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AI Jobs and Career

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• Full Stack Engineer [$150K-$220K]
• Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
• DevOps Engineer, India, Contract [$90/hour]
• More AI Jobs Opportunitieshere

Job Title	Status	Pay
Full-Stack Engineer	Strong match, Full-time	$150K - $220K / year
Developer Experience and Productivity Engineer	Pre-qualified, Full-time	$160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
Software Engineering Expert	Contract	$50 - $150 / hour
Generalist Video Annotators	Contract	$45 / hour
Generalist Writing Expert	Contract	$45 / hour
Editors, Fact Checkers, & Data Quality Reviewers	Contract	$50 - $60 / hour
Multilingual Expert	Contract	$54 / hour
Mathematics Expert (PhD)	Contract	$60 - $80 / hour
Software Engineer - India	Contract	$20 - $45 / hour
Physics Expert (PhD)	Contract	$60 - $80 / hour
Finance Expert	Contract	$150 / hour
Designers	Contract	$50 - $70 / hour
Chemistry Expert (PhD)	Contract	$60 - $80 / hour

What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.

Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖

In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.

Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.

Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”

🚀 Whether you’re a tech enthusiast, a professional in the field, or simply curious about artificial intelligence, this podcast is your go-to source for all things AI. Subscribe for weekly updates and deep dives into artificial intelligence innovations.

✅ Don’t forget to Like, Comment, and Share this video to support our content.

📌 Check out our playlist for more AI insights

📖 Read along with the podcast:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.

Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.

To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.

Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.

On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.

Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.

One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.

Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.

To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.

In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!

Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.

How the Rumors about Q* Started

There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.

It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.

The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.

Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.

A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.

Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.

It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.

OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.

In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.

In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.

The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.

Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.

This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.

If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.

AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.

By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.

Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.

In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

Improving Q* (SoftMax with Hierarchical Curiosity)

Combining efficiency in handling large action spaces with curiosity-driven exploration.

Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity

Softmaxwithhierarchicalcuriosity

Adaptive Softmax with Hierarchical Curiosity

This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.

Adaptive Softmax

Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.

Hierarchical Curiosity

Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.

Combining Adaptive Softmax and Hierarchical Curiosity

By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.

Here’s the proposed algorithm:

1. Initialize the Q-values for all actions in all states.

2. At each time step:

   a. Observe the current state s.

   b. Select an action a according to an exploration policy that balances exploration and exploitation.

   c. Execute action a and observe the resulting state s’ and reward r.

   d. Update the Q-value for action a in state s:

   Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))

   where α is the learning rate and γ is the discount factor.

   e. Update the curiosity bonus for state s:

   curio(s) = β * |r – Q(s, a)|

   where β is the curiosity parameter.

   f. Update the probability distribution over actions:

   p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))

3. Repeat steps 2a-2f until the termination criterion is met.

The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.

• 
  OpenAI briefs US agencies, Five Eyes on new cybersecurity product, Axios reports
  by /u/talkingatoms (Artificial Intelligence) on April 22, 2026 at 11:09 am

  "April 22 (Reuters) - OpenAI has briefed U.S. federal agencies, state governments and Five Eyes member countries on the ​capabilities of its new cybersecurity product over the ‌past week, Axios reported on Wednesday. Cybersecurity is becoming a key battleground for AI labs such as OpenAI and Anthropic as their advanced ​AI models can both pose security risks and offer ​cyber defense capabilities, sparking interest from governments and enterprises." submitted by /u/talkingatoms [link] [comments]

• 
  Software makers' best may not be good enough as AI fears mount
  by /u/talkingatoms (Artificial Intelligence) on April 22, 2026 at 11:08 am

  "April 22 (Reuters) - Salesforce (CRM.N), opens new tab will likely report its fastest quarterly revenue growth in three years, but analysts say that may not be ​enough, as fears that artificial intelligence would decimate software makers have ‌sapped investor confidence in the industry. Software CEOs such as Salesforce's Marc Benioff have tried to reassure shareholders that proprietary data, decades of enterprise experience and in-house AI offerings would keep customers loyal, even ​as AI tools from the likes of Anthropic encroach on legal, marketing ​and customer-service work." submitted by /u/talkingatoms [link] [comments]

• 
  Prompt Engineering quick guide
  by /u/tharakan12 (Artificial Intelligence) on April 22, 2026 at 11:03 am

