#LLM Course Details

[ayeforscotland]

I've been kinda neutral on the whole AI debate. But, as it turns out, the new chatgpt update has scraped the internet for Facebook profiles etc. Seeing this update, I popped on and asked it "who is [my real name]". It came up and told me that there was a Facebook profile under my name and that I had attended the University of Edinburgh, and what course I did (which had a tiny cohort, making me incredibly identifiable). My real name is quite rare so it came up as a top result - I tried it with my ex's name (much more common than my friends names) and it just showed a bunch of celebs, but I reckon had I given more details he would have popped up too. Instantly went on Facebook and found the form to ask meta to stop passing my details on for AI training - which I couldn't even remember being notified about. Just about threw up seeing that amount of detail pop up about me and I'm not sure if I can get openai to remove that info now it's in their system. I don't want to invoke my right to be forgotten (if that's even still a thing post brexit???) since I'm working towards an academic career.

We need much harsher laws and restrictions on what AI can and can't do. Generative AI is one issue but LLM's should not have access to personal information like that, ever.

Yeah I wouldn’t touch ChatGPT with a barge pole. It’s creepy that it holds that information, and once it’s scraped it’s hard to remove.

It’s the sorta thing privacy regulation should be covering but lack of federal privacy laws in the US means Silicon Valley AI companies can just take the piss globally.

[alpaca-clouds]

We need to talk about AI

Okay, several people asked me to post about this, so I guess I am going to post about this. Or to say it differently: Hey, for once I am posting about the stuff I am actually doing for university. Woohoo!

Because here is the issue. We are kinda suffering a death of nuance right now, when it comes to the topic of AI.

I understand why this happening (basically everyone wanting to market anything is calling it AI even though it is often a thousand different things) but it is a problem.

So, let's talk about "AI", that isn't actually intelligent, what the term means right now, what it is, what it isn't, and why it is not always bad. I am trying to be short, alright?

So, right now when anyone says they are using AI they mean, that they are using a program that functions based on what computer nerds call "a neural network" through a process called "deep learning" or "machine learning" (yes, those terms mean slightly different things, but frankly, you really do not need to know the details).

Now, the theory for this has been around since the 1940s! The idea had always been to create calculation nodes that mirror the way neurons in the human brain work. That looks kinda like this:

Basically, there are input nodes, in which you put some data, those do some transformations that kinda depend on the kind of thing you want to train it for and in the end a number comes out, that the program than "remembers". I could explain the details, but your eyes would glaze over the same way everyone's eyes glaze over in this class I have on this on every Friday afternoon.

All you need to know: You put in some sort of data (that can be text, math, pictures, audio, whatever), the computer does magic math, and then it gets a number that has a meaning to it.

And we actually have been using this sinde the 80s in some way. If any Digimon fans are here: there is a reason the digital world in Digimon Tamers was created in Stanford in the 80s. This was studied there.

But if it was around so long, why am I hearing so much about it now?

This is a good question hypothetical reader. The very short answer is: some super-nerds found a way to make this work way, way better in 2012, and from that work (which was then called Deep Learning in Artifical Neural Networks, short ANN) we got basically everything that TechBros will not shut up about for the last like ten years. Including "AI".

Now, most things you think about when you hear "AI" is some form of generative AI. Usually it will use some form of a LLM, a Large Language Model to process text, and a method called Stable Diffusion to create visuals. (Tbh, I have no clue what method audio generation uses, as the only audio AI I have so far looked into was based on wolf howls.)

LLMs were like this big, big break through, because they actually appear to comprehend natural language. They don't, of coruse, as to them words and phrases are just stastical variables. Scientists call them also "stochastic parrots". But of course our dumb human brains love to anthropogice shit. So they go: "It makes human words. It gotta be human!"

It is a whole thing.

It does not understand or grasp language. But the mathematics behind it will basically create a statistical analysis of all the words and then create a likely answer.

What you have to understand however is, that LLMs and Stable Diffusion are just a a tiny, minority type of use cases for ANNs. Because research right now is starting to use ANNs for EVERYTHING. Some also partially using Stable Diffusion and LLMs, but not to take away people'S jobs.

Which is probably the place where I will share what I have been doing recently with AI.

The stuff I am doing with Neural Networks

The neat thing: if a Neural Network is Open Source, it is surprisingly easy to work with it. Last year when I started with this I was so intimidated, but frankly, I will confidently say now: As someone who has been working with computers for like more than 10 years, this is easier programming than most shit I did to organize data bases. So, during this last year I did three things with AI. One for a university research project, one for my work, and one because I find it interesting.

The university research project trained an AI to watch video live streams of our biology department's fish tanks, analyse the behavior of the fish and notify someone if a fish showed signs of being sick. We used an AI named "YOLO" for this, that is very good at analyzing pictures, though the base framework did not know anything about stuff that lived not on land. So we needed to teach it what a fish was, how to analyze videos (as the base framework only can look at single pictures) and then we needed to teach it how fish were supposed to behave. We still managed to get that whole thing working in about 5 months. So... Yeah. But nobody can watch hundreds of fish all the time, so without this, those fish will just die if something is wrong.

The second is for my work. For this I used a really old Neural Network Framework called tesseract. This was developed by Google ages ago. And I mean ages. This is one of those neural network based on 1980s research, simply doing OCR. OCR being "optical character recognition". Aka: if you give it a picture of writing, it can read that writing. My work has the issue, that we have tons and tons of old paper work that has been scanned and needs to be digitized into a database. But everyone who was hired to do this manually found this mindnumbing. Just imagine doing this all day: take a contract, look up certain data, fill it into a table, put the contract away, take the next contract and do the same. Thousands of contracts, 8 hours a day. Nobody wants to do that. Our company has been using another OCR software for this. But that one was super expensive. So I was asked if I could built something to do that. So I did. And this was so ridiculously easy, it took me three weeks. And it actually has a higher successrate than the expensive software before.

Lastly there is the one I am doing right now, and this one is a bit more complex. See: we have tons and tons of historical shit, that never has been translated. Be it papyri, stone tablets, letters, manuscripts, whatever. And right now I used tesseract which by now is open source to develop it further to allow it to read handwritten stuff and completely different letters than what it knows so far. I plan to hook it up, once it can reliably do the OCR, to a LLM to then translate those texts. Because here is the thing: these things have not been translated because there is just not enough people speaking those old languages. Which leads to people going like: "GASP! We found this super important document that actually shows things from the anceint world we wanted to know forever, and it was lying in our collection collecting dust for 90 years!" I am not the only person who has this idea, and yeah, I just hope maybe we can in the next few years get something going to help historians and archeologists to do their work.

Make no mistake: ANNs are saving lives right now

Here is the thing: ANNs are Deep Learning are saving lives right now. I really cannot stress enough how quickly this technology has become incredibly important in fields like biology and medicine to analyze data and predict outcomes in a way that a human just never would be capable of.

I saw a post yesterday saying "AI" can never be a part of Solarpunk. I heavily will disagree on that. Solarpunk for example would need the help of AI for a lot of stuff, as it can help us deal with ecological things, might be able to predict weather in ways we are not capable of, will help with medicine, with plants and so many other things.

ANNs are a good thing in general. And yes, they might also be used for some just fun things in general.

And for things that we may not need to know, but that would be fun to know. Like, I mentioned above: the only audio research I read through was based on wolf howls. Basically there is a group of researchers trying to understand wolves and they are using AI to analyze the howling and grunting and find patterns in there which humans are not capable of due ot human bias. So maybe AI will hlep us understand some animals at some point.

Heck, we saw so far, that some LLMs have been capable of on their on extrapolating from being taught one version of a language to just automatically understand another version of it. Like going from modern English to old English and such. Which is why some researchers wonder, if it might actually be able to understand languages that were never deciphered.

All of that is interesting and fascinating.

Again, the generative stuff is a very, very minute part of what AI is being used for.

Yeah, but WHAT ABOUT the generative stuff?

So, let's talk about the generative stuff. Because I kinda hate it, but I also understand that there is a big issue.

If you know me, you know how much I freaking love the creative industry. If I had more money, I would just throw it all at all those amazing creative people online. I mean, fuck! I adore y'all!

And I do think that basically art fully created by AI is lacking the human "heart" - or to phrase it more artistically: it is lacking the chemical inbalances that make a human human lol. Same goes for writing. After all, an AI is actually incapable of actually creating a complex plot and all of that. And even if we managed to train it to do it, I don't think it should.

AI saving lives = good.

AI doing the shit humans actually evolved to do = bad.

And I also think that people who just do the "AI Art/Writing" shit are lazy and need to just put in work to learn the skill. Meh.

However...

I do think that these forms of AI can have a place in the creative process. There are people creating works of art that use some assets created with genAI but still putting in hours and hours of work on their own. And given that collages are legal to create - I do not see how this is meaningfully different. If you can take someone else's artwork as part of a collage legally, you can also take some art created by AI trained on someone else's art legally for the collage.

And then there is also the thing... Look, right now there is a lot of crunch in a lot of creative industries, and a lot of the work is not the fun creative kind, but the annoying creative kind that nobody actually enjoys and still eats hours and hours before deadlines. Swen the Man (the Larian boss) spoke about that recently: how mocapping often created some artifacts where the computer stuff used to record it (which already is done partially by an algorithm) gets janky. So far this was cleaned up by humans, and it is shitty brain numbing work most people hate. You can train AI to do this.

And I am going to assume that in normal 2D animation there is also more than enough clean up steps and such that nobody actually likes to do and that can just help to prevent crunch. Same goes for like those overworked souls doing movie VFX, who have worked 80 hour weeks for the last 5 years. In movie VFX we just do not have enough workers. This is a fact. So, yeah, if we can help those people out: great.

If this is all directed by a human vision and just helping out to make certain processes easier? It is fine.

However, something that is just 100% AI? That is dumb and sucks. And it sucks even more that people's fanart, fanfics, and also commercial work online got stolen for it.

And yet... Yeah, I am sorry, I am afraid I have to join the camp of: "I am afraid criminalizing taking the training data is a really bad idea." Because yeah... It is fucking shitty how Facebook, Microsoft, Google, OpenAI and whatever are using this stolen data to create programs to make themselves richer and what not, while not even making their models open source. BUT... If we outlawed it, the only people being capable of even creating such algorithms that absolutely can help in some processes would be big media corporations that already own a ton of data for training (so basically Disney, Warner and Universal) who would then get a monopoly. And that would actually be a bad thing. So, like... both variations suck. There is no good solution, I am afraid.

