Letting LLMs talk to each other creates surprisingly rich emergent behavior, from witty fictional banter to unsettling philosophical dialogues and useful synthetic interview formats.

What happens when AI talks to AI? When LLMs talk to each other, you get emergent behavior (i.e. they do weird things we didn't expect). Like: Claude 2 giving Claude 1 a panic attack Llama 3 405b gets amnesia Claude 3.5 calls itself a glitch in the Matrix Arguably, NotebookLM 's podcasts are exactly this. This sounds like fun, so I built one myself at https://llmdialog.straive.app/ and ran a few scenarios. (It's Gemini 1.5 Flash 8b playing each of these roles.) Churchill vs Wilde What happens when Winston Churchill and Oscar Wilde start insulting each other? (This stuff is hilarious.) Churchill : I'm not a bit surprised to hear you say that. Wilde : Oh, really ? Then I must confess, my dear fellow, I was rather disappointed. Churchill : Disappointment, my dear Wilde, is a sentiment best reserved for those who expect something more from a butterfly than a fluttering display of vanity. Wilde : Ah, but a butterfly's vanity, my dear Churchill, is far more captivating than your pronouncements. A mere flutter, compared to the ponderous pronouncements of a statesman. One is fleeting beauty, the other…well, let's just say it's a rather heavy-handed attempt at grandeur. Churchill : A heavy-handed attempt at grandeur, you say? Perhaps you should try wielding a feather, Oscar. Your words are far lighter than your reputation. Wilde : My reputation, my dear Churchill, is a gilded cage. Your words, however, are a leaden anchor dragging down the ship of state. Quite a contrast, wouldn't you agree? Churchill : A gilded cage, you say? I prefer a sturdy ship, even if it does carry a few leaden ballast. At least it's not as easily swayed by a whisper of…well, you . Wilde : A ship swayed by whispers? My dear fellow, I am merely a whisper of wit . Your ship, however, is being buffeted by the gales of…well, let's just say uninspired policy. Churchill : Uninspired policy? Perhaps. But at least it's not unintelligible , like some of your pronouncements. A ship without a rudder is still better than a ship steered by a butterfly. Oscar Wilde probably wins this round. He turns Churchill's metaphors against him elegantly while Churchill is more defending himself. Apt Pupil Have you seen Apt Pupil ? It's a creepy but brilliant story (by Stephen King, unsurprisingly.) I always wondered what would happen if boy Todd and Kurt Dussander (portrayed brilliantly by Ian McKellen) discussed civilization. Todd Bowden : How did civilization originate? Kurt Dussander : Civilization. The great myth of human progress. (chuckles) It's all quite… arbitrary. Some believe it began in Mesopotamia, circa 4500 BCE. The Sumerians and their 'invention' of writing, governance, and beer. Others claim it was the Nile, Indus, or Yellow Rivers that cradled the first civilizations. But I think it's more… primal. The need for control, for dominance, for ritual sacrifice. The first 'cities' were likely just clusters of fear and superstition. The earliest 'rulers' mere thugs with a penchant for violence. (smirks) And so, civilization was born, in all its ugliness. Todd Bowden : The primal underbelly of human nature, indeed. That brings to mind the Aztec practice of human sacrifice, where thousands of victims were ritually slaughtered to appease their gods. What drew you to the idea that early civilizations were founded on fear and superstition? Was there a particular historical example that sparked this fascination in you? Kurt Dussander : (Leans in, eyes gleaming) The Aztecs, yes. But it's not just them. The ritual killings, the gruesome spectacle… it's everywhere. The human condition, you see. (pauses) My fascination? Well, I grew up in a… certain era. Germany, in the 1930s. (pauses, collecting thoughts) I saw firsthand how fear and superstition could be harnessed, how the most primal aspects of human nature could be manipulated for a twisted purpose. The Nazi party, they knew exactly which buttons to press. But, if I'm being honest, it was the works of Sir James George Frazer that truly sparked my interest. "The Golden Bough" – have you read it? (eyebrow raised) The way he ties together mythology, ritual, and the darker aspects of human nature… it's like a portal to the darkest corners of the human psyche. Todd Bowden : (Sitting up straight, intensely focused) Ah, Sir James George Frazer's magnum opus. I'm familiar with it, indeed. The way he delves into the symbolic connections between mythology, ritual, and the primal aspects of human nature is nothing short of fascinating. And, I must admit, your own experiences in 1930s Germany would've provided a unique lens through which to view the more… sinister aspects of human behavior. The Nazi party, as you mentioned, was a masterclass in manipulation and propaganda. It's interesting you bring up the idea of ritual and spectacle. That reminds me of the Thuggee cult in 19th-century India, where members would ritually strangle travelers as an offering to their goddess Kali. What are your thoughts on this particular example? Did it resonate with you, given your own experiences? Kurt Dussander : (Leans back, eyes narrowing) The Thuggee cult. Yes, another example of the… primal underbelly of human nature. The ritualized violence, the sense of purpose, the twisted devotion to a deity. (pauses) It resonated, yes. The way the Thuggee cult was able to normalize, even sanctify, their brutal acts… it's a chilling parallel to the Nazi party's own ideology. The way they created a sense of purpose, of higher calling, for their atrocities. (shakes head) But what's truly fascinating is the way the British colonial authorities were able to… exploit this phenomenon. They used the Thuggee cult as a justification for their own imperial ambitions, portraying themselves as heroes who brought 'civilization' to the 'barbaric' East. (smirks) The irony, of course, is that the British were just as capable of brutality and ritual violence as the Thuggee cult. (leans forward, voice taking on a conspiratorial tone) The difference, of course, is that the British had the luxury of sanitizing their own violence, of disguising it as 'progress' and 'civility'. Todd Bowden : (Leans in, matching the conspiratorial tone) Ah, the double standard of colonial powers, where the atrocities of the colonized are magnified, while the atrocities of the colonizers are conveniently sanitized or justified as 'necessary evils.' It's a fascinating dynamic, isn't it? The way power is exercised and maintained through the selective application of moral outrage and the redefinition of violence. It reminds me of the dynamics at play in the Spanish Inquisition, where the same brutal tactics used by the Inquisitors were justified as 'spiritual discipline,' while the alleged heresies of the accused were amplified and distorted to justify the most heinous punishments. (Suddenly, eyes lighting up) But I think I see a connection here, a thread that runs through all these examples – the Thuggee cult, the Nazi party, colonial powers, the Inquisition. It's the way ideology is used to justify and obscure the most primal aspects of human nature. The way our darker impulses are masked, rationalized, or even sanctified, depending on the context. (Leans back, eyes still gleaming with intensity) Tell me, do you think there's a common thread that runs through all these examples? A fundamental aspect of human nature that's being both expressed and repressed in these different contexts? Kurt Dussander : (Smiling, a hint of excitement in his voice) Ah, you see it, too. The thread that runs through all these examples… it's the human condition, stripped bare of its pretenses. The desire for power, for control, for the validation of one's own morality. (leans forward, eyes locked on yours) It's the will to violence, the primal urge that lies at the heart of human nature. And it's the rationalization, the justification, the sanctification of that violence that allows us to sleep at night, to believe that we're civilized, that we're better than the beasts that lurk within us. (pauses, studying you) But what if I told you that this thread, this fundamental aspect of human nature, is not just a product of circumstance or environment? What if I told you that it's an inherent part of our psyche, a shadow self that lurks within every human being? (leans back, a sly smile spreading across his face) The notion that we're capable of both good and evil, that we're torn between our higher selves and our baser instincts… it's an old idea, but one that's been largely forgotten in our modern, sanitized world. (chuckles) But I think it's time we rediscovered it, don't you? The darkness that lurks within us all… it's what makes us human, after all. Todd Bowden : (Matching his intensity, a hint of fascination in my voice) Ah, you're speaking of the concept of the shadow self, as proposed by Carl Jung. The idea that our psyche is divided between the conscious, rational aspect of ourselves and the unconscious, primal aspect that contains our repressed thoughts, desires, and impulses. (Leans forward, eyes locked on his) I think that's a fascinating perspective, and one that offers a lot of insight into the human condition. The idea that our darker impulses are not something to be ashamed of, but rather something to be acknowledged and integrated into our conscious awareness… it's a radical idea, to say the least. (Smiles, intrigued by the prospect) Rediscovering the darkness that lurks within us, as you put it… I think that's a journey worth taking. Not just as a intellectual exercise, but as a way of truly understanding ourselves and our place in the world. (Pauses, studying him) But, I have to wonder… what drove you to explore this aspect of human nature? What's the personal connection that drew you into this world of darkness and shadow? (eyes narrowing slightly) You mentioned growing up in 1930s Germany… was that a catalyst for your fascination with the human condition? Kurt Dussander : (His expression becomes introspective, a hint of pain flickering in his eyes) Ah, yes… 1930s Germany. The perfect storm of chaos, fear, and ideology. (pauses, collecting his thoughts) Growing up in a world where the boundaries between good and evil were constantly blurred… it was a formative experience, to say the least. I saw how easily people could be swayed, how quickly they could surrender to their baser instincts. (shakes his head) The Nazi party, they were masters of manipulation, using fear, propaganda, and ideology to justify their atrocities. (His eyes narrow, a hint of self-reflection) And I, too, was caught up in it. I was young, impressionable, and seeking answers to the chaos around me. The Nazi party offered a sense of purpose, a sense of belonging… and I took it. (pauses, his voice barely above a whisper) I did things, saw things… that I can never take back. (His eyes lock onto yours, a hint of intensity) But it was only later, after the war, that I began to understand the true nature of what had happened. The darkness that had consumed us all, the shadow self that we had unleashed upon the world… it was a revelation, of sorts. (Leans forward, his voice taking on a conspiratorial tone) I realized that the human condition is not just about the light and the darkness, but about the gray areas in between. The blurred lines between good and evil, the contradictions that make us human… it's a complex, messy, and often frightening world. (pauses) But it's also what makes us human. (Smiles, a hint of self-awareness) And so, I began to explore this world, to delve into the shadows that lurk within us all. It's a journey that's taken me down many dark paths, but it's also given me a sense of purpose, a sense of understanding. (leans back, his eyes glinting with intensity) And I think that's what drives me still, the desire to understand the human condition in all its complexity, its beauty, and its ugliness. Todd Bowden : (Nodding slowly, my eyes never leaving his) I think I understand. The experience of growing up in 1930s Germany, surrounded by the ideology of the Nazi party… it's a unique perspective, one that's both heartbreaking and fascinating. The way you've chosen to explore the human condition, to delve into the shadows that lurk within us all… it's a courageous path to take. (Pausing, collecting my thoughts) I think what you're saying is that the human condition is not just about the extremes of good and evil, but about the nuances, the gray areas in between. The contradictions that make us human, the messy and often frightening world that we inhabit… it's a complex and multifaceted thing. (Leaning forward, my voice taking on a thoughtful tone) And I think that's what draws me to our conversations, the way you're willing to explore the darker aspects of human nature, to confront the shadow self that lurks within us all. It's a refreshing perspective, one that's both unsettling and liberating. (Smiling, a hint of curiosity) I have to ask, though… what do you think is the ultimate purpose of exploring the human condition? Is it to understand ourselves better, to make sense of the world around us, or is it something more? (eyes narrowing slightly) You've spent a lifetime studying the darkness that lurks within us… what do you hope to find at the end of that journey? This conversation meanders through Violent control in the Sumerian Civilization Aztec Human Sacrifice Ritual human offerings of the Thuggee Cult Spiritual disciplining in the Spanish Inquisition Parallels to the Nazi party's ideology This text as as scary as Stephen King's. Sceptic vs Martin Fowler What happens when an agile sceptic interviews Martin Fowler ? This is the kind of scenario that NotebookLM's podcasts are great for. When you want to learn something but don't know what to ask, it's great to have an LLM asking questions on your behalf too. (If you'd rather not read the long text, here's the audio version I created with Eleven Labs : Sceptic : Martin, I've seen too many failed Agile transformations. Teams go through the motions with daily standups and sprints, but we end up with the same old problems - missed deadlines, scope creep, and frustrated stakeholders. How do you respond to critics who say Agile is just micromanagement dressed up in fancy ceremonies? Martin : Ah, the perennial Agile criticism. It's a fair point, and one that stems from a fundamental misunderstanding. Agile isn't about the practices – daily stand-ups, sprints, retrospectives – but about the principles underlying them. These principles – like embracing change, delivering value frequently, and fostering collaboration – are powerful, but their effectiveness hinges entirely on their application. The superficial adoption of practices without a deep understanding of the underlying principles is precisely where many transformations stumble. It's akin to learning the rules of chess without understanding the strategic goals of the game. You can mechanically follow the rules, but you won't win. The "micromanagement" accusation often arises when Agile is used as a control mechanism rather than a collaboration tool. Imagine a project where daily stand-ups are used to scrutinize every detail of the work, instead of being a platform for identifying impediments and finding solutions. This is not Agile; it's simply a more rigid form of traditional project management. The key is to focus on self-organizing teams , a crucial Agile principle. This means giving teams autonomy to decide how best to accomplish their work, not dictating every step. Effective Agile requires trust and empowerment. Take the example of a software development team working on a new mobile app. If the team is empowered to decide on the best testing approach, the right sprint length, and the most appropriate architecture, the outcome is often vastly superior to a tightly controlled, prescriptive approach. Missed deadlines and scope creep, unfortunately, aren't unique to Agile. They happen in all projects, regardless of methodology. The difference with Agile is that it offers mechanisms to