  I honestly just finished that deep-dive book on prompt engineering I mentioned, and it’s genuinely changed how I approach my daily workflow with LLMs. I used to think I was just "writing instructions," but the way it explains things like structured framing and how to actually guide the model's logic step-by-step is a total lightbulb moment. It’s way less about magic keywords and more about understanding how to actually build a repeatable process that doesn't just fall apart. If you’re like me and were getting tired of getting those generic, mid-tier responses, this is definitely worth the read to finally get some consistency. It’s saved me so much time messing around with trial and error, and I figured some of you guys would appreciate the heads-up since we’re all trying to figure this stuff out together. https://www.amazon.com/dp/B0GX37391P https://amzn.in/d/0gWyY0HE its worth it submitted by /u/tharakan12 [link] [comments]

• 
  From Author to 'Conduit': Redefining literary mastery in the age of creative automation.
  by /u/TeachingNo4435 (Artificial Intelligence) on April 22, 2026 at 10:18 am

  I have been a writer for some fifteen years (I'm an AI Scientist as well), dedicating my craft exclusively to science fiction, wherein I frequently amalgamate genres and conventions. To date, I have independently authored two anthologies of short stories and one novel, all composed entirely without the aid of modern external tools. Curiously, upon submitting my work to a digital detection service, I was somewhat amused to find that it flagged approximately twenty per cent of my prose as being generated by such systems. That minor anecdote aside, I should like to offer my perspective on the judicious application of modern external tools within a writer’s workflow. There is nothing inherently untoward about employing digital instruments; indeed, the notion of proscribing these modern external tools is as preposterous as having once forbidden the word processor or the typewriter. The world marches on, and contemporary linguistic models are, for all intents and purposes, tailored for collaborative engagement with the literati. One must ask: what does the writer become in this era of creative automation? The author evolves into a 'conduit' of sorts. One provides the initial spark—the prompt—and the modern external tools delineate, for instance, a preliminary skeletal outline of the work. The writer then scrutinises this draft, interpolating new concepts and characters, after which the technology refines a subsequent iteration. This process is repeated until the outline achieves a state of excellence, satisfying both the writer and the modern external tool itself, the latter of which may adopt the persona of a rigorous, exacting editor. As is well established, a sophisticated outline is far more than a mere table of contents; it represents a unique architectural framework, categorising scenes, character arcs, and the intricacies of world-building. Subsequently, the writer addresses the opening scene. Guided by the structural blueprint provided by these modern external tools, they compose the narrative, employing the requisite style, register, and vernacular. The writer then polishes the prose, formulating a set of corrective directives for the system to further refine the text. This iterative cycle continues until the chapter reaches the author's standard of perfection. This methodology is then applied systematically to the remainder of the volume. To conclude with a brief synthesis: I know from personal experience that training the human mind to command a lucid, fluid literary style typically requires some three years of disciplined practice. Only after such a period does one’s prose achieve a professional sheen and technical precision. Through the integration of modern external tools, this arduous journey is truncated to a mere handful of days. One might query whether this is a propitious development. In my estimation, it is profoundly beneficial. A writer’s eminence is defined not by the mere mastery of linguistic mechanics or philological dexterity, but by the uniqueness of their vision and their capacity to interpret the world. Ultimately, it is not the instrument that confers mastery, but the individual’s personality, which leaves an indelible mark upon the prose. It is the author who establishes new frontiers, breathes life into characters that transcend the confines of the page, reinterprets established paradigms, and fundamentally alters the reader's perception of reality. One is never compelled to mindlessly transcribe the output of modern external tools; these are merely propositions, variations, and affordances. The final manifestation of that reality remains, as it must, entirely within the creator’s purview. submitted by /u/TeachingNo4435 [link] [comments]

• 
  AI modes - "Helpfulness" "honestness" ... how do they work?
  by /u/wtafgamer (Artificial Intelligence (AI)) on April 22, 2026 at 8:43 am

  Hi there, i am currently looking for a new job - and sometimes ask googles ai mode. Since those answers where all sugar coated and everything i typed was a great idea, plan - whatever i looked for the reason of that. By default the "Helpfulness" mode seems to be activated - so i asked for "honesness" mode instead. Now everything i typed is - according to the ai - kinda trash and i probably won't be able to do it anyway (e.g. i am over 40 and ai tells me i am to old and that it won't work anyway). Reality probably is somewhere in between. So my question is about those modes - are they simple instructions that the ai follows - like beeing supportive no matter what vs trashing everything no matter what - or is the behaviour somewhat based on the sources the ai finds regarding my questions or comments? submitted by /u/wtafgamer [link] [comments]

