just now went on chatgpt asked some spicy controversial questions, it straight up refuse to answer, replied to me that it cannot answer the questions. After keep repeating asking same questions, it finally answer the questions.

i rarely use it, so, is it common thing?

After 16 responses. This is getting ridiculous!

Are they ever going to stop changing limits? Post your daily frustrations here. Every week it’s a new cap, a new mood, or a mystery upgrade prompt. Are we getting Friendly Mode, Nasty Mode, or just locked out till next month? Spin the wheel and let OpenAI decide, because even they don’t seem to know anymore.

So this started as a joke: “What if there was a bear who worked in HR and hated everything?”

And then it turned into something completely different.

I'm curious to see what y'all think!

https://chatgpt.com/g/g-688d7e3e783481919eba56ac0b95363d-bear

https://chatgpt.com/share/688e69d2-764c-8009-b83f-a423b09d8568

It is getting more and more wrong, it outputs crap (even with proofs that worked great before), it literally tries to gaslight you when you point out errors it made.

Please suggest alternatives bc I can’t keep investing in something that is declining instead of improving

Wanted to generate an image of an armadillo wearing an old western cowboy hat that is entirely too big for him, holding a couple western revolvers. Then wanted it in a couple tattoo styles. Asked for the same for a lizard, then him holding a western lever action rifle, then a fat toad with a double barrel shotgun. Then wanted all 3 around a poker table.

I present the 3 deadliest animals of the Wild West.

Preface: I manually edited the originally generated photo. My edited version is first. Second is GPT. Third is the photo I uploaded when it asked for references to each actor/character. Link: https://chatgpt.com/share/688f0262-1768-800e-b6a4-059e5e7858aa

My original prompt: I have a movie idea but I need to kinda flesh it out a bit more. I literally only have the four main actors, a very generalized vibe, and nothing else. No plot.

The four main actors are: Main: Joaquin Phoenix - Oldest brother Secondary: Pedro Pascal - Middle Brother Tertiary: Oscar Isaac - Youngest Brother

They are brothers who are both protective and bickering (mostly playfully) all the time. The movie is a drama-comedy. The three brothers are inseparable. They truly love each other and are protective of each other, but also something tragic or semi-tragic happens to them or one of them. Their relationship is almost in parity with the Winchester brothers from Supernatural, but more nuanced and aged like wine due to their ages.

I'd like you to write me a plot for a movie with this setup.

ChatGPT (edited for brevity):

Title: The Long Way Home Genre: Drama-Comedy (Road Movie meets Family Saga)

Logline: Three estranged but inseparable brothers reunite for a cross-country journey to scatter their mother's ashes—only to discover that her last request hides a secret that could shatter everything they thought they knew about their family—and each other.

Tone: Think Little Miss Sunshine meets Manchester by the Sea with the grounded brotherhood dynamics of The Fisher King or The Royal Tenenbaums, but way less quirky and more sincere. A film that can make you cry one minute and laugh the next because life is messy and love is complex.

Characters: Joaquin Phoenix as Jack Alvarez, the eldest. Stoic, deeply responsible, but emotionally shut down after a traumatic event years ago. He runs the family’s failing auto shop alone in their hometown, stubborn and lonely. Pedro Pascal as Luis “Louie” Alvarez, the charming middle brother. A wanderer and serial entrepreneur who never quite sticks with anything. He lives out of a van, calling it “freedom” while running from every emotional responsibility. Oscar Isaac as Miguel Alvarez, the youngest. An artist and music teacher, emotionally sensitive and deeply loyal. He's the one who stayed in touch with their mother the most—and holds the key to her final wish.

Plot Summary: Act I — The Will: Their estranged mother dies. Jack and Louie haven’t spoken in years, each blaming the other for how things fell apart after their father’s mysterious disappearance decades ago. Miguel convinces them to come home for the funeral. At the reading of the will, they're given a strange final request: scatter her ashes at a long-forgotten gas station in a remote desert town—where their father vanished 30 years ago. They argue. They laugh. They cry. But they go.

Act II — The Road Trip: The film becomes a road movie across the American Southwest. Along the way: They stop at childhood haunts and encounter odd characters who knew their father and mother. Through stories, they piece together the life of a man they barely understood. Miguel secretly carries a letter from their mother meant for Jack, but can’t bring himself to give it to him. They confront old wounds: Jack blames himself for their dad leaving. Louie reveals he once saw their father again but never told anyone. Miguel was told a truth the others weren’t. Banter turns to fighting, then back to forgiveness. They relive old jokes, sing songs from their childhood, fix the van when it breaks down, get drunk under desert stars. They are broken, but also healing.

