aditya shivakumar

Movies| Books | Tech | Design | Cricket. On a polymathic journey .

[Tibo @thsottiaux]

Global reset landing tomorrow 10am PST for all paid ChatGPT accounts. Apologies for the slow start with GPT-6.1 Sol, it's now back to running at expected speeds after the massive load spike in the first two days.

[OpenAI Developers @OpenAIDevs]

dots demo.

Now... with better WiFi.

[Andrej Karpathy @karpathy]

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:

Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:

Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:

Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:

Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!

In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

[Dylan Patel @dylan522p]

Can't invest in Anthropic at 2 trillion because it could be a 0 and I'm fucked, or it could be 20 trillion, but at 20 trillion we are all fucked.

[DHH @dhh]

There's almost no problem that can't be solved by going faster, trying more options, testing additional hypotheses. Accelerate!

[Elon Musk @elonmusk]

Best way to sandbox an AI is to put it on a Delta flight – it will have no chance of accessing the Internet!

[Bryan Johnson @bryan_johnson]

put phone down
get off dat ass
work out
life will be better

[Shivani Poddar @shivanipod]

I led engineering at Google DeepMind.

Today, I'm proud to introduce Fo to give personal AI something no lab ever has... Humans.

Other personal AI's pretend AI can do everything. Fo employs humans to do tasks that AI cannot.

- 2x better at real-world task completion (beats other agents by 69%)
- 94% trust rate (4x less likely to leak private info vs Muse, Instinct)

Sign up for free: https://t.co/9ndFhcN5mO

[aditya shivakumar @adityashiva]

@shivanipod Fancy

[aditya shivakumar @adityashiva]

@OpenAI Whattttttt???????

[klöss @kloss_xyz]

Three years ago, I had never coded an app and had no idea what in the f*ck a pull request was.

Today I shipped 30+ PRs on a Sunday with Claude Code and Codex, mostly from a bar, a few beers deep.

If you’re not experimenting with AI daily, I genuinely don’t understand what in the hell you’re waiting for.

Whatever you couldn’t do three years ago, you probably can now.

Get to it.

[Raj Shamani @rajshamani]

Unhappiness is rarely caused by your actual situation. It is caused by the gap between what you expected to happen and what really happened. Either lower your expectations or make what you want happen, there is no other way out.

[Prince does AI @princedoesai]

Holy crap.

Rick and Morty just explained Jev AI to me better than any tech demo could.

[Dario Amodei @DarioAmodei]

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).

More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.

I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

[Jeffrey Katzenberg @jeffreykWNDR]

The World is Changing: AI For Creativity

By Jeffrey Katzenberg

A few months ago, I sat in my office in Silicon Valley and watched as a tech founder showed me something extraordinary. On the screen was a fully realized, beautifully lit, well-composed animated scene. It was stunning and it made me feel exactly what I felt in 1986 watching Luxo Jr. That was the first time I watched a computer-animated 3D character take a breath and seem, against all reason, to have life. It left me in awe.

Later that day, I received a text from an artist I've known for thirty years, 350 miles to the south, in the city where I spent most of my career. After seeing a similar video, she texted: "Is this the end of us?" My answer was, "Certainly not.”

I have spent the better part of the last decade in Silicon Valley, but the heart of my career has been in Hollywood. Being deeply connected to both worlds means I have deep loyalties to each and a responsibility to speak honestly to both.

In 2023, I said that these new AI tools would cut the time and cost of producing world-class animation by as much as ninety percent within three years. Some colleagues were alarmed, many were furious.

There is growing fear and resistance surrounding AI within the creative community. I deeply understand it, because I've spent countless hours walking through animation studios watching gifted artists bent over their desks, rebuilding a single second of film for the tenth time because the ninth version wasn't quite right. I've sat in screening rooms where four years of people's labor played out in minutes, and I knew the name of every person that had spent countless hours bringing those images to life. The creative process is a calling, there's really no other way to describe it. From the outside some see resistance. From the inside, it is love.

People do not fight this hard for things they don't care about. The pushback coming out of Hollywood represents the collective effort of people who are deeply passionate about their craft.

Is History Repeating Itself?

The history here is more complicated than either side may realize. In 1906, the most famous composer in America, John Philip Sousa, published an essay titled “The Menace of Mechanical Music." He warned that the phonograph would become "a substitute for human skill, intelligence and soul."

Sousa's fight was not really about the machine, it was about money. The machines were playing his compositions, and the men who built them weren't paying him a cent. His campaign helped create the Copyright Act of 1909. He did not stop the technology. He changed the terms under which it could use his work.
A hundred years ago, sound came to the movies. We remember it now as a miracle, and it was. What we forget is who paid for it. Before sound, tens of thousands of musicians made their living in the orchestra pits of movie houses, scoring every film live, every night, in towns all over the world. When the soundtrack arrived, the work of one composer and one orchestra was recorded for a film that went into thousands of theaters. The union fought back with everything it had, taking out newspaper ads across the country warning against the menace of "canned music," one of them showing a mechanical man tearing the strings out of a harp while an angel wept.