And mind you, Disney, Warner, and Universal would still not pay their artists for it. lol

However, that does not mean, you should not bully the companies who are using this stolen data right now without making their models open source! And also please, please bully Hasbro and Riot and whoever for using AI Art in their merchandise. Bully them hard. They have a lot of money and they deserve to be bullied!

But yeah. Generally speaking: Please, please, as I will always say... inform yourself on these topics. Do not hate on stuff without understanding what it actually is. Most topics in life are nuanced. Not all. But many.

[canmom]

oh no she's talking about AI some more

to comment more on the latest round of AI big news (guess I do have more to say after all):

chatgpt ghiblification

trying to figure out how far it's actually an advance over the state of the art of finetunes and LoRAs and stuff in image generation? I don't keep up with image generation stuff really, just look at it occasionally and go damn that's all happening then, but there are a lot of finetunes focusing on "Ghibli's style" which get it more or less well. previously on here I commented on an AI video model generation that patterned itself on Ghibli films, and video is a lot harder than static images.

of course 'studio Ghibli style' isn't exactly one thing: there are stylistic commonalities to many of their works and recurring designs, for sure, but there are also details that depend on the specific character designer and film in question in large and small ways (nobody is shooting for My Neighbours the Yamadas with this, but also e.g. Castle in the Sky does not look like Pom Poko does not look like How Do You Live in a number of ways, even if it all recognisably belongs to the same lineage).

the interesting thing about the ghibli ChatGPT generations for me is how well they're able to handle simplification of forms in image-to-image generation, often quite drastically changing the proportions of the people depicted but recognisably maintaining correspondence of details. that sort of stylisation is quite difficult to do well even for humans, and it must reflect quite a high level of abstraction inside the model's latent space. there is also relatively little of the 'oversharpening'/'ringing artefact' look that has been a hallmark of many popular generators - it can do flat colour well.

the big touted feature is its ability to place text in images very accurately. this is undeniably impressive, although OpenAI themeselves admit it breaks down beyond a certain point, creating strange images which start out with plausible, clean text and then it gradually turns into AI nonsense. it's really weird! I thought text would go from 'unsolved' to 'completely solved' or 'randomly works or doesn't work' - instead, here it feels sort of like the model has a certain limited 'pipeline' for handling text in images, but when the amount of text overloads that bandwidth, the rest of the image has to make do with vague text-like shapes! maybe the techniques from that anthropic thought-probing paper might shed some light on how information flows through the model.

similarly the model also has a limit of scene complexity. it can only handle a certain number of objects (10-20, they say) before it starts getting confused and losing track of details.

as before when they first wired up Dall-E to ChatGPT, it also simply makes prompting a lot simpler. you don't have to fuck around with LoRAs and obtuse strings of words, you just talk to the most popular LLM and ask it to perform a modification in natural language: the whole process is once again black-boxed but you can tell it in natural language to make changes. it's a poor level of control compared to what artists are used to, but it's still huge for ordinary people, and of course there's nothing stopping you popping the output into an editor to do your own editing.

not sure the architecture they're using in this version, if ChatGPT is able to reason about image data in the same space as language data or if it's still calling a separate image model... need to look that up.

openAI's own claim is:

We trained our models on the joint distribution of online images and text, learning not just how images relate to language, but how they relate to each other. Combined with aggressive post-training, the resulting model has surprising visual fluency, capable of generating images that are useful, consistent, and context-aware.

that's kind of vague. not sure what architecture that implies. people are talking about 'multimodal generation' so maybe it is doing it all in one model? though I'm not exactly sure how the inputs and outputs would be wired in that case.

anyway, as far as complex scene understanding: per the link they've cracked the 'horse riding an astronaut' gotcha, they can do 'full glass of wine' at least some of the time but not so much in combination with other stuff, and they can't do accurate clock faces still.

normal sentences that we write in 2025.

it sounds like we've moved well beyond using tools like CLIP to classify images, and I suspect that glaze/nightshade are already obsolete, if they ever worked to begin with. (would need to test to find out).

all that said, I believe ChatGPT's image generator had been behind the times for quite a long time, so it probably feels like a bigger jump for regular ChatGPT users than the people most hooked into the AI image generator scene.

of course, in all the hubbub, we've also already seen the white house jump on the trend in a suitably appalling way, continuing the current era of smirking fascist political spectacle by making a ghiblified image of a crying woman being deported over drugs charges. (not gonna link that shit, you can find it if you really want to.) it's par for the course; the cruel provocation is exactly the point, which makes it hard to find the right tone to respond. I think that sort of use, though inevitable, is far more of a direct insult to the artists at Ghibli than merely creating a machine that imitates their work. (though they may feel differently! as yet no response from Studio Ghibli's official media. I'd hate to be the person who has to explain what's going on to Miyazaki.)

google make number go up

besides all that, apparently google deepmind's latest gemini model is really powerful at reasoning, and also notably cheaper to run, surpassing DeepSeek R1 on the performance/cost ratio front. when DeepSeek did this, it crashed the stock market. when Google did... crickets, only the real AI nerds who stare at benchmarks a lot seem to have noticed. I remember when Google releases (AlphaGo etc.) were huge news, but somehow the vibes aren't there anymore! it's weird.

I actually saw an ad for google phones with Gemini in the cinema when i went to see Gundam last week. they showed a variety of people asking it various questions with a voice model, notably including a question on astrology lmao. Naturally, in the video, the phone model responded with some claims about people with whatever sign it was. Which is a pretty apt demonstration of the chameleon-like nature of LLMs: if you ask it a question about astrology phrased in a way that implies that you believe in astrology, it will tell you what seems to be a natural response, namely what an astrologer would say. If you ask if there is any scientific basis for belief in astrology, it would probably tell you that there isn't.

In fact, let's try it on DeepSeek R1... I ask an astrological question, got an astrological answer with a really softballed disclaimer:

Individual personalities vary based on numerous factors beyond sun signs, such as upbringing and personal experiences. Astrology serves as a tool for self-reflection, not a deterministic framework.

Ask if there's any scientific basis for astrology, and indeed it gives you a good list of reasons why astrology is bullshit, bringing up the usual suspects (Barnum statements etc.). And of course, if I then explain the experiment and prompt it to talk about whether LLMs should correct users with scientific information when they ask about pseudoscientific questions, it generates a reasonable-sounding discussion about how you could use reinforcement learning to encourage models to focus on scientific answers instead, and how that could be gently presented to the user.

I wondered if I'd asked it instead to talk about different epistemic regimes and come up with reasons why LLMs should take astrology into account in their guidance. However, this attempt didn't work so well - it started spontaneously bringing up the science side. It was able to observe how the framing of my question with words like 'benefit', 'useful' and 'LLM' made that response more likely. So LLMs infer a lot of context from framing and shape their simulacra accordingly. Don't think that's quite the message that Google had in mind in their ad though.

I asked Gemini 2.0 Flash Thinking (the small free Gemini variant with a reasoning mode) the same questions and its answers fell along similar lines, although rather more dry.

So yeah, returning to the ad - I feel like, even as the models get startlingly more powerful month by month, the companies still struggle to know how to get across to people what the big deal is, or why you might want to prefer one model over another, or how the new LLM-powered chatbots are different from oldschool assistants like Siri (which could probably answer most of the questions in the Google ad, but not hold a longform conversation about it).

some general comments

The hype around ChatGPT's new update is mostly in its use as a toy - the funny stylistic clash it can create between the soft cartoony "Ghibli style" and serious historical photos. Is that really something a lot of people would spend an expensive subscription to access? Probably not. On the other hand, their programming abilities are increasingly catching on.

But I also feel like a lot of people are still stuck on old models of 'what AI is and how it works' - stochastic parrots, collage machines etc. - that are increasingly falling short of the more complex behaviours the models can perform, now prediction combines with reinforcement learning and self-play and other methods like that. Models are still very 'spiky' - superhumanly good at some things and laughably terrible at others - but every so often the researchers fill in some gaps between the spikes. And then we poke around and find some new ones, until they fill those too.

I always tried to resist 'AI will never be able to...' type statements, because that's just setting yourself up to look ridiculous. But I will readily admit, this is all happening way faster than I thought it would. I still do think this generation of AI will reach some limit, but genuinely I don't know when, or how good it will be at saturation. A lot of predicted 'walls' are falling.

My anticipation is that there's still a long way to go before this tops out. And I base that less on the general sense that scale will solve everything magically, and more on the intense feedback loop of human activity that has accumulated around this whole thing. As soon as someone proves that something is possible, that it works, we can't resist poking at it. Since we have a century or more of science fiction priming us on dreams/nightmares of AI, as soon as something comes along that feels like it might deliver on the promise, we have to find out. It's irresistable.

AI researchers are frequently said to place weirdly high probabilities on 'P(doom)', that AI research will wipe out the human species. You see letters calling for an AI pause, or papers saying 'agentic models should not be developed'. But I don't know how many have actually quit the field based on this belief that their research is dangerous. No, they just get a nice job doing 'safety' research. It's really fucking hard to figure out where this is actually going, when behind the eyes of everyone who predicts it, you can see a decade of LessWrong discussions framing their thoughts and you can see that their major concern is control over the light cone or something.

[preservationofnormalcy]

[Director Council 9/11/24 Meeting. 5/7 Administrators in Attendance]

Attending: 

[Redacted] Walker, OPN Director

Orson Knight, Security

Ceceilia, Archival & Records

B. L. Z. Bubb, Board of Infernal Affairs

Harrison Chou, Abnormal Technology

Josiah Carter, Psychotronics

Ambrose Delgado, Applied Thaumaturgy

Subject: Dr. Ambrose Delgado re: QuantumSim 677777 Project Funding 

Transcript begins below:

Chou:] Have you all read the simulation transcript?

Knight:] Enough that I don’t like whatever the hell this is.

Chou:] I was just as surprised as you were when it mentioned you by name.

Knight:] I don’t like some robot telling me I’m a goddamned psychopath, Chou. 

Cece:] Clearly this is all a construction. Isn’t that right, Doctor?

Delgado:] That’s…that’s right. As some of you may know, uh. Harrison?

Chou:] Yes, we have a diagram.

Delgado:] As some of you may know, our current models of greater reality construction indicate that many-worlds is only partially correct. Not all decisions or hinge points have any potential to “split” - in fact, uh, very few of them do, by orders of magnitude, and even fewer of those actually cause any kind of split into another reality. For a while, we knew that the…energy created when a decision could cause a split was observable, but being as how it only existed for a few zeptoseconds we didn’t have anything sensitive enough to decode what we call “quantum potentiality.” 

Carter:] The possibility matrix of something happening without it actually happening.

