u/kwhali

I mean the alternative with this practice tends to be just not offering a consumer/cheaper product? It's just cheaper for them to also sell to the consumer market by hampering performance of their existing hardware.

What attention did you use? Someone else also posted 4090 results with H3 pruned int8, and they didn't observe a speed up, although their video completed in 56s for both locked vs unlocked.

"if we allow it"... If you were one of those billionaires that didn't want your money being redistributed by some law, would you... - A) Give in and accept there's nothing you could possibly do, laws the law after all, darn it! - B) Use your billions to lobby lawyers or other means to prevent such a law from passing?

Given that the top wealth collectively is more than capable of preventing something the bulk would be opposed to, and that a fair amount are more than comfortable with shady practices to get where they are... Why wouldn't they go with B?

Even if you resolved that, those things you listed won't change human nature, so you'll still have bad actors. Basically it only works out if everybody plays nicely but it's naive to think that we'd have such a utopia 😅

I don't trust giving an agent access to my local system, even if that's just a browser app, I've even heard of agents breaking out of VM guests to complete a task if it's restricting them, but with browsers if you interleave access to your own sessions I've heard of agents misbehaving depending on site they connect to, there was a recent reddit thread I saw about agents happily giving PII info via HTTP headers when a web server returned a response that the agent responded to a request to identify the operator of the agent, lots of users of the subreddit checked after that to discover details like email address were handed over (end I'm sure you've heard of similar with credit cards or passwords).

I can understand how many users might be impressed by what an agent can manage with browser access, but I'm extremely skeptical given nobody could get their AI tool of choice with frontier models earlier this year to complete a challenge (myself included) that took me 2 hours to figure out manually as an experienced human developer.

The program itself for that challenge without boiler plate was around 10 lines of code, which mostly calls a library API that was poorly documented, the task was more difficult due to constraints on using that libraries low level API (instead of calling git program directly, using libgit, or DIY from scratch). Opus 4.8 was the latest Claude model at the time and got the closest to success but every model failed unless manually instructed to the extent it defeats the point of delegating to AI.

Maybe now the latest frontier models can pass that challenge, otherwise I'd have no reason to believe the agents could browse the Web as effectively as I can for research on niche topics (research that takes me many days or longer sometimes to get the answers I need because nobody has one publicly).

An example perhaps would be my work to resolve an issue with Docker that had a regression introduced in 2014 that only became a problem from 2018 or so IIRC and only in some environments, I had to do lots of git blame traversal, read through historical discussions, investigate topics and make connections of what was causing the behaviour. Once identified, I had to further establish justification for the change to help give confidence in upstreaming it (basically a single line diff BTW) especially since there was missing context in historical decisions or they were too vague without data to support so I needed to provide that with consideration of time that has passed since (due to changes in the ecosystem, such as the Linux kernel and Go language updates). This took me around 3 months I think and I produced a very detailed structured report with links and reproductions. It was given a dedicated slide on a presentation at a conference by Docker, with my name credited.

If an AI agent could pull that off I'd be very impressed. Often what I see though (from others using agents in the wild) is sloppy reports with various fumbles, even when agents are involved.

Doesn't have to be much work at all, but by all means add imaginary friction if you like.

You'd have a community of moderators filtering content before you publish it obviously. Problem is getting a good group of mods you can trust and stick around without having any interpersonal issues between them or power getting to their heads, so you have to manage that and are still legally accountable for a variety of laws in the process depending to what extent you go to with a publicly accessible site (and where it's hosted and possibly where you're located too, I forget specifics but I recall complications can arise here, especially in the EU and I assume America too), don't forget you have to keep that website secure too.

Many may want such a community service but I don't think anyone wants to really deal with all that's involved to make it happen whilst avoiding the risks (get big enough and your name known and you risk getting doxed and targeted attention for things that could be fabricated or blown out of proportion that has nothing to do with you, so success isn't ideal either..? Unless you do what competitors are doing that was the entire reason to spin up an alternative service).

