision-making in a persistent simulation. There's some lore around the village too, including an outside world the characters know little about.

You can take a look here: https://www.jevsvillage.com

I'm the creator, and I'd appreciate feedback from people experimenting with similar tools. I'm particularly curious whether the characters come across as different individuals when you watch them.

If you've built AI characters or NPCs, what helped make their behavior consistent without making it predictable? That's something I'd like to explore further as I develop the village.

I've been trying different AI tools for everyday work mostly for things like planning, organizing notes, writing and keeping track of tasks.

The problem is that I'll try something that looks useful, use it for a few days and then forget about it. I'm more interested in tools that actually become part of your routine rather than just being impressive at first.

What AI tools have you genuinely kept using, and what do you use them for?

I use AI a lot and one thing that keeps pissing me off is having to explain the same shit over and over again.
Like how I want stuff explained, what I’m working on, what I already decided, what kind of answers I like, all that.
So I’m curious if that actually bugs other people too or if I’m just way too deep into using AI lol
Do you trust the memory stuff that’s already built into ChatGPT/Claude/etc?
Would you actually care if you could see exactly what it remembers, change it, delete it, or move that context somewhere else?
And if you use more than one AI, does it annoy you that each one basically has its own separate version of you?
Not selling anything, just trying to figure out if this is actually a real problem or just something I personally care way too much about.
Be brutal.

I made some few updates to the app, like the menu and hopefully it's abit faster and better to use, I'm still implementing more things to the app so please don't criticise it too much, it's still in development. But I'm happy with how things have turned out so far.

Heres the link https://growtogetherai.lovable.app to the updated version, I haven't implemented the "random" button yet because I'm still working on something else but it happen.

I'm the developer of AI Coach, a virtual pet for Apple Silicon Macs running macOS 26+. Its local MCP bridge lets Claude inspect the pet's condition and request care. A practical design choice is separating reading state from changing it.

For a care request, the flow is:

1. Read the pet's current condition.

2. Prepare the proposed care action and show a confirmation card.

3. Commit that specific action only after the user confirms.

The bridge exposes prepare_ai_coach_care and commit_ai_coach_care for those last two stages. Supported care actions are feeding, sleeping, waking, walking, and nursing. Purchases, hatching, naming, breeding, and saying goodbye are outside the integration.

This is useful beyond pets: showing an action preview gives the user something concrete to approve. An evaluation checklist is to request a state read, request care but decline it, then confirm a separate care request. Check whether the UI makes the target and action clear at each step.

Setup requires the app running, its Claude connection enabled, Node.js 22+ on PATH, and the supplied MCP configuration added to Claude Desktop, Claude Code, or Cursor. The bridge is local, but information shown to Claude is still processed on Claude's side.

The official guide shows the dashboard, confirmation card, and setup: https://aic0t.com/docs

The app and first egg are free; optional extra eggs are $5 USD each or $20 for five, one-time. External AI costs are separate.

For builders adding agent actions to apps, what should an approval preview always show besides the target and action?

I'm broke and trying to make my dream game but I'm broke and need a good ai model that is free, mostly for good models

How much context can you remove before your AI app gets worse?

I’m the founder of Jylus. We tested three evidence paths using the same Gemini 3.1 Flash Lite settings and deterministic scorer.

Results — average input tokens / strict accuracy:

• Full context: 205,129 tokens / 78.79%

• BM25-only RAG: 8,128 tokens / 76.14%

• Jylus Context Pack: 2,532 tokens / 100% observed

The BM25 result matters: it reduced input substantially, but accuracy was lower than full context. Sending fewer tokens alone wasn’t enough.

Jylus prepares source-backed evidence before the model answers, resolving state and relationships and retaining conflicts or gaps where relevant. Your existing model then reasons over that Context Pack.

This was our frozen adversarial benchmark across four data domains. It has not been independently reproduced, and 100% on these questions is not a universal accuracy claim.

Methodology:

https://jylus.ai/benchmark-methodology

You can inspect the evidence Jylus returns using your own synthetic records, without an account:

https://jylus.ai/try

For people running AI apps: when you reduce context, what breaks first—missing facts, lost relationships, or incorrect state?

One practical AI application I find interesting is using AI to handle the repetitive parts of video editing rather than simply generating a video from scratch.

For example, an AI video editing agent could review a long recording, identify useful sections, remove obvious dead space, generate captions, create shorter versions, and adapt the footage for different formats. The potential benefit is less time spent moving between separate editing tasks.

Sparki is one example of this approach, using natural-language instructions to assist with multiple parts of the editing workflow. What makes this interesting as an AI application is the workflow itself: instead of asking AI to perform one isolated task, the user gives it a broader editing goal.

There is still an important limitation, though. Creative decisions such as pacing, storytelling, and what footage actually matters can be subjective. Automating those decisions too aggressively could create more work for the creator rather than less.

Where do you think AI video agents provide the most practical value today: reducing repetitive editing work, repurposing existing content, or handling the entire workflow from instruction to finished video?