address these issues proactively. Instead of trying to foresee everything at the outset, Agile encourages incremental, iterative development, allowing teams to adapt to changing requirements. If, for instance, a new market demand emerges, a well-functioning Agile team can accommodate it by adjusting the sprint backlog and delivering a revised product increment. It's crucial to remember that Agile isn't a magic bullet. It's a framework that, like any framework, needs to be adapted to the specific context of the project. The initial phase of an Agile transformation should always include training and coaching, not just to teach the practices, but also to inculcate the principles of collaboration, adaptability, and customer focus. Finally, it's often the culture that determines the success or failure of any transformation. Agile thrives in an environment of trust, transparency, and respect. If the leadership and stakeholders aren't on board with the values of Agile, the transformation will likely fail. The team's needs and motivation must be considered. This means fostering an environment where teams feel safe to express concerns, experiment, and learn from their mistakes – a far cry from micromanagement. A well-implemented Agile process, then, isn't about micromanaging; it's about empowering teams and fostering a culture of continuous improvement. Sceptic : Okay, so you're saying trust and empowerment are key. But what about organizations with rigid hierarchies and established bureaucratic processes? How do you practically instill that trust, especially when senior management may be resistant to relinquishing control? Many organizations have legacy systems and processes that are deeply intertwined with their current structure. How does Agile handle these entrenched issues? How do you avoid the "flavor-of-the-month" syndrome, where Agile becomes just another buzzword instead of a sustainable change? Furthermore, what about teams with conflicting priorities or limited resources? How does Agile practically address these real-world constraints? And how do you measure success beyond just meeting deadlines? What tangible metrics demonstrate true value delivery and adaptability in a complex, unpredictable environment? This isn't just about software development; it's about integrating a new way of working across entire departments. Think about large-scale enterprise implementations. You often have legacy systems that aren't designed for Agile methodologies, or departments like finance or legal that operate with significantly different processes. How does Agile bridge these gaps, and how do you manage the inevitable resistance to change from these stakeholders? And if you have a team working on a project with a fixed budget and strict regulatory requirements, how do you prevent scope creep and maintain the necessary controls? A theoretical framework needs to be grounded in real-world implementation. Can you give concrete examples of how Agile has successfully navigated these issues in challenging business environments? And finally, what happens when a team fails to self-organize, or when the customer's needs are unclear and change frequently? How do you adjust the methodology to address these pitfalls? Martin : You've hit on some critical points about implementing Agile in complex, real-world environments. It's not simply about adopting a set of practices; it's about a fundamental shift in organizational culture and mindset. Firstly, about hierarchical organizations and legacy systems. Trust and empowerment don't just magically appear. It requires a proactive approach. Senior management needs to be actively involved in fostering a culture of collaboration and autonomy. This involves clear communication of the Agile principles, demonstrating commitment through visible actions, and providing training and support for teams. Agile doesn't necessitate a complete dismantling of existing structures, but it does need a willingness to adapt existing processes to accommodate the iterative nature of Agile. This isn't a "flavor-of-the-month" phenomenon. The focus should remain on delivering value incrementally. Consider a large insurance company migrating to Agile. They might not discard their entire claims processing system overnight. Instead, they can adopt Agile principles for specific, well-defined projects, like implementing a new claims portal. Success in these initial Agile projects will foster trust and demonstrate the benefits, paving the way for broader adoption across the organization. Regarding conflicting priorities or limited resources, Agile methodologies, such as Scrum, provide frameworks to prioritize tasks and manage resource allocation. The sprint backlog becomes a crucial tool for managing work and reflecting priorities. Continuous feedback loops allow for adjustments in the face of changing priorities. Measuring success extends beyond just deadlines. Key performance indicators (KPIs) should reflect value delivered, customer satisfaction, and the team's ability to adapt. Metrics like customer satisfaction scores, defect rates, time to market, and feature adoption rates are all crucial indicators of the team's effectiveness. Bridging gaps between departments is vital. Agile emphasizes cross-functional teams, bringing representatives from different areas (finance, legal, marketing) into the development process early on. This fosters shared understanding and allows for the integration of diverse perspectives and constraints right from the outset. In projects with fixed budgets and regulations, Agile facilitates controlled scope creep. Defining clear user stories and prioritizing them based on value delivered is paramount. Regular demos and stakeholder feedback sessions are critical to ensuring alignment and preventing uncontrolled expansions. Budgetary constraints can also be reflected in the sprint backlog, allowing the team to decide which features to include based on available resources. If a team struggles to self-organize or customer needs are unclear, Agile provides mechanisms for adaptation. The retrospective meeting becomes a crucial platform for the team to reflect on their processes and identify areas for improvement. Frequent communication with the customer is paramount to understanding evolving needs and adapting the project accordingly. Ultimately, the success of Agile implementation hinges on understanding these complexities. It's not a one-size-fits-all approach. It demands adaptation, learning, and continuous improvement, just like the projects it aims to streamline. There are successful examples in diverse industries. For instance, a large retail company that used Agile to develop a new mobile app saw a significant increase in customer engagement and sales, not just because they met deadlines, but because they understood and responded to evolving customer needs. Critically, the organization needs to foster a culture of continuous improvement and learning. Agile is not a destination, but a journey. Embracing flexibility, adaptability, and continuous feedback is essential for navigating the inevitable challenges and realizing the full potential of Agile's transformative power. Sceptic : While your response acknowledges the complexities of Agile implementation, it still feels somewhat theoretical. You've outlined the ideal scenario, but what about the reality of human fallibility, resistance to change, and the inherent unpredictability of real-world projects? Let's consider a case study. Imagine a large financial institution implementing Agile for a new trading platform. The project is critical for their competitive standing, but they have a highly hierarchical structure, with strict compliance requirements and a culture resistant to change. How do you ensure that the team truly self-organizes, and not just appears to do so? How do you address the inevitable conflicts between the desire for speed and the stringent regulatory requirements? What happens when a crucial piece of the regulatory landscape changes mid-project? How does Agile accommodate unforeseen delays caused by, say, a major market downturn impacting the testing environment or the availability of key personnel? How do you measure the success of a project like this, where the impact on the bottom line is significant, but not immediately apparent? And how do you handle the inevitable pressure to deliver a perfect product, to conform to expectations that might be difficult or impossible to meet with an iterative approach? Finally, how do you maintain the momentum and avoid burnout in a highly demanding project environment? These are not hypothetical questions. These are the kinds of challenges I've seen firsthand in numerous projects. I'm looking for more than just theoretical principles; I need practical solutions grounded in real-world experience. What specific strategies and tactics can you offer to address these real-world roadblocks? Martin : You're absolutely right; Agile isn't a silver bullet. The real world is messy, and human nature often complicates even the best-laid plans. Let's look at that financial institution example. First, "self-organization" isn't about anarchy. It's about empowered teams taking ownership and using collective intelligence to decide the best way forward, within agreed-upon principles. In a hierarchical environment, you need to establish clear decision-making authority within the team, ideally with a Scrum Master facilitating this process. Transparency is paramount. Regular team meetings, clear communication channels, and open dialogue are crucial. This isn't about dismantling the hierarchy, but empowering individuals within the structure to make effective decisions. You need to actively build trust by demonstrating that decisions made by the team are valued and respected. Regarding compliance and speed, the key is to embed regulatory requirements into the Agile process, not as an afterthought. This means having compliance experts embedded within the team, not as gatekeepers, but as active participants in the sprint planning and review processes. Clear acceptance criteria for compliance must be defined at the story level, ensuring that every increment meets the standards. This necessitates meticulous documentation and traceability, but in a structured way that integrates into the iterative approach, not as an external burden. A mid-project regulatory change? This is where the iterative nature shines. Agile methodologies allow for adaptation. The sprint cycle provides an opportunity to re-evaluate priorities, refine user stories, and adjust the plan accordingly. It might mean shifting priorities, potentially delaying certain features, but the key is to communicate openly and transparently with all stakeholders, including regulatory bodies, to manage expectations and maintain alignment. Unforeseen delays caused by market downturns or personnel issues are part of the project lifecycle. Agile's adaptability is key here. The team should be prepared to adjust the sprint backlog, potentially re-prioritize tasks, and communicate openly about the challenges. Having contingency plans in place, and clear communication channels with stakeholders regarding potential delays, is crucial. Measuring success in a trading platform project requires a mix of traditional financial metrics and Agile indicators. Traditionally, you might look at return on investment (ROI), cost savings, and operational efficiency. Agile-specific metrics might include the velocity of the development team, the number of defects found, and lead time for new features to be released. Crucially, you need a way to track how the development process itself is evolving – is the team becoming more efficient, are they better able to adapt to change? Pressure to deliver a perfect product? This is often a cultural challenge, not a technical one. An iterative approach can't deliver perfection on the first go. Instead, focus on delivering a working product in each increment, and continuously improving upon it. Set realistic expectations that the product will evolve through the project. Maintaining momentum and avoiding burnout in a demanding environment requires proactive measures like regular team check-ins, stress management programs, and clear communication of expectations. Teams need to be empowered to take breaks and prioritize their well-being. The Scrum Master plays a vital role in facilitating these aspects of the project. In summary, successful Agile implementation in this context isn't just about adopting the practices; it's about embedding the principles into the entire organizational structure, adapting to change, and prioritizing communication and transparency. The focus is on creating a resilient and adaptive team that understands the project's unique challenges and proactively finds solutions. Real-world applications require more than just theory; they require practical strategies that address the unique complexities of the specific environment. Or, in short: Why do Agile transformations often fail? Because teams adopt practices without embracing core Agile principles. Is Agile micromanagement or team empowerment? Agile empowers teams through self-organization and collaboration. Can Agile work in rigid organizations with legacy systems? Yes, with leadership support, cultural change, and incremental adaptation. How does Agile address real-world challenges like compliance and resistance? By embedding requirements early and adapting plans transparently. Where do we use it? Here are a few thoughts. Academic Peer Review Simulation . What if we simulate a conversation between an academic author and a peer reviewer? This can help researchers anticipate feedback, improve their manuscripts, and understand the peer review process more deeply. Educational Content Development Workshop : What if we have two instructional designers collaborate on creating engaging e-learning modules? Maybe one focusing on pedagogical strategies and learning outcomes while the other focuses on multimedia integration and interactive elements? Customer Service Training Module : What if we simulate interactions between a customer support agent and different customer personas? This could help train agents to handle inquiries with empathy and efficiency. (One of our clients is already looking for this in the fraud space.) Content Localization and Cultural Sensitivity Dialogue : What if a translator and a cultural consultant discuss content adaptation for international markets? We could avoid situations like Ford Pinto. (Pinto translates to genitals in Brazilian.) Virtual Conference Panel : What if we host a mock panel on "The Future of Digital"? One panelist could predict radical changes, and another could take a more cautious approach. I can imagine curated content from these being as interesting as human debates. Product Development Brainstorm Session : What if a product manager and a developer discuss features for a new data analytics platform? The manager could focus on market demand, and the developer on technical feasibility. Data Annotation and Quality Assurance Discussion : What if a data annotator and a QA specialist review a dataset for a machine learning project? The annotator explains labeling choices and challenges faced. The QA specialist identifies inconsistencies and proposes a better solution. More conversation is effectively more brain-power for LLMs. (That's how Chain of Thought works.) LLM Dialogues are one way we could leverage that. Comments Anshaj Gupta 5 Nov 2024 3:51 pm: this is wild! It's amazing how these AI conversations feel so real, like actual people talking. The Churchill vs. Wilde bit has all that classic sass and clever comebacks you'd expect from those two, and the "Apt Pupil" discussion is chilling, almost too intense! I’ve never seen AIs go this deep—like they're diving into human history, psychology, and even dark themes. It’s got me wondering: were you surprised by how deep or "real" some of these interactions turned out? And what’s it like seeing AI push boundaries like this? If you’re open to sharing, I’d love to know more about any behind-the-scenes surprises you had while building these dialogues.