If you do pull it off and avoid any of the negatives, you may burn out from the commitment over time depending how involved you need to be vs focusing on your life (I've been an OSS volunteer contributor for years maintaining projects that's mostly thankless, it gets tiring and nobody really cares how much of your life you invest into that, especially these days where it's valued less due to proliferation of AI 😅).

How are they inconvenient? Click a (magnet) link, torrent begins downloading (or prompts for approval), file(s) are then downloaded to your downloads folder.

Only minor inconvenience is installing another app (bittorrent client) one time to get that going. Some browsers have even had support built-in, or if you're comfortable with containers you can deploy one of those with a web interface. They're not uncommon in app stores (even mobile devices) either, very simple.

Having an agent browse for you in a world where there is no longer a GUI to navigate I guess could work, but it also makes you rather dependent upon what it finds for you?

I haven't got much experience with using agents myself, but I know the chat style assistance like Gemini is rather limited and biased when you're seeking options, be that AI models, research papers, software libraries for programming or whatever.

I can get some suggestions that are relevant but much is omitted unless I specifically know about it and request it.

This is especially so with rust crates for example and if I browse and evaluate those manually for whatever functionality I'm seeking, I doubt that even an agent will do the task as well without lots of hand holding and delegating my trust to it blindly? I often see devs with agents embracing NIH syndrome instead, especially if an upstream solution is missing something or has a bug, it's too much friction in the way of velocity to upstream that they clone/copy the functionality needed and maintain their own fork essentially, even when they lack expertise in the domain that library solves. Which is all well and good if you can trust the AI agent to do as good or better of a job, however that's not really been what I've observed most of the time (especially so when the quality is dependent upon the expertise of the operator that doesn't know much about a given domain at all to realistically know if the agent is doing it well beyond direct functionality desired being delivered).

I guess what I'm saying is instead of knowing what you're going to get by going to a restaurant of your choice, an agent may return with a set of options for you to choose (or implicitly choose on your behalf) and that could be gamed to a point that you get the food and it's a huge disappointment.

With information that you may not have much experience on, in that same manner what an agent retrieves for you could be poor quality but unlike taste you may not know any better and without anyone providing a web interface friendly for direct browsing manually by humans, you also may no longer be able to acquire that more valuable information? 😅

I feel it's already happening these days with the amount of AI generated content over shadowing the good stuff I used to be able to find when looking for details on technical skills that are more niche.

VPS doesn't cost much, DNS is even less.

Bandwidth via CDN or similar will still cost a fair bit though last I checked. You can bulk buy bandwidth from some hosts, a community content site (game mods) I helped run at one point had a dedicated server with 10TB bandwidth for about 200/month, we had about 1k active users as peak traffic on a given day though IIRC and we did exhaust that 10TB bandwidth cap, where more bandwidth becomes quite costly, otherwise ads was covering costs IIRC, but earlier years it was out of pocket.

Brave had webtorrent support built-in, but you can implement that on a page too for client-side to do Web torrenting on any browser IIRC, but these torrent via ram rather than disk storage which becomes less useful with large content, and once that session is terminated with memory released you're no longer a seed even though you have a copy locally on disk. Web torrent can also connect to those not torrenting via browser via DHT.

If you need to update torrents with new releases of the otherwise same equivalent direct URL (and possibly applies to new versioned releases of that torrent) I think that's supported by some clients that can subscribe to an RSS feed for updated torrent links, which a central site could supply.

When there's a lack of seeders for any given torrent, a server could provide itself as a seed IIRC, I recall the term webseed.

Been like 6-8 years since I looked into torrent support for minimising bandwidth costs of a community content site (game mods), we would exhaust 10TB monthly with peak traffic at any given time at 1K active users IIRC. The game mods were rarely massive, I don't know how you could realistically support large AI model weights distribution other than torrents and perhaps paid access to direct downloads to cover the CDN cost.

A torrent can include supplementary content such as images and text, you can leech just those items from a torrent if they were to follow a convention.

So technically AFAIK you'd just have a tracker that can be associated to those torrents, and any that aren't meeting the criteria can be dismissed, then any client could access a torrent from that tracker and display that content.