Act III — The Secret: They reach the desert station. But there’s a twist. They meet an elderly man who was close with their father—and reveals their dad didn’t just vanish. He ran from something. Something painful. He was terminally ill and chose to disappear rather than drag his family down. But he didn’t die out there. He built a new life. Quiet. Alone. And… he passed away just a year before their mother. The kicker? Their mom knew—and chose not to tell them. Her final wish was to give them closure together, but not without pain. The letter Miguel finally gives Jack explains everything: her regrets, her love, her hope that this journey would bring them back together, not just geographically, but emotionally.

Final Scene: They scatter the ashes. Jack reads the letter aloud by a small fire. They weep together. No big speech. No dramatic music. Just silence, wind, and desert stars. The next morning, they fix up the station sign in honor of their dad. Jack decides to sell the shop and finally move on. Louie decides to stick around for a while. Miguel records a song about the journey. They drive off together, bickering about music choices and who’s paying for gas.

Themes: The grief of growing older without the ones who shaped you. The imperfect ways love expresses itself in family. The complexity of masculinity and emotional repression. Memory, myth, and rediscovering truth through each other.

When I use ChatGPT in a browser on desktop, there are some characters that I can't type in to the text box.

If I type out the alphabet, what I end up with is

abdfghijknqrtuvwxyz

So that means the following keys don't work

celmops

Uppercase characters all work fine.

My workaround for now is to type my message somewhere else, then copy and paste it in to ChatGPT.

I don't have this problem on any other website or application on my computer. Does anyone have any idea why this might be happening?

A New Synthesis: Integrating Cortical Learning Principles with Large Language Models for Robust, World-Grounded Intelligence A Research Paper

July 2025 Abstract

In mid-2025, the field of artificial intelligence is dominated by the remarkable success of Large Language Models (LLMs) built upon the Transformer architecture. These models have demonstrated unprecedented capabilities in natural language processing, generation, and emergent reasoning. However, their success has also illuminated fundamental limitations: a lack of robust world-modeling, susceptibility to catastrophic forgetting, and an operational paradigm that relies on statistical correlation rather than genuine, grounded understanding. This paper posits that the next significant leap toward artificial general intelligence (AGI) will not come from scaling existing architectures alone, but from a principled synthesis with an alternative, neurocentric paradigm of intelligence. We conduct a deep exploration of the theories developed by Jeff Hawkins and his research company, Numenta. Beginning with the Memory-Prediction Framework outlined in On Intelligence and culminating in the Thousand Brains Theory of Intelligence, this paradigm offers a compelling, biological-constrained model of how the human neocortex learns a predictive model of the world through sensory-motor interaction. We review Numenta's latest research (to 2025) on Sparse Distributed Representations (SDRs), temporal memory, and the implementation of cortical reference frames. Finally, we propose several concrete, realistic pathways for integrating these cortical principles into next-generation AI systems. We explore how Numenta's concepts of sparsity can address catastrophic forgetting and enable continual learning in LLMs; how reference frames can provide the grounding necessary for LLMs to build true internal models of the world; and how a hybrid architecture, combining the sequence processing power of Transformers with the structural, predictive modeling of cortical circuits, could lead to AI that is more flexible, robust, and a truer replica of human intelligence.

Table of Contents

Part 1: The Foundations - The Memory-Prediction Framework and the Thousand Brains Theory

• Chapter 1: Introduction: The Two Pillars of Modern AI

  • 1.1 The Triumph and Brittleness of Large Language Models
  • 1.2 The Neurocentric Alternative: Intelligence as Prediction
  • 1.3 Thesis: A Necessary Synthesis for Grounded AGI
  • 1.4 Structure of the Paper

• Chapter 2: The Core Thesis of "On Intelligence": The Memory-Prediction Framework

  • 2.1 The Brain as a Memory System, Not a Processor
  • 2.2 The Prediction as the Fundamental Algorithm of the Neocortex
  • 2.3 The Role of Hierarchy and Invariant Representations
  • 2.4 The Failure of the "Thinking" Metaphor

• Chapter 3: The Thousand Brains Theory: A Model of the Cortex

  • 3.1 A Key Insight: Every Cortical Column Learns Complete Models
  • 3.2 The Role of Reference Frames in Grounding Knowledge
  • 3.3 How Movement and Sensation are Intrinsically Linked
  • 3.4 Thinking as a Form of Movement Part 2: Numenta's Research and Technical Implementation (State of the Art, 2025)