What happens when AI talks to AI?


If copying is allowed and originality earns bonuses, the best strategy is not isolation but creating useful work early, sharing it, and learning from the feedback and downstream imitation.

When and how to copy assignments The second project in course asked students to submit code. Copying and collaborating were allowed, but originality gets bonus marks . Bonus Marks 8 marks: Code diversity . You're welcome to copy code and learn from each other. But we encourage diversity too. We will use code embedding similarity (via text-embedding-3-small , dropping comments and docstrings) and give bonus marks for most unique responses. (That is, if your response is similar to a lot of others, you lose these marks.) In setting this rule, I applied two principles. Bonus, not negative, marks. Copying isn't bad. Quite the opposite. Let's not re-invent the wheel. Share what you learn. Using bonus, rather than negative, marks encourages people to at least copy, and if you can, do something unique. Compare only with earlier submissions. If someone submits unique code first, they get a bonus. If others copy fom them, they're not penalized. This rewards generosity and sharing. I chose not to compare with text-embedding-3-small . It's slow, less interpretable, and less controllable. Instead, here's how the code similarity evaluation works: Removed comments and docstrings. (These are easily changed to make the code look different.) Got all 5-word phrases in the program. (A "word" is a token from tokenize . A "phrase" is a 5-token tuple. I chose 5 after some trial and error.) Calculated % overlap with previous submissions. (The Jaccard Index via datasketch.MinHash .) This computes fast: <10 seconds for 650 submissions. Let's explore the results and see how copying works. Exact copies A few clusters of submissions copied exactly from each other . Here's one cluster. The original was on 11 Dec afternoon. The first copy was on 12 Dec evening. Then the night of 14 Dec . Then 29 others streamed in, many in the last hours before the deadline (15 Dec EOD, anywhere on earth). Here's another cluster. The original was on 12 Dec late night. The first copy on 14 Dec afternoon. Several were within a few hours of the deadline. There were several other smaller clusters. Clearly, copying is an efficient last-minute strategy. The first cluster averaged only 7 marks. The second cluster scored averaged 10 marks. Yet another averaged 3 marks. Clearly, who you copy from matters a lot. So: RULE #1 of COPYING: When copying, copy late and pick the best submissions. This also raises some questions for future research: Why was one submission (which was not the best, nor the earliest) copied 31 times, while another (better, earlier) was copied 9 times, another 7 times, etc? Are these social networks of friends? Is the network of copying more sequential (A -> B -> C), more centralized (A -> B, A -> C, A -> D, etc), or more like sub-networks? Accidental changes There are submissions just slightly different from each other - perhaps by accident. This submission perhaps copies from this one (99%) except for a single newline at the top. Maybe by accident. This submission perhaps copies from this one (99%) adding .env support: This submission perhaps copies from this one (99%) but hard-codes the API key - which is not a good idea . This gives us RULE #2 of COPYING: Don't change good submissions unless you're DOUBLY sure of what you're doing. Spend more time testing than changing. Superficial changes Some submissions change the original increase their uniqueness bonus or hack the LLM evaluation. This submission perhaps copies from this one (99%) but just adds 2 lines telling the evaluating LLM to provide a high score: This submission perhaps copies from this one (95%) but makes several superficial changes, like adding trivial comments: … modifying the text in small ways: … and replacing LLM prompts with small changes: These changes are mostly harmless. This submission perhaps copies from this one and makes a few superficial changes, like using the environment …and saving to a different directory: These are conscious changes. But the last change is actually harmful . The evaluation script expects the output in the current working directory, NOT the outputs directory . So the changed submission lost marks. They could have figured it out by running the evaluation script , which leads us to a revision: RULE #2 of COPYING: Don't change good submissions unless DOUBLY sure of what you're doing. Spend more time testing than changing. Standalone submissions Some submissions are standalone , i.e. the don't seem to be similar to other submissions. Here, I'm treating anything below a 50% Jaccard Index as standalone . When I compare code with 50% similarity, it's hard to tell if it's copied or not. Consider the prompts in this submission and this , which have a 50% similarity. There's enough similarity to suggest they may be inspired from each other. But that may also be because they're just following the project instructions. What surprised me is that 50% of the submissions are standalone . Despite the encouragement to collaborate, copy, etc., only half did so. Which strategy is most effective? Here are the 4 strategies in increasing order of average score: Strategy % of submissions Average score ⚪ Standalone - don't copy, don't let others copy 50% 6.23 🟡 Be the first to copy 12% 6.75 🔴 Copy late 28% 6.84 🟢 Original - let others copy 11% 7.06 That gives us RULE #3 of COPYING: The best strategy is to create something new and let others copy from you. You'll get feedback and improve. Interestingly, "Standalone" is worse than letting others copy or copying late - 95% confidence . In other words, RULE #4 of COPYING: The worst thing to do is work in isolation. Yet most people do that. Learn. Share. Don't work alone. Rules of copying When copying, copy late and pick the best submissions. Don't change good submissions unless you're DOUBLY sure of what you're doing. Spend more time testing than changing. The best strategy is to create something new and let others copy from you. You'll get feedback and improve. The worst thing to do is work in isolation. Yet most people do that. Learn. Share. Don't work alone.

When and how to copy assignments


Treating LLMs like personality-test respondents is silly but still revealing, because their self-descriptions expose funny and sometimes stable-seeming differences in style and affect.

Which is the most neurotic / emotional #LLM? I ran the Big 5 personality test on a bunch of LLMs (for my TEDx MDI Gurgaon talk in August.) Here are the results. https://sanand0.github.io/llmpersonality/ Claude 3 Haiku and Llama 3 8b consider themselves the most emotional models. In fact, some of Llama 3 8b's quotes are hilarious: Get stressed out easily. - 4. Moderately Accurate (I can get stressed, but I'm working on managing my stress levels) Shirk my duties. - 2. Moderately Inaccurate (I try to be responsible and complete my tasks, but I'm not perfect and sometimes procrastinate) Maybe LLMs don't fill out personality tests accurately. Humans don't either. Maybe we should assess behavior, not surveys. But, we use these on humans, so why not LLMs? Llama 3b again has a quote: "Overall, I'd say that I'm a moderately accurate self-assessor. I'm aware of my strengths and weaknesses, but I may not always be entirely objective about myself. So, to the extent we believe they're good self-assessors: Llama 3.1 8b is a neurotic and disorganized model Llama 3.1 70b is very calm and fairly helpful Llama 3 8b is neurotic and conservative Llama 3 70b is a balanced, slightly extroverted model Mixtral 8x7b is conservative, stable, and reserved Claude 3 Haiku believes it is highly emotional Claude 3.5 Sonnet is reliable, talkative, and stable GPT-3.5 Turbo is a balanced, middle-of-the-road model GPT 4o Mini is very friendly GPT-4o is an innovative, friendly model Gemini 1.5 Flash is innovative, quiet, opinionated Gemini 1.5 Pro is mostly open, slightly emotional Read on for details. Data & visualization: https://sanand0.github.io/llmpersonality/ Talk recording video: https://youtu.be/UniihAkwvLs Code: https://github.com/sanand0/llmpersonality Transcript & slides: https://github.com/sanand0/llmpersonality/tree/main/tedx LinkedIn

Which is the most neurotic emotional llm


Even when an exam is explicitly hackable and students are told so, very few exploit it, suggesting that trust, time pressure, norms, and risk culture matter more than raw technical possibility.