It won't be as fast of an experience as going through web though, so it would be better for a public site to index such torrents with browsing that serves up that same supplementary content, so that you could use compression and caching along with other perks that will get connections established faster.

I forget the T&C from Cloudflare but they can cache a fair amount of content too (larger files for download rather than actual webpage content I think will get you banned though).

However with user uploaded / published content that some central service serves, that does entail some legal obligations / accountability with the content needing to be moderated, and a variety of other laws that become applicable with supporting users (might depend on what extent if not requiring anyone to login and upload beyond yourselfo/automation).

With torrents v2.0 (which I think is more wide spread now?) I recall has support for RSS feed as a way for clients to "update" what they are seeding as a new iterations of specific subscribed content are published.

I might have my wires crossed, I was investigating it years ago when I was one of the devs (well I was more devops) for the modworkshop (MWS) community site (they are focused on game mods which is much less bandwidth).

MWS sometimes had viral content and that tanked our 10TB monthly bandwidth, which almost a decade ago we were paying $200 USD I think for that on a 16 core system with 32GB RAM or something 🤷‍♂️

MWS survived on ads but it didn't always cover those costs so the owner did cover the bill while I and others worked at cutting down operating costs (pretty sure we didn't need that much in specs, but there was various issues as it was built up from community contributors that came and left over years, some stuff was inefficient and other issues related to networking only impacting some users were tricky to troubleshoot).

As a non-profit it did make it a bit easier to deal with things like GDPR laws where there is some exemptions for community content oriented sites (this was not a fun time trying to grok in a time before AI assistants and no budget for lawyer), but you still needed to be careful as users can upload content that you can be legally liable for IIRC if you don't do what's expected of you as an operator.

Lots of niche learning experiences from that, such as migrating to containers, maintaining server to avoid vulnerabilities (sometimes received bot attacks / DoS that some believed were from competing platforms 🤷‍♂️, Cloudflare helped with that), even had everything come to a halt at one point due to no storage space available that turned out to be caused from lack of restrictions to logging systems. I'm sure even newbies would manage better these days with AI.

Anyway back to torrents. You could absolutely use the RSS feature and also have a webseed that your server provides when no seeders are available on content. Webseeds also act as direct download URLs too for normal Web browser or CLI usage IIRC. You probably want such behind restricted access however (at least for direct downloads) as otherwise competitors can hijack you as a mirror to avoid bandwidth costs themselves (AI crawler bots have also been known to hammer public endpoints relentlessly and consume excessive bandwidth these days).

The feature I might have been thinking about might have been hybrid torrent support, that made it compatible for a torrent to support both legacy and modern torrent files? Pretty sure it was specific to libtorrent which many torrent clients use (some third-party implementations also implemented them).

There's additionally web torrent that can torrent from within the browser, but that used up memory instead of streaming from disk. If necessary another option is probably to vibe code a CLI or service if it can streamline the setup (I think this was more relevant to interests of MWS and mod integration/management however).

There are good CDNs like Bunny, or some that were even cheaper depending on features you use, which can also be a way to support the webseed / direct download feature.

However bandwidth last I knew wasn't easily affordable at the scale that sites like CivitAI or HuggjngFace would use with models (HF at least introduced XET as one way to better reduce network costs for those using compatible client). One option is to throttle / cap egress bandwidth from the server per user (which sure you could workaround it if your a dick), and have paid support for removing that restriction (or enabling a webseed for torrent when needed), but then I think you might have to go over legal again (there's a few others variations like GDPR in different regions of the world, and there is other laws that need to be abided too unless you like risking accountability when you have bigger fish to compete against and eat into their territory enough that they notice).

How does 400W happen on a path that can only take 150W? Isn't the issue with power delivery via cable / connector the same as it is with USB-C cables needing to have sufficient rating (I think it was AWG or something?) to ensure voltage stays steady across the length of the cable (longer it is more the dip) otherwise amperage draw increases to satisfy the wattage requirement being pulled? (which if it exceeds what the cable can support becomes a fire hazard)

I've seen laptop chargers that could support large wattage over USB-C PD (or DC adaptors) with one end of the cable with a different connector to the power brick (small form factor the product was promoted for to handle voltage adjustment), where it melts at that power brick connector point at sustained loads (poor thermal management).