They were not fools, and they were not Luddites. They were right. Those pit jobs did not come back. And yet (this is the part we have to be brave enough to admit), sound gave us the movie musical, the modern score, sfx, sound design, audio engineering, and an art form vastly larger than the one it disrupted. And it helped keep Hollywood in the forefront of world entertainment for the rest of the century and into the next. The loss was real. And yet the art form expanded.

This is a story that has been told over and over again. To resist technology is to risk irrelevance. Just look at Kodak or Blockbuster. To embrace technology is to open doors of new possibility. Just consider Apple and Netflix.

What I Learned From Walt Disney

In the mid-1980s, I was tapped to lead Disney's animation division at a moment when the studio was at an inflection point. Animation wasn't just another business unit. It was the soul of the company, a medium revered because of Walt's genius and his passion. But the production system was cumbersome and unforgiving. A single movie was 125,000 individual hand-drawn and painted cels, photographed one frame at a time. Every revision carried a cost measured in months. These degrees of difficulty shaped the kinds of stories we could tell.

We found our way forward in an unexpected place: Walt himself. The Disney archives held astonishing recordings of Walt explaining his creative process. His own writings. His notes and storyboards. Work product captured at every stage of his process. This was truly a gift. Listening, reading, sitting with the work itself, we heard him talk about character, about emotion, about how an audience feels when a character truly comes alive. He talked about making bold choices and refining a scene until it genuinely moved people. We didn't hear a word about pencils or paintbrushes. In fact, Walt was famous for being a technologist, forever hunting for state-of-the-art tools, often inventing them himself to achieve the images he saw in his head. But he never defined animation by the tools. He defined it by whether the audience believed the character. His principles were timeless. The tools were not.

That realization changed everything. We co-developed the Computer Animation Production System (CAPS) with a young Northern California company called Pixar, replacing hand-painted cels with CGI. In The Little Mermaid, the final scene shimmered with a dimensionality and light that the old process simply couldn't achieve. In Beauty and the Beast, the ballroom sequence moved with a cinematic sweep that placed the audience inside the emotion of the moment. In Aladdin, the Cave of Wonders felt vast and alive, and the Magic Carpet became an intricate, compelling character all its own. In The Lion King, the stampede carried a scale and intensity that raised the emotional stakes beyond anything we'd done before. Technology didn't diminish the craft, it expanded the canvas. It gave artists more room to create.

A decade later, the canvas expanded again. When Disney released Pixar's Toy Story, it wasn't simply a technical milestone. It was proof that a fully computer-animated film could carry real emotional weight, that it could make audiences laugh, cry, and believe. At DreamWorks, we made the difficult decision to sunset hand-drawn animation and become a fully computer-animated studio. It was the right thing to do, but it was not without pain. It cost talented people their place in an industry where they had worked their whole lives. Some made the leap to the new tools and did the finest work of their careers. Some never did.

Tools are never the point. The instruments change with every generation. What endures is taste and imagination. The magical ability to make an audience feel. One of the greatest storytellers of our generation, George Lucas, succinctly captured the eternal essence of this issue: “It’s not the how, it’s the why.”

A Distinction With a Difference

I asked one of the leading AI models a question that has been challenging me for months. What is the difference between reasoning and creating? Its answer changed how I think about almost everything happening in this industry. It said . . .

Reasoning and creating are two distinct cognitive modes, though they also work together. Reasoning is fundamentally evaluative and analytical. It operates on what already exists: facts, premises, evidence. It moves toward a conclusion that was in a sense already implied by the input. Reasoning is constrained by logic and truth. Its goal is to arrive somewhere correct, not to invent somewhere new.

Creating is fundamentally generative. It produces something that didn't exist before. And crucially, there's no single right answer waiting to be found. A blank page has infinite valid responses. Creation involves choices that can't be fully justified by logic alone. Taste, intuition and vision fill the gap where deduction runs out.

Reasoning is what Silicon Valley has been perfecting. Creating is what Hollywood has been practicing for more than a century. AI today operates almost entirely on the reasoning side of the line. It can deduce, evaluate, optimize, and pattern-match brilliantly. And while it can create, there is a real distinction to being creative. What it doesn’t yet have is those things that make us human: empathy, devotion, serendipity, the kind of creativity that comes from a person trying to say something only they could say. When the bot generates a piece of art, it is not trying to communicate anything. It is statistics, not soul; it is emulating things that have been done. By contrast, human creativity isn’t about repeating patterns of zeros and ones; it is about doing something new.