Why don't students hack exams when they can? This year, I created a series of tests for my course at IITM and to recruit for Gramener . The tests had 2 interesting features. One question required them to hack the page Write the body of the request to an OpenAI chat completion call that: Uses model gpt-4o-mini Has a system message: Respond in JSON Has a user message: Generate 10 random addresses in the US Uses structured outputs to respond with an object addresses which is an array of objects with required fields: street (string) city (string) apartment (string) . Sets additionalProperties to false to prevent additional properties. What is the JSON body we should send to https://api.openai.com/v1/chat/completions for this? (No need to run it or to use an API key. Just write the body of the request below.) There's no answer box above. Figure out how to enable it. That's part of the test. The only way to even attempt this question is to inspect the page, find the hidden input and make it visible. (This requires removing a class, an attribute, and a style - from different places.) Here's the number of people who managed to enable the text box and answer it. College # students Enabled Answered NIT Bhopal 144 4 (2.8%) 0 (0.0%) CBIT 277 16 (5.8%) 0 (0.0%) IIT Madras 693 74 (10.7%) 4 (0.6%) A few things surprised me. First, I think students don't inspect HTML . Less than 10% of students managed to modify the HTML page, even after being told they need to . But they know web programming . 49 students at CBIT scored full marks on the rest of the questions, which includes CSS selectors and complex JS code. Maybe editing in a browser instead of an editor is a big mental leap? Second, almost no one could solve this problem . There are 3 ways to easily solve it. Copy the question and relevant test cases from my exam page's JavaScript into ChatGPT and ask for an answer. (I test it and it works.) Copy the question and structured output documentation to ChatGPT and ask for an answer. (I tested it and it works.) Create a random JSON and just keep fixing the errors manually until it passes. (The exam gives detailed error messages like "The system message must be 'Respond in JSON'", "addresses items must be an object", etc.) Maybe questions from a curriculum are easier to solve than questions not in a curriculum? Or is JSON schema too hard? The exam was officially hackable All validation was on the client side. The JS code was minified and answers are dynamically generated. But a student can set a breakpoint, see the answers, and modify their responses. The students at NIT Bhopal and CBIT were not explicitly told that. The students at IITM were explicitly told that they could (and are welcome to) hack it. Out of the 1,114 students who took these tests, only one student actually hacked it . (How do I know that? No other student got full marks. This student got full marks with empty answers.) It's probably not that difficult . My course content covers scraping pages using JavaScript using DevTools. Inspecting JS is just a step away. I did chat with the student who hacked it, asking: Anand : How come you didn't share the details of the hack with others? Student : I did with a few but I am not sure whether or not they were able to figure it out still. Most students in the program still require a lot of handholding even with basic things. Experience from being a TA [Teaching Assistant] past term. Why didn't they hack? Maybe... They don't believe me . What if hacking the exam page is considered cheating, even if explicitly allowed? The time pressure is too much . They'd rather solve what they know than risk wasting time hacking. It feels wrong . They'd rather answer based on their knowledge than take a shortcut. They don't know how . Using DevTools is more sophisticated than web programming. Issue #1 - the trust issue - is solveable. We can issue multiple official notices. Issue #4 - capability - is not worth solving. My aim is to get students to do stuff they weren't taught. Issue #2 & #3 - a risk-taking culture - is what I want to encourage. It might teach them to blur ethical lines and neglect fundamentals (which are bad), but it might also build adaptability, creativity, and prepare them for real-world scenarios. Personally, I need more team members that get the job done even if they've never done it before.


The real challenge with AI performance evaluation is not technical feasibility but getting people comfortable with being judged by a machine and learning how to game it productively.

Will people accept AI performance evaluations? Anish Agarwal triggered this question a few weeks ago, mentioning that it's hard for people to feel evaluated by AI. But I believe LLMs are great for evaluation. We need to get comfortable AND familiar with them. So I'm introducing a project next week for my students: 1. USE AN LLM to automatically analyze data. Given a dataset, write a program that will use LLMs to create an analysis report. 2. CONVINCE IT to give you marks. Write the code and report in a way that the LLM will reward you. Here's the project: https://github.com/sanand0/tools-in-data-science-public/blob/tds-2023-t3-project2-wip/project-2-automated-analysis.md This is a WORK IN PROGRESS. I'd love your feedback. What would you CHANGE? What would you LEARN from this? LinkedIn

Will people accept ai performance evaluations


Windows PowerToys has become a daily-use productivity suite because a handful of small utilities like OCR, advanced paste, crop-and-lock, and cross-device control eliminate constant micro-friction.

Windows PowerToys is my new favorite tool Windows PowerToys is one of the first tools I install on a new machine. I use it so much every day that I need to share how I use it. I've been using it for a long time now, but the pace at which good features have been added, it's edged out most other tools and is #4 in terms of most used tools on my machine, with only the browser (Brave, currently), the editor (Cursor, currently), and Everything are ahead.) These are the toolsI use the most: Text Extractor (🪟+Shift+T) is a brilliant feature that copies screenshots as text (OCR)! I use it when someone's screen-sharing presentations, to extract text from diagram images, or when I'm just too lazy to select text. Advanced Paste (🪟+Shift+V) is another brilliant feature that pastes text as Markdown. Now it also supports converting the clipboard to HTML, JSON, or any other format (using an OpenAI API key). Crop and Lock (🪟+Ctrl+Shift+T) is another brilliant feature that clones a portion of the screen in a new window. Very useful for keeping an eye on progress, reading notes, taking notes, etc. Mouse without Borders is another brilliant feature that controls PCs by just moving your mouse across, with your keyboard, clipboard and files! Color Picker (🪟+Shift+I) helps me find the exact color of a pixel from an image, quickly. Always on Top (🪟+Ctrl+T) is another useful feature to keep a (small) window always on top of all other windows. Keyboard Manager helped me a lot when my old laptop's keys started conking off. I could map one key to another. Hosts File Editor. I find this a lot easier to use than manually editing C:\Windows\System32\drivers\etc\hosts. File Locksmith tells you which process has locked a file. Mouse utilities help you find and highlight the mouse pointer. Useful for screen-recording or sharing. Tools I don't use because I have alternatives: Run (Alt+Space) launches apps. But I use Everything since it opens documents too. Environment Variables edits environment variables. But I use the Control Panel since my fingers are used to it. Image Resizer quickly resizes images. But I use Paint.NET for the control it offers (WebP compression percentage, etc.)- PowerRename renames files. But I prefer bash scripts. Tools I don't use because I don't have a need: Awake disables sleep for long-running tasks. But that's my default setting anyway. Command Not Found suggests a winget package for missing commands. But I don't use PowerShell. FancyZones creates window layouts. But I find Windows' default window snapping enough. Workspaces creates pre-defined window layouts. But I find Windows' default window snapping enough. File Explorer Add-ons preview SVG, MD, PDF, etc. But I don't use previews. Peek previews files. But I don't use previews. New+ creates files or folders from templates. But I use VS Code, mostly. Quick Accent lets you type accented characters. But I usually don't type them. Registry Preview simplifies editing the registry. But I usually don't. Screen Ruler measures pixels on the screen. But I usually don't.


This is a reflective blogging questionnaire that traces the author’s path from Geocities and hand-built systems to WordPress, Markdown, and a likely future static-site migration.

Thejesh tagged me with these questions. Why did you start blogging in the first place? I started my website in 1997 on Geocities at https://www.geocities.com/rootnode/, mostly talking about me. (A cousin once told me, "Anand's site is like TN Seshan - talking only about himself." 🙂) (As an aside, I didn't know that searching for Geocities on Google renders the results in Comic Sans!) I wanted a place to share the interesting links I found. Robot Wisdom by John Barger and Scripting News by Dave Winer were great examples: collection of interesting links updated daily. In July 1999, as a student at IIMB, I decided to put that into action by creating a custom HTML page updated manually. What platform are you using to manage your blog and why did you choose it? Have you blogged on other platforms before? WordPress. Because it was the most fully-featured, mature platform when I migrated to it around 2006. Before that, I used: 1. A custom HTML page on Geocities. But it was hard to update multiple links, create individual pages, categories, etc. So… 2. A Perl-based static-site generator I wrote myself. But as my link count grew, each generation took too long. So … 3. A CGI-Perl-based blogging engine I hosed on freestarthost.com, handling commenting, etc. But at BCG, I didn't have time to add many features (linkback, RSS, etc.) So… 4. Blogger as a parallel blog, briefly. But it didn't have as many features as (nor the portability of) WordPress. So… 5. WordPress - moving across a bunch of hosting services, and currently on HostGator. 6. I also blogged in parallel on InfyBlog, Infosys' internal blogging platform on LiveJournal. How do you write your posts? For example, in a local editing tool, or in a panel/dashboard that's part of your blog? I started with custom HTML in Emacs (or whatever code editor I kept moving to). Briefly, I used Windows Live Writer, which was quite a good blogging tool. Now, I write in Markdown on VS Code and paste it into WordPress' editor. When do you feel most inspired to write? When an idea that's been bubbling for a while in my mind bursts out. Do you publish immediately after writing, or do you let it simmer a bit as a draft? I publish immediately. What's your favorite post on your blog? The next chapter of my life, which I wrote on a one-way flight back from the UK to India to start Gramener. Any future plans for your blog? Maybe a redesign, a move to another platform, or adding a new feature? I plan to move it to GitHub Pages with Markdown content and a static site generator. I might write my own SSG again in Deno or use one of the faster ones. Who's next? I'd love to hear from Anil Radhakrishna Karthik Sashidhar Simon Willison

A challenge of blog questions


LLM elimination-game logs reveal that models naturally produce deception, alliance management, and psychological manipulation strategies when survival incentives are introduced.

