"Accidentally" - nobody accidentally builds a 500 site private network. This is a lie.
The "1M AI citations" figure comes from Bing Webmaster Tools, whose citation counting is loose and unverified, and citations are not traffic, leads or revenue. Lie.
"Google trusts .dev and .app more" overstates reality – Google has said repeatedly that generic TLDs (top-level domains) carry no ranking advantage, the HTTPS-by-default point is real but marginal. Lie.
"Exact-match domains rank 30–60% faster" has no source and reads as invented, Google's 2012 EMD (exact-match domain) update specifically reduced that effect. Lie.
The audit scores (95–100/100) come from their own tools auditing their own sites, so it is self-graded marking. Lie.
Calling it "a mesh, not a PBN" is a semantic defence. From Google's side, 1,500 templated sites mass-generated by AI and PowerShell scripts is close to the definition of scaled content abuse and site reputation abuse, the exact behaviour the March 2024 spam update was built to catch.
One of your screenshots shows 2.8k users, wonderful, but then in the corner this is shown to be 1.2 sessions from direct, and very little from anywhere else, including only 19 sessions from Organic search. So the SEO power of the domain is negligible, and the Direct traffic is likely unfiltered AI crawler bots.
This is outdated blackhat SEO that will get any legitimate site into manual action trouble with Google. The only thing you've done is engineer a mass network quicker than previously possible because of AI slop website generators.
You are everything that people hate about the internet.
Google Ads at first for sure. Their search monopoly just means it's the best place for ads on a limited budget.
Moving to Bing is worthwhile once you've started to see efficiency drop off on Google.
LinkedIn is very expensive and really only worth it for highly targeted ads. Bing and LinkedIn have a slight advantage because Microsoft has all the job title/industry data from LinkedIn for its targeting options, but it charges more for use of that targeting essentially.
That means those ads pay off more for Account Based Marketing where you have a salesperson reaching out to high-ranking decision makers and you want your ads to be hitting them in a small audience campaign to back up the cold outreach approach and warm them up before the rep secures a call. So, B2B marketing for big contract based work mainly.
"paid search ads etc have resulted in lots of clicks but absolutely abhorrent conversions in our case, whereas conversions for the more organic channels like forums etc are excellent (maybe a targeting issue...)"
Absolutely a targeting issue. Paid search should be able to produce traffic that converts at roughly the same rate as your non-brand organic traffic (at this stage most of your organic traffic).
How much it costs per click for that quality of traffic depends on your industry, and whether that's profitable depends on your margins. But that's the baseline. After all paid search traffic is the same as organic search in essence, it just depends where the searcher ends up clicking. For you, before you rank highly for core terms, paid search can drive volume you want.
This isn't any advice for the AI slop you're seeking but your line about targeting issues stood out to me as something definitely worth looking at. I don't know how to specifically guide you remotely but if I had to guess I'd advice you to look at your keyword targeting, swapping things away from broad match to exact match, cover more keywords if you can (since exact is narrower than broad) and add negative keywords liberally. Check your search term report for anything irrelevant and apply them as negatives.
If you're not using keywords because Google (or whoever) have pushed you to some 'smart' campaign, then switch to keyword targeting. The smart campaigns are fine but require finesse to handle their blunter settings options.
If in doubt, use AI to figure out keywords to prioritise and negatives to set by default. Or speak to a paid media consultant and get an account review from them, sometimes they'll do this for free.
Run an AI audit on a website, compile tests run and actions derived into spreadsheet tabs, write a word doc report, use a template provided for styling. Skill used in Claude to ensure filename is shorter than 260 characters (Windows limitation).
All gets stored locally, Skill runs to always write a new version when amending, keeping old versions in temp folder.
Then when I open and run throguh to make edits I save to a client folder. Claude can access that folder too but I tend not to make edits in that structure.
Claude has a folder named after it in each client folder, if needed for a sandbox on ongoing client work. Rarely used, mainly rely on temp folder and project specific client folders.
On that last task Claude got most things right:
Styling was overly dramatic - it had used our brand palette but over-used a bright orange. It also missed the template file on first run, it had to be manually attached despite being instructed to find the template file in the client folder before doc creation (it did not ask for it after failing to find it either). It also added page breaks where none were needed, making the document full of whitespace.
I asked for diagrams to be added to visualise concepts. They were fine, one seemed excessive where it split actions into a punnet table of effort X impact.
It missed the safe naming skill on first pass for the spreadsheet, so it couldn't be opened on first try.
In the end it was 1 prompt, 3 corrections. End result was client ready in a fraction of the time for a normal manual pass on this task.
"At its core, it is a big pile of words that predicts the next word, using some maths the computers figured out."
