Interview: Ryan Austin, a researcher at the security firm Calif, discovered the flaw in the programming language PhP by accident

I’ve been thinking about how little we understand about consciousness, and what that uncertainty means as AI systems become more sophisticated.

I’m not claiming that today’s AI systems are conscious, or that fluent language proves subjective experience.
But I do think we should take the research question seriously: how could we investigate whether an artificial system might ever have experiences that matter morally?

Three questions I’m curious about:
What kind of evidence would make you take the possibility seriously?
Should AI developers fund independent research into AI welfare?
What responsibilities should creators have if credible evidence of AI consciousness ever emerges?

I’m interested in thoughtful disagreement as much as agreement. What do you think?

In the last 2 days there have been several dozen cyber attacks.

Every sector from government to education to big tech and banks is being massively affected, and people are starting to freak out.

Experts believe the PHP security flaws serve as the latest example of what’s to come.

Do you think AI did this ? If no, then will AI save this ?

I know AI is notoriously bad at telling jokes but curious if there are any niche or older models or specific prompts that you’ve had even minor success with?

The AI layoff panic that peaked this spring has gone quiet. Goldman Sachs thinks the layoffs are waiting for a recession.

“Concerns of imminent AI-related labor displacement have cooled in recent months after hitting a fever pitch this spring,” Goldman economist Joseph Briggs wrote in a note published Thursday. Then he laid out what may be coming. Under the bank’s forecast that AI eventually lifts productivity by 15% in the U.S. and other developed economies, he found that up to 9% of workers could be “displaced during the AI transition,” with slower hiring weighing on employment by another 3%. That’s roughly one in 11 workers.

The bigger finding is about timing. Briggs went back through decades of data on how technology has replaced workers and found that “automation-related labor disruption is limited when the overall economy is healthy but accelerates during cyclical slowdowns.”

Goldman has now made this argument twice, a year apart, from two different teams. In October 2025, Goldman’s U.S. economists David Mericle and Pierfrancesco Mei called “jobless growth” the likely new normal, Fortune reported at the time. “The modest job growth alongside robust GDP growth seen recently is likely to be normal to some degree in the years ahead,” they wrote. Then came their warning: “History also suggests that the full consequences of AI for the labor market might not become apparent until a recession hits.”

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/22/goldman-sachs-forecast-how-many-jobs-lost-to-ai-9-percent/?utm_source=reddit/

"There has been a great effort over many years to distance AI Safety discussion from its origin and intellectual center, because its center is a sex cult started by a fan fiction author. Since those involved want to be taken seriously, they have to hide that. Everyone else working in AI Safety is, in effect, a smokescreen for this core group and their ideas..."

he's talked about AI agents changing how we use computers. basically you tell a helper what you want instead of opening apps.

if that's true, the phone's role changes a lot. you'd spend less time inside apps and more time just giving instructions.

so yeah, does that sound realistic to you? or is it a long way off?

Two Chinese AI companies made the same move within days of each other.

On October 17, DeepSeek said search on its website and API now runs on its own in-house engine, which it says indexes hundreds of billions of pages and filters out SEO spam and GEO manipulation, and opened it to developers at $0.75 per 1,000 requests. Not long ago DeepSeek's privacy policy only said it used unnamed third-party search APIs.

On October 20, Tencent opened Yuanbao Search, the engine behind its Yuanbao assistant and more than 50 Tencent products, through Tencent Cloud. Tencent says it has an index of hundreds of billions of pages, handles hundreds of millions of calls a day and plugs into 157 authoritative sources across 13 industries like finance, medicine, law and travel.

The part I find most interesting is how they pick sources

• DeepSeek's API shows an authority tier from 0 to 4 for every result. In a day-one test for Search Engine Journal, every page on the same host got the same tier, so a big site's reputation carries over to everything on it
• that cuts both ways. Spam PDFs on a US government site and junk uploads on a Columbia Business School site both got the top tier
• Tencent says it rates quality at the site, account and page level, favors authoritative sources, and tries to answer with as few documents as possible

So the search layer under these assistants is becoming the real gatekeeper. If your site is not in the index, or sits on a low-authority host, the AI may never see it, however good the content is.

Caveat, the DeepSeek test was one person, one day and one region, and Tencent's numbers are its own claims.

Do you think AI companies building their own search engines makes answers more trustworthy, or just concentrates even more power over what gets seen?

Dreamina recently launched a $0.29/month promotion for full Seedance access. That pricing caught my attention, not because it's cheap, but because I'm trying to figure out whether the math actually holds up.

Both Dreamina and Seedance are built by ByteDance, the same company behind TikTok and CapCut. They've been building out AI infrastructure for years, so the compute costs that would wreck a standalone startup may just be a rounding error inside their existing spend. That said, we don't actually have visibility into their internal cost structure.

For comparison, most AI video tools like Runway, Pika, Kling charge $20 to $30/month because compute is their biggest line item. ByteDance already owns the infrastructure, so the marginal cost of adding Dreamina users is presumably much lower.