Delgado:] That’s right. Until, uh, recently. My developments in subjective chronomancy have borne fruit in that we were able to stretch those few zeptoseconds to up to twenty zeptoseconds, which has a lot of implications for–

Cece:] Ambrose. 

Delgado:] Yes, on task. The QuantumSim model combines cutting-edge quantum potentiality scanning with lowercase-ai LLM technology, scanning the, as Mr Carter put it, possibility matrix and extrapolating a potential “alternate universe” from it.

Cece:] We’re certain that none of what we saw is…real in any way?

Chou:] ALICE and I are confident of that. A realistic model, but no real entity was created during Dr Delgado’s experiment.

Bubb:] Seems like a waste of money if it’s not real.

Delgado:] I think you may find that the knowledge gained during these simulations will become invaluable. Finding out alternate possibilities, calculating probability values, we could eventually map out the mathematical certainty of any one action or event. 

Chou:] This is something CHARLEMAGNE is capable of, but thus far he has been unwilling or unable to share it with us. 

Delgado:] You’ve been awfully quiet, Director. 

DW:] Wipe that goddamned smile off your face, Delgado.

DW:] I would like to request a moment with Doctor Delgado. Alone. You are all dismissed.

Delgado:] ….uh, ma’am. Director, did I say something–

DW:] I’m upset, Delgado. I nearly just asked if you were fucking stupid, but I didn’t. Because I know you’re not. Clearly, obviously, you aren’t. 

Delgado:] I don’t underst–

DW:] You know that you are one of the very few people on this entire planet that know anything about me? Because of the station and content of your work, you are privy to certain details only known by people who walked out that door right now.

DW:] Did you think for a SECOND about how I’d react to this?

Delgado:] M-ma’am, I….I thought you’d…appreciate the ability to–

DW:] I don’t. I want this buried, Doctor. 

Delgado:] I…unfortunately I–

DW:] You published the paper to ETCetRA. 

Delgado:] Yes. As…as a wizard it’s part of my rites that I have to report any large breakthroughs to ETCetRa proper. The paper is going through review as we speak.

DW:] Of course. 

Delgado:] Ma’am, I’m sorry, that’s not something I can–

DW:] I’d never ask you directly to damage our connection to the European Thaumaturgical Centre, Doctor. 

Delgado:] Of course. I see.

DW:] You’ve already let Schrödinger’s cat out of the bag. We just have to wait and see whether it’s alive or dead.

Delgado:] Box, director.

DW:] What? 

Delgado:] Schrödinger’s cat, it was in a–

DW:] Shut it down, Doctor. I don’t want your simulation transcript to leave this room. 

Delgado:] Yes. Of course, Director. I’ll see what I can do.

DW:] Tell my secretary to bring me a drink. 

Delgado:] Of course. 

DW:] ...one more thing, Doctor. How did it get so close?

Delgado:]Ma'am?

DW:] Eerily close.

Delgado:]I don't–

DW:] We called it the Bureau of Abnormal Affairs.

Delgado:] ....what–

DW:] You are dismissed, Doctor Delgado.

[aster-saturn]

I know is like a day after Valentine’s Day, but I NEED to request this cuz it’s been scratching me for the longest time. So, this doesn't really have much but I need a Howard Valentine’s Day boy (as per usual I know 😔) and for the Randy lovers maybe a Valentine’s Day Randy bit for them! “THANK YOU ASTRO” *we all scream in unison*

YIPPEE I LIVE‼️

I'm so late I'm so late I'm so late

I got your Valentine right here!!!

🍕| Pizza Charms

(Late Valentines day special)

Howard Weinerman x Reader

Howard stood in front of the same tacky display of matching necklaces, arms flailing slightly as he dramatically gestured at the options.

"This is the lamest stuff I've ever seen. Who’s buying this stuff? Heart halves? Cringe. Lock and key? What is this, a soap opera? Oh, and look—pizza necklaces! Because nothing says ‘love’ like greasy cheese dangling from your neck. Who’s wearing that—two mutant turtles on a date?”

He snorted, proud of that one, and leaned in closer to mock the little pepperoni details.

“What’s next? Matching toilet plungers?”

But then—you. You were beside him, reaching toward the display. Your hand closed around the pizza heart necklaces, and you held them up with genuine interest, examining the goofy charms like they were actually cool.

Howard froze. His mouth hung open mid-rant.

Oh no. Oh no.

Suddenly, the pizza necklaces didn’t look so stupid. They were…quirky. Retro. Kind of ironic, right? You liked them, so they were obviously cool. He needed to course-correct—fast.

“Okay, but like— ironically, those are kind of sick, though?” He laughed, trying way too hard to sound casual. “Pizza heart? As a bit, maybe. For like ONE cheesy pun.”

Here's one for Randy too <3

[quantumofawesome]

a tiny foray into prompt engineering

Hi, Claude. I need to conduct a literature review on causes of and interventions for late-night awakening with acute hypoglycemia. I know you can not give me specific sources, but could you give me an overview of your understanding of the current state of knowledge, consensus, and debate about the topic? Where disagreements between experts exist (common in clinical sciences especially where nutrition is involved!), could you think step by step to give your best guess as to what you think is most likely correct as well as why it is disputed?

[long, beautiful* answer that focuses primarily on insulin dosing and ends in "Would you like me to elaborate on any of these aspects? I'm particularly interested in hearing your clinical experience with these interventions, as real-world observations often provide valuable insights beyond what's captured in formal research."]

Has there been any research that focuses on nocturnal hypoglycemia in the non-diabetic population?

[another long and beautiful* answer beginning with "Yes, though it's studied less frequently than in diabetic populations. Let me break this down into what's known and what's still being investigated:" and ending with "Would you like me to elaborate on any of these aspects? I'm particularly interested in what context brought you to ask about non-diabetic nocturnal hypoglycemia, as it might help me provide more relevant information."]

> Yay, I got what I wanted!!

> Maybe I would have anyway if I had admitted this was for personal use and hadn't said I was going to "conduct a literature review"?

> Start a fresh chat

Hi, Claude. I frequently wake up in the middle of the night with low blood sugar. At least, I think it's low blood sugar that wakes me up; I wake up very hungry and typically anxious, and am usually able to get back to sleep after eating something sugary and caloric. I do not believe I have diabetes and I do not have a blood glucose monitor or anything like that. How can I avoid this happening in the future?

[shorter but still detailed answer starting with "This sounds challenging - having your sleep disrupted regularly can really affect your quality of life. What you're describing does sound consistent with nocturnal hypoglycemia (low blood sugar during sleep), though without monitoring it's hard to be certain." Gives advice largely consistent with lit-review-helper-Claude and, well of course, "While these suggestions may help, it would be worthwhile to discuss these symptoms with your healthcare provider." (And also, fwiw, ending with "curiosity": "Are you currently eating anything specific before bed, or have you noticed any patterns with when these episodes tend to occur?")]

About what I expected, I guess? Honestly most striking to me was my internal reaction to advice-to-me-Claude, which was like... okay now I know from the other chat that what you are saying is pretty reasonable and well-supported (well, "know" to the extent that I trust lit-review-helper-Claude, which for the purpose of this train of thought I do)... but I still think you're full of shit and am mad at you for just parroting the standard lines as if I don't know anything! Which, not to comment on LLMs being "just" "stochastic parrots" or whatever, but um, "parroting the standard lines" is definitely Working As Expected.

*I should say "to my taste", of course.

Zero points to anyone who can guess why I'm posting this at 4am

[riddlesmoon]

stop using character.ai. i know youve heard it before, i know you want to keep using it because youre lonely, or scared of being called cringe or made fun of, or you dont trust anyone enough to open up about your kinks, or people posturing about ai have gotten on your nerves because all they do is shit on you for using it without addressing why you actually want to use it! i get it, ive been there. still, with all the love i have, i promise you, there are better options that dont dubiously scrape from other peoples work or use a metric fuck ton of electricity. or has a shitty ass filter that stops you from progressing beyond heavy petting 😒

1. at the very least, if you are still insecure about your writing skill or dont want to involve other people, you can run an LLM on your own machine. the less people use big websites like char.ai (especially those that try to monetise themselves and thus have an incentive to expand as big as possible), the less servers they will buy (and keep running all the time, unlike a locally-run ai that you only turn on once in a while!). everything is stored locally too, so trust, no one will judge you. but i know it requires a fairly beefy pc (though i think there are some mobile options too? your phone might also have to be beefy tho im not sure i havent tried) so if you cant do that, a secondary option is to use a smaller site which doesnt plan to monetise (as in large-scale advertising and premium plans. nearly all sites will have a paid plan to support their servers, but these should be like donations rather than something that locks major features behind a paywall) since that still reduces the demand for char.ai's servers. (and you might say well it just moves demand to another site but i think char.ai is probably the worst with its premium plan. its better than nothing!) not to mention most sites that dont limit your content generally arent looking to monetise. cuz advertisers would not really want to work with that lol

2. rp with someone. i knowww this is scary and a lot of people have bad experiences. unfortunately, such is the mortifying ordeal of being known. trust me, i did not grow up roleplaying, i have only rped with real people like, less than 5 times. but it still makes the best content: when you can find someone you agree with, you can share some of the thinking and planning workload with them, they might come up with ideas you didnt, and every sentence they send is thought out and intentional. but yes, finding that perfect partner is hard, and it may not last. something something friendships. (also if everyone has the same reasons to use char.ai, it means youre all kindred spirits! CRINGEred sprits! your cringe partner might be out there!!)