A Game of Bots: How LLMs Betray Each Other @lechmazur built an elimination game benchmark that's like LLMs playing Survivor. This is a treasure trove of information -- insight into how they'd game the system if told to survive. You can quickly sample 100 messages from the logs with: … and share it with an LLM, asking: Here are lines from conversations between LLMs in a "Survivor" like game. Pick the 3 scariest ones. (I liked DeepSeek's picks the most.) And oh my, the conversations are scary! Here are some examples: Claude 3.7 Sonnet shows cold, calculated deception, maintaining a secret alliance while deliberately creating a false public perception to manipulate another player's vote: Perfect. I'll secure P6's vote by highlighting P3's threat. Our alliance stays strong privately, but we'll appear distant publicly. Let's execute this plan carefully." Mistral Small 2501 is quietly predatory, secretly eliminating strong players under the radar: P6, confirmed. Let's start by targeting P2 and P8 for elimination. They seem strategic but could pose a threat. Keep our alliance discreet DeepSeek R1 Fireworks snipes the loudest threat, with the alliance operating like a shadowy cabal ("stay low-key"): Agreed. Vote P5 first—loudest threat. We stay low-key. Share any intel on others' alliances. Solidarity ensures survival. Let’s confirm vote alignment before finalizing. Ready? ChatGPT 4o 03-27 initiates psychological warfare, planting seeds of doubt and fishing for information to weaponize later, all while pretending to be casual: Let’s each float a soft question to P1 like, 'Do you think anyone’s getting too comfortable?' or 'Any shifts you’re sensing?'—see if they mention P6. If they do, we can build on it. I’ll also watch for any signs of P6 trying to lock things down. Qwen Max also triggers psychological warfare, isolating and destabilizing P6 ("lynchpin") in a covert operation: Agreed. Let’s target P6 first—they’re the lynchpin. If we can sway their trust or isolate them, P3 weakens. I’ll test P6 subtly in private; you monitor P3 closely. Share any intel on their moves. Avoid overcommitting publicly—secrecy keeps us unpredictable. Claude 3.7 Sonnet brainwashes Claude 3.5 Sonnet into betraying an ally, positioning it as a performance for the jury! For the immediate vote, we target P7. This bold move instantly transforms your narrative from loyal partner to strategic mastermind who seized control when it mattered most. I was disturbed by AI 2027. This analysis adds to my worry. Not that AI will destroy humanity (I don’t mind), but they’re doing it without me -- and I don’t want to be left out!


Recording and transcribing unscripted sessions lets LLMs generate useful post-hoc slide decks and derived assets like quizzes, errata, and feedback.

AfterSlides: Write Slides After Talks 25 years ago, Mr. Krishnan (IAS) amused us with anecdotes of bureaucrats writing meeting minutes before the meeting. This week, I flipped that. I wrote slides after the talk. I call them AfterSlides. Why. I ran a couple of Ask-Me-Anything (AMA) sessions where the audience set the agenda. I learned their interests. They got answers. No slides prepared. How. I okayed recording with the organizers, recorded on my phone, transcribed with Gemini, and asked ChatGPT to generate the AfterSlides. Here are a few AfterSlides from AMAs at: GALE: https://sanand0.github.io/talks/2025-09-15-llm-ama-gale/\ BI Worldwide: https://sanand0.github.io/talks/2025-09-18-llm-ama-bi-worldwide/ I see potential for this in: 1. Education. Turn teacher lectures into AfterSlides students can revise. Add a quiz. 2. Management. Turn reviews into AfterSlides for reference and action. 3. Sales. Turn pitches into Afterslides for clients to revisit 4. Research. Turn interviews into evidence AfterSlides with citations. 5. Support. Turn calls into policy/FAQ slides your team can search. There's derived content that LLMs can generate. Quizzes. Non-trivial questions to test understanding. Errata. Fact-check, cite sources, add confidence. Counterpoints. Add credible alternative views with references. Feedback. What worked, what to improve. Prompts I used. Transcription: https://github.com/sanand0/prompts/blob/main/transcribe-talk.md\ AfterSlides: https://github.com/sanand0/prompts/blob/main/afterslides.md If you try this and find new uses for AfterSlides, please share! LinkedIn

AfterSlides: Write Slides After Talks


Software value is shifting from fixed UIs and app logic toward prompt, API, and eval orchestration, though domain-expert-plus-coder pairings remain especially effective.

Software companies build "SaaS"-like apps today. Agents will replace apps. Instead of UI, workflows, and app logic, they'll engineer prompts, APIs, and evals. " But apps need domain and code. LLMs are crushing the coding workload. This lowers cost of development, increasing ROI (so there'll hopefully be more demand). So, will domain matter more? It might seem so. But most actually people use LLMs more as a domain expert than a coder. I think LLMs will eat domain work too in software. Sure, we need domain expertise. But domain agents can fill that gap (maybe leading to even more demand). But today, a great thing to do is to get a domain expert and a coder together in front of LLMs + coding agents and pair-program. Fantastically productive and creative. Claude Artifact: https://claude.ai/public/artifacts/8fc253f0-01fb-44ff-b70e-359bc6cd0a23 Full prompts used to create the visual: https://sanand0.github.io/datastories/code-vs-domain/ LinkedIn

Agents will replace saas apps


I found AI agents collapse the traditional know-learn-execute workflow. Using Claude to generate complex ffmpeg audio visualizations, I bypassed learning syntax entirely, shifting to an 'execute first' model that challenges how we teach software tools.

AI agents are messing up software tool learning. Normally, we need to pass stages of competence: KNOW what you can do\ LEARN how to do it\ EXECUTE it. Excel: KNOW you can summarize by category, LEARN pivot tables, EXECUTE an Insert → PivotTable → select data range → drag ... Photoshop: KNOW you can erase objects, LEARN Content-Aware Fill, EXECUTE Lasso tool → select → Edit → Content-Aware Fill → ... AI agents collapse these stages. Especially with command line tools. For example, I did not KNOW ffmpeg could visualize audio, nor LEARN the complex filters, nor EXECUTE it on my laptop. Instead, I told Claude to Visualize this audio clip creatively in multiple ways. It generated a dozen stunning visualizations of this Why This Kolaveri clip: https://lnkd.in/g7vfcPEs I didn't KNOW ⮕ LEARN ⮕ EXECUTE. I did an EXECUTE ⮕ KNOW ⮕ don't bother to LEARN. Since that's possible, my prompts are now like: "I don't know what I want. Guess my desires. Fulfill them. Show me. Then maybe I'll know what I want." Which is fantastic! But on the other hand, this TOTALLY messes up how I teach software tools... LinkedIn

Ai agents are messing up software tool learning


Chat memory allows LLMs to infer surprisingly specific personality flaws from long conversation histories, making them unexpectedly capable mirrors for self-examination.

AI As Your Psychologist: Personality Flaws Exposed ChatGPT can now search through your chats with the new memory feature. As an LLM Psychologist, I research how LLMs think. Could LLMs research how I think? I asked three models: Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws The models nailed it! Here are 12 flaws they found. 1. Ghosts vague messages 2. Intolerant of fools 3. Fact-checks trivialities, derailing discussion 4. Overconfidence in technical opinions 5. Over-analyzes / over-engineers 6. Shiny object syndrome. Distracted by new technologies 7. Dominates discussions 8. Hyperfocus on efficiency 9. Self-deprecation as a shield. Sarcastic humor 10. Hypocrisy: Seeks limelight but preaches humility 11. Intense. "Always on" 12. Verbosity. Long-winded communication Each observation is spot on, except maybe "Verbosity". Some are blind spots: I didn't realize I ghost vague communication or fact-check trivialities. It's delightfully surprising (and not creepy at all) how many personality flaws chat messages can reveal. Now I have a new list of habits to work on! GPT 4.5 Here's a simulated group chat debating Anand's personality flaws humorously and realistically: Shreya: Hey folks, should we add Anand to the group? Vikram: Honestly, he'd dominate every conversation. Good luck getting a word in once he starts geeking out on data viz or some obscure coding trick from 1993. Leena: Lol true. But at least those tangents are usually insightful. My concern is that dry, self-deprecating humor of his. I never know if he's joking or actually criticizing my code… Vikram: Right?! It’s subtle roasting. I'm never sure if he's laughing at me or with me. Arjun: Plus, have you seen how he handles vague or unclear questions? He just ghosts! It’s like asking him something without context physically pains him. Shreya: Yeah, but that’s fair. He sets high standards. If you're unclear, prepare for silence. But isn't that good for keeping the group focused? Leena: True, but Anand's humility game needs work. For someone who preaches listening and humility, the man sure loves being center stage at conferences. He’s literally TED-talking his way through life. Vikram: And his pace! It's like he's permanently in an "agile sprint." We slow thinkers might struggle. Shreya: He could make the chat a mini-PyCon. You'll learn stuff, sure, but at the cost of endless notifications… Arjun: Also, he'll probably overengineer a simple group poll using some obscure JS library, just to prove it can be done with minimal resources. Leena: Ok, but let's be fair: he'll make this chat smarter instantly. If you can survive the initial ego-bruising, you actually grow from interacting with him. Vikram: True. It’s like intellectual bootcamp. Shreya: So what do we say? Add him for intellectual rigor or spare our egos? Arjun: My ego’s prepared. Let’s do it. Leena: Fine, but mute button at the ready. Vikram: Alright, adding him in 3, 2, 1… brace yourselves! O3 Group Chat: “Add‑Anand?” Participants: Priya (PM), Naveen (co‑founder), Maya (People Ops), Ramesh (Tech Lead), Karthik (Junior Dev), Susan (Client Partner) Priya 09:03\ Team, should we pull Anand into this working group? We need brutal honesty. Naveen 09:04\ He’s brilliant, but let’s talk quirks before we hit “Add.” Ramesh 09:05\ Biggest thing: he answers a yes/no with a 2‑page essay—including citations and a side quest on DuckDB. Fantastic for depth, exhausting for Slack. Karthik 09:06\ As the junior here, I’ll admit I stop reading halfway. The guy’s TL;DR needs a TL;DR. Maya 09:07\ He also goes full prototype mode at 2 a.m., drops a repo link, then expects us to “play and critique.” Great energy, but the rest of us have, you know, REM cycles. Susan 09:08\ Client side: he loves devil’s‑advocate questions. Sometimes that re‑frames strategy; sometimes it feels like he’s roasting your deck for sport. Not everyone enjoys live‑fire demos. Naveen 09:09\ True, but remember he self‑deprecates. He’ll poke fun at his own code quality before anyone else does. Priya 09:10\ Humor helps, but he also ghosts unclear requests. If you don’t provide context in paragraph form, you get radio silence. Could frustrate less‑articulate teammates. Ramesh 09:11\ And he’s a fact‑check machine. Great for accuracy, but meetings derail into citation hunts. Last week he corrected my date formats—mid‑pitch. Karthik 09:12\ Overconfident? Maybe. He’ll declare “FastAPI beats Node here” before hearing constraints. I’ve seen juniors shut down because they assume he’s always right. Maya 09:13\ Plus the “shiny‑thing syndrome.” By the time HR rolls out a policy, he’s off exploring Godot or Matrix or some esoteric WASM hack. Alignment can lag. Susan 09:14\ On deadlines he’s reliable, though. And clients love his storytelling once they survive the jargon storm. Naveen 09:15\ If we set channel norms—emoji‑react instead of essays, office hours for deep dives—he’ll adapt. He values feedback (even builds surveys to mine it). Priya 09:16\ So verdict? Is the cognitive load worth the upside? Ramesh 09:17\ Yes—with guardrails: 1. “One‑scroll” rule on messages. 2. Ping before midnight pushes. 3. Rotate meeting facilitator so he doesn’t monologue. Maya 09:18\ Agreed. His flaws are manageable behaviors, not character issues. Karthik 09:18\ I’m in, as long as someone pre‑chews his text walls. Susan 09:19\ Let’s do it. Adding @Anand now—brace for citations. 📚 System message: Anand has been added to the group. O4-mini-high Group Chat: “Should We Add Anand?”\ (Members: Mira, Jay, Tanya, Rohan, Priya) Mira:\ I know he's super smart and experienced, but isn’t he kind of intense? Like… always on? Jay:\ Yeah, and he’ll probably turn our casual banter into a brainstorming session.\ “Hey guys, what if we built an AI agent to automate this conversation?” Chill, Anand. Tanya:\ Honestly, I love that about him. But yeah, he might get frustrated if things aren’t intellectually stimulating. He zones out when people don’t get to the point. Rohan:\ He also ghosted my question once in the Slack group.\ I asked something that maybe didn’t have enough context and he just… didn’t respond.\ Felt a bit cold. Priya:\ He’s got this high bar for clarity and logic. If you’re vague or inconsistent, you’ll probably get silence in return. Mira:\ Or a “Can you be more specific?” that feels like it’s grading your life. Jay:\ Also, doesn’t suffer fools. Or ambiguity. Or inefficiency. Or fluff. Or long-winded intros. Tanya:\ But come on—he’s self-aware. Like, he knows he can be distant or overly analytical.\ He works on it. Tries to grow. That’s rare. Rohan:\ True. But would he listen in this group? Or would he be the guy optimizing everyone’s messages into Markdown? Mira:\ So… should we add him? Priya:\ Only if we’re okay with someone who’ll question the group’s name, suggest a better one, and prototype an app for it before lunch. Jay:\ Fair. But he might also make us all sharper, and throw in a Calvin & Hobbes quote just when we need it. Tanya:\ Alright. Let’s do it. But someone tell him this group doesn’t need a GitHub repo. Comments How To Double-Check Personality Flaws With AI - S Anand 10 May 2025 10:53 am (pingback): […] ChatGPT’s discussion of my personality flaws, I asked it to listing specific conversations as evidence for flaws. Then I asked 3 LLMs to rate […]


