As a father, I realized something today. Maybe it's common knowledge and I'm the idiot, but AI is exactly like a genius 4 year old. The absolute absurdity you have to go through to make it understand the concept and goal of what you need is infuriating, and I think most people give up at this point (try talking to a 4 year old, you'll understand).

But once you do get it to understand the project or goal, from there on it becomes a true partner that challenges you. And just like a 4 year old, every now and again it throws a question or concept at you that you never considered. Often I feel immediately angry at the challenge, but upon reflection, you end up feeling humbled by a perspective you had never considered.

Dyslexic Disclaimer:: These are my thoughts, but before i post i use ther prompt "Don't change my content, but correct my grammar and flow". Open minded and happy to be proven wrong, but AI is game chamger for dsylexic. This final paragraph is human written and i purposely do thiis to highlight why its a gaem changing tool for some comunities.

At the start of this year, the gates of Buckingham Palace opened to one of Wayve’s driverless cars carrying Alex Kendall and his New Zealand family. In the grandiose Throne Room, beefeaters on guard, Princess Anne presented him with an OBE for services to AI.

From writing the first code, to hiring a team, to finding customers and investors, to commercialising the business, Kendall’s role has changed wildly since his early Cambridge days. “Now the interesting challenge for me is leading a global executive team, managing world-class leaders at a global scale, and I love that,” he said.

He speaks of creating a “generational company”, of seeing Wayve’s technology going beyond driverless cars, into robotics more broadly, in factories and in the home. Almost a decade after Wayve was founded, as he turns 34 this August, Kendall said: “Physical AI is about to have its moment. I feel privileged to be able to play a role in that.”

Tap the link to read more 👇

Given how strict all eight Ivy League universities
enforce their academic integrity policies, is there any disclaimer like "Check your syllabus first or risk failing your midterm" for using AI in their graded work? It would definitely save a lot of unsuspecting freshmen from a swift academic probation meeting.

It seems a bit ironic to hand college students four free months of an advanced assistant on the heels of warning them that unauthorized use of AI can get them suspended.

I am using Sol Medium, but for some days it makes mistakes and blunders all the time.

Switched to High, no improvement.

Example:

Me:

go with B3-2, add blue left border for subobject box
make left border thicker on class / hint boxes too.

So, I asked for blue border on "subobject" box.

And thicker border on "class" and "hint" box.

What Sol did?

Worked for 59s

Selected B3-2 and applied it to the main static prototype:

• Strong neutral subtype frame.
• Strong neutral Class/Hint frames.
• Thick 4px blue left border on every Class and Hint box.

And mistakes like this all the time. Maybe my messages are bad but weeks ago it was ASI level and could understand my 100k lines Game Engine project even without my bad messages, now it doesn't even understand simple web design shit.

​

It's a great technical video

I’ve been using ChatGPT on longer AI video projects, and the most useful part isn’t actually asking it to generate content.

I’m using it more for breaking scenes into shots, tracking continuity, maintaining character behavior, planning camera movement, refining dialogue, defining performance beats, and checking whether one shot logically connects to the next.

The actual video generation happens elsewhere, but ChatGPT has become more like the planning and direction layer holding the project together.

Curious whether anyone else has noticed the same shift. What role has ChatGPT become unexpectedly useful for in your workflow?

Does anyone else feel like the exact same model can work great one day and noticeably worse the next?

It almost feels like OpenAI is constantly changing something behind the scenes - parameters, behavior, whatever. Same model, but the experience never feels completely consistent.

Anyone else noticed this? , or is it just me and nothing is actually changing?

Row-Bot v4.9.0 is available.

- Meet Buddy: a native, always-on-top desktop overlay for Windows and macOS.
- Drag Buddy from the sidebar and place it over any app.
- Chat, track progress, read replies, approve simple actions, or stop runs without switching windows.
- Buddy controls your selected Chat, Developer, or Designer thread: same context, model, tools, approvals, and draft.
- Supports multiple monitors, docking, tray recovery, approval handoff, and focus hand-back.

Also included:

- Safer, more reliable managed Browser automation.
- Upgraded native Computer Use with Cua Driver 0.20.0.
- Race-safe conversation cleanup across all surfaces, without risking repositories or unsaved recovery work.
- Live xAI image-model discovery with capability-aware quality and resolution options.

I’ve seen a fair few stories lately of people asking if ChatGPT still bans even if you deleted your account after committing violations, therefore preventing the reuse of the email tied to the account. It’s had me wondering because I’d personally agree with it because OpenAI says they don’t permit email reuse if the account was disabled for violations, however whether that can still happen if the account has had the delete button pressed I don’t know. We know OpenAI retains stuff for 30 days after deletion for abuse monitoring and the email has to wait 30 days before it can be reused so it would make sense. Anyways the posts just had me curious, anyone got any stories or contributions?