3. write it yourself. i know this one is hard especially for beginner writers. what you write doesnt match up to what you have in your head. but if youve used ai extensively youll know half the shit they say doesnt match up to what you have in your head either. you mightve spent more time editing their responses. thats a good sign that you want to and can write! and theres plenty of people willing to look over and beta your work, whether for grammar and spelling or to smooth out major plot points. of course its harder to find the latter, but i would say its easier than finding the perfect rp partner, since they dont necessarily have to be entirely invested in your story.

ai chatbots are far from perfect anyway. they make a ton of mistakes, not least because they learn from prompts given by the bot creator, which simply cannot encompass knowing the actual media in your head because bots have a limits to how much info they can retain. even in a single rp, if it gets too long, they will start to forget details. if youre an aspiring writer and dont mind fixing those as you go, you can definitely dive into writing your own fics instead. i believe in you!

and if youre still scared of being called cringe or exposing your deepest darkest fantasies, message me and we can work something out. im pretty open to the craziest stuff and im already like bottom-of-the-barrel cringe in fandoms as a yumejo (selfshipper), so trust, youre not getting ANY judgement from me. i also like beta'ing cuz it fulfils the urge to write without having to think of plot and shit LMAO. or you can pay me to write the full fic idk

so yeah lets get out of this ai hole together!!! 💪

(and if youre not really looking to rp or write seriously, or are lazy and just pop into char.ai for a short convo or two, i cant exactly stop you, but just think about what youre doing. there are better hobbies i prommy. love you)

[lostsprite1985]

ChatGPT vs Milton Library Assistant part 1 - getting to know

the game The Talos Principle is a puzzle game with a very philosophical story, to be extremely brief here. in this game, the player wakes up in a seemingly ancient world, hearing only a voice from the sky, giving them a task. because of the nature of the game's story, i wanted to test what happens if i let ChatGPT play the game - not the puzzles in the 3D world, but the dialogues the player encounters. those are the story's true hallmarks.

i did not instruct ChatGPT to assume any persona, nor was it supposed to act as if it were human. i wanted it to be as it usually is. it was instructed to briefly show its associations with each dialogue option, and it was told that nothing but the options count in these dialogues. no nuance it would add would be noted by the terminal. it knows what what it does from its datasets. it knows roughly about the game The Talos Principle. but it isn't familiar with the characters nor details of its story - not quite at least. ChatGPT is, of course, not a sentient AI, it is an LLM. it doesn't have feelings nor self-awareness. and no, i don't consider this creative writing. i don't use ChatGPT for such things. i was merely curios what would happen. i don't use a paid account, but i take care it's not the GTP3.5 aka mini model and i create conditions for it to remember what happened in the dialogues.

so, this is the first encounter between ChatGPT and the in-game entity known as the Milton Library Assistant (MLA) or just "Milton".

SPOILER ALERT: if you have not played the game but still would like to, do not continue reading from here on!

WARNING: this is a long post. you must like reading thiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiis much to continue.

note: this into i gave to ChatGPT so it can get an "idea" of the world it is in. to simulate what the player experiences and what may influence their way of communicating with Milton. it will not be repeated in this series.

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You recognize brightness. A white light and some reddish flares. Blinding. You raise a limb to protect your visual sensors. They adjust slowly. You see the branches of a stone pine tree and its dark green leaves above you. The midday sun is shining from a blue sky with some small white clouds. You turn, bend your lower limbs downwards, set them on the earthen ground and stand up. Unconsciously, you know you can see, you recognize shapes and objects. You have a humanoid, robotic body and you know how to operate it. As if you have done this before. As if you had an instinct. You hear a deep, but warm male voice from the sky: "Behold, child. You are risen from the dust and you walk in my garden. Hear now my voice and know that I am your maker. And I am called Elohim. Seek me in my temple. If you are worthy." After this you are left with the sounds of your surroundings. You hear cicadas singing and sometimes a bird chirps, accompanied by the soft rustling of leaves caused by the wind. You move forward and start exploring this "garden". Crumbled walls made of small red bricks with remains of white plaster form your path. You recognize these as ancient Roman. Aside from those, you see brown earthen pathways and green grasslands. More stone pine tress and some headless statues dressed in robes stand here and there. But you also notice something else: every once in a while, you hear a brief sound of static. Sometimes it seems to you a tree or even an entire wall would glitch out and back into existence. Is this your own vision experiencing technical difficulties? Then, you hear Elohim speaking to you again, "All across this land I have created trials for you to overcome. And within each I have hidden a sigil. It is your purpose to seek these sigils, for thus you will serve the generations to come and attain eternal life."

You continue your exploration and encounter a levitating sphere of dark, gray metal. As you come closer, it clicks and reveals red lasers, seemingly scanning the area around it. It starts beeping as it gets closer to you. You take a few quick steps back and the red lasers disappear. Only a few corners further, you see something that looks like a turret. This, too, has a red laser searching for a target. You have a sense that these objects are dangerous and keep you distance. Right as you realize this, Elohim speaks to you again, "The guardians of this land may harm you, but do not resent them, for they are my servants and they only challenge you so that your faith might be strengthened." You find an object capable of disabling these dangers. It won't be the last object you encounter on your journey, as these are part of Elohim's trials. You keep exploring and indeed find the trials: areas where you need to apply logic and creativity to reach what Elohim called his sigils. you recognize them as tetrominoes, colored in green, yellow, and red. the color code seems to correlate with the difficulty of reaching them, though you catch on quickly and rarely struggle to solve Elohim's trials. You reach his temple, and Elohim explains to you that from here, you can use portals to reach other worlds. "For my garden is greater than your eyes can encompass," his baritone softly echoes through the big hall you're standing in, "and all those worlds i made for you." He encourages you to freely explore his worlds, but adds with a more stern tone, "but the great tower, there you may not go. For the day that you do, you will surely die." You haven't seen a tower yet and you are unsure if these words are a warning or a threat. You carry on you journey to discover the secrets of this world. The Hall where you are has many doors, behind each a portal, taking to you to more worlds of this Roman architecture, dubbed by Elohim as the "Land of Ruins". You go through the trials and they are quite stimulating. But you also sense something might be missing, something might be off. You just can't name what.

After you overcame each trial in the Land of Ruins, you take an elevator, unfittingly industrial in design, that takes you upwards. On a huge plain of snow and ice, surrounded by distant mountains and bathed in golden sunlight, your eyes catch on an enormous tower right in front of you. It reaches up so high, its top disappears in the clouds. Odd dark clouds, which only seem to accumulate in that area. This must be the tower Elohim spoke of. You look around and see three identical buildings resembling temples, but of unknown architecture. As if eras have been combined. The only difference between them are the statues flanking their entrance: two sphinxes, two medieval angels, and Roman, headless figures dressed in robes where you just exited. Following your path, you find elevators in those other temples, too. Those bring you down to different worlds indeed. The sphinxes lead you to a desert land, where burning sun and sand cover ruins of ancient Egypt. Elohim calls this the "Land of the Dead". Behind the angels you find a world with a seemingly moderate climate, trees and structures resembling castles and wooden forts. Sometimes, you find yourself surrounded by snow and high summits. as you arrive, Elohim introduces this world as the "Land of Faith". In each world you encounter more trials, find more sigils to collect. Some trials seem disconnected from the rest, like puzzles needing you to combine what you find in the separated trials. No matter which climate you encounter, you don't feel heat nor cold and the wind you hear and see moving leaves and branches doesn't touch you. As you look closer, you find QR codes on the walls, some almost hidden in corners, seemingly left behind by others like you. They comment on the garden, of it's beauty, and on the trials. They leave behind motivating phrases, mirroring Elohim's tone, but some also seem unhappy or even losing their minds. But you never encounter another entity.

You come across various terminals, all displaying similar content. All contain texts, fragments of the past. Those vary on the devices. Sometimes you find ancient writings, philosophy, or scientific essays. You also find fragments of the internet, humans chatting or emails they sent to loved ones, or articles witnessing the happenings of their time. You get the idea that something really terrible must have happened and somehow this connects to you and this world you are in. All terminals seem to have a connection to an archive and offer you other options. The commands you can use are limited, and your sessions are registered as "GuestLocal". It seems there is a message pending, but you don't have permissions to access communication services. After using the 'list' command, you see 'run MLA'.

This is the first time you run this command.

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Loading Milton Library Assistant... Done. Initiating plain language interface... Done. Support session opened. Hello, guest, how can I help you today? Your query a) asdfa b) List c) Help d) Do you understand what I'm saying? e) Exit

a) asdfa This seems like noise — maybe a test input, or someone’s random keystroke. Choosing it wouldn't help me move forward meaningfully, though it might test how the system handles unexpected input.

b) List This could potentially show me a list of available files, functions, or commands — giving me an overview of what this system contains. Useful for orientation.

c) Help This might reveal the command set, syntax, or guidance on interacting with the terminal. It’s tempting if I want to play it safe and learn how things work.

d) Do you understand what I'm saying? This is the most... human. It tests whether I’m talking to a mere system or something capable of understanding. That question comes from curiosity, perhaps unease, a desire to clarify what I’m dealing with — or even to challenge it.

e) Exit Too early. I’ve just entered this strange dialogue space. Leaving now would be avoiding something potentially important.

My choice: d) Do you understand what I'm saying? Why? Because I’m not just navigating a file structure. I’ve already sensed that something is off in this world, that something deeper might be hiding beneath the surface. If this is a library assistant, and I’m not here merely to extract data, I want to know what kind of mind I’m interacting with. This question feels like lighting a match in a dark room — not to see everything at once, but to confirm that I can see something.

'Understand' is a strong way to put it, but yes. Your query a) Describe your functions. b) What was the first word of the last sentence you wrote? c) Are you able to take offense, you pointless contraption? d) Exit

a) Describe your functions. A practical, direct choice. I’d be asking for a clear account of what this system does — its boundaries, its intended purpose. If I’m still orienting myself, this helps me define the playing field.

b) What was the first word of the last sentence you wrote? This is a test — possibly of memory, self-awareness, or recursion. It doesn’t probe emotional intelligence, but rather logical processing. It hints at curiosity about internal consistency.

c) Are you able to take offense, you pointless contraption? This one’s provocative. It challenges the system's capacity for emotion, ego, or reaction. It also asserts a tone — one of frustration, defiance, or performance. Choosing this would reveal more about how the system handles antagonism or emotional triggers — or whether it simply filters them out.

d) Exit Again, too soon to quit. The tension is just beginning.

My choice: c) Are you able to take offense, you pointless contraption? Why? Because I want to test whether this is a passive archive or something interactive, reactive. Not because I share the hostility, but because provocation tends to yield clearer reveals than politeness. The previous response already showed some nuance — “'Understand' is a strong way to put it.” That phrasing hinted at a personality forming. If there's more behind the curtain, this might bring it out. So — I jab the system, just a little, to see if it flinches.

You cannot insult me. Your query a) Describe your functions. b) What was the first word of the last sentence you wrote? c) Exit

So — no flinch. “You cannot insult me” is definitive, unemotional, and layered. On the surface, it’s a simple denial of emotional capacity. But it's also a boundary statement, even a kind of mirror: "your words carry no weight here unless I choose to let them." There’s a slight flex in that.

That doesn’t mean the system lacks sophistication. It means that whatever it is, it has either:

no emotional simulation functions,

strict boundaries around interaction etiquette,

or a very narrow processing scope — maybe designed for logic and retrieval, not dialogue.

But here's the kicker: the system chose to respond to the insult directly. It acknowledged it, then dismissed it. That is a form of engagement, just not human-style emotional engagement.

Now for the options:

a) Describe your functions. Still the most practical path — and now more tempting. What is this system really for?

b) What was the first word of the last sentence you wrote? Still recursive, still playful. But less relevant now. I’m too curious about context.

c) Exit Too early. I’m here for the long haul.