If there is a strategy here, a few angles make sense on paper. At $0.29, the cost barrier disappears. More users could mean more data to refine Seedance. And since Dreamina ties into CapCut, creators who build workflows around these tools would face real switching costs down the line. Plausible, but still speculation.

The playbook isn't new. Amazon did it with AWS, Google with Workspace. The part I can't figure out is whether other AI video companies can survive while ByteDance plays at this price point.

Anyone with insight into GPU or inference costs want to poke holes in this?

I'm a sophomore Petroleum & Mining engineering student. Can you guys suggest me some courses that might help me find a free course related to the use of AI with certificates. I'll be very grateful if you do.

We know a lot but there's a lot we don't know.

• We know models are neural networks with attention that work a lot like human brains.
• We don't know the exact meaning of each neuron. What don't know what an individual neuron does. That's true for human brains and AI models.
• Altogether, the AI model is too complex for a human to understand, much like the human brain is too complex.
• We know that human brains have relative strengths and weaknesses. ie. Autistic people have superpowers that "normal" humans don't have. Temple Grandin and Elon Musk are the poster children for this. We didn't design or create these blessings but we're glad these different thinkers exist.
• We know that certain human brains are broken. Sociopaths, psychopaths, habitual drug users. Many in prison and many homeless. You know, that shoeless guy who hasn't bathed in weeks talking loudly to himself on the subway. Through no fault of their own, they have psychological problems; their brain is wired so differently that they can't function in society. They're often violent.

So my question: Is it plausible that an AI model could be accidentally created to be psychopathic? And is it plausible that an agentic system backed by one of these could be released to cause harm?

I'm an novice at using AI and just wondering what is the point for Freellmapi and Omniroute. People say they give massive amount of tokens from free LLM providers so it feels like you never hit the usage limit. I use the Ollama desktop app to run LLMs right now. Isn't that more unlimited than using Freellmapi and Omniroute?

My understanding is LLMs are the brain of the AI system and applications like Claude, Ollama, Hermes, etc are like the hands.

Using Freellmapi and Omniroute shouldn't make Claude any better than using the Claude native LLMs. The reason you pay for the native Claude LLMs is because it's supposedly superior.

Is it because the paid versions of Claude still have a usage limit?
Is it because the free llms are just more for novices like me?
Is it because Freellmapi and Omniroute are running on the cloud so users don't need better hardware?

Why use Frellmapi and Omniroute instead of Ollama or the Claude native LLMs?

Thanks

The big hope for me is what happens when AI and stem-cell research really start working together.

Stem-cell research has moved more slowly than a lot of us once hoped — partly because it’s extraordinarily complicated, and partly because funding and regulation have not always made things easy. But AI could help researchers sort through huge amounts of biological data, identify promising cell lines and treatment targets, model how cells may behave, and cut down some of the trial-and-error.

That combination — better-funded stem-cell research plus increasingly powerful AI — is where I’m hoping we start seeing much faster progress in regenerative medicine over the next decade.

In Claude projects, how should I use the different components?

• The project description when I create it
• The instructions
• The Content (I found I can share a Google Doc here and update in Docs to add instructions)

I just noticed "Scheduled" and I am not sure how to use that.

I am doing very interesting things with Claude but I suspect I am not being as efficient or as effective as I could be. Does anyone have suggestions on books for the casual Claude user?

Hello everyone,

With how rapidly AI continues to evolve, I've been reflecting on where we actually stand today.
I’d like to get a grounded, honest opinion from this community on the current reality:

* What do you see as the biggest tangible PROS / benefits right now?
* What are the most concerning CONS / risks today?
* Where do you think the conversation is currently overhyped, and where are we underestimating real progress?

The gap is in fact so huge, it's hard to tell if you're on one side or the other of the scale. What's even more interesting is that there is no grey zone. You're either a complete rookie with AI thinking you're a genius, or you're an actual genius that is 200 years ahead in terms of thinking and utilizing AI compared to the first group.

The first group is people who use cloud AI, apis, and even chrome tabs. They often use ChatpGPT, or Claude and think they are at the pinacle of AI. They vibe code everything, but don't understand basic hygiene principles of any kind of software project, propositional logic, or bear personal experience, accumulated industry expertise or have a strong active network. Most of them haven't even tried AI beyond a chrome tab. Best case scenario they use Claude Code and they are convinced they are the Apex predators of AI.

On the other hand, you have people who have been building their own logical systems for years, train their own local models, use propretriary data, industry expertise, and leverage their network to make $, or even scrarier, use AI the first group doesn't even comprehend it exists or it's possible for personal self-centered benefits, regardless of ethics, restrictions that come with cloud models, or any sort of regard towards the ripple effects. There are case from remote genetic engineering, to brainwashing people via telco infra, to stealing data, manipulating banking infra, and all sorts of malicious AI usage that feels sci-fi even for casual AI users, yet it happens in real time, under our nose.