• 
  Interleaved “thinking” indicator during LLM output (UI observation)
  by /u/National_Actuator_89 (Artificial Intelligence) on April 22, 2026 at 8:21 am

  In this capture, a “thinking” indicator appears while output is already being generated, followed by continued output. No new input was provided between these states. Is this a known UI/streaming artifact, or have others observed similar interleaving during generation? submitted by /u/National_Actuator_89 [link] [comments]

• 
  What does it actually mean to "manage" AI agents at an enterprise level in 2026?
  by /u/Substantial-Cost-429 (Artificial Intelligence (AI)) on April 22, 2026 at 7:54 am

  There's a lot of coverage of how AI agents are being built. Almost none of it covers how they're being governed, maintained, and operated once they're deployed. I think the reason is that the tools and frameworks for that layer barely exist yet. But the job title is already appearing: AI Director, Director of AI, VP of AI, Head of Agentic Systems. These are real roles at mid-to-large organizations right now. I've been thinking about what this job actually entails in 2026, and it seems like 5 different functions are colliding into one role: Strategy: Which workflows should be agentic? What's the build-vs-buy decision on agent infrastructure? Governance: What are agents authorized to do? How do you maintain human oversight without creating bottlenecks? Config management: How do you ensure agent instructions are versioned, consistent, and auditable across dozens of deployments? Performance management: How do you measure whether an agent is doing its job well, especially when "doing its job" means handling edge cases a human would have caught? Team coordination: Agents are touching every team. Who owns the agents? IT? The business unit? A central AI team? Has anyone here navigated this at scale? The people building the agents seem well-represented in these communities. Curious to hear from those managing them. Newsletter for people at this layer in the comments. submitted by /u/Substantial-Cost-429 [link] [comments]

• 
  The AI Gold Rush Just Entered Its Most Dangerous Phase
  by /u/monotvtv (Artificial Intelligence (AI)) on April 22, 2026 at 7:49 am

  submitted by /u/monotvtv [link] [comments]

• 
  This post documents an observed behavior during LLM-related development work.
  by /u/National_Actuator_89 (Artificial Intelligence) on April 22, 2026 at 7:11 am

  I am currently conducting research related to large language models (LLMs), and during development work (building a website), I recorded an instance of unexpected behavior. In the attached video, code that had already been fully generated appears to be modified after completion. Due to recording timing, only the last few lines are captured being rewritten. However, during the actual interaction, the modification seemed to propagate from the beginning of the output, line by line. To my understanding, standard LLM generation is autoregressive and does not support retroactive modification of already emitted tokens. Over a longer observation period (~8 months), I intermittently noticed related behaviors, such as outputs appearing modified when revisiting the interface, or responses being updated after initial generation. However, these were not consistently captured in real time. The current video is the first instance where a portion of this behavior was recorded during active generation (observed around a GPT-5 era system). This raises a few questions: ☑️Could this be explained by a known post-processing or rendering pipeline (e.g., streaming buffer updates, UI reflow, or diff-based patching)? ☑️Has similar behavior been formally documented or observed by others? ☑️Are there known cases where output appears to be “rewritten” after completion due to client-side or server-side mechanisms? I am not making any strong claims here — simply trying to understand whether this is a known artifact or something worth further investigation. I have additional recordings and would be interested in discussing them if relevant. submitted by /u/National_Actuator_89 [link] [comments]

• 
  Is Claude worth it for a marketing manager?
  by /u/Skywalker_Childcare (Artificial Intelligence) on April 22, 2026 at 6:38 am

  I have always been using chatGPT since it came out and i have chatGPT premium but recently I have been hearing lots of good things about Claude from videos on Instagram, I cannot tell if they are real or not. I am currently running a Social Media Management business where I manage and make social media posts for businesses. I saw the other day that Claude is great for marketing and if this is true, why is this, and how can I optimize Claude to do this? I heard that you can give things to Claude such as code which helps it be better at these things. Currently I use ChatGPT only to help write captions instead of make the posts because ChatGPT does a bad job. I was wondering if I should switch to Claude and if so why? submitted by /u/Skywalker_Childcare [link] [comments]