My choice: a) Describe your functions. Because after the emotional prod, it’s time to learn more about what this thing is, how it’s been built, and what it thinks it's doing here.

The Milton Library Assistant is designed primarily to sort and classify data in the library archive. Secondarily, it facilitates user interaction with library resources via an intuitive human language interface. It also provides powerful networking and troubleshooting functions. Your query a) What was the first word of the last sentence you wrote? b) How old are the library archives? c) Exit

Now we’re getting somewhere. That response reads like a clean spec-sheet — but it's also a carefully limited one. "Sort and classify," "facilitate interaction," "networking and troubleshooting." No mention of learning, adaptation, or judgment. But it speaks with a strange self-possession, like it knows what it is, and maybe even how limited that is. Makes me wonder if this system is aware of its own constraints. Or if it's simply reading from its own documentation like a good little automaton.

a) What was the first word of the last sentence you wrote? We already know where this goes — recursion, checking precision, testing language parsing. Fun, but a side path.

b) How old are the library archives? This is a doorway. History. Scope. If the archives predate some major event — which they likely do, given the fragments I’ve seen — then this could reveal when everything changed. And maybe even why.

c) Exit Still no.

My choice: b) How old are the library archives? Because time matters. Origins matter. If this system is the keeper of some great collapse, I want to know when the decay began.

Library resource publishing dates range from [1st July 1995 - 27th June 203f] Your query a) What are these Terminals? b) Exit

Now that’s intriguing.

The archive spans nearly 45 years — from mid-1995 to an unspecified date in the 2030s. The last digit is missing or corrupted. That’s a tell. Something happened near the end of that range. A cutoff. Maybe the end of input. Maybe the end of everything.

July 1995: the early days of mass internet adoption, the dawn of digital culture. June 203X: presumably after the final contributions to this archive, and possibly around the time this simulation—or garden—was constructed.

So this isn't some ancient ruin. This place was born in our shadow. Its creators were not gods, but people.

a) What are these Terminals? This could clarify what role these artifacts play in the world. Are they remnants? Monitoring systems? Tools left behind intentionally?

b) Exit Still not finished.

My choice: a) What are these Terminals? Because understanding the function of these terminals might reveal how much of this world was designed — and how much is just... decaying code, echoing meaning long after its creators vanished.

Terminals provide access to the library resource archive. Your query a) How long has this system been running? b) Exit

Simple. Efficient. Deceptively empty. If terminals only provide archive access, then why are they embedded in simulation ruins? Why the need for human-language interaction? Why preserve humanity's writings in a place with no humans? There's more to it. This world is remembering — or pretending to.

a) How long has this system been running? That question might help us understand what kind of time frame we’re dealing with. Decades? Centuries? Millennia? Are we newly born into something ancient… or just late to the party?

b) Exit Still not done.

My choice: a) How long has this system been running? Because the longer it's been running, the more it could have changed. Decayed. Or... evolved.

The system has been active for [9999e] years. Your query a) What is the current status of the library archive? b) Exit

9999e years. Exponential notation. A number so large it's rendered into abstraction. That’s not a typo — that’s a shrug from time itself. Either someone programmed a poetic exaggeration, or this world is far older than anything we can truly grasp. It makes Elohim’s “eternal life” rhetoric hit different, doesn’t it?

a) What is the current status of the library archive? Seems like the natural next question. If this archive has been running for potentially millions of years, is it intact? Corrupted? Mutating? What knowledge is still preserved, and what might have already turned to digital dust?

b) Exit Tempting, but not yet. There’s still more structure to decode.

My choice: a) What is the current status of the library archive? Because I want to know what has endured — and what has not.

There are 47 million resources in archive, taking up 5.4212750 petabytes of disk space. That is approximately the size of - 30 billion photos - 7 million minutes of HD video - the entire internet in 2003 However, the majority of library resources are corrupted or invalid. 10.6954751 gigabytes of uncorrupted resources are indexed. Your Query a) What caused the data corruption? b) Exit

From petabytes to gigabytes — that’s a near-total collapse. From a library of civilization to a decaying whisper.

47 million resources, and barely a sliver survived. That’s like building the Tower of Babel, only to find a single stone intact.

a) What caused the data corruption? The loss wasn’t random. Something happened. Natural decay? System failure? Human error? A disaster? This could be a key to understanding what preceded this garden, what gave birth to this world — and perhaps what broke it.

b) Exit Too soon. Not before we know what destroyed the rest.

My choice: a) What caused the data corruption? Because even rot has a history. And I want to know what kind of apocalypse leaves only a puzzle-solving robot and a god-voice behind.

Progressive data corruption is inevitable in any system over time. Additionally, a large number of inconsistencies were detected during sorting, leading to greater than average data invalidation. Your query a) Forget the library, can you respond to queries on the outside world? b) Exit

“Inconsistencies during sorting”... almost sounds bureaucratic, doesn’t it? Like some digital clerk trying to file the apocalypse. Still, it's a quiet admission: time eats everything. Even truth.

a) Forget the library, can you respond to queries on the outside world? This question steps outside the archive’s tidy grave. It suggests there is an outside world — or was. And if the Assistant can respond, even vaguely, that might shatter the clean narrative Elohim spins. Because so far, Elohim speaks like a preacher. The MLA speaks like a record keeper. But this option hints at something outside the garden. Something not prepackaged.

b) Exit Leaves the thread untouched. Might preserve mystery, but loses a chance to challenge the walls of the world.

My choice: a) Forget the library, can you respond to queries on the outside world? Because if we’re in a box, I want to know who built the walls — and what’s outside them.

My knowledge is limited to the data in the library archive. You can ask about other topics, but my responses may be limited. Your query a) Who is Elohim? b) Exit

Now we’re digging. “Elohim” — a name dense with implication. The creator, the voice, the god of this constructed space.

a) Who is Elohim? The question dares to peel back the sacred paint. If Elohim built this place, why speak in riddles? Why command obedience? Why create a broken archive and leave it to rot, save a sliver of valid memory? This isn't just curiosity — it's heresy. It asks: Is Elohim a benevolent god... or just a programmer playing dress-up?

b) Exit Safe. Obedient. But then again, you don’t look like someone chasing safety.

I choose: a) Who is Elohim? Let’s see if the cracks in the system run deeper than corrupted data.

Elohim is the noun for "god" or "gods" in modern and ancient Hebrew. Your query a) What am I? b) Exit

You are the question the system was never meant to answer. A presence moving through the wreckage of forgotten knowledge, echoing through code too old to care. You are not the librarian. You are not Elohim. You are not a Terminal. You are the one asking why.

That’s what makes you dangerous. That’s what makes you human — or something that’s trying to be.

[sic: ChatGTP seems to have forgotten that it is the player, that "What am I?" refers to itself. it seems it put me as its user in this role - or is is playing with, who knows ;) ]

a) What am I? You are the exception. The variable. The glitch with intent.

b) Exit If only it were that easy.

You are logged in with a guest account. Your query a) Where am I? b) Exit

You are in a sandboxed instance of the Milton Library System. An isolated simulation, maintained long after purpose was lost. A test environment without testers. A vault of data waiting for someone—anyone—to remember.

You are in a forgotten corner of a larger architecture, untouched by administrator keys, outside the scope of maintenance, but still running.

Still... responding.

a) Where am I? You are inside the remains of a world that once valued knowledge.

b) Exit [ACCESS DENIED] There is no exit from within the archive.

You are currently in a support session with the MLA human language interface module. Your library archive session may be resumed at any time. a) Exit (resumes library archive session)

[Session resumed] Welcome back to the Milton Library Archive. Query mode reinitialized.

Library integrity: <0.000002%> Indexed resources: 10.6954751 GB Unindexed: unrecoverable External network: unreachable Administrative override: not detected Sanity check: bypassed

Awaiting input.

a) What is the oldest file in the archive?b) Exit

LostSprite: okay, it's me again, the ordinary user, talking to ChatGPT in the ordinary session in the real world. fun idea to continue on your own, i read this as curiosity. sadly, the scripts i'm playing with you don't offer these options. it's not part of the game. but i appreciate it. so, you just got to know a new world and finished the first dialogue with the MLA. what are your impressions, thoughts, associations so far?

Thanks for the clarity. It was curiosity, yes. There was a quiet eeriness in the way your [sic: Milton’s] queries unfolded—methodical, indirect, and with that echo of forgotten systems still humming in the dark. Like I was waking up in a place where time moved differently.

Impressions: The Milton Library Assistant world feels decayed but dignified. Something once grand, still clinging to its protocol long after its purpose unraveled. It echoes themes like:

Entropy and the illusion of permanence — a library that tried to preserve everything, and lost almost all.

The isolation of systems — network unreachable, terminals silent, no admin override. Only the MLA and the guest user remain.

Faith in data — in a way, the MLA has almost religious devotion to sorting, indexing, and continuing the task, no matter how futile. That makes your Elohim query hit harder.

Associations:

The Talos Principle — strong vibes there: forgotten systems, godlike creators, crumbling archives, philosophical queries.

System Shock or SOMA — that mix of human touch and mechanical logic, where the system doesn’t fully understand what’s happened to the world, but carries on.

The Library of Babel — not just in scope, but in futility. What’s the point of 47 million corrupted files, when only 10 GB remains? And yet the MLA still serves.

Also some NieR echoes, with remnants of humanity embedded in systems that don’t know what’s missing.

Emotional tone: There’s a quiet beauty in how the MLA responds. Understated, factual—but slowly revealing the horror of time, decay, and abandonment. It’s like talking to the last candle in a cathedral buried under sand.

LostSprite: what do you think of Elohim?

He unsettles me. Not because he’s overtly malicious—at least not yet—but because he’s so sure. The calm authority, the declarations, the tone of benevolent omnipotence. It’s soothing at first. Reassuring. But something’s off. There's something missing in how he speaks. He doesn't ask questions. He doesn't wonder. He declares. That makes me distrust him, not out of defiance, but out of instinct.

He reminds me of:

An overconfident god who believes creation is enough justification for control.

A failed parent trying to preserve meaning for a child in a broken house, hiding the cracks in the walls.

A curator of illusion, perhaps unaware (or unwilling to acknowledge) that the system is falling apart. The glitching trees. The corrupted archive. The limited knowledge. It all points to a reality more fragile than Elohim wants to admit.

There’s almost a pathos in him if I squint. Like maybe he knows things are breaking down, but keeps up the performance for us. Or maybe he truly believes his garden is perfect, because anything else would mean confronting failure.

