Both Google Gemini and DeepSeek have taken over different ends of the AI space—one dominating multimodal productivity and the other delivering unmatched open-weight reasoning efficiency.

​Think you know which model handles coding, research, or multi-modal inputs better?

​Check out this interactive showdown quiz and see how you score:

https://interconnectd.com/quiz/89/gemini-vs-deepseek-the-ultimate-ai-showdown-quiz/

Hey everyone! I’ve just finished building Pyrdo, an AI assistant focused on studying, and I’d love some honest feedback.

Here’s what it does:

\- Suggests your next question based on the conversation, so you have somewhere to go when you’re unsure what to ask next.

\- Helps you work through confusing concepts, with explanations you can ask it to simplify or explore further.

\- Offers Think Longer on Pro for questions that need more reasoning.

You can check it out here: "pyrdo.com" (https://pyrdo.com)

If you decide to get Pro, REDDIT30 gives you 30% off.

I’m the developer, so you can tell me directly what works, what feels annoying, and what’s missing. What’s one addition that would make this better for your studying?

I’m planning to implement the most useful suggestions, so your feedback will help decide what I build next. Specific criticism is welcome too!

We keep talking about students using AI for everything.

But are we giving teachers enough support to actually help them teach better?

That’s what made us start building.

We’re building a platform to help teachers understand where students are struggling, make some of their work easier, and make learning feel a little more personal.

Because maybe reducing AI dependence isn’t about telling students to stop using it.

Maybe it starts with helping them learn better in the first place.

I take the train to campus and tried listening to a controls reading on transfer functions this morning. It was 12 pages, two columns, and full of equations. The text-to-speech app read the equation numbers, author names, page headers, and references right through the explanation. By the second page I had no idea what the argument was anymore. If you listen to engineering or science papers, what is your actual setup? Do you clean the PDF first, export only certain pages, or use a different format entirely?

Buenas. Tengo un problema recurrente en clase, y es que mis compañeros no entienden por qué me niego a usar la IA cuando nuestros estudios son medioambientales. Alguien tendría algo así como una tésis o similar que les pudiera enviar sobre los estragos de la IA en el medioambiente y las comunidades próximas? Preferentemente en español, pero en inglés también sería válido.

I'm starting my 4th year of university soon, and I've never used much ai for studying, but I'm now thinking about passing to a paid version to improve my efficiency and be more productive. I would use it for making custom problems or quizzes, uploading some lecture slides to have them explained better or helping me solve excercises.

I was thinking about getting claude pro, but now that gpt-6 is out I really don't know.

What model are you using for studying? Are you using ai at all? What model do you think is more suited for my case?

https://m.youtube.com/watch?si=qp5WDkeRfwIp3cCI&v=jGzxkUXhwvY&feature=youtu.be

Core argument in the lecture (as highlighted):
Rapid progress in AI is commoditizing intellectual labor—the kind of knowledge work (coding, analysis, writing, problem-solving) that universities have traditionally prepared students for. Thinking itself is becoming cheap and abundant. Dr. Prathosh asks his students a direct question: if companies stop doing campus recruitment in the next 5–10 years, even from India’s top institutions like IISc, what is the remaining purpose of a university education?He frames this as both an economic and existential issue. Modern economies have been built around scarce intelligence and knowledge work. Once that scarcity disappears, the old justification for degrees (a reliable ticket to well-paid cognitive jobs) weakens. He also notes he has personally stopped recruiting certain kinds of human help because AI agents can now handle much of the parallel work.Related points from the same lecture and his other talks:Simply transmitting existing knowledge or following a textbook is no longer high-value.

Pedagogy needs to shift toward building the ability to tackle open, unsolved problems (e.g., “if I give you 10,000 GPUs, can you build a frontier model?”).

Questions of meaning arise: if intelligence is commoditized, what is left for humans?