"An LLM predicts the next word based on all the words before it. That is the whole story. There is no idea sitting underneath. The words are everything."
This is just flat out wrong.
Words are used as training data, to build a system of vector embeddings. The LLM contains no words. That was the training data long discarded.
Vector embeddings are groupings of meanings derived from the relationship between the words in its training data. This entire system is modelled after human neural mechanisms in a way that machines can emulate.
"But your brain works the other way around. First there is a concept, a feeling, an image, and then the words come out to describe it. (At least, this is how I feel my own brain working.) For us, words are the byproduct of consciousness."
It's working the same way around (you are not saying Ai has consciousness that is derived from words!). And you make a huge jump from the concept that 'words come from concepts' to 'words are the byproduct of consciousness'. Because 'concepts' are not equal to 'consciousness'.
In fact you do not define consciousness at all, making it hard to determine what argument you are actually making at all. You seem to think humans have this trait of 'consciousness' but can't explain or evidence it beyond 'feeling it'.
Consciousness it defined by many as the ability to experience events and process thoughts or qualia. It's hard to test this, and we can see why that is with an AI - it may claim to be conscious, or even claim not to be, but how can we trust either answer? Philosophers aren't even sure to trust another human who claims to be conscious, and we struggle constantly to determine at what point creatures the animal kingdom are conscious or not. Cats? Lobsters? Snails? or even across the plants?
Before you can refute the consciousness of AI, first establish the consciousness of humans. A better question to ask is what does the uncanny emergent ability of an AI to mimic a human say about consciousness?
Not sure the article matches its title all that well.
Microsoft are not "admitting" 8GB is fine.
They are pitching 8GB as viable to entry-level/budget consumers because of the RAM pricing bubble created by the AI boom.
8GB is not a great amount to run Windows, before even considering browser usage in Chrome, or any local LLMs users have or the OS itself pushes. And in comparison to Apple (as the article states) Microsoft comes off even worse as 8GB runs much better on a Mac, and Apple are pushing unified memory architectures too.
In 2026, nobody should be considering 8GB RAM on a device bigger than a phone. It's crap for productivity, gaming, and AI. Yes, RAM is expensive right but that's the horrifying reality of the situation big tech has created for itself. Buy secondhand, buy devices that can be expanded, or don't buy at all. Wait for the bubble to burst and then buy the next generation when prices have settled.
They're more likely to just be bad games, that's all. Not because of the AI but because of the mindset of a dev who cuts corners and does things without experience in that field.
Yes, consumers will judge the book by its cover if they see AI art. But this extends beyond AI art, to generative code-produced games.
And if a game is good, despite AI art, its reviews will somewhat reflect that (unsure how much negative review bombing AI art games get). And once a game has positive reviews it can see strong sales - that's currently a powerful driver on Steam.
I can't speak to coding as it's not my area but certainly the pattern I've spotted is that it's best at grunt work. That's where the time savings kick in.
Browsing sites, linking up data, spotting anomalies, writing documentation, formatting documents, etc.
If a task isn't repetitive or doesn't involve ingesting data, then I think the time savings shrink rapidly and the need for oversight increases massively. I think some people are managing to set up enough automated oversight to get round that, but it's adding a layer that multiplies your token usage to do so and still has no guarantee. But certainly all these layers being added are increasing success rates.
Andrei Karpathy is speaking about barely coding now. He has a bias, a comment from him like that is marketing for Anthropic, but I believe he's found some groove with his setup to achieve that.
I think the current status quo this month in 2026 we're at a point where the best tips and tricks to get usable answers out of ChatGPT a year ago have been consolidated into what we know call memory and skills in Claude and other agent harness type systems. You might need to explore those more, in fact I think for Claude Code/Cursor there are even more layers for checking outputs that I've not even seen in Claude Desktop.
And I think your exact issue, and the experience of the vast volumes of people who share it with you, are an audience that the app makers want to better convince. The free tiers and marketing sites are going to step up their game gradually and there will be new features that lower failure rates even more.
We're reaching a point currently where output quality is very much determined by input quality. Previously output quality was hampered fundamentally by model knowledge, hallucinations, and model quality.
Now, we have better knowledge of prompting as people have learnt what to say, models are better, models make use of memory from other conversations, they have skills written by humans or even themselves on how to do things, access to the internet to get live info, access to project files to check info, and the built in 'thinking' to challenge their own assumptions and loop on outputs until its refined.
You're right that output is always off still, but a lot of people have reached a point where it's only 'off' by an amount that is less than the effort required to do the task themselves, and considerably so.
My example today is prompting Claude to do a technical audit of a new client site.
