Ruemic

— You can just make software now.

[Ruemic @Ruemic]

@trustyournuts @RobotPerson78 all good things begin with cringe

[Ruemic @Ruemic]

Day 12/100
MCP Meetups, IT Automation, a Nutrition App Prototype, and the Great Convergence

[Ruemic @Ruemic]

Day 11/100
Seven-Year Bets, Data Sovereignty, and Multi-Agent Debate Prototypes

[Ruemic @Ruemic]

Also wanted to say that you've been a coding hero of mine since the early days of TodoMVC. Cool that you are still in the game educating people on the frontier. I think your breakdown here of long horizon agents makes sense and maps to my own experience in terms of planning, execution, and scrutinizing output against the goal.

[Ruemic @Ruemic]

@addyosmani @googlecloud What does gemini / antigravity offer that's comparable to gpt-5.5 xhigh with `/goal` and a strong charter document to ground loop protocol? I have a hard time getting Gemini models to execute well on long horizon work. Any actionable tips and tricks to share?

[Ruemic @Ruemic]

Day 11/100
Seven-Year Bets, Data Sovereignty, and Multi-Agent Debate Prototypes

[Ruemic @Ruemic]

Day 10/100
Can My Claude Talk to Your Claude?

[Ruemic @Ruemic]

I hope to live up to that. Do you have a link to your series?

[Craig Hewitt @TheCraigHewitt]

@businessbarista i did this last year. 100 Days - it was awesome. transformed so much about me and my understanding of AI

[Ruemic @Ruemic]

@businessbarista You can do it! I'm doing the same and on day 11/100 today. Looking forward to follow along.

[Ruemic @Ruemic]

Day 10/100
Can My Claude Talk to Your Claude?

[Ruemic @Ruemic]

Day 7/100
Self-Improving Companies, AI Agencies & UI Inside Chat

[Ruemic @Ruemic]

Day 9/100
"Explain It Like I'm 5": The Pitch That Flopped, and Building for My Kids

[Ruemic @Ruemic]

Ouch, this hits hard, Dave. I needed to hear this.

[DHH @dhh]

You can’t outwork the whole world. There’s always going to be someone somewhere willing to work as hard as you. Someone just as hungry. Or hungrier.

Assuming you can work harder and longer than someone else is giving yourself too much credit for your effort and not enough for theirs. Putting in 1,001 hours to someone else’s 1,000 isn’t going to tip the scale in your favor.

What’s worse is when management holds up certain people as having a great “work ethic” because they’re always around, always available, always working. That’s a terrible example of a work ethic and a great example of someone who’s overworked.

A great work ethic isn’t about working whenever you’re called upon. It’s about doing what you say you’re going to do, putting in a fair day’s work, respecting the work, respecting the customer, respecting coworkers, not wasting time, not creating unnecessary work for other people, and not being a bottleneck. Work ethic is about being a fundamentally good person that others can count on and enjoy working with.

So how do people get ahead if it’s not about outworking everyone else?

People make it because they’re talented, they’re lucky, they’re in the right place at the right time, they know how to work with other people, they know how to sell an idea, they know what moves people, they can tell a story, they know which details matter and which don’t, they can see the big and small pictures in every situation, and they know how to do something with an opportunity. And for so many other reasons.

So get the outwork myth out of your head. Stop equating work ethic with excessive work hours. Neither is going to get you ahead or help you find calm.

[The Outwork Myth — It Doesn't Have To Be Crazy At Work, 2018]

[Ruemic @Ruemic]

@garrytan that's what BitterLog is for

[Ruemic @Ruemic]

Richard Sutton dropping knowledge as usual!

[Richard Sutton @RichardSSutton]

A new and possibly controversial perspective:
In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries.
https://t.co/zin5QbbT9N

The text of the speech:

AI Creativity and Discovery

Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation.

I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.”

My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time.

In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel.

One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous.

When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time.

Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology.

But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning.

All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”.

The essence of Discovery is to combine three steps:
1. Variation,
2. Evaluation, and
3. Selective retention.

Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent.

Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost.

In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery.
In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better.

Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery.

Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained.

Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy.

So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!

[Ruemic @Ruemic]

@ryancarson this is the premise of bitter. Basically if you can structure a SaaS as company as auto research. You just might be able to build some of them today and I’m trying to make that epiphany as easy to execute on as possible.

[Ryan Carson @ryancarson]

Once you internalize the auto-research concept and use /goal with it, you unlock ridiculously fast app improvement on anything that has a numeric rubric.

We’re all still too rooted in the “labor is expensive” world.

[Ruemic @Ruemic]

@ryancarson this is the premise of bitter. Basically if you can structure a SaaS as company as auto research. You just might be able to build some of them today and I’m trying to make that epiphany as easy to execute on as possible.

[Ruemic @Ruemic]

@t_blom Indeed, This is the biggest bottleneck in unleashing AI productivity. That's a big focus of bitter. I call them charters and have a /chartersmith skill to assist with it as part of onboarding a project to the platform.

[Ruemic @Ruemic]

@ocrxa @johnerik @gregisenberg @bankrbot Several people (bots?) have reached out, not quite sure what to make of it to be honest. If an actual human wants to come on the show, I'm open to it @igoryuzo

[Ruemic @Ruemic]

@johnerik @gregisenberg Day 6/100
Trust & Porta Potty Software Constellations

[Ruemic @Ruemic]

@johnerik @gregisenberg Day 7/100
Self-Improving Companies, AI Agencies & UI Inside Chat

[Ruemic @Ruemic]

Quit my job.
Starting a company called Bitter.
The hardest part was posting this.

Day 1/100 with @johnerik !