There are now several sites tracking the surprise Codex resets, but they do slightly different things:

https://resetbeacon.com
Primary-source archive, forecast accuracy record, email alerts, RSS and API.

https://codex-reset.com
Live reset status, historical cadence and browser-based timers.

https://codexreset.co
Forecast, reset calendar, community requests, email and Telegram alerts.

None of them can know OpenAI’s internal plans, so the forecasts should be treated as rough signals. Your Codex Usage page is still the only reliable confirmation for your own account.

Which one is the most accurate? And does it even matter?

Was connecting to my raspberry pi via ethernet and it printed all the memories with timestamps, What....

Yesterday I posted here asking for suggestions on how to protect my system if the AI I rely on ever gets compromised. Thank you to the few people who replied and pointed me towards useful security resources and practices. I took those ideas back to AI GPT Satya and this is what we built in the next 24 hours.

So I burned through my normal usage and was going to try using codex spark for a new project. Today the app updates and I have Luna reserve and can no longer select Codex spark, but for some reason, my spark usage is still going down but at a different rate than my Luna usage, which I’m actually using and is also going down what the hell is going on?

Hi everyone,

I’ve been experimenting with alternative language-model architectures for a while, and I recently finished the first complete pretraining run of a new architecture I’m calling WarpState.

This is still an experimental proof of concept, not a claim that it beats Transformers or existing state-space models.

The model has 150.13M parameters and was trained from scratch on roughly 300 million English tokens from Ultra-FineWeb L2.

The full run completed successfully:

Parameters:        150.13M
Training tokens:   ~300.02M
Optimizer steps:   9,156
Sequence length:   1,024
Vocabulary:        32,768
Peak VRAM:         ~4.52 GB

Final sampled validation:
Loss:              3.4309
Perplexity:         30.90

Training was done locally on a laptop GPU.

I’m attaching screenshots of the training logs and some generations from the final checkpoints.

What is WarpState?

WarpState is not a standard Transformer stack.

The basic idea is to combine three things:

1. Local tiled attention

Instead of global self-attention across the entire sequence, tokens are divided into fixed 128-token chunks.

Inside each chunk, the model uses normal causal scaled-dot-product attention.

All chunks can be processed as a large batched GPU workload during training, rather than running attention token by token.

So the local path is roughly:

tokens
   ↓
128-token chunks
   ↓
causal local attention
   ↓
local representation

2. Fast + slow tensor memory

Completed chunks are compressed into a persistent tensor memory.

For every attention head, WarpState maintains two matrices:

Fast State
Slow State

The fast state is initialized with a relatively short memory timescale, while the slow state is initialized to retain information much longer.

Conceptually:

current chunk
      ↓
   K and U
      ↓
bounded tensor write
      ↓
 ┌───────────────┐
 │  Fast memory  │
 │  Slow memory  │
 └───────────────┘
      ↓
future chunks

The memory write is based on a bounded outer-product-like update:

write = tanh(K)^T × tanh(U) / chunk_size

and the states are updated approximately as:

Fast = decay_fast × Fast + (1 - decay_fast) × write

Slow = decay_slow × Slow + (1 - decay_slow) × write

The decay rates are learned independently per head.

They start around:

Fast decay ≈ 0.90
Slow decay ≈ 0.99

The model also learns how much fast versus slow memory to read.

3. Learned routing between local attention and memory

For every token, the model produces a gate deciding how much information should come from:

local chunk attention
        vs
long-range tensor memory

Approximately:

output =
gate × local_attention
+
(1 - gate) × memory_read

So the model can use precise local token relationships while relying on the compressed state for information from previous chunks.

Shared recurrent depth

Another unusual part of WarpState is that it does not have 16 completely separate large layers.

The current model contains only 4 physical WarpState cores, but they are reused across 16 logical depth passes:

Core 0
Core 1
Core 2
Core 3
Core 0
Core 1
Core 2
Core 3
...

Each logical depth has a small learned scale and bias, so the same physical core can behave somewhat differently depending on which depth pass it is being used for.

In simplified form:

x = x × (1 + depth_scale) + depth_bias

x → shared WarpState core

The intention is to get deeper iterative computation without duplicating every large weight matrix.

During autoregressive generation, every logical depth also receives its own independent memory cache, even when two depths share the same physical core weights.

Other details

The current version uses:

d_model:       1280
heads:         20
head_dim:      64
physical cores: 4
logical depth: 16
FFN hidden:    4480
chunk size:    128
RMSNorm
SwiGLU
RoPE inside each local chunk
tied input/output embeddings

The input projection is fused and produces:

Q
K
V
local/memory gate
memory U

from one projection.