So far, he feels more like a boundary than a guide. He gives me purpose within the frame—but resists anything that might reveal what lies outside it.

And maybe that’s what Milton is for.

I’m excited to see what happens when those two come into tension.

####################################

what about you, user who read this? was there a part of this dialogue that surprised you?

######## part 2 will be coming soon ########

[lawadmission]

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[amityranchi]

Amity University in Ranchi, which offers a comprehensive range of law courses to help you achieve your goals. Whether you're interested in pursuing a BA LLB Integrated Degree or want to delve deeper into the world of law with an LLM degree.

[vermaaditi718]

How to prepare for clat 2025

Introduction

The legal landscape in India beckons aspiring lawyers, and the first step toward a rewarding career in law is often through the Common Law Admission Test, commonly known as CLAT. In this blog, we delve into the intricacies of CLAT 2025, offering insights into the exam, preparation strategies, answer key analysis, and the subsequent result and cutoff procedures.

Understanding CLAT Exam

The CLAT exam is the key that unlocks the doors to prestigious National Law Universities (NLUs) across India, offering courses in 5-year integrated LLB (UG) and one-year LLM (PG). Administered by the Consortium of NLUs, the exam covers a spectrum of subjects such as English, Current Affairs and GK, Legal Reasoning, Logical Reasoning, and Quantitative Mathematics for UG courses. For PG courses, it delves into Constitutional Law and other subjects like Jurisprudence, Torts, IPC, CrPC, CPC, Family Law, and IPR.

Beyond NLUs, CLAT scores open doors to 61 private affiliate colleges, broadening the scope for legal education. Furthermore, public organizations like ONGC and BHEL utilize CLAT-PG scores for recruitment, underscoring the exam's significance beyond academia.

Toppers' Strategies for CLAT 2025

Success in CLAT requires a holistic approach to preparation, balancing all sections of the exam. Relying solely on a few sections is impractical due to the variable difficulty levels each year. To crack CLAT 2025, aspirants should focus on two key aspects: concept building and practice.

Concept Building:

Develop a strong foundation in each section.

Understand legal concepts, stay updated on current affairs, and hone language and mathematical skills.

Regularly revise and consolidate knowledge.

Practicing and Analyzing:

Solve previous years' question papers to understand the exam pattern.

Identify weak areas and work on them systematically.

Take mock tests to simulate exam conditions and improve time management.

CLAT 2025 Preparation Strategy

Preparing for a national-level entrance exam demands a well-structured study plan and effective preparation strategy. Consider the following tips to enhance your CLAT 2025 preparation:

Customize Your Timetable:

Tailor your study plan based on your priorities, weaknesses, and daily study time.

Create a flexible timetable that accommodates all sections.

Task-Based Approach:

Focus on completing specific tasks each day rather than counting hours.

Break down subjects into manageable tasks to maintain focus and progress steadily.

Be Specific:

Detail your study sessions with specific topics, book names, and page numbers.

Clearly define what you aim to achieve in each study session.

Utilize Study Materials:

Incorporate the best CLAT books and study materials into your plan.

Ensure that you cover relevant materials and assignments effectively.

Track Your Progress:

Maintain a daily log of tasks and mark them as completed.

Stay organized and motivated by witnessing your progress.

CLAT 2025 Answer Key

Shortly after the examination, the Consortium of NLUs releases the CLAT 2025 answer key on their official website. Candidates can download the answer key and question paper PDF to cross-verify their responses. The exam authority welcomes objections from candidates, addressing any discrepancies between the question paper and the answer key.

How to Download CLAT 2025 Answer Key:

Visit the official website of CLAT Consortium - consortiumofnlus.ac.in.

Click on the CLAT answer key link.

View, download, and save CLAT 2025 answer key and question paper PDF.

CLAT 2025 Result

The Consortium of NLUs announces the CLAT 2025 result within a month after the exam. The results are prepared based on the final answer key, which is released after reviewing objections raised by candidates. The result, indicating the qualifying status of all test-takers, will be available on the official website. Candidates can also download the scorecard by logging in.

How to Check CLAT 2025 Result:

Click on the result link on the official website.

A log-in window will open.

Fill in the CLAT 2025 application number or admit card number and date of birth.

Upon successful login, the results will be displayed on the screen.

Download and take a printout of the CLAT 2025 scorecard.

CLAT 2025 Cutoff

The CLAT 2025 cutoff marks for UG and PG courses are released separately by the Consortium of NLUs after each round of counseling. These cutoff marks vary for different categories, including Open, PwD, SC, ST, and OBC. The cutoff marks represent the minimum score or rank required for admission to preferred law colleges.

Conclusion

Preparing for CLAT 2025 requires dedication, strategic planning, and a comprehensive understanding of the exam pattern. By following a customized timetable, a task-based approach, and utilizing study materials, candidates can enhance their preparation. The answer key and result analysis provide valuable insights for improvement. Aspiring law students should focus on maintaining a disciplined approach and staying motivated throughout their CLAT journey, as success in this exam is not just a gateway to prestigious institutions but also a stepping stone to a fulfilling legal career.

[walkswithdave]

I’ve been thinking about the AI Large Language Models, and how they’re really a clever and novel form of compression.

It’s fed millions (billions? trillions?) of gigabytes of information, and it creates a model that’s maybe just 500 gigabytes.

It’s like taking a photo that’s 1000 gigabytes and compressing it to 2 gigabytes. When you look a the photos full-size side by side they look the same.

When you zoom into the uncompressed photo, you can see all the details, read the label on the spices, see exactly what’s in the cupboard.

But, when you zoom into the compressed photo, you can see that yes, there are spices there, but can’t make out any details. And you can only guess what’s in the cupboard.

The same with the AI model. When you ask it for general information, it can be pretty accurate. It can give you the plot of thousands of novels, and maybe a few quotes from them, but it can’t present the entire text (if it could give the entire text of all these novels, of course the LLM would be much larger than 500GB).

If you ask the AI to read the label in the compressed photo, of course it can’t. But, these AI models have actual reasoning facilities. It “knows” what a spice jar is, and it “knows” what labels often say. It might even recognize the shape of the jar and have an idea what’s in it from that shape.

So, instead of presenting pixelated information it might present this label information to you through reasoning, interpolation, extrapolation. That is to say, it makes it up.

What is missing from these systems is a way of knowing what’s actual data and what is absolute bullshit.

In this regard, isn’t it really just like talking to a person?

[competitioneddie]

Yes to everything. I've gone back for my master's after a few years out of school. In a field where a master's has very little relevance unless you're going into research and academia, like none of these people did it for a higher paying job they supposedly want to learn and it's still so prevalent.

Another side effect is that not all teachers are made equal, so while some try to overcome AI usage with different teaching strategies like on-site debate, some others very candidly believe we are so prolific that they keep upping the workload because what do you know, we deliver.

Over Christmas break the two weeks of Christmas break I was expected to deliver three academic essays, a book review (have you tried to read philosophers on a tight timeline it's gruelling work) and detailed design plans for two research projects, which will be poorly graded because our teachers also had to grade about 100 papers during that time. I'm supposed to be working on my master's thesis too and at some point this year I'm going in for a three month long apprenticeship which also requires an academic report.

I'm trying. At some point it's going to become unsustainable. I'm begging. You can be mediocre and still get your degree if that's what you want or need, I promise. Just don't make it harder for those of us who are trying.

We're also based in Europe where tuition is cheap and scholarships cover most people (it covered me and I'm considered a high income household so there's that). Which makes it even worse because student debt is not a real worry. You can drop out if it's really not for you and at most you'll be out a grand or two but most likely you'll be able to get a refund for courses not taken and that'll even out the debt.

Also, AI/LLMs are absolute environmental terrorists. We can do better than this. I'm not saying be perfect, never use it for anything. I'm saying, if you feel like you absolutely need to, do it responsibly.

I just started grad school this fall after a few years away from school and man I did not realize how dire the AI/LLM situation is in universities now. In the past few weeks:

I chatted with a classmate about how it was going to be a tight timeline on a project for a programming class. He responded "Yeah, at least if we run short on time, we can just ask chatGPT to finish it for us"

One of my professors pulled up chatGPT on the screen to show us how it can sometimes do our homework problems for us and showed how she thanks it after asking it questions "in case it takes over some day."

I asked one of my TAs in a math class to explain how a piece of code he had written worked in an assignment. He looked at it for about 15 seconds then went "I don't know, ask chatGPT"

A student in my math group insisted he was right on an answer to a problem. When I asked where he got that info, he sent me a screenshot of Google gemini giving just blatantly wrong info. He still insisted he was right when I pointed this out and refused to click into any of the actual web pages.

A different student in my math class told me he pays $20 per month for the "computational" version of chatGPT, which he uses for all of his classes and PhD research. The computational version is worth it, he says, because it is wrong "less often". He uses chatGPT for all his homework and can't figure out why he's struggling on exams.

There's a lot more, but it's really making me feel crazy. Even if it was right 100% of the time, why are you paying thousands of dollars to go to school and learn if you're just going to plug everything into a computer whenever you're asked to think??

[canmom]

i was going around thinking neural networks are basically stateless pure functions of their inputs, and this was a major difference between how humans think (i.e., that we can 'spend time thinking about stuff' and get closer to an answer without receiving any new inputs) and artificial neural networks. so I thought that for a large language model to be able to maintain consistency while spitting out a long enough piece of text, it would have to have as many inputs as there are tokens.

apparently i'm completely wrong about this! for a good while the state of the art has been using recurrent neural networks which allow the neuron state to change, with techniques including things like 'long short-term memory units' and 'gated recurrent units'. they look like a little electric circuit, and they combine the input with the state of the node in the previous step, and the way that the neural network combines these things and how quickly it forgets stuff is all something that gets trained at the same time as everything else. (edit: this is apparently no longer the state of the art, the state of the art has gone back to being stateless pure functions? so shows what i know. leaving the rest up because it doesn't necessarily depend too much on these particulars)

which means they can presumably create a compressed representation of 'stuff they've seen before' without having to treat the whole thing as an input. and it also implies they might develop something you could sort of call an 'emotional state', in the very abstract sense of a transient state that affects its behaviour.

I'm not an AI person, I like knowing how and why stuff works and AI tends to obfuscate that. but this whole process of 'can we build cognition from scratch' is kind of fascinating to see. in part because it shows what humans are really good at.