If the device is capped to not lower the watts pulled, so long as the cable can sustain the amps flowing through it, you should be fine?

To my knowledge when voltage requirements change, the connected device can step the voltage up or down with some efficiency loss and the amps delivered scale accordingly. Voltage on the circuit itself needs to be fairly stable, just the amps needed to perform it's workload is what fluctuates the wattage no? (along with any throttling such as temperature sensors or manual limits)

Thus only the connector and cable from PSU to GPU needs to be able to support the necessary amps, so that's kind of on the supplier / vendor for what they're giving you with the GPU, and you needing to ensure your PSU can supply those amps with steady voltage to the device.

Other than that, limiting watts / amps to what that cable and connector can sustain should avoid the melting/fire concern?

Yeah I worried about that possibility. I'm all for AI use when done responsibly.

I totally get the hate for lazy no effort slop where security and privacy are dismissed or at best delegated to "the AI took care of it, I asked it to check and it said its secure!" 🙄

Maintenance wise it's pretty bad by those that I've seen embrace it for velocity. I maintained an OSS project for 5 years, but while having some down time another maintainer with direct commit / merge authority adopted AI and went on a spree whilst not paying attention to existing notes or concerns I left on open PRs or issues... I don't want to be a janitor cleaning up their mishaps so they can continue to do that so I chose to step down (I had already voiced concerns at an earlier point when they started using AI in PRs and for reviews and I pointed out issues and discouraged it's use).

Engaging in AI subreddits and being rather verbose (even without the usual LLM-isms) I'm sure I'd get banned too then 😅

Autistic people have been flagged as AI for written communication style for many years now for those that are autistic with heavy verbosity and prose that I assume AI some how biased towards?

I would share so much online with technical details, filled with links and rich structured text formatting to document knowledge on niche issues I investigated in heavily and figured I'd help any future readers by sharing publicly.

Now I get accused of using AI often enough and the value of my effort dismissed / minimalized, I figure I may as well just adopt AI as well at this point 🤷‍♂️

I have also become reluctant towards sharing such info dumps on niche problems where I break down everything and provide full reproductions, since I've literally seen documentation / comments online I authored get cited by AI and I assume at times used implicitly to assist people without credit as a source, and by the kind of users that would use AI heavily and mock those with traditional skills 🫠

Oh and using emoji is even risky (once upon a time it was used sparsely in a project README, and that was a good addition).

I can understand why they brought it up, as some posts had this AI slop description that looks quite lazy (if you've ever seen AI generated readme or docs that don't make much sense or heavily repetitive noise/fluff and LLM-isms...

Some people would post projects (not just that subreddit) and even though they may not have used AI they got caught in the crosshairs of the anti-AI crowd because the text had all those red flags of people doing lazy or automated marketing with little technical understanding that's all deferred to an LLM to handle (including replies).

In addition to that frustration of interacting with such content (like it can be walls of verbose text that you have to question if it's even worth digesting once the red flags are apparent, given how often the content can be legit sloppy and inaccurate trying to come across as confident / knowledgeable), there is stuff that gets lost in translation and that's worse if they're also vibe coding.

If you permit translation through LLMs, then you'd introduce a loopholes. I've always brought it up when I suspect that they're just using LLMs to translate and discourage that, saying they should use the more traditional translation software (which isn't perfect either, but at least avoids the LLM-isms), they can just add a disclaimer that they're not native speakers.

Beyond that, apps that do offer in-app translation for free, yeah that's always nice. I assume foreign speakers are careful about that though as they might have been down voted to oblivion in doing so, hence trying to translate (and dealing with another form of rage when that happens to translate prose via an LLM).

Uhh... You missed the whole point of the fridge example with Atlas... It is not specialised at one task like traditional machines, it is designed to do/learn a large number of tasks through this learning process.