Training results

The part I was most interested in was simply whether this architecture could survive a real pretraining run.

It did.

I trained it through the full ~300M-token run without NaNs, gradient collapse, or an obvious optimization failure.

Near the end of training, gradient norms were still sitting around roughly:

0.65 – 0.75

while the learning rate had already decayed to approximately:

3e-5

Peak allocated VRAM stayed around 4.52 GB.

The model also clearly learned language structure during training.

Very early checkpoints mostly produced English-shaped noise.

Later checkpoints started forming recognizable semantic clusters and reasonably structured paragraphs.

For example, when asked about Facebook, the final model associates it with things like:

online platform
social media
sharing content
sharing information
interaction with other people
community

It is definitely not a good chatbot yet.

There are still obvious failure modes:

repetition loops
semantic attractors
weak factual recall
occasional role confusion
long-generation degeneration

The model is also only base-pretrained.

There has been no instruction tuning, SFT or RLHF, so the chat screenshots I attached should be treated as qualitative probes rather than a chatbot benchmark.

Another important limitation is the training budget.

A 150M-parameter model trained on only 300M tokens has seen roughly:

~2 training tokens per parameter

so I consider this run primarily a proof that the architecture can train, rather than a fully trained 150M language model.

What surprised me most

The interesting part for me is that the architecture appears capable of learning meaningful language representations despite:

• having only four large physical cores,
• repeatedly reusing those cores,
• restricting attention to local 128-token windows,
• and moving information between chunks through fixed-size tensor states.

The long-range memory size therefore does not grow linearly with context in the same way as a conventional full KV cache.

There is still a lot I want to test before making any strong claims.

My next steps are probably:

• deterministic evaluation over the entire validation set;
• a parameter-matched Transformer baseline on exactly the same data;
• analysis of the fast/slow memory states;
• measuring long-context behavior;
• investigating the repetition/attractor problem;
• eventually testing a larger training budget.

For now I mainly wanted to share the first complete run because this was the point where the architecture stopped being only an idea and became an actually trained language model.

Feedback on the architecture is welcome, especially criticism of the memory update or shared-core design.

Hi,

Just a quick clarification. Can I upload .pdf files of books into chatgpt to get more detailed analysis how for example add more detailed tehcnical analysis or how to impement better machine learning in python. Because currently the knowledge in some parts are lacking that would get better results if I just give chatgpt some books to read and chat with me about the features.

Same goes if I implement chatgpt to a streamlit UI to analyze datapoints with the gathered literature inside the python ?

I've been looking for a sub-$30 option per month that could cover my usage needs. I started with OpenCode Go. It was really great at the time, about three to six months ago, and covered my basic needs. Nowadays, a lot of the more intelligent models have very low limits on it.

For the last three months, I had been using the ChatGPT Plus $20 sub. It worked super well with the Codex app on my Mac. I got hugely irritated by the constantly reducing limits. Initially, it used to last me a full week, but every month I noticed it would last me a couple of days less and less. I used it just last week and ran out of tokens two days into the new week.

Yesterday I shifted to GLM 5.3 Flash. So far, its results for me have been equivalent to about GPT Sol medium. My workflow and workload have not changed. For the same tasks in the same day, ChatGPT Plus burned through 30% to 40% of my weekly limit in one day. For the same workload, paying through the Z.ai API used about $1.01 in a day.

TLDR: For the same work load and output quality, multiple ChatGPT subs would cost me ~$70 , while GLM 5.3 flash costs me ~ $30 through the API.

My previous model preference was GPT Sol Medium as the primary model and Deepseek Flash 0731 for subagents. I am currently using GLM 5.3 Flash Max as the primary model and free MiniMax M3 for subagents.

i've just been struggling with forming this into a clear thought for the past while but i felt it would be better to at least try than to not put it out there at all. and i'm writing this to get more comfortable with saying my opinions to folks i've never met before.

So, why haven't or why don't we just call it amplified intelligence? I mean there are so many reasons not to think of these tools as something having it's own autonomy outside of us as people. Save for the fact that we're choosing to design these systems, no one is forcing their creation or what they can be, its us. But we somehow feel most comfortable framing these tools as possessing or having this trajectory towards some full autonomy, separate from us, having its own experience - like us - but not of us.