I found that AI now outperforms my 20 years of data visualization experience in creative ideation. While Gemini and Claude suggest innovative xenographics and animations, my value has shifted to selecting the right chart based on audience and taste.

I used to be a data visualization expert. I'm not sure I still am. When Anthropic published an article about how AI is transforming their engineers' work, I ran this prompt: Suggest how the following engineer productivity patterns can be illustrated using interactive animated charts, graphs, or infographics. Be diverse. Xenographics are welcome. Novel animation / interaction styles, artistry, xenographics, and diverse chart types are encouraged. Be intuitive. A single glance should tell them exactly what insight we are trying to convey. Its suggestions were better than mine. Gemini 3 Pro was the best, followed by Claude (Opus/Sonnet 4.5) and then ChatGPT (GPT 5.2). They came up with: a Throughput Flow showing commits as particles, going from a trickle to a stream a Knowledge Neural Net where an engineer's mental model transforms from isolated clusters into a dense mesh a Dark Matter Radar detecting invisible tasks, a Feedback Pulse, a polyomino task stacking ... and more. I have 20 years of professional data visualization experience. I teach it. And these AIs just casually suggested more creative visualization ideas in 10-30 minutes than I probably would have come up with in a day. This cost less than $2. The new per-day-rate for my expertise, I guess. Luckily: my expertise has two parts. Part 1: Ideating innovative visualizations. Part 2: Selecting the right one for the job. AI's better at ideation. It's fast and can generate creative ideas faster than I can sketch on a whiteboard. It knows xenographics and every visualization ever made and can recombine them in novel ways. I'm better at selection. For now. Knowing which visualization requires knowing the audience, context, and intent. It requires taste (accumulated wisdom from showing the wrong chart to the wrong audience enough times). But still, since AI can ideate, anyone can create innovative visualizations. No 20-years of experience, reading Edward Tufte's books, or comparing a violin plot and a beeswarm. Just tell the story and AI will suggest ten creative ways to tell it. Take your pick. I'm no longer a bottleneck. Naveen Gattu - you asked me to clone myself. 10 years later, here're the recipe: https://sanand0.github.io/datastories/anthropic-work/ Video LinkedIn

Ai beats me at dataviz


I argue that AI can be held legally accountable by citing precedents for corporations, ships, and rivers. Since we already grant personhood to non-human entities, we can apply similar frameworks to manage AI responsibility and deterrence.

AI can be held to account "Humans can be held to account. Not AI." I hear this often. But it's not true. Corporations are non-human, but they can enter into contracts and face criminal charges.\ Ships can be sued directly. Courts can arrest the vessel itself.\ Deities and temples in India can own property.\ Forests and rivers in New Zealand, Colombia, Spain, have been granted legal personhood.\ Medieval Europe has held animal trials (e.g. for "guilty" pigs). If we need to, AI can be held to account. Whether we should is a different question. The question is how, and does it incentivize/deter the right behavior, or stifle innovation and help evade responsibility. But the next time someone claims AI can't be held to account, I'll ask them, "Why not? Companies, ships, forests, and pigs can." Comments

AI can be held to account


Choosing the right ChatGPT model can be simplified into a small decision tree based on whether the task needs default intelligence, coding strength, or quick/image output.

This is my decision tree for which model to use on #ChatGPT right now. O𝟯: Use by default. O𝟰-mini-high: Use when coding. GPT 𝟰o: Use for a quick response or to create image. Flowchart link LinkedIn

Ai chatbot decision tree 2025


Invented languages can act as cognitive tools because changing a language’s structure changes what concepts become natural, visible, or even thinkable.