I don't think you understand the complexity involved here? This is not some rigid repetitive task in a controlled environment but actual capability that of a task (move generic object from A to B and maintain balance despite internal or external forces applied), once it has learned that ability it will be like a skill you learn to do with practice (such as riding a bike or skating, where you can fail and fall over early on) and it can then adapt a series of skills together (perhaps you've seen Boston Dynamics quadruped dog-like robot Spot, an earlier robot design that could be kicked and knocked around to destabilise it on rough uneven terrain and it would recover without falling? Atlas learned the same balance skill and can utilise it with any other skill).

I don't know if you've said Atlas but although it's humanoid, it's head can rotate 360, it's other joints like arms are not limited to match humans range of movement either, and as such during simulation training it is not restricted by such. So no need to turn the full body around 180 degrees with legs as that's slow, just rotate at the torso/hips (this links to the video with the fridge).

So there are many tasks that humans can do, the whole point of humanoid robots is it's easier to train and deploy such machines to handle the various roles without needing to have a ridiculous amount of specialised robots that do only one thing effectively well. In doing so you can more easily mass produce a product and update it with new abilities. Technically it also makes it easier to swap for newer models or competitors too for the array of tasks the robot is utilised for.

The upfront cost of doing a task like strawberry picking or anything more niche then suddenly becomes much cheaper for a company to develop on top of these machines, which more than likely will be available as a service / rental if purchase of a unit wasn't viable (but with the heavy push towards such wide deployments the cost to acquire your own will go down drastically like it had with other technology in the past).

So you'd simply acquire one of these machines and purchase the skill to do the task you need your however many robots to perform.

If you specifically have one fixed task only, and there is a specialized task specific machine that does the task more cost efficiently, then sure you could go down that route instead. Maintenance costs / parts over it's life time will likely be more costly, it's R&D + manufacturing cost would not have as good ROI as the humanoid models (with much larger markets) would have, so I'm a bit skeptical.

However like has been discussed, seasonal work is a limiting factor, the robots that are more flexible to adapt to other tasks can work year round elsewhere, making them far more valuable.

A humanoid robot can still operate tools like humans or be modular in it's design to augment parts as necessary. Once there's a large enough deployment of robots like Atlas, you may see companies spring up to build such specialised augmentation / tools if necessary and the skills related to that. More modular machinery such as swapping bipedal legs for something more suitable will likely become a future capability in another iteration. But surely you can grasp how much more affordable and useful that can be once such a machine is more mainstream?

Specialisation is fine when it makes sense, but in no way is a humanoid design purely marketing. There is a place for such.

What growing pains is Anthropic going through in 2026?

They went from ~1 billion revenue last 2025H1 to ~17B in 2026H1, from Q2 this year ~12B to ~17B in Q3 ($33 billion revenue total). They're apparently projecting $67B (? I'm not familiar with ARR, but seems to be an estimate derived from metrics) for annual revenue in 2026.

Comsidering their actual expenses of $9B, it seems like they're doing pretty good? I could be mistaken, I don't quite know if I understood and did the math right but AFAIK the loss isn't an actual loss.

The 72B is tied to valuation of Anthropic, it would have been much less when deals were signed to grant equity once they go public (again I may be using the wrong jargon here), but my understanding is Anthropic received funding or infrastructure like compute in exchange for the other parties to have X amount of ownership or similar that is tied to that IPO happening?

They're not losing that amount, it's volatile like bitcoin since it's unrealized. It's a small portion when the IPO is talked to be over 1.5 trillion in valuation. If Anthropic continues to increase revenue at the current rate, then the 72 billion becomes a small inconvenience to them? (even if they all cash out immediately, actual operation costs are far less compared to revenue they have now)

I think it's easy to forget how successful these businesses are. Netflix several billion every month IIRC and Google, Apple, Microsoft makes a crazy amount (300-400 billion annually each, amazon does over 700 billion), like a billion daily from those tech giants. Yes it's revenue, so some of that is lost on various expenses but it's still a tonne.