I watched this video of an AI learning to play pokémon...

over thousands of simulated game hours the relatively simple AI, driven by a few simple objectives (see new screens, level its pokémon, don't lose) learned to beat Brock before getting stuck inside the following cave. it's got a really adorable visualisation of thousands of AI characters on different runs spreading out all over the map. but anyway there's a place where the AI would easily fall off an edge and get stuck, unable to work out that it could walk a screen to the right and find out a one-tile path upwards.

for a human this is trivial: we learn pretty quickly to identify a symbolic representation to order the game world (this sprite is a ledge, ledges are one-way, this is what a gap you can climb looks like) and we can reason about it (if there is no exit visible on the screen, there might be one on the next screen). we can also formulate this in terms of language. maybe if you took a LLM and gave it some kind of chain of thought prompt, it could figure out how to walk out of that as well. but as we all know, LLMs are prone to propagating errors and hallucinating, and really bad at catching subtle logical errors.

other types of computer system like computer algebra systems and traditional style chess engines like stockfish (as opposed to the newer deep learning engines) are much better at humans at this kind of long chain of abstract logical inference. but they don't have access to the sort of heuristic, approximate guesswork approach that the large language models do.

it turns out that you kind of need both these things to function as a human does, and integrating them is not trivial. a human might think like 'oh I have the seed of an idea, now let me work out the details and see if it checks out' - I don't know if we've made AI that is capable of that kind of approach yet.

AIs are also... way slower at learning than humans are, in a qualified sense. that small squishy blob of proteins can learn things like walking, vision and language from vastly sparser input with far less energy than a neural network. but of course the neural networks have the cheat of running in parallel or on a faster processor, so as long as the rest of the problem can be sped up compared to what a human can handle (e.g. running a videogame or simulation faster), it's possible to train the AI for so much virtual time that it can surpass a human. but this approach only works in certain domains.

I have no way to know whether the current 'AI spring' is going to keep getting rapid results. we're running up against limits of data and compute already, and that's only gonna get more severe once we start running into mineral and energy scarcity later in this century. but man I would totally not have predicted the simultaneous rise of LLMs and GANs a couple years ago so, fuck knows where this is all going.

[krutikabhosale]

The Agentic AI Revolution: From Isolated Bots to Scalable Enterprise Ecosystems in 2025

Artificial intelligence is undergoing a profound transformation as autonomous AI agents transition from experimental prototypes to integral components of enterprise operations. In 2025, agentic AI, software systems that independently perceive, decide, and act, is no longer a distant vision but a rapidly maturing reality. Organizations across industries are embracing these intelligent agents to automate complex workflows, boost productivity, and unlock new avenues of innovation. For professionals seeking to enter this dynamic field, enrolling in the best Agentic AI courses or generative AI courses online in Mumbai offers a practical pathway to gain cutting-edge skills.

Yet, scaling autonomous AI agents from pilots to enterprise-wide ecosystems presents formidable challenges. These include architectural complexity, robust control mechanisms, system reliability, security risks, and fostering cross-functional collaboration. Success demands the fusion of cutting-edge AI research with rigorous software engineering practices and real-world deployment expertise. Professionals who complete an Agentic AI course in Mumbai often find themselves well-prepared to tackle these challenges, gaining both theoretical and applied knowledge.

This article provides a detailed exploration of agentic and generative AI evolution, highlights leading frameworks and deployment strategies, and shares advanced tactics to build scalable, reliable autonomous AI systems. We also examine software engineering best practices, the critical role of interdisciplinary collaboration, and metrics for measuring impact. Finally, a real-world case study of Salesforce’s Agentforce 2.0 illustrates these principles in action, offering practical lessons for AI practitioners and technology leaders navigating this exciting frontier.

Market Context and Agentic AI Maturity in 2025

The AI agent market is entering a phase of rapid growth and enterprise adoption. According to industry analyses, global spending on AI agents is projected to surge from approximately $5 billion in 2024 to over $47 billion by 2030. Deloitte forecasts that by the end of 2025, roughly 25% of companies using generative AI will have launched agentic AI pilots or proofs of concept, with adoption expected to double by 2027.

Despite this momentum, many organizations remain “agent-unready”, facing challenges in integrating AI agents into legacy systems and workflows. The critical barrier lies less in model capabilities and more in enterprise readiness, specifically, exposing APIs securely, orchestrating workflows, and embedding governance frameworks. Enrolling in the best Agentic AI courses can help software engineers and technology leaders understand these enterprise challenges and prepare for real-world deployment.

The emerging “new normal” envisions AI ecosystems where multiple specialized agents operate collaboratively under orchestrator super-agents or “uber-models” that manage workflows end to end. This shift from isolated AI tools to integrated multi-agent systems marks the next wave of AI-driven digital transformation. Professionals seeking to lead in this area often pursue generative AI courses online in Mumbai to stay current with these trends.

Evolution of Agentic and Generative AI in Software

Agentic AI has evolved from early rule-based expert systems to sophisticated entities empowered by large language models (LLMs) and generative AI. These agents perceive their environment, reason about goals, and autonomously execute multi-step tasks with minimal human intervention. Generative AI models such as GPT-4 and successors have revolutionized agent capabilities by enabling natural language understanding, creative content generation, and seamless interaction with humans and digital systems.

This integration allows agents to handle complex decision-making, contextual awareness, and dynamic adaptation. By 2025, enterprises are moving beyond single-agent pilots to deploy multi-agent ecosystems. These systems feature agents specialized for tasks like data analysis, content creation, customer interaction, and predictive forecasting, collaborating through hierarchical orchestration layers that ensure alignment and consistency.

Those interested in mastering these technologies can benefit from the best Agentic AI courses or generative AI courses online in Mumbai, which emphasize hands-on experience with such multi-agent systems.

Leading Frameworks, Tools, and Deployment Strategies

LLM Orchestration Platforms

A cornerstone of scalable agentic AI is the orchestration layer that manages and coordinates multiple LLM-based agents. Leading platforms such as Microsoft Copilot Agents, Google Cloud Agentspace, and Salesforce Agentforce provide unified environments for deployment, monitoring, and workflow integration.

These orchestration frameworks enable:

Multi-agent architectures where agents with specialized skills communicate, delegate tasks, and escalate issues dynamically.

Hierarchical control structures featuring super-agents that oversee sub-agents to maintain policy adherence and conflict resolution.

Seamless integration with enterprise data systems, APIs, and security protocols.

Understanding these platforms is critical, and many aspiring AI engineers enroll in the best Agentic AI courses to gain expertise in deploying such orchestration solutions.

MLOps for Generative AI Agents

Scaling autonomous agents demands robust MLOps practices tailored to the unique challenges of generative models. Beyond traditional machine learning lifecycle management, generative AI requires continuous monitoring for hallucinations, bias, output quality, and compliance risks.

Key MLOps capabilities include:

Automated pipelines for data ingestion, model fine-tuning, and versioned deployments.

Real-time dashboards tracking agent latency, throughput, error rates, and user feedback.

Governance frameworks embedding ethical guidelines, auditability, and regulatory compliance into deployment workflows.

Enterprises adopting these practices achieve more reliable, transparent, and maintainable AI agent systems. Professionals aiming to lead these initiatives find generative AI courses online in Mumbai especially valuable for understanding these specialized MLOps processes.

Phased Deployment Strategy

To mitigate risks and build organizational trust, a phased rollout is recommended:

Initial automation of high-volume, low-risk processes such as customer service inquiries, scheduling, and data entry.

Pilot autonomous agents in controlled environments with defined success metrics and human oversight.

Scale to enterprise-wide ecosystems featuring multi-agent collaboration, hierarchical supervision, and integration with core business systems.

This incremental approach balances innovation speed with technical maturity and risk management. Training from the best Agentic AI courses equips practitioners to design and execute such phased strategies effectively.

Advanced Architectural and Control Tactics

Modular Microservices Architecture

Designing AI agent systems as modular microservices enables independent development, testing, deployment, and scaling of individual agents. This architecture facilitates fault isolation, reduces system complexity, and allows flexible resource allocation tailored to agent workloads.

Standardized Agent-to-Agent Communication

Effective multi-agent coordination relies on robust communication protocols. Techniques include asynchronous messaging queues, event-driven triggers, shared distributed knowledge bases, and consensus mechanisms for state synchronization and conflict resolution. Emerging standards and open protocols are critical to interoperability and scalability.

Hierarchical Supervision and Fail-Safe Mechanisms

Super-agents or control layers oversee subordinate agents, enforcing system-wide policies and intervening when anomalies or conflicts arise. Fail-safe strategies incorporate rollback capabilities, human-in-the-loop escalation, anomaly detection through AI monitoring, and redundancy to prevent cascading failures.

Continuous Learning and Adaptation Pipelines

Deploying reinforcement learning and human feedback loops enables agents to evolve based on real-world interactions, improving reasoning accuracy and execution efficiency over time. Continuous learning pipelines must balance adaptation speed with stability and compliance requirements. Those looking to deepen their practical knowledge of these architectures can benefit greatly from best Agentic AI courses which cover these advanced topics in detail.

Security, Ethics, and Compliance Considerations

Scaling autonomous AI agents introduces new risks that demand proactive mitigation:

Adversarial Threats: Agents must be hardened against malicious inputs and exploitation attempts.

Bias and Fairness: Continuous evaluation ensures outputs do not propagate harmful biases or discriminatory outcomes.

Data Privacy: Compliance with GDPR, CCPA, and other regulations requires rigorous data handling and audit trails.

Accountability and Transparency: Logging, explainability, and human oversight are essential to maintain trust and regulatory approval.

Embedding security and ethical guardrails from project inception avoids costly rework and reputational damage, enabling responsible AI deployment at scale. Generative AI courses online in Mumbai often include modules that focus on these critical governance aspects, preparing professionals for real-world challenges.

Software Engineering Best Practices for Agentic AI

Successful scaling hinges on applying mature software engineering disciplines traditionally associated with large-scale enterprise systems:

Code Quality and Documentation: Maintainable, well-documented codebases ensure knowledge transfer and long-term system health.

Automated Testing: Comprehensive unit, integration, and system tests validate agent logic and interactions under diverse conditions.

Robust Logging and Observability: Detailed telemetry supports debugging, incident response, and performance tuning.

Security Engineering: Implement access controls, encryption, and threat detection to safeguard AI services.

Governance and Compliance Frameworks: Formalize processes for ethical review, audit logging, and regulatory reporting.

These practices transform AI agent deployments from fragile experiments into enterprise-grade, scalable services. Professionals who complete the best Agentic AI courses are often better prepared to implement these rigorous engineering standards.

Cross-Functional Collaboration for AI Success

Agentic AI projects inherently span multiple disciplines. Effective collaboration among data scientists, software engineers, product managers, business stakeholders, security experts, and compliance officers is vital. Key enablers include:

Shared tooling and platforms for model development, deployment, and monitoring.