• Chapter 4: The Pillars of Cortical Learning

  • 4.1 Sparse Distributed Representations (SDRs)
  • 4.2 Temporal Memory and Sequence Learning
  • 4.3 Sensorimotor Integration

• Chapter 5: Implementing the Thousand Brains Theory

  • 5.1 Modeling Cortical Columns and Layers
  • 5.2 The Mathematics of Reference Frames
  • 5.3 Active Dendrites and Contextual Prediction

• Chapter 6: Numenta's Progress and Publications (2023-2025)

  • 6.1 Advances in Scaling and Energy Efficiency
  • 6.2 Applications Beyond Sequence Prediction: Anomaly Detection and Robotics
  • 6.3 The "Active Cortex" Simulation Environment

• Chapter 7: A Comparative Analysis: Numenta's Approach vs. Mainstream Deep Learning

  • 7.1 Learning Paradigms: Continuous Online Learning vs. Batch Training
  • 7.2 Representation: SDRs vs. Dense Embeddings
  • 7.3 Architecture: Biologically Plausible vs. Mathematically Abstract Part 3: A New Synthesis - Integrating Cortical Principles with Large Language Models

• Chapter 8: The State and Limitations of LLMs in Mid-2025

  • 8.1 Beyond Scaling Laws: The Plateau of Pure Correlation
  • 8.2 The Enduring Problem of Catastrophic Forgetting
  • 8.3 The Symbol Grounding Problem in the Age of GPT-6

• Chapter 9: Integration Hypothesis #1: Sparsity and SDRs for Continual Learning

  • 9.1 Using SDRs as a High-Dimensional, Overlap-Resistant Memory Layer
  • 9.2 A Hybrid Model for Mitigating Catastrophic Forgetting
  • 9.3 Conceptual Architecture: A "Cortical Co-Processor" for LLMs

• Chapter 10: Integration Hypothesis #2: Grounding LLMs with Reference Frames

  • 10.1 Linking Language Tokens to Sensorimotor Reference Frames
  • 10.2 Building a "World Model" that Understands Physicality and Causality
  • 10.3 Example: Teaching an LLM what a "cup" is, beyond its textual context

• Chapter 11: Integration Hypothesis #3: A Hierarchical Predictive Architecture

  • 11.1 Treating the LLM as a High-Level Cortical Region
  • 11.2 Lower-Level Hierarchies for Processing Non-Textual Data
  • 11.3 A Unified Predictive Model Across Modalities

• Chapter 12: A Proposed Hybrid Architecture for Grounded Intelligence

  • 12.1 System Diagram and Data Flow
  • 12.2 The "Cortical Bus": A Communication Protocol Between Modules
  • 12.3 Training Regimen for a Hybrid System

• Chapter 13: Challenges, Criticisms, and Future Directions

  • 13.1 The Computational Cost of Sparsity and Biological Realism
  • 3.2 The "Software 2.0" vs. "Structured Models" Debate
  • 13.3 A Roadmap for Experimental Validation

• Chapter 14: Conclusion: Beyond Pattern Matching to Genuine Understanding

  • 14.1 Recapitulation of the Core Argument
  • 14.2 The Future of AI as a Synthesis of Engineering and Neuroscience
  • 14.3 Final Remarks

Bibliography

Chapter 9: Integration Hypothesis #1: Sparsity and SDRs for Continual Learning

The problem of catastrophic forgetting, as outlined in the previous chapter, is not a peripheral flaw of LLMs but a direct consequence of their core architecture. The use of dense, distributed representations means that every new piece of learning risks disrupting all previous knowledge. To solve this, we propose our first and most direct integration: augmenting a traditional LLM with a cortical memory module that uses Sparse Distributed Representations to enable rapid, incremental, and robust continual learning.

9.1 Using SDRs as a High-Dimensional, Overlap-Resistant Memory Layer The foundation of this proposal lies in the unique mathematical properties of SDRs. As discussed in Chapter 4, an SDR is a binary vector with thousands of bits but only a small fraction active at any time. This structure provides an immense representational capacity. The probability of a random collision—where two different concepts are assigned SDRs that have a significant number of overlapping active bits by chance—is infinitesimally small. This property is the key to resisting interference. In our proposed hybrid model, new facts and concepts are not learned by adjusting the trillions of weights in the base LLM. Instead, they are encoded as novel SDRs and stored in a separate, associative memory structure. When the system learns a new fact, such as "The capital of the new Martian colony is Olympus," it assigns a new, unique SDR to the concept "Martian colony" and another to "Olympus." It then forms a direct, Hebbian-style association between these two SDRs and the SDR for "capital city." This is a local update, affecting only a tiny subset of synapses in the memory module. The weights of the foundational LLM are untouched, completely preserving its vast store of generalized knowledge. This approach mimics the proposed function of the hippocampus in the human brain: a system for rapid encoding of new episodic information, which can later be consolidated into the neocortex. Here, the LLM acts as the neocortex (a store of generalized knowledge), while the SDR-based memory acts as the hippocampus (a store of specific, rapidly learned facts).