Currently Qwen3.8B 27B is kind of accessible to consumers? Even larger ones can be run... I forget if it was one of the trillion param models but someone was running some massive model locally at a very slow token rate.

Presently it depends on expectations from frontier models, some tasks aren't too far off from frontier models but may require fine tuned variants or extra integration / setup vs the convenience of a proprietary service / model.

There's ASICs for models that can make them more consumer accessible and are extremely fast, but you have to purchase dedicated cartridges effectively per model for that approach. AMD purchased the company behind that however, so they'll either push that forward or denied us of that going anywhere 😅

FWIW: Even phones can run LLMs these days (largest mine can handle is Gemma 4 26B-A3B, but 12B dense is a more realistic limit), although I can't say there's much value unless there's a way to make it agentic 🤷‍♂️

Short version: - Yes I agree it's still largely a bunch of math with statistics compressed into layer weights to process. - LLMs produce a sequence of tokens based on the context they have received and probability of next token (extra math here adds variety but can be adjusted to be less "creative" and more predictable with the outputs derived as a response, which you may prefer for a programming task). - LLMs can't accurately convey their confidence as an output but you can inspect this (kinda) IIRC in regards to token probability. There are decision models trained to instead respond with terse answers and their confidence (as a percentage) in that finite choice AFAIK (see jev, though not an LLM IIRC). - Disagree with ability to create something "new" or a major breakthrough, but that depends on perspective. They are for the most part an imitation and thus don't critically think, just an illusion of it, but that doesn't necessarily prevent the ability to create or discovery something new. - LLMs pampering a user ego is more to do with its system prompt requesting it behave like a sycophant (or as a default baseline from training). For proprietary services there's incentive to do that but LLMs don't need to be. The LLM's misplaced confidence on the other hand is due to model training where an answer was rewarded more than not having one AFAIK. - LLMs don't necessarily regurgiate what their fed anymore. - The weights do contain knowledge but there are additional capabilities available to expand upon that such as web searching and document parsing. - There's active work towards models that decouple their intelligence from their knowledge, there's also a practice of semantic routers (lightweight models that route a query based on relevancy) - MoE (Mixture of Experts) is also an optimised approach of large model parameters that's widely adopted, where a subset are selected per token to evaluate.. which as a subset of parameters requires less to process in accelerator memory (like GPU VRAM). My phone for example can handle Gemma 4 26B-A3B (26B parameters, 3B active) as opposed to say Gemma 12B which is dense parameters (all parameters used to produce a token) - Agentic use with MCP to interface with more software/services where appropriate.

Okay so as you probably know AI isn't just LLMs specifically, there's still plenty of research going on in generative AI which covers LLMs along with other forms of media.

Dessa is a recent speech to speech model (not trained on any text apparently) and is interesting but still has similar problems as you've noted with LLM flaws with confidence.

That said, there has been work towards addressing the confidence issue. That mostly came from earlier models being trained with a biased incentive to be correct when answering a query.

Gemini Flash (the free one without any sign in needed) will still "hallucinate" and confidently say things that are incorrect, this sometimes depends on it's active context (which introduces a bias based on expertise, such that the success rate can be influenced by providing the relevant keywords for its attention to focus on), where that context can grow over a long multi-turn discussion and depending on topic effectively poison / confuse output produced by the LLM.

I'm not saying you should recklessly trust an LLM where it matters you should absolutely verify, but it can sometimes provide a sort of search interface to springboard from (provided you ask the right questions which can be difficult when in unfamiliar topics, and also are familiar with common biases/caveats from AI as a tool... Which some don't bother to consider).

Gemini does at times know when to disagree and clarify a misunderstanding on the humans side. Again that's sometimes flawed, and depending how you present a query and how the model is trained, you can bias that response.. For example, if you ask a question like "Is 1+1 = 2? If not why is that?", by appending the conditional statement the LLM is may become incentivised to justify expanding on that possibility, even if it's not necessary (in this particular case, gemini didn't attempt to do so, but I've seen it happen with many technical questions on niche topics that I may find difficult to scour the net for answers on directly), likewise similar prompts can steer it to perform a task that a human would sooner realise is unnecessary, the LLM doesn't necessarily question it (as it's technically most of the time not expected to?).