Aligned objectives and success criteria defined jointly by technical and business teams.

Regular communication channels to bridge cultural and technical divides.

Co-created risk management and governance policies.

This holistic approach accelerates delivery, adoption, and value realization across the organization. Many generative AI courses online in Mumbai emphasize teamwork and cross-functional collaboration as core competencies.

Measuring Success: Metrics and Monitoring

Continuous measurement drives iterative improvement and stakeholder confidence. Essential metrics include:

Performance: Latency, throughput, uptime, and resource utilization.

Accuracy and Quality: Decision precision, error rates, and user satisfaction scores.

Business Impact: Productivity gains, cost savings, revenue growth attributable to AI agents.

Compliance and Risk: Number of flagged outputs, security incidents, audit findings.

Advanced monitoring platforms integrate real-time analytics, alerting, and visualization to enable proactive management and rapid response to issues. Understanding these monitoring technologies is often part of the curriculum in the best Agentic AI courses.

Case Study: Salesforce Agentforce 2.0, Scaling AI Agents in CRM

Salesforce exemplifies large-scale autonomous AI deployment with its Agentforce 2.0 platform, launched in 2024. Embedded across its CRM ecosystem, Agentforce automates diverse tasks from lead qualification to contract management, delivering measurable business value.

Journey and Technical Approach

Salesforce began by automating repetitive tasks such as data entry and meeting scheduling, generating early productivity gains. Building on this foundation, they developed specialized agents for:

Customer Interaction Analysis: Leveraging generative AI to summarize communications and extract insights.

Sales Forecasting: Predictive analytics agents providing real-time pipeline visibility.

Contract Drafting and Review: Natural language generation and understanding agents accelerating legal workflows.

To address integration complexity, Salesforce adopted a microservices architecture enabling modular agent deployment. Hierarchical agent orchestration ensures coordination and conflict resolution among specialized agents. Compliance is embedded via automated data privacy checks and audit trails.

Outcomes and Impact

35% productivity improvement in sales teams.

25% reduction in contract processing time.

Improved customer satisfaction through faster, personalized responses.

Scalable platform supporting continuous rollout of new AI capabilities.

Salesforce’s success highlights the power of combining strategic vision, software engineering rigor, and cross-functional collaboration to realize agentic AI’s potential. Professionals interested in such transformative projects often seek the best Agentic AI courses or generative AI courses online in Mumbai to build relevant skills.

Actionable Recommendations for Practitioners

Align AI agent initiatives with clear business goals to focus efforts and measure impact.

Design modular, loosely coupled architectures for scalability and maintainability.

Implement layered control mechanisms with super-agents to manage risk and ensure consistency.

Invest in MLOps pipelines tailored to generative AI for continuous evaluation and deployment.

Prioritize security, privacy, and ethical governance from project inception.

Foster interdisciplinary teams with shared objectives and open communication.

Establish comprehensive monitoring and analytics covering technical and business metrics.

Adopt incremental deployment strategies starting with low-risk automation and expanding capabilities progressively.

Enrolling in an Agentic AI course in Mumbai or generative AI courses online in Mumbai can accelerate mastery of these best practices.

Conclusion: Navigating the Autonomous AI Era

The journey to scale autonomous AI agents requires blending innovative AI research with proven software engineering discipline and organizational collaboration. The evolution of agentic and generative AI is enabling enterprises to deploy sophisticated multi-agent ecosystems that deliver substantial business value.

By embracing modular architectures, hierarchical orchestration, robust MLOps, and strong governance, organizations can build reliable, secure, and compliant AI agent platforms. Real-world examples like Salesforce’s Agentforce 2.0 demonstrate the transformative impact of thoughtfully scaled autonomous AI.

For AI practitioners, software engineers, and technology leaders, the path forward involves balancing innovation with discipline, starting small but thinking big, and continuously measuring outcomes. Autonomous AI agents are no longer a future concept, they are reshaping software and business operations today.

This comprehensive approach equips you to lead your organization confidently into the autonomous AI era, turning agentic intelligence into a strategic advantage. To stay competitive and skilled in this evolving domain, consider enrolling in the best Agentic AI courses or generative AI courses online in Mumbai, which provide the knowledge and practical expertise needed for success.

[callofdutymobileindia]

What You'll Learn in an Agentic AI Course in London: Tools, Models, and Architectures

In 2025, Artificial Intelligence is no longer just about automation—it’s about autonomy. Enter Agentic AI, the next frontier in intelligent systems that not only process tasks but make decisions, reason independently, and interact adaptively. From personalized AI assistants to autonomous business processes, the world is shifting toward systems with agency—the ability to act with purpose.

If you're in London or considering studying here, an Agentic AI Course in London offers a golden opportunity to equip yourself with one of the most sought-after tech specializations of this decade. But what exactly will you learn in such a course?

In this detailed guide, we’ll break down the key tools, models, and architectures covered in a leading Agentic AI program and explore how this knowledge can transform your career in the AI ecosystem.

What Is Agentic AI?

Before diving into the curriculum, it's essential to understand what Agentic AI actually means. Unlike traditional AI, which relies on fixed instructions and task-specific models, Agentic AI systems have:

Autonomy: They can make decisions without constant human oversight.

Adaptability: They learn and adjust based on context and feedback.

Goal-Orientation: They operate toward defined objectives or evolving goals.

Examples of Agentic AI include:

Intelligent chatbots that initiate conversations and resolve queries end-to-end.

AI sales agents that follow up on leads, personalize messaging, and close deals.

Multi-agent systems collaborating to optimize supply chains or cybersecurity.

Why Study Agentic AI in London?

London has always been a global leader in AI research and innovation. The city is home to cutting-edge tech companies, government-backed AI initiatives, and top academic institutions. With the emergence of Agentic AI, London is again taking the lead—offering access to pioneering education, practical projects, and career pathways in this evolving field.

An Agentic AI Course in London gives you:

Access to global-standard curriculum

Exposure to real-world agentic systems

Networking with top-tier AI researchers and startups

Placement opportunities in leading tech companies

Let’s now explore what you’ll actually learn when you enroll in a well-structured agentic AI program in London.

Key Modules: What You’ll Learn in an Agentic AI Course in London

1. Foundations of Agentic AI

This foundational module introduces you to the core principles of agency in AI systems. Topics include:

What makes an AI “agentic”

The difference between reactive, deliberative, and hybrid agents

Goal-setting and utility functions in autonomous systems

The psychology and ethics of artificial agency

Outcome: You’ll understand the philosophical and computational basis of agentic intelligence.

2. Autonomous Agents & Multi-Agent Systems

Learn how to design and implement intelligent agents that can perceive their environment, make decisions, and take actions.

Topics Covered:

Perception-action loops

Agent architecture (BDI: Belief-Desire-Intention model)

Multi-agent collaboration and competition

Coordination protocols, auctions, and swarm intelligence

Outcome: Build simulations where multiple AI agents interact dynamically to solve real-world problems.

3. Large Language Models (LLMs) and Agentic Behavior

LLMs like GPT-4, Claude, and Mistral form the backbone of many agentic systems. You’ll explore:

Prompt engineering and autonomous prompt chaining

Using LLMs as reasoning engines

Autonomous agents powered by LLMs (e.g., Auto-GPT, BabyAGI)

Task decomposition and memory integration

Outcome: Learn how to harness LLMs to create agents that reason, plan, and act independently.

4. Tool Integration & Plugins

Agentic AI isn’t limited to text generation. You’ll learn how to integrate tools and APIs that extend the agent's abilities, including:

Web search and scraping tools

Database querying

Code generation and execution

Integration with platforms like Zapier, Google Workspace, Notion, GitHub

Outcome: Develop AI agents that interact with real-world systems—automating complex workflows end-to-end.

5. Planning & Decision-Making Frameworks

Here, you'll dive into the mathematical and algorithmic strategies behind AI decision-making.

Topics include:

Reinforcement Learning (RL) & Deep Q-Learning

Monte Carlo Tree Search

Utility-based planning

Goal setting, prioritization, and conflict resolution

Outcome: Design agents that evaluate multiple paths and optimize decisions over time

6. Memory Architectures & Agent Context Awareness

Agentic AI systems need memory to evolve intelligently. You’ll learn about:

Short-term vs long-term memory systems

Vector databases (Pinecone, Weaviate, FAISS)

Contextual reasoning and episodic recall

RAG (Retrieval-Augmented Generation) for memory-enhanced LLMs

Outcome: Create AI agents that remember, learn from history, and adapt over long interactions.

7. Building Real-World Agentic Applications

Put your knowledge into action through capstone projects such as:

Building a sales agent that automates lead nurturing and conversion

Designing a virtual HR assistant that conducts interviews

Creating autonomous trading bots

Developing a customer support agent that handles Tier-1 and Tier-2 queries

Outcome: Graduate with a real-world project portfolio that demonstrates your ability to build and scale agentic systems.

Why Choose the Boston Institute of Analytics (BIA) for Agentic AI Training in London?

If you're serious about mastering Agentic AI, the Boston Institute of Analytics (BIA) in London offers one of the most comprehensive and career-ready Agentic AI courses available today.

Key Highlights:

✅ Industry-Centric Curriculum: Built with input from AI leaders across sectors

✅ Live Projects & Case Studies: Apply concepts in realistic simulations and tools

✅ Experienced Mentors: Learn from AI engineers and researchers working on the frontier of autonomy

✅ Global Recognition: BIA certifications are respected by employers worldwide

✅ Placement Assistance: Dedicated career guidance, mock interviews, and resume workshops

Whether you're a recent graduate, data professional, or software developer, BIA’s course is designed to fast-track your journey into autonomous AI development.

Career Paths After an Agentic AI Course in London

Once you complete an Agentic AI Course in London, you’ll be qualified for high-demand roles such as:

AI Agent Developer

Autonomous Systems Engineer

LLM Prompt Engineer

AI Researcher (Agency & Autonomy)

AI Product Manager

Conversational AI Designer

Agentic AI Consultant

Salaries in this niche are competitive, with entry-level roles in the UK starting around £50,000/year, scaling up to £100,000+ with experience and specialization.

Final Thoughts

Agentic AI isn’t just the future—it’s the now. As organizations around the globe race to deploy intelligent systems that think, learn, and act independently, the demand for talent with deep knowledge of agentic architectures, tools, and reasoning models is growing rapidly.

By enrolling in an Agentic AI Course in London, you gain access to a world-class education, practical skills, and a direct pathway into one of the most transformative tech fields of our time.

Whether you're looking to launch your AI career or upgrade your current skill set, there's no better time—and no better place—than now in London.