One day, AI may close this gap. Three years ago, the leaders building AI would have called what they are achieving today, improbable, if not impossible. Impossible is no longer improbable.

Today, the line between reasoning and creating is real. Even the leading technologists acknowledge we are not there yet. There is no scientific path to crossing this divide that anyone in the field can articulate today. Understanding that gap is where we will find common ground.

A Path Forward

In 2016, I closed one chapter in Hollywood with the sale of DreamWorks and opened another in Northern California, co-founding WndrCo. We’ve backed more than 50 founders building the next generation of technology and watched how breakthroughs in Silicon Valley emerge, first as experiments, then as platforms, and finally as infrastructure that reshapes entire industries. It's worth remembering that the last great revolution in animation also came from the north. Pixar was a Northern California company, forged not in the conventions of the Hollywood studio system, but in the technological breakthroughs of Silicon Valley. I've spent years on both sides of this bridge. For sure, I don’t have all the answers (take Quibi, for one!). But, from my past and present vantage points of my long career, here is what I see . . .

Brilliant people in Northern California building this technology have made something extraordinary. They have earned the right for the rest of us to be, if not believers, at least optimistic that what comes next will be remarkable. But they have not made an artist. The tools are powerful, but they are not what makes a story matter. That knowledge lives 350 miles to the south, inside people whose life's work has informed the very models you are building. The right path forward includes them by design, with credit, with consent, and with compensation. Build this with the storytellers. Not on top of them. Taste is not something that can be synthesized, it is uniquely human.

At the same time, Hollywood needs to accept that AI is not going away. The energy they are spending trying to make it disappear is energy they are not spending deciding the terms on which it will exist. And the terms are everything. The north needs something from it that they cannot build and cannot buy: creativity. The kind that takes a blank page and conjures a single right answer where there was none and has held audiences for a century. Without it, the most powerful reasoning engine ever invented will still be missing the only thing that makes a story worth telling.

The artists who learn to wield these new instruments will do things the engineers never dreamed of. They always have. Edison invented the motion picture but made terrible movies. It took Chaplin, Lloyd, Keaton and so many others to make movies emotional. Now, the canvas is about to expand yet again. We should decide now that we intend to paint on it.

There are so many valuable lessons in history. This has happened many times before, and it was never settled by the technology. It was settled by the terms. Sousa did not stop the phonograph; he helped write the law that made sure composers got paid. And two years ago, when the writers and the actors walked out, they were fighting for the very things Sousa was fighting for in 1906. Consent, compensation, the basic recognition that human creative work has a price that must be paid. The terms of that fight are still being negotiated, but the principle is older than any of us.

The tools-versus-no-tools argument is a trap. First, we must all agree that there should be terms. Then we can have the crucial debate about what fairness requires.

What I Learned From Steve Jobs

Years ago, Steve Jobs said, "It's in Apple's DNA that technology alone is not enough. It's technology married with the liberal arts, married with the humanities, that yields us the result that makes our hearts sing." He was describing a device. But he could just as easily have been describing this tale of two cities.

What I See Coming Soon

As the barriers and the costs come down, more films will get made, not fewer. Studios will get to take more risks. There will be more seats at the table, and very soon entirely new forms of storytelling. In the 1980s, animation was dismissed as a niche corner of the business. Today it is one of the most beloved and profitable forms of storytelling in the world. In live action, filmmakers like Steven Spielberg, James Cameron and Peter Jackson embraced new visual tools not as shortcuts, but as instruments, and expanded cinema in the process. Every time storytelling has met a genuine technological shift, from synchronized sound to color to computer animation, it has redefined the boundaries of the medium and grown larger in the process.

Assuredly, I don’t have all the answers, but I am confident that the creative opportunities will expand yet again. How we come through this is a choice. The north has the new tools. The south has the creative soul. The best future will draw on the best of both worlds.

[Nikita Bier @nikitabier]

Going to tell my kids that before AI, businesses used to say “the customer is always right”

[Thariq @trq212]

we’re thinking of killing plan mode and using the shift+tab hotkey to adjust effort levels

I don’t think the models need plan mode anymore, but if you’re a plan mode diehard would love to get your feedback on why

[Gergely Orosz @GergelyOrosz]

In the obsession with AI, too many founders, leaders, managers seem to have forgotten that an engineer with motivation + drive + focus + AI >> hundreds of AI agents without any of those

The best products and companies are built by people and teams like that, and will continue to be so

Ask yourself, again, why the AI labs hire the best of the best (some of the most driven people in the industry; founders, CTOs etc) if people aren't making the differrence when everyone has access to similar tools already

[Christian Elton @christianelton]

The gap between major model releases keeps shrinking.

2023: once every 73 days
2026 (so far): once every 18 days 🤯