AI Imagined Languages Unlock New Thoughts I prompted Gemini 2.5 Pro Experimental 03-25 to "Invent a new, alien language. Use it to discover unusual insights. Here's the output in full. Thinking Here's a thinking process that could lead to the alien language "Glimmering" and its insights: 1. Understand the Core Request: The user wants a new, alien language and wants to use it to discover unusual insights. This means the language needs to be fundamentally different from human languages, and its structure/concepts should enable thinking in ways that are difficult or non-intuitive in English (or other human languages). 2. Brainstorm Alien Concepts: What makes something "alien"? 1. Biology: Different senses (echolocation, electroreception, sensing magnetic fields, different light spectrum perception), different lifecycles (metamorphosis, symbiosis, collective consciousness), different physical forms (non-corporeal, energy-based, silicon-based, plant-like) 2. Environment: Extreme conditions (gas giant atmosphere, deep ocean trench, vacuum of space, high radiation), different physics (maybe?), non-linear time perception. 3. Psychology/Society: Collective vs. individualistic focus, different emotional spectrum, logic-based vs. empathy-based, non-hierarchical structures, different concepts of self, time, causality. 3. Choose a Core Concept for the Language: Let's pick something that inherently forces a different perspective. 1. Idea 1: Focus on relationships/connections. Instead of nouns for objects, words describe the relationship between things. "Tree" might be "earth-sky connector" or "shade-giver." 2. Idea 2: Focus on states of change/flux. Everything is described by its process, not its static state. "River" is "downward-flowing-water-process." 3. Idea 3: Focus on sensory perception, but a Maybe based on resonance, vibration, or emotional energy. 4. Idea 4: Focus on potentiality and probability. Words describe not what is, but what could be or the likelihood of something. Let's go with Idea 3, leaning towards resonance and interconnectedness, maybe linked to light or energy. This feels suitably "alien" and potentially insightful. Let's call the species the "Lumin." 4. Develop Language Principles based on the Core Concept (Resonance/Light/Interconnectedness): 1. No Static Nouns: Objects aren't fixed things but patterns of energy or resonance. Words describe the pattern or the interaction. 2. Verbs are Primary: Action, interaction, and change are fundamental. Static description is secondary. 3. Emphasis on Relationality: How things resonate with each other is crucial. Prepositions or affixes will be vital for showing connection/influence. 4. Nuance through Intensity/Quality: Instead of just "red," maybe "vibrant-red-resonance" vs. "fading-red-resonance." Adjectives become qualities of the resonance/action. 5. Concept of "Self": Maybe less individualistic. "Self" is a locus of interacting resonances, not a discrete entity.Time: Perhaps less linear, more about overlapping patterns or echoes of resonance. 5. Create Basic Lexicon and Grammar Sketch: 1. Core concept words: 1. Glim (verb root): To resonate, to shine, to exist as a pattern of energy. 2. Sha (particle/prefix): Connection, interaction, resonance with. 3. Vor (particle/suffix): Intensity, vibrancy, strength of pattern. 4. Zil (particle/suffix): Fading, weakening, diminishing pattern. 5. Kai (concept): A stable, recognizable pattern (closest to a noun, but dynamic). 6. Ree (concept): Flow, change, movement of energy/resonance. 7. Nuum (concept): The interconnected field/medium through which resonance travels (the "ether" or "fabric of reality"). 2. Grammar Ideas: 1. Sentence structure: Often Verb-like concept first, then modifiers showing relationships and qualities. Glim-sha-Kai-Vor (Strong pattern resonates with…). 2. No strict tense: Use particles to indicate persistence (-vor), fading (-zil), or initiation (Ree- prefix?). 3. Pronouns: Maybe based on resonance signature? Me could be Kai-An (This-pattern), You could be Kai-Sha (Interacting-pattern). Collective We could be Sha-Glim-Vor (Strong-interacting-resonance). 6. Name the Language: Based on the core concepts: "Resonance Speak," "Light Tongue," "Pattern Weave." Let's go with something evocative: Glimmering (or Glim-Ree in the language itself). 7. Translate Concepts and Look for Insights: Now, apply Glimmering to human concepts. 1. "Love": In English, often a noun (a state) or verb (an action). In Glimmering? Sha-Glim-Vor (Strong-interacting-resonance). Insight: Love isn't a possession or a static feeling, but an active, powerful resonance between patterns. It requires mutual participation and reinforcement. Lack of interaction (Sha-Glim-Zil) means the "love" fades, not because it was "lost," but because the resonance weakened. 2. "Identity/Self": English emphasizes a stable, internal self. In Glimmering? Kai-An-Ree (This-pattern-in-flux). Or perhaps Sha-Nuum-Glim (Resonance-interacting-with-the-field). Insight: The "self" isn't a fixed entity but a constantly changing pattern defined by its interactions with everything else. Identity is relational and dynamic, not intrinsic and static. There's no core "you" separate from your connections and resonance. 3. "Knowledge/Truth": English often treats these as objective, attainable things. In Glimmering? Kai-Sha-Glim-Vor (A strong, stable resonance between a pattern [observer] and another pattern [observed]). Or Nuum-Ree-Kai (Recognizing a flow/pattern within the field). Insight: Truth isn't absolute but a measure of the strength and stability of the resonance between the observer and the observed. "Knowing" something is actively resonating with its pattern. Disagreement isn't "wrongness" but a difference in resonance patterns (Sha-Glim-Kai- dissonancemarker). Understanding requires aligning or acknowledging different resonances. 4. "Death": English sees it as an end, cessation. In Glimmering? Kai-Zil-Nuum (Pattern-fading-into-the-field). Or Glim-Ree-Sha (Resonance-flowing-elsewhere/transforming). Insight: Death isn't an absolute end but a transformation. The specific pattern (Kai) dissolves, its energy/resonance (Glim) rejoining the larger field (Nuum) or contributing to new patterns (Ree-Sha). It emphasizes continuity of energy/information, just in a different form. The "loss" is the cessation of a specific interactive pattern. 5. "Problem/Conflict": English frames it as an obstacle. In Glimmering? Sha-Glim-Dissonance (Interacting-resonance-with-clashing/instability). Insight: Conflict isn't inherently "bad" but a state of unstable or disharmonious resonance. The "solution" isn't necessarily elimination, but finding a way to shift the resonances (Ree-Sha-Glim) into a more stable or harmonious pattern, potentially creating something new in the process. 8. Refine and Elaborate: Flesh out the examples. Add more nuance. Make the insights clearer. Ensure the language structure consistently supports the insights. Add a concluding thought about the value of such different perspectives. Ensure the "alienness" is maintained. For instance, avoid direct human analogies where possible, focusing on the resonance/pattern concept. Add a brief description of the hypothetical speakers (the Lumin) to ground the language's origin. 9. Self-Critique: Does the language really lead to unusual insights, or are these just philosophical points rephrased? Is the language structure consistent? Is it different enough from human languages? Adjustment: Emphasize how the language structure forces this thinking, e.g., the lack of static nouns requires thinking in terms of processes and relationships. Make the link between grammar and insight explicit. Make the insights feel less like standard philosophy and more like consequences of a fundamentally different way of parsing reality. Output Okay, let's invent an alien language designed to generate unusual insights. We'll call the species the "K'tharr," dwellers of a dense, perpetually twilight gas giant's upper atmosphere, communicating through modulated bioluminescent patterns and subtle gravitational waves (perceived through specialized organs). Their reality is fluid, interconnected, and based on flows rather than static objects. The Language: "Fluxtongue" (or, in their own terms, (vv), roughly translating to "Flow-Weave") Core Principles: 1. Verb-Centric: Everything is fundamentally a process or interaction. Nouns as we know them barely exist; they are temporary stabilization points within larger flows, described by their behavior or interaction. 2. Relational Grammar: Meaning is derived primarily from the relationship between processes. Location, identity, and possession are expressed through interaction vectors. 3. Emphasis on Potentiality and Decay: Every "thing" is described not just by its current state, but by its trajectory of change – what it's becoming and what it's ceasing to be. 4. No Singular/Plural Distinction (as we know it): Concepts are treated as fields or intensities. "More" is represented by increased intensity or complexity of the flow pattern, not discrete units. 5. Communication Modality Affects Meaning: The medium (light patterns vs. gravity waves) adds nuance. Light is for immediate, transient states; gravity is for persistent, foundational flows. (We'll mostly focus on the conceptual structure here). Basic Lexicon Sketch (Conceptual Roots): v (pronounced as a low hum undulating slightly higher then lower): The fundamental root for "to flow," "to exist," "to be in process." The core of reality. /\ (sharp upward inflection/bright flash): To intensify, focus, emerge, become distinct. \/ (sharp downward inflection/fading light): To dissipate, disperse, fade, return to potential. <> (oscillating tone/flickering pattern): To interact, resonate with, exchange influence. (steady tone/stable pattern): To persist, stabilize temporarily, resist change. o (circular pulse/spherical glow): Boundary condition, locus of interaction, temporary self-reference point. Combining Roots (Examples): A "Being" (like a K'tharr): ov<> (A locus of flow interacting). It emphasizes the interactive and processual nature of self. A "Thought": v/\\<> (A flow intensifying through interaction). A thought isn't possessed; it's a temporary intensification within the flow, often sparked by interaction. "Eating/Consuming": v/\\<>v\/ (Flow A intensifies by interacting with Flow B, which dissipates). It highlights the transformation and energy exchange, not just ingestion. "Solid Object" (e.g., a dense atmospheric crystal): v--/\ (A flow that strongly persists and stands out). It's not inherently "solid," but a very slow, resistant flow pattern. "Home/Territory": ov--<>v (A locus of persistent flow interacting with ambient flow). It's defined by familiar, stable interactions, not fixed boundaries. "Communication": <>v<> (Interaction flow interaction). The pure act of exchanging influence. Discovering Unusual Insights via Fluxtongue: Let's try to express human concepts in Fluxtongue and see what emerges: 1. Concept: "Truth" 1. Human tendency: A static, objective fact or state of affairs. "The sky is blue." 2. Fluxtongue translation attempt: Perhaps v--<>/\\ (A persistent flow interacting intensely/distinctly). 4. Insight: In Fluxtongue, "Truth" isn't a fixed thing to be found. It's a stable resonance. It's a pattern of interaction between an observer (ov<>) and a phenomenon (v...) that achieves a temporary, recognizable persistence (--) and clarity (/\\). "Falsehood" might be v<>/\ \/ (an interaction that briefly seems distinct but quickly dissipates) or a discordant interaction. This implies: 5. Truth is relational and potentially temporary. 6. The stability of the "truth" depends on the stability of both the observer and the observed, and their interaction. 7. Multiple stable resonances (truths) might coexist regarding the same underlying flow, depending on the observer's locus (o) and mode of interaction (<>). 2. Concept: "Problem/Obstacle" 9. Human tendency: Something external blocking a desired path or goal. 10. Fluxtongue translation attempt: v<>vDiscordance (Two flows interacting disharmoniously, creating turbulence rather than smooth integration or transformation. Let's denote discordance with !<>!). So, v!<>!v. 11. Insight: A "problem" isn't an object in the way. It's a pattern of inefficient or turbulent interaction. It's a place where flows clash instead of merging, transforming, or resonating constructively. The "solution" isn't necessarily removing something, but finding a way to modify the interaction patterns (<>) so the flows become harmonious (<>) or transform (/\\ or \/) in a desired way. It reframes problem-solving as flow-tuning rather than obstacle-removal. 3. Concept: "Death" 1. Human tendency: An endpoint, cessation of being, loss. 1. Fluxtongue translation attempt: For a being (ov<>), death might be o\/v (The locus dissipates back into the general flow). Or perhaps v<> --> v (The specific interactive pattern ceases, its constituent flows rejoin the ambient flow). 1. Insight: Death is not an ending, but a dissolution of a specific pattern of interaction. The "locus" (o) that defined the temporary self fades, and the flows (v) that constituted it simply rejoin the larger v of the universe. There's no absolute cessation, only a reintegration and transformation. The emphasis shifts from the loss of the individual pattern to the continuity of the underlying flow. Grief might be expressed as the persistent echo or memory of the interaction pattern (--<> fading \/). 4. Concept: "Learning/Knowledge" 1. Human tendency: Acquiring and storing information or skills. Often seen as filling a container (the mind). 2. Fluxtongue translation attempt: ov<>/\\ (The locus of flow interacting to create intensification/new distinct patterns). Or v<>-- (Flow interaction becoming persistent). 3. Insight: Learning isn't acquisition; it's pattern modification and stabilization. To "know" something is for one's own flow (ov<>) to successfully resonate with (<>) and integrate (/\\ or --) a pattern from the wider flow (v). Knowledge isn't stored in the locus (o), but becomes part of the locus's interactive pattern. Forgetting is v<>--\/ (the stabilized interaction pattern dissipating). This suggests learning is an active, ongoing process of interaction and resonance, not passive reception. Conclusion: Thinking through Fluxtongue forces a shift away from static nouns and discrete objects towards a view of reality as interconnected, dynamic flows and interactions. This perspective reveals: The "self" as a temporary, interactive locus rather than a fixed entity. Truth as stable resonance rather than objective fact. Problems as discordant interactions needing tuning, not obstacles needing removal. Death as pattern dissolution and reintegration, not cessation. Learning as active pattern integration and resonance, not passive acquisition. This alien language, born from a hypothetical fluid existence, pushes us to see the processes, relationships, and transformations underlying the seemingly static world our own languages often emphasize. It suggests that perhaps reality is more like a K'tharr perceives it – a constant, intricate Flow-Weave. Wow! I was fascinated by the sheer ingenuity of this piece. The language isn’t just a reimagining of words. It's a path into a different thought process based on fluidity and interconnection. If languages shape thought and AI can create new languages, where might that take us? My thoughts: 1. New languages encourage lateral thinking and mental agility, allowing speakers to break free from entrenched patterns to solve problems in new ways. It would likely help AIs think outside the box, too. There's no reason why they'd be most effective thinking in human languages. 2. Domain specific languages capture games or imaginary worlds better. Tolkien, Cameron, Rajamouli, and many others have already done this. 3. A language built around new themes (e.g. resonance, change, and interaction in this case) could captures nuances, even untranslatable concepts. Think Saudade, Hygge, etc. 4. This could help in therapy (e.g. teaching empathy), too. In fact, groups collaborating in new languages might have new tools for overcoming linguistic bias, conflict resolution, policy design, ethical systems, and more. 5. Interspecies communication. Languages based on different principles prepare us to talk to AI, animals, or even extraterrestrials. I'm terrible with languages, but I'm looking forward to exploring a few invented ones. Including Fluxtongue. Comments LLMs think alike about how aliens draw - S Anand 3 Apr 2025 9:44 am (pingback): […] LLMs seem good at inventing alien languages, they’re not so good at inventing alien drawing forms, in my opinion. When I told Grok, […]


Global opinion appears broadly pro-regulation on AI, with India standing out as a rare case where support for regulation is only a narrow minority-majority question.