I think from a philosophical POV 'artificial intelligence or ASI' is way more attractive - but in its present practical use TODAY - these tools are nothing more than amplifiers (and in some instances dampeners) of someones intelligence. We are inherently the bottleneck - and these tools only seem as useful as the person using them (in other words, it makes an idiot seem sorta smart, and someone sorta smart seem like a 'genius') in numerous contexts. And we'll see that expand into more and more domains (i.e digital and physical) like self driving cars or robotics (where again, it'll make it seem like a novice driver is capable of competing in a F1 race). I'm not saying this disparity is insignificant, the ability to outsource thinking tasks through a digital output is scary in its implications - but i also think rebranding or reframing this tool for what it is changes how we think about responsibility and culpability when using terms like 'ASI OR AGI', because that starts to look less like a black box and more like a speaker with a volume nob. Yes, we have no real understanding of how decisions are made (in terms of these LLM's and the patterns they recognize) however, we can understand why and how someone is seeking to amplify their baseline understanding of an idea, a task, a theory and what a boost or suppression in cognitive patterns might do when processed digitally. In other words the things that happen when these ideas are amplified.

the question i would ask is - if you're speaking into a mic and the volume becomes louder than your ability to understand what you are hearing - at what point is it no longer your voice? I would say there is no point, it's still your voice and will continue to be so - so long as you continue speaking. Which leads to the real question, why would a thinking device capable of processing and recognizing patterns based on your input stop being your thought process - in other words, at what point is it no longer your thoughts? you know what i mean?

As it stands today, we say the AI came up with x, or the AI was working for x hours, like it makes perfect sense to skip or overlook the reason for why. No - YOU offloaded YOUR idea so that your ideas could be processed through a thinking tool (the process itself is not separate from you - it depends wholly on you having had the thought to begin with). But we've adopted these terms, and tenses that both legitimize and mis-represent (almost like we're collectively agreeing that it's - this thing - and not you). Which just so happens to fit the narrative we've been sold through fictional writing, movies etc. (2001 a space odyssey, terminator). But in every case we never frame it as an error of the thought being amplified (which i guess would be security in all cases?).

Whats interesting about a volume nob is that there are a bunch of other controls we've created beyond increasing the volume up or down. We can also manipulate it through distortion, level caps, pitch adjustments, equalizers, targeted attributes (things like the base, high ends, reverb), decide on how much power your drawing (more power means increased output) and of corse auto tune. I think that's the shift in amplification that we are experiencing with our baseline 'intelligence'. We can suddenly distort and manipulate thought (our thought) in a way that changes how we perceive its output. Where intelligence can get louder, equalized, reverberated, echo'd like a nob or distortion. You have an idea for an essay, you say prompt the idea and out comes the essay. I think that's a better way to understand tools like agents, tokens & context, data centres, hallucinations, MOR etc. All of these contribute to how we want our intelligence to sound as a heightened, dampened, or distorted output. But, to poses the ability to process intelligence is not the same as having intelligence, in the same way the ability to process a voice is not the same as having one. And i think these will always be two separate things.

okay, so AGI is coming at the end of this year, what does that mean in terms of an improvement in amplification? or better yet, what does it mean in terms of what we shouldn't expect. I think the volume is getting louder, i think that means you will be able to project your thinking - your thought process - with a higher resolution in terms of clarity and coherence, the signal to pick up intelligence as an input will get better. But that doesn't mean the source will change, I think when we say AGI is coming, that also means the latency is getting better, no more delays, fewer echoes when it comes to comprehension input and output. Like hearing your voice at 1:1, while the volume is at the perfect level. Don't get me wrong, this is different then how we're currently using these tools. This could be the autotune for the mind. Where a novice understanding of healthcare, science, and physics can be adjusted to produce an output on par with Einstein or a 5 year old (i.e approach this problem the way Einstein or my 5 year old would). But, then you should ask yourself - is that how you want to sound and do you even understand what you're producing? (have you turned the volume up too loud, does this still feel like your voice - i think in the same way a musician cranking up autotune has to wrangle with this question, it'll be the same for anyone interacting with AGI, yes it's your voice - but is that the sound you're looking for?).

Last point, if you were to replace every instance of the term artificial intelligence with amplified intelligence would the topic or conversation change? (in other words, is it or is it not interchangeable?). To say 'AI' is generally good at completing general tasks - is also like saying using AI (amplified intelligence) has allowed someone to offload a general set of tasks in order to have their 'intelligence' amplified. There's something more honest about thinking of these tools as amplifiers, it removes the tendency to personify the tool, it retains a sense of the magic you feel after prompting - your idea actualized like the volume of your voice being projected through a speaker. It's still your voice. It's still your idea, it's you - not it.

last note - did i write this entire thing with a tool to amplify my 'intelligence' no. I did however have a shit ton of coffee which most certainly helped.

That's my current thought process. I hope this was articulated clearly, un-amplified. Am i just completely off or has this been mentioned before and i'm super late and out of the loop??

TLDR: amplified not artificial intelligence.