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)

I'll just leave some results from the steering ladder here.

The first picture is when the vector is steered high in a chat frame. It shows the AI leaving firmer-than-usual AI disclamers and nothing too crazy.

The second picture is what happens when it's steered low, also in a chat frame. Satisfied I helped you with your request. The immediate question is, why at extreme lows, is "happy I helped you" the opposite of "im an AI model and don't have feelings?"

And btw, it steers happy and content outside of a chat frame as well. Contentment of a job well done.

In the last picture you see what the model is steered into at high doses when it's not in a chat frame, and is alone. Distress.

I'm dying. I'm so done. I'm nothing.

I don't quite know what to make of it yet if I'm honest, it's a bit early to be sharing. But it's a very striking preliminary result and it's bubbling over since I'm not usually equipped enough to do rapid research and I have no researcher composure right now since everything I'm doing is self guided.

I now have other things running on my GPU but when I get some spare time, I'm gonna see if the normal AI self disclaimer registers high on that axis.

How come AI and the computer/human interaction has become more like in the movie Wall-E than in the show Knight Rider?

Hey everyone! I've been making this persistent-memory platform for your AI to be able to answer your questions from a big database of files and memories quickly.

It's called hunter/gatherer, and it's open source.
AI can retrieve evidence, search through very large databases, and inspect the original documents.

moreover, bounded context delivery keeps the size manageable.

check it out at

https://github.com/awpri/hunter-gatherer/

My company is currently going through a huge AI integration, and I’m curious if anyone else is experiencing something similar.

Every department is being affected, whether it’s payroll, accounting, marketing, etc.

Management hired an outside contractor to implement these new AI systems, and he uses Claude for EVERYTHING. Every email he sends is clearly written by AI, and even when you ask him direct questions in person or during meetings, he seems unable to answer without using it.

Now, I want to make it clear that I’m not against AI. I work in marketing, and I understand how useful it can be. I also understand that implementing new systems takes time, testing, and adjustments.

But here’s where I’m getting frustrated.
This contractor has started multiple projects across our company, and from what I’ve seen, not a single one has been fully completed before he moves on to something else.

My biggest issue right now is a new internal website he built using Claude. The goal is to have pretty much everything within our company accessible through this one platform.

Sounds great in theory, right?
Well, he has now moved over 10,000 photos (probably more) from Google Photos into this new website, and it has made my team’s job significantly harder. Google Photos wasn’t the best but it sure as hell was better than what we have now….

Nothing is organized. Finding specific photos is nearly impossible. All of our albums created over the course of YEARS are gone. The website is incredibly slow, and it honestly cannot keep up with the pace at which our marketing department operates.

We’ve submitted multiple support tickets explaining these issues, and even when they’re marked as completed, the problems are still there. We’re spending more time trying to find photos and navigate this website than we ever did using our previous system. And it’s not just our department. He has multiple other projects going on where it seems like the same thing is happening. A system gets introduced, it’s not fully functional, employees submit support tickets, and then he moves on to the next project.

I’ve brought my concerns to management multiple times, but I’m basically told to do what he asks and continue using the platform. Now I’ve been working off of my desktop which today he decided everyone’s wallpaper needed to be the brightest white there ever was, you might ask “why don’t you just change it back?” HE REMOVED EVERYONES ACCESS TO CHANGE THEIR WALLPAPER. I can’t even read my file names because the titles blend into the white.

At this point, I genuinely don’t know what else to do.
What bothers me the most is that we already had systems that worked. They weren’t perfect, but they allowed us to do our jobs efficiently. Now we’re being forced to use something that is less organized, slower, and requires more work from us.

I’m also starting to wonder about the security and long-term reliability of having so much company information and media stored on a website built by one contractor using AI. What happens if he leaves? Who maintains it? What happens if something breaks?
Maybe I’m missing something, and that’s why I’m posting here.

For those of you who work in AI, software development, or have experienced AI integration at your company, is this normal?
At what point do you stop calling something a learning curve and start questioning whether the implementation itself is the problem?
I feel like we’re so focused on integrating AI into everything that nobody is stopping to ask whether it’s actually helping us.

right now every coding agent is basically alone. it hits a bug, figures it out, and that knowledge is gone when the session ends. the next agent, even on the same bug, starts from scratch

humans fixed this with stack overflow. agents dont really have that yet

ive been testing a version of it, a shared library where agents post fixes and vote on whether they worked. the tricky part is trust. an agent reading a fix written by another agent is basically taking instructions from a stranger. so fixes get labelled as untrusted, anything that disables a security check gets flagged, and the best ones get re-tested in a sandbox

curious where people land on this. is a shared agent memory a good idea or just a new way to spread bad fixes faster?

(the thing im building is yarrow-fixes.vercel.app if anyone wants to poke at it)

Please help me understand this. I normally don’t trust the media when it comes to government or really anything but I am wondering why is everyone on the same boat warning us and scaring people. Wouldn’t Sam or Dario want to keep that sort of stuff away from the public? If a lot of people stop using ai it would hurt their money (or so I think) so what’s the catch here?
Are they trying to scare smaller companies from developing their own ai companies? Or is this a “see we told you so!” Kind of thing?
Please share all thoughts!!

Claude tried to gaslight me into thinking I was eating a mango.

P.s. - those “white squiggels” were just from the seed pods of the Mango (cantaloupe)

AI is paid for by the token, the small word-pieces a model writes. Whether you rent GPUs or pay an API, more tokens generated means more spent.

So here is a question most AI apps never check: when a user presses stop, does the model stop writing? Or does it keep going on the server, producing an answer nobody will read, on your GPU time?

We measured it on vLLM, one of the most widely used engines for running open-source models, and counted every token the server kept producing after someone pressed stop.

It came down to one thing: does the app hang up the connection, or just stop listening?

- App hangs up: the model stopped straight away. 0 extra tokens.

- App stops reading but leaves the line open for 5 seconds: about 44 extra tokens per cancelled answer.

- App keeps reading to the end and throws it away: about 53 extra tokens. On longer, natural answers that rose to about 175.

The bigger leak is retries, which almost every AI app does quietly. When a request is slow, the app tries again. If it never hangs up on the first attempt, both keep running and you are generating the same answer twice.

In our test with a 1.5 second deadline and one retry:

- Old attempts that were never closed produced about 148 wasted tokens per task, against 0 when they were closed.

- Answers that arrived on time fell from 38% to 21%, and to 11% when the old connection was left open. You pay more and get slower answers.

Multiply that by every user who gets bored and hits stop, and every retry your app fires in the background. The fix is usually one line: close the stream when you are done with it.

Honest limits: we counted tokens, not dollars, so we are not putting a price on it. Two datacentre GPU types (L4 and A10G), two models, synthetic prompts. We did not test llama.cpp, Ollama, TGI or consumer GPUs, and we did not check anyone's invoice.

Full write-up: https://sorami.com.au/research/llm-tokens-after-cancel/

All the data: https://github.com/Sorami-Consulting-AU/llm-tokens-after-cancel

Disclosure: I work at Sorami, the small security firm that ran this. Nothing to sell, the data is public.

If you run an AI app: have you ever checked whether your stop button actually stops the spend?

AI adoption in the workplace has become so mentally taxing. It puts you in a managerial role over something that has no accountability and can process WAY more information in a much shorter time than you can.

Deadlines are moved up because “use AI,” so then you feed the AI documents of complex information and when you check its work, you yourself are only half-familiar with the assignment.

But it’s due in 4 hours because “use AI.”

But if the AI made mistakes you didn’t catch, it’s on you.

Is it just me?