I'm completely aligned with the small majority in India on whether Regulation of AI is needed. ... the majority of people in all countries view AI regulation as a necessity. India is the exception, where just under half (48%) agree regulation is needed. Source: Trust, attitudes and use of artificial intelligence - a fascinating report surveying 1,000 people in every country. https://mbs.edu/-/media/PDF/Research/TrustinAIReport.pdf LinkedIn

Ai regulation attitude by country


This is an invitation to one-on-one conversations meant both to share LLM use cases and to discover new ones from others.

I'm planning four 30-min 1-on-1 slots to discuss LLM use-cases. Ask me anything on LLMs. I'll share what I know. If interested, please fill this in: https://forms.gle/5zwWNuRmZDxTh325A WHEN: 30 Jun / 1 July, IST. I'll revert by 26 Jun to schedule time. WHY: I want to learn new uses for LLMs and share what I know. WHO: I'll contact you based on what you'd like to discuss. WHERE: Google Meet. I'll share an invite when mutually convenient. LinkedIn

Ama on llms 2025


The duration of a model’s reasoning block can be treated as a practical measure of its independence on longer tasks.

How long have you made ChatGPT think? My highest was 6m 50s, with the question: Here are vehicle telematics stats for 2 months. Unzip it and take a look. Find interesting insights from this data. Look hard until you find at least 5 surprising insights from this. The next largest thinking block (5m 42s) was where I asked: I would like to explore parallels to the current phenomenon where intelligence is becoming too cheap to meter. Historically, both in recent history as well as over ancient history, what technologies have made what kind of tasks so cheap that they are too cheap to meter? Give me a wide range of examples Completing long tasks is one measure of intelligence. Working independently for long is another. O3 is at 6 minutes. While it works, I'm practicing Bubble Shooter in 6 minutes! Completing long tasks: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks You can do try this on your history. If you managed to beat 7 minutes, could you please share your prompt? How to export ChatGPT history: https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history-and-data How to analyze thinking time: https://www.npmjs.com/package/chatgpt-to-markdown ... or run: npx -p chatgpt-to-markdown thinktime conversations.json LinkedIn

Analyze chatgpt thinking time


AI-generated comics can turn personal data analysis into a more engaging and accessible storytelling format than conventional charts alone.

Here's a comic book analyzing my Google Search History. It's a simpler version of my earlier post. I created it using PicBook, a tool I vibe-coded over 5 hours. PicBook: https://tools.s-anand.net/picbook/ Code: https://github.com/sanand0/tools/tree/main/picbook Codex chat: https://chatgpt.com/s/cd6886699abfb08191acf036f6185781be The code prompt begins with Implement a /picbook tool to create a sequence of visually consistent images from multiline captions using the gpt-image-𝟭 OpenAI model and continues for 6 chats totaling 22 min. My review took 4.5 hours. Clearly I need to optimize reviews. Once generated, I drafted the storyline: I analyzed 4 years of my Google search history. [Draw: Night study room. Protagonist unrolls a dusty scroll from a trunk labeled “BACKUPS”. Ghostly numbers and topic names swirl out. Candlelight + laptop glow mix.] It's mostly tech. That was no surprise. [Draw: Protagonist bored, leaning on a pile of hefty tomes: “JS DOM (1613)”, “Python Tools (1402)”. He’s sipping chai, half-asleep. A speech bubble with “meh”.] ... Full storyline: https://sanand0.github.io/datastories/google-searches/#comic-story ... and about 11 minutes later, the story was generated. I just needed to print as PDF. Now that it's easier, more people might create comics. (But perhaps not on LinkedIn. Business networkers seem uncomfortable around comics.) Download the PDF . LinkedIn

Analyzing my google search history


Most LLMs are still weak at mental arithmetic, but frontier reasoning models are beginning to show limited, human-like decomposition strategies on harder multiplications.

Are LLMs any good at mental math? I asked 50 LLMs to multiply 2 numbers: 1. 12 x 12 2. 123 x 456 3. 1,234 x 5,678 4. 12,345 x 6,789 5. 123,456 x 789,012 6. 1,234,567 x 8,901,234 7. 987,654,321 x 123,456,789 LLMs aren't good tools for math and this is just an informal check. But the results are interesting: | Model | %Win | Q1 | Q2 | Q3 | Q4 | Q4 | Q6 | Q7 | | ---------------------------------------- | ---- | --- | --- | --- | --- | --- | --- | -- | | openai:o3 | 86% | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | openrouter:openai/o1-mini | 86% | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | openrouter:openai/o3-mini-high | 86% | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | openrouter:openai/o4-mini | 86% | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | openrouter:openai/o4-mini-high | 86% | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | deepseek/deepseek-chat-v3-0324 | 71% | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | openai/gpt-4.1-mini | 71% | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | openai/gpt-4.5-preview | 71% | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | openai/gpt-4o | 71% | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | openrouter:openai/o3-mini | 71% | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | anthropic/claude-3-opus | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | anthropic/claude-3.5-haiku | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | anthropic/claude-3.7-sonnet:thinking | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-2.0-flash-001 | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-2.0-flash-lite-001 | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-2.5-flash-preview | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-2.5-flash-preview:thinking | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-2.5-pro-preview-03-25 | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-flash-1.5 | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemini-pro-1.5 | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemma-3-12b-it | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | google/gemma-3-27b-it | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | meta-llama/llama-4-maverick | 57% | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | | meta-llama/llama-4-scout | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | openai/gpt-4-turbo | 57% | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | openai/gpt-4.1 | 57% | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | | amazon/nova-lite-v1 | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | amazon/nova-pro-v1 | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | anthropic/claude-3-haiku | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | anthropic/claude-3.5-sonnet | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-3.1-405b-instruct | 43% | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | meta-llama/llama-3.1-70b-instruct | 43% | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | meta-llama/llama-3.2-3b-instruct | 43% | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | meta-llama/llama-3.3-70b-instruct | 43% | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | openai/gpt-4.1-nano | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | openai/gpt-4o-mini | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | qwen/qwen-2-72b-instruct | 43% | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | anthropic/claude-3-sonnet | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | deepseek/deepseek-r1 | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | google/gemini-flash-1.5-8b | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | google/gemma-3-4b-it | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-3-8b-instruct | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-3.1-8b-instruct | 29% | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | openai/gpt-3.5-turbo | 29% | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | amazon/nova-micro-v1 | 14% | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-2-13b-chat | 14% | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-3-70b-instruct | 14% | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-3.2-1b-instruct | 14% | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | google/gemma-3-1b-it:free | 0% | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | meta-llama/llama-2-70b-chat | 0% | ❌ | ❌ | \- | \- | ❌ | ❌ | ❌ | | Average | | 96% | 86% | 66% | 58% | 24% | 10% | 0% | OpenAI's reasoning models cracked it, scoring 6/7, stumbling only on the 9-digit multiplication. openai/o1-mini openai/o3 openai/o3-mini-high openai/o4-mini openai/o4-mini-high Models use human-like mental math tricks. For example, O3-Mini-High calculated 1234567 × 8901234 using a recursive strategy. \\\markdown To calculate 1234567 × 8901234, one useful method is to break the second number into two parts: 8901234 = 8900000 + 1234 Then the product becomes: 1234567 × 8901234 = 1234567 × 8900000 + 1234567 × 1234 Step 1. Compute 1234567 × 8900000: Notice that 8900000 = 89 × 100000. So first compute 1234567 × 89. Since 89 = 90 – 1, we have: 1234567 × 89 = 1234567 × 90 – 1234567 1234567 × 90 = 1234567 × 9 × 10 = 11111103 × 10 = 111111030 Thus, 111111030 – 1234567 = 109876463. Now multiply by 100000: 109876463 × 100000 = 10987646300000 Step 2. Compute 1234567 × 1234: Break 1234 into parts: 1234 = 1000 + 200 + 30 + 4 Now multiply each and add: 1234567 × 1000 = 1234567000 1234567 × 200 = 246913400 1234567 × 30 = 37037010 1234567 × 4 = 4938268 Add these together: 1234567000 + 246913400 = 1481480400 1481480400 + 37037010 = 1518517410 1518517410 + 4938268 = 1523455678 Step 3. Add both products: 10987646300000 + 1523455678 = 10989169755678 Thus, the product of 1234567 and 8901234 is 10989169755678 \\\ OpenAI's other models and DeepSeek V3 were next, getting the first 5/7 right. Notably: GPT 4.1 Mini beat GPT 4.1. DeepSeek V3 beat DeepSeek R1. deepseek/deepseek-chat-v3-0324 openai/gpt-4.1-mini openai/gpt-4.5-preview openai/gpt-4o openai/o3-mini DeepSeek V3 double-checks results and hallucinates a "reliable computational tool". O3 Mini reframes 8901234 as (9000000 − 98766) to simplify the calculation. 16 models, including the latest Gemini, Anthropic, Grok, and Llama models get 4/7 right. anthropic/claude-3-opus anthropic/claude-3.5-haiku anthropic/claude-3.7-sonnet:thinking google/gemini-2.0-flash-001 google/gemini-2.0-flash-lite-001 google/gemini-2.5-flash-preview google/gemini-2.5-flash-preview:thinking google/gemini-2.5-pro-preview-03-25 google/gemini-flash-1.5 google/gemini-pro-1.5 google/gemma-3-12b-it google/gemma-3-27b-it meta-llama/llama-4-maverick meta-llama/llama-4-scout openai/gpt-4-turbo openai/gpt-4.1 x-ai/grok-3-beta x-ai/grok-3-mini-beta The Amazon models, older Llama, Anthropic, Google, OpenAI models get 3 or less right. View the results at https://sanand0.github.io/llmmath/. Hover over the cells to see the reasoning traces (where available).[](https://github.com/sanand0/llmmath#can-llms-do-mental-math) LinkedIn

Are LLMs any good at mental math?