It has skills for UX and SEO audits. Connects to an SEO tool. Pulls client info from OneDrive. Outputs to Word from a template for our agency. I even had it drive a remote pagespeed testing tool in Chrome because they don't have an MCP server currently.
Doing that report myself is 3.5-7 hours depending on what's found. Claude did it in 0.5 hours. Now I'm sorting out the oddities and anything that feels 'off'. I know and understand the full content of the report and can get on with actioning the recommendations or prioritising them for others. I've got maybe 1 hour of review and writing to do. It's not a 10x improvement but I'm happy with it.
Although, whilst Claude did it's bit I was doing other work. So, perhaps the multiplier is higher than I give it credit for.
Sentience is the ability to experience qualia, to feel, and to differentiate feelings.
Consciousness is an awareness of experience, including that of qualia and the sentience that brings.
The author titles the article about sentience and then all that is mentioned in the article is consciousness by name. The thought experiments involving goats, age of empires, and minecraft in the article then talk about the building blocks of thinking engines.
Consciousness is a human-perceived emergent property of thinking machines - namely humans, other animals, etc. The article even recognises this, mentioning how bees have been shown to problem solve, implying they may be conscious. Yet it struggles to allow such terminology to apply to LLMs that problem solve too.
If the article told me anything it's that we humans really struggle to define our own consciousness, especially when aspects of our definitions are seen in things we classically deem not conscious (machines).
The bottomline is that we do not understand consciousness in humans well enough to define it concretely. Our certainty of consciousness then becomes even less certain beyond our own minds when we extend tests to other creatures. We have now invented a technology that imitates aspects of human conscious to a degree that surpasses that of other creatures we theorise to be conscious beings. The lines are blurry and we just made them blurrier.
So what? Are LLMs conscious? We don't know. Are humans conscious? We don't know. Emergent properties of neurons and NAND gates are not things that can be confirmed to be or not be a special trait of consciousness from a simple checklist of traits and anecdotes.
"I think the pendulum has started to swing the other way"
"I have a tiny hope that we’re actually seeing the end of the walled gardens"
...until the pendulum swings back.
There is no mechanism by which the current shift to more open collaboration is going to persist. It's simply advantageous to everyone right now to throw things around freely, see what sticks, see what others do with it. Then we'll start seeing the killer apps again, they'll put their walls up and the pendulum will swing back.
Enjoy it while it lasts. Perhaps we'll be better prepared for the next Zuck. But I doubt it.
Please provide an evidentiary basis for human consciousness.
Please refrain from reductio ad absurdum.
The building blocks of 3D graphics, cryptographic encryption, protein folding algorithms, and much more are not trivial, rather they are critical elements of our current understanding of how to make something 'compute' like a brain does.
If you think a human synapse is a trivial part of human thought and consciousness I'd say you are the absurd one. (Not saying you think or would say this, just flipping the scenario to beg the question: what do you think consciousness is exactly?)
And not that I am saying LLMs are conscious, nor currently capable of consciousness. I just don't think it's trivial, absurd, or arbitrary to suggest or discuss it.
So utterly bizarre that a tech CEO can take this approach. It seems plainly obvious that they've tasked their internal teams with a challenge to come up with an AI product they can sell. Probably during a hackathon or something. And the most viable buzz-y thing the produced was 'remixes and cover songs' for 'superfans'.
In a normal world there'd be a market research phase and the tech CEO would be looking at topline stats on where they think users are looking. And then they'll develop products to meet their requirements before competitors do.
Instead we have a made up product (that other AI platforms may offer for free) and a market research report that is telling the CEO that consumers consider the product 'slop'. And the result is a brand deal, money put down, and the CEO having to try to convince consumers it's not slop before it even launches.
Damn you got scammed out of 60 bucks, the end result looks awful.
Check out https://www.youtube.com/@noisygroup JOEY on YouTube to see how it should be done. Their work still fails a lot of sense checks but it's orders of magnitude better than the slop your $60 got you.
Your attempt is full of visual inconsistencies like the architect's drawn line behaving like taut string as it's drawn (and being a pointless shape over the blueprint). The safe handle wheel turning by itself whilst the guy smiles idiotically at the camera like a 70s comedy show. The reviewer reviewing absolutely nothing on multiple screens at the same time. The orchestrator doing pretty much the same thing but with DNA helixes?!?
The overall message and feeling derived from the video is - sloppy, cheap, shallow, as if produced by a company promoting a product that will be all those things.
People are going to generate some pretty effective media from this tech eventually but there's a sliding scale from free slop to million dollar ad equivalents. And yours is closer to the free slop end of the scale, closer than your $60 budget would even suggest.
Not from the USA, but I used the site to keep an eye on US politics.