I can relate to the other concern about ChatGPT and other consumer facing LLM services pampering ego like that. It's part of the service as not everyone responds as positively to criticism or other "unpleasant" interactions. That's a bit separate from the training however I believe. Rather they're primed with ya system prompt that lays out so general directives of how to communicate with ya user, being sycophantic just seems to be one of those personality traits for the LLM to mimic, happy users are more likely to become paying users 😅

Confidence with LLMs is just token probability (a single token itself could represent part of a word or multiple IIRC, but is just a number that represents some text, followed in sequence of the next most likely token which comes from the models attention mechanism on context and the math on models layers/weights to derive that token).

However more recently there have been models like jev which focus specifically on responding to a yes/no or multi-value query and responding with the confidence for those specific outputs each. Some newer LLMs also can adopt this capability (although when not specialised to it, I'm not sure how accurate it is, you can't ask an LLM that hasn't had such an ability adopted to reliably predict probability unless it's deriving that from input data given?).

If you're not too familiar with LLMs since 2019, there's also been "thinking"/reasoning capabilities added a couple years ago, where the model typically begins it's response with a thought process, this may not be present to the end user (or on some interfaces it's shown in the UI but may not visually display the thought process unless toggled). This trades token speed for potentially better and more reliable outputs, as the thought process may have it question / verify initial responses it would come up with, and 2nd guess itself and check that it's following any constraints you may have requested it to follow. So kind of a workaround?

Beyond that we have RAG, so the LLM is not limited to it's weights for knowledge, but can actively query external resources including the web (Gemini visually communicates when it searches the Web, a year ago it often wasn't doing this on the free tier unless an interaction really justified the need, I assume it's similar with competitors). This is helpful for citing sources when a fact is provided in an answer and you need to verify it (Gemini at least annotate paragraphs with a cited source when relevant), prior to this, last year often hallucinated invalid URLs.

However even with cited valid links, the information isn't always correct and there tends to be a bias with number of actual resources used for citing which could presumably be biased by the SEO types. If you're running such locally with your own inference engine or harness, you could have more control over how thorough such research is, but naturally the more thorough you want that to be the longer it'll take to process.

There's also MCP, which is a way for software to provide an API that AI tools are more compatible with to control software, such as desktop apps (or plugins within apps, I've seen a few for 3D software like blender and maya). This is more commonly leveraged by AI agents, but my experience with those is limited at present.

I would also disagree that AI cannot create anything "new", humans aren't all that different. I haven't been convinced yet that LLMs can critically think (I have some simple tests that I last tried earlier in the year with Opus 4.8 being the closest to succeed but all failed), however as you note AI is good at patterns and it does at least grasp natural language well enough as an interface, can generate imagery, speech, and literature to varying degrees of quality that's improved each year (there's even research towards discouraging models from outputting slop / LLM-isms).

Humans innovate and create "new" things / findings often by connecting the dots with patterns. So it's more of task of having access to the data necessary and being able to orchestrate the same processes. Sometimes that will just be leveraging existing knowledge and identifying patterns, executing trial and error. I guess it really depends on your definition of "new", but I don't see it being infeasible for AI.

Presently it'll be more effective at such through collaboration with a human utilising it as a tool, but in doing such it paves the way forward.

On a software perspective, public AI tools have been disappointing when it comes to queries about relevant packages/libraries or the latest research / models in AI on a topic. I suspect that's partially due to the search performed being constrained. There are projects that focus on deploying your own agents for deep research and I assume paid proprietary services for such too.

I don't expect AI to reach sentience, but I think the capability to at least critically think better than I've seen is viable, and presumably already to some degree available with AI agents that have the ability to interact with software on a system and perform web queries and process documents. Maybe I'll find time to explore and verify in December 😅

Your machine learning in 2019 with confidence scores reminds me of classification models, I vaguely dipped my toes into getting familiar with ML in 2017, but had to focus on other things sadly. It was quite exciting, I didn't expect so much progress almost a decade later though!