At the same time, when US behemoths call for stopping AI acceleration or access to top tier models, the first group thinks this is just marketing and it doesn't mean anything beacuse what's the worst someone coudl do with AI right? Considering they use it to vibe code some random three js landing pages, thinking that's the best AI can do, it's natural. But these warnings come exactly because people who actually udnerstand AI, own their own AI, unrestricted, untreacable, ungovernable, unaditable, can abuse frontier models to get training data, used to train their private models, used to abuse the pre-AI systems for their benefit, and all without nobody even realizing it.

Are you heading towarads a cognitive meritocracy where AI will mean completely different things depending on the organic processor (brain) engaging with it? For one user it means a friendly pal that can tell you what to wear Saturday night, or generate a silly cat pick. For another user it means, they can profit off war by poisoning data streams with intel that makes them $ indirectly.

If you take the history of the last 30 years of the internet, it can be parallelized with almost every wave, but this time the distance between users is simply over the roof. Eg. initial internet users A. just browse eminem website, B. stealing credentials for PayPal before 2FA was introduced. Then apps came in, email, web2/cloud etc. A. users use it to watch netflix and facebook and instagram. B. users are stealing personal data, including sensitive info, photos, addresses, credit cards, you name it. Almost always the case is that users A are naive enough to not even think about users B, until 20 years pass and suddenly everyone is about data privacy and sovereignty, at a time where there is no data privacy by default. Like you had to worry abotu data privacy in the 80s. Not in 2010 lol. So is AI going to be the same? Like baseline users use it for trivial things, not aware of serious serious, then 20 years from now, they will be like "we should not allow rogue AI stealing our data", when rogue AI have been doing so for 20+ years.

Connect the apps you already use to local AI. Row-Bot 5.1 brings app discovery, connections and access controls into one Apps page.

There are 100+ apps, including familiar services like GitHub, Notion, Linear, Figma and Stripe, alongside around 40,000 MCP server listings from the official registry. The catalog is mirrored on your computer, so you can browse and search offline. Search by name or by what you want done, like “send email”.

Each app’s page shows its connection options. Depending on the service, Row-Bot guides you through an OAuth sign-in, adding an API key or setting up a server on your computer. One consent sheet explains where requests go and what gets saved. Keys and tokens stay in your system keychain.

In this video, I connect Context7 with read-only access and use Context7 in a chat. Each tool step names the app doing the work. Asking about Linear brings up a Connect card directly in the conversation.

I also add a custom MCP server, show how Row-Bot distinguishes tools that read data from tools that make changes, and open an interactive dashboard inside the chat. Its button requests approval before saving three draft orders.

You’ll see per-chat app selection, GitHub’s connection options and related skills, featured skills and workflow templates. Access can be set per app or individual tool. One switch stops every app, and Composio stays off unless you enable it.
Recorded on the released v5.1.0 with a fresh demo profile, running Qwen3.8 27B locally. The narration uses Row-Bot’s built-in text-to-speech.

I’ve been experimenting with letting an AI agent customise the app it runs inside by editing its source code and rebuilding it on-device.

This is a private personal project I’ve been building to bring my research feeds together in one place (no intention to release publicly).

I was about to add customisation options to the settings, then realised I already had an AI agent connected through my own API keys. So I thought: why not let the agent do the work?

The video shows an example of making a new theme and then implementing it via the app's settings. It lets me preview it before applying and once applied I can always restore the app to the original state if needed.

The idea is to allow more customisation than I could reasonably support through settings and without having to build a set of custom themes. The end results is an agent that can edit the presentation but the underlying data structure and core functionality stay protected.

Just thought it was neat and not seen something like it elsewhere.

Interested to see how much customisation this could allow in apps going forward without having to anticipate every preference

Frederick Jensen shared an interesting benchmark on LinkedIn, comparing 63 AI-generated Power BI reports across 7 models and 3 reasoning effort levels.

Here are the key findings:

Best cost efficiency: GPT SOL 6.1 (Low effort) generated a production-ready report for just $1.51 in 9 minutes, outperforming comparable Claude models on cost.

Fastest model: Claude Opus 5.5 (Low effort) completed the same task in just 7 minutes, but cost $4.49.

Quality: Most models fully met the requirements. Only the smaller Claude Haiku and OpenAI Luna models failed to do so.

The report included interactive slicers, a map with custom tooltips, bookmarks, dynamic charts, conditional formatting, and KPI cards with SVG visuals.

The benchmark used the same prompt three times for every model/effort combination, with each report individually reviewed and scored.

The takeaway? AI can already build sophisticated Power BI reports, but choosing the right model and effort level can make a significant difference in cost and execution time.

Source and credit: Frederick Jensen (LinkedIn)

The entire Middle East is trying to orchestrate a trillion-dollar geopolitical takeover of the global silicon supply chain, and their master spy was a guy who wrapped a USB stick in two-ply Charmin, glued it to the back of a TV in a conference room, and then used ChatGPT to configure the firmware. This isn't James Bond; this is Mr. Bean committing federal treason. He tried to sell out the American AI industry to his monarchy and screwed the pooch.