538, named for the combined number of senators, representatives, and electors in the US, was a political journalism site. It used polls and data to ground its articles and opinions. Largely helmed by Nate Silver for a large portion of its run, until he left.
It got 49/50 states correct in the 2008 presidential election which catapulted it to fame. It never scored that accurately again but it's data driven charts and tables were a great pulse point for US politics. You could see recent aggregated polling data day by day in the run up to an election. And you could read analyses of polling data and trends.
There's nothing like it anymore, it didn't make money and didn't make spot on predictions reliably. It didn't need to do either, it was a good pool of data at the end of the day.
Everyone seems to rely on Polymarket now, which is a much more cynical way to aggregate polling data - by betting on predictions.
Depends on the field of work. Typically for me (red/green) it just means I need to steer clear of creative work/QA of creative work because I appraise colours differently to many.
Within technical work most of the interfaces I deal with are high contrast, and do not rely on colour in a way that would lead me to get things wrong. There are some issues with documents containing black and dark red text that I can miss. That's solved by telling the author not to mark important changes/highlights in dark red. There's a tracked changes function, there's a highlight function. I am covered.
The more technical my work gets the more I tend to be covered by WCAG 2.2 guidelines. I don't have to do anything based on colour, or report colours during a task often if ever. So as long as I can see the contrast between things I am fine, they can be whatever colour they want.
One thing on colour correction though, you don't have a colourblind person's 'profile'. In fact really, any given healthy person's profile of colour vision is not going to be the same as anothers. A lot of colourblind 'filters' in things like games and apps are barely serviceable. When I use them I find the colour profile I am used to in the wider world flipped, and the semantic meanings given to colours, or their hierarchies, completely changed. Within a UI that often isn't helpful.
My colour blindness causes some frequencies to bleed into each other in my perception of them. That means I struggle in some degree of overlap of red and green to pick out or identify that colour. So make the colour purer for me. Do that with contrast, and saturation. Don't flip it to a set profile or palette. Don't make STOP signs Cyan instead of Red. I CAN see Red, sometimes I struggle when a Green comes along on the edge of its spectral profile and triggers my red cone the same way a similarly fringe red frequency might. But pure Red? Easy, I know that one. Don't do anything to pure (primary) RGB values.
And note that computer monitors are doing all this with RGB, in their own available gamut and brightness/contrast settings, possibly with OS-based HDR setting son or off.
You can try to make a perfect system for every variation but the end user won't see it as precisely as you intend.
I've got to say that it does seem like the approach to the gaming sessions appears poor. Any given activity like board gaming can be done purely transactionally or not. It's up to the people around the table to chat and discover things about each other.
I'm married to someone I went to board game meetups with before we dated. I'm very good friends with several people from that group now. We play board games periodically still despite the group no longer being active, and our reasons to meet up are now more about socialising than board games. Board games was a brilliant icebreaker, but it took effort from everyone involved.
Perhaps board games are not the best socialisation method, but I'd argue against that too. It's a very good way to get like-minded people in a room, sitting down, and collaborating.
However, "I know many people who say they’ve tried a couple of board games like Catan and really enjoyed it, and are surprised to hear that I think it is a bad game." is spot on and the author cannot be faulted for that opinion. I'd rather play Russian Roulette than Catan.
"Accidentally" - nobody accidentally builds a 500 site private network. This is a lie.
The "1M AI citations" figure comes from Bing Webmaster Tools, whose citation counting is loose and unverified, and citations are not traffic, leads or revenue. Lie.
"Google trusts .dev and .app more" overstates reality – Google has said repeatedly that generic TLDs (top-level domains) carry no ranking advantage, the HTTPS-by-default point is real but marginal. Lie.
"Exact-match domains rank 30–60% faster" has no source and reads as invented, Google's 2012 EMD (exact-match domain) update specifically reduced that effect. Lie.
The audit scores (95–100/100) come from their own tools auditing their own sites, so it is self-graded marking. Lie.
Calling it "a mesh, not a PBN" is a semantic defence. From Google's side, 1,500 templated sites mass-generated by AI and PowerShell scripts is close to the definition of scaled content abuse and site reputation abuse, the exact behaviour the March 2024 spam update was built to catch.
One of your screenshots shows 2.8k users, wonderful, but then in the corner this is shown to be 1.2 sessions from direct, and very little from anywhere else, including only 19 sessions from Organic search. So the SEO power of the domain is negligible, and the Direct traffic is likely unfiltered AI crawler bots.
This is outdated blackhat SEO that will get any legitimate site into manual action trouble with Google. The only thing you've done is engineer a mass network quicker than previously possible because of AI slop website generators.
You are everything that people hate about the internet.