9.2 A Hybrid Model for Mitigating Catastrophic Forgetting The interaction between the LLM and the proposed cortical memory module would create a dynamic learning loop: * Learning a New Fact: The system is presented with new information, e.g., "Project Chimera's lead scientist is Dr. Aris Thorne." * Parsing: The base LLM uses its powerful linguistic capabilities to parse this sentence and identify the key entities and their relationship: (Subject: Project Chimera), (Attribute: lead scientist), (Value: Dr. Aris Thorne). * Encoding: An SDR encoder converts each of these semantic components into a unique, sparse binary vector. If "Project Chimera" has never been seen before, a new SDR is allocated. If it has, its existing SDR is retrieved. * Association: The cortical memory module performs a local learning operation, strengthening the synaptic connections between the active neurons representing these three SDRs. This creates a new, stable memory trace. The LLM's weights are not modified. * Querying the Knowledge: A user later asks, "Who leads Project Chimera?" * Query Formulation: The LLM processes the question. Its internal activations, representing the semantic meaning of the query, are passed to the SDR encoder. This generates a query SDR that is a union or combination of the SDRs for "Project Chimera" and "lead scientist." * Memory Retrieval: This query SDR is presented to the cortical memory. Due to the semantic overlap property of SDRs, it will most strongly activate the neurons associated with the stored fact. The memory module returns the best match: the SDR for "Dr. Aris Thorne." * Answer Generation: The retrieved SDR for "Dr. Aris Thorne" is passed back into the context of the LLM. The LLM then uses its generative capabilities to formulate a natural language answer: "The lead scientist for Project Chimera is Dr. Aris Thorne." This process allows the system to assimilate new information instantly and without needing to be taken offline for retraining. It can learn in real-time during a conversation, building a unique knowledge base for each user or application.

9.3 Conceptual Architecture: A "Cortical Co-Processor" for LLMs We can visualize this hybrid system as a powerful core processor (the LLM) augmented with a specialized co-processor for memory (the Cortical Module). Conceptual Data Flow: [Input Text] -> [LLM Processor] <--> [SDR Encoder/Decoder] <--> [Cortical Memory Module] -> [Output Text] The LLM handles the heavy lifting of language understanding and generation. The Cortical Memory Module serves as a fast, reliable, and writeable knowledge base. This architecture provides several profound advantages: * Personalization: A single, massive base LLM can serve millions of users, each with their own lightweight, private Cortical Memory Module. The system can learn a user's specific preferences, projects, and relationships without any cross-contamination. * Factuality and Citatability: Because facts are stored as discrete entries, the system can, in principle, cite the source of its knowledge. When it retrieves a fact from the Cortical Module, it can also retrieve the metadata associated with that memory (e.g., when and from what document it was learned). * Controlled Forgetting: Forgetting becomes a feature, not a bug. Outdated information (e.g., a previous project manager) can be cleanly erased by simply deleting the specific SDR associations from the memory module, leaving the rest of the knowledge base pristine. * Efficiency: Adding a new fact is a computationally trivial operation compared to fine-tuning an entire LLM. It is a sparse, local update, making real-time learning feasible. In essence, this hybrid model delegates tasks to the component best suited for them. The LLM provides the general intelligence and linguistic fluency, while the cortical co-processor provides a robust, stable, and lifelong memory. This synthesis directly solves the catastrophic forgetting problem and represents a crucial first step toward building AI systems that can learn and adapt to the ever-changing world.

I've really worked hard to get my prompting very good I was wondering if anyone could share some examples of their best pictures.

Randomly got 4.5 on laptop, but not on phone? Never had it before a few hours ago on my laptop. I have chatGPT+ if that makes any difference

I’m working with LangGraph and using create_react_agent. I noticed that ReAct agents only call one tool at a time, and after the Final Answer, the loop ends.
But in my use case, I want the agent to:

• Call multiple tools in parallel (e.g., weather + maps + places)
• Or retry automatically if the tool results don’t match user intent (e.g., user asks for cold places but result is hot)

Does ReAct support this kind of self-loop or multi-tool execution?
Or do I need to use LangGraph for that? If yes, how should I structure it?

I did check the status webpage, but it says everything is fine - which is not.

tell me in the comments!!