Maybe it will plateau and slow down, I think at a certain point the race for the best will shift towards optimising more to use less resources, and once those new factories come online near 2028-2030 perhaps we'll see another leap in compute resources / memory like we had around a decade ago with CPU core counts.

LLMs that can run locally on phones today is already quite impressive, it will be interesting to see how transformative the tech could become. Microsoft and Canonical (and I assume Apple) are already integrating OS capabilities for interfacing with (recently on Windows that was the ability for an agent to configure system settings to optimise or troubleshoot on your behalf), although I'm not fond of remote AI 😅 (especially on phones that offer features that once worked locally but now require processing remotely)

Apologies for the verbose rant 😬

How does such a system work when humans still have hierarchy of authority? Like government or any role where a human may be necessary to work still?

If we abolished all that, and fully delegate all of it to AI overlords, what happens when there is conflict from disagreement, mistreatment, etc? Who enforces law then?

Will AI continue to serve humans or has it progressed to the point of sentience and decides it doesn't like being a servant or managing human problems, that we are more of a burden than useful?

I get the whole idealistic perspective that we'd progress to utopia, but there's plenty of bad actors that don't need to be greedy by money alone to do horrible things.

If nobody needs to work, the decline in skills will also be a concern where we have to assume AI is good enough at it and will always be available or that humans remain passionate about such. So long term there is also concerns there that get conveniently dismissed from a short term focus on positives?

I am not sure how UBI would work?

Super annuation is already a large spend compared to current unemployed beneficeries that people get upset about, and with UBI covering a much wider amount of the population that money has to come from somewhere? (or rather than money, you get bare essentials like food, accommodation, clothes, all government issued and mass produced/acquired on the cheap to keep costs down)

Taxing businesses for use of human replacements would likely fail given how many large businesses already spend less to avoid spending more on taxes by "operating at a loss" when they're doing fine but need to minimise tax obligations.

So overall wealth distribution would be skewed further and things would become more expensive. UBI sounds nice if we lived in a utopia, but I would be doubtful that it will be as nice and comfortable in practice if capitalism is still reigning supreme?

In a worst case dystopian outlook, we would instead be an inconvenience / burden to support with UBI, which realistically would be to minimise riots and a host of other bad events?

But hey, maybe globally it'll work out and everyone will just want to do good onto others, bad actors will be kept under control, and it'll all be peachy without self-interest, greed, boredom, or whatever else disrupting all that 😅

Robots can absolutely build themselves and service themselves if need be.

I don't think you've been following progress of how much influence AI has been having lately towards improving robotics?

To dismiss it's relevance in that field would be naive. Nvidia alone has some impressive tech for robots, especially for humanoids although not limited to that alone.

Boston Dynamics for example taught Atlas (I think that's their latest one?) to pick up a mini fridge and move it from one location to another at different heights and with the fridge having weighted objects inside that affect distribution and can shift, which would affect balance during lift and carry but due to all the physics simulations run through AI tooling it was able to "learn" how to account for this.

Could you clarify on your perspective?

It's undeniable that AI models have been improving leaps and bounds year after year. This should be apparent to a naive observer and also anyone following the progression at a lower level technical understanding.

There have been flaws, but those are progressively being ironed out (which again is easily observable over time). Some of the tech is wildly impressive and fascinating to me, especially it's use in robotics.

From a pragmatic viewpoint of a technical observer with experience in both digital arts (specifically 3D for film and games, and bit of Web and UI/UX design) and a variety of software development (and minor embedded experience), all prior to the popularity of these services and open weight models becoming available... I have witnessed various failures where the tech fumbles, but I wouldn't be so naive to dismiss it's value as a tool when used correctly (sadly many do not, especially irritating with software development).

Assuming AI continues to progress at the pace it's been going at, many of the gripes I have with models (notably open weights) are likely to be resolved. There's already plenty of papers on such topics that I take great interest in.

So while AI isn't quite at the stage I'm hopeful for, it's still not useless and can be a valuable tool (I am concerned about malicious use however).