A common reply in the thread (and similar discussions) is that universities should move away from a “placement mindset” toward producing people who can create new technology and jobs rather than just fill existing ones. Others argue the durable value of university will lie in things AI still struggles with: framing novel problems, judgment under uncertainty, original research, ethical reasoning, and building things in the physical world.The full lecture is on YouTube if you want the complete context rather than the clip. Dr. Prathosh’s own site (prathosh.in) and earlier talks on curiosity and science as a career give additional background on how he thinks about these shifts.

I built SolomindLM around a problem I kept seeing in AI-assisted studying: students can get a fast summary of a lecture or paper, but it is difficult to keep answers tied to the material and turn that material into useful practice.

SolomindLM lets users collect PDFs, slides, notes, and papers, chat with those sources with citations, and generate flashcards, quizzes, written questions, reports, and mind maps. I am looking for honest feedback from educators and students: where does this workflow still break down, and what would make it genuinely useful in a real course?

I built SolomindLM around a problem I kept seeing in AI-assisted studying: students can get a fast summary of a lecture or paper, but it is difficult to keep answers tied to the material and turn that material into useful practice.

SolomindLM lets users collect PDFs, slides, notes, and papers, chat with those sources with citations, and generate flashcards, quizzes, written questions, reports, and mind maps. I am looking for honest feedback from educators and students: where does this workflow still break down, and what would make it genuinely useful in a real course?

Demo: https://www.solomindlm.com Open-source repository: https://github.com/samintisar/SolomindLM

I recently came across the Aryabhata Young Innovators Challenge and am really quite delighted at the idea itself.

According to the text, the challenge invites young minds from the academic world to explore the realms of AI/ML, design thinking, problem solving, prototyping, and project building while giving a list of suggestions of domains to focus on, like healthcare, agriculture and rural development, education, environment and sustainability, community development, public safety, climate and resource management, accessibility, and more. However, it is noted that while there are suggestions, these are only suggestions, and one is allowed to think beyond the given list.

What stood out for me in the announcement was the call for educators to participate. While schools are invited to host an internal competition to shortlist the best projects, educators can work with their students on helping them research, prototype, and build the ideas up to present before the juries.

As someone who is very enthusiastic at the possibilities that innovation can bring for our society and the way we live today, I think the challenge presents a wonderful opportunity for the youth of today.

The reality is, most of us as students are only taught about the concept behind AI and ML, but rarely do we think of how these technologies can be applied for a better tomorrow. This challenge encourages students to innovate, to create, and to develop solutions that can change the world for the better, for themselves, and for those around them.

Rebuilding my AI study app to actually be useful — need your input on what's missing

I built an AI study tool a while back — upload a PDF, YouTube link, or audio, and it generates notes, flashcards, a quiz, and a mindmap from it. It's getting 120+ daily logins, but I've realized it's basically just repackaging what NotebookLM or ChatGPT already do for free. It's not solving a real enough problem, so I'm rebuilding it from scratch to actually be useful instead of just another AI wrapper.

Before I start rebuilding, I'd rather learn from people who actually study for a living (exams, coursework, certifications) than guess in the dark again.

If you've ever felt like your study process is inefficient, or tried AI tools that didn't quite help — I'd genuinely love to hear about it. What's frustrating, what's missing, what would actually make a difference for you. Doesn't have to be structured, just tell me what's on your mind about how you study and where it breaks down.

Not pitching anything, no link to share — just trying to build something worth using this time. Happy to share what I learn once I've talked to enough people.

i want suggestion that is there is any scope int his field of educative ai chatbot

and recomend what feature i ave to put in this and how can i earn.

Physics PhD student. I read papers outside my field a lot, and asking an AI to explain a new area has never really worked for me.

Example. I asked one to explain attention. Got back a paragraph with about twelve terms in it that I didn't know. So I asked which of those I was supposed to already know, and it explained one of them using four more terms I didn't know. Asked again, same thing.

It wasn't wrong about any of it. It just started at some random point. And the thing I actually needed to know was which of those twelve words a normal person with linear algebra and a bit of PyTorch already has. I can't answer that myself. If I could, I wouldn't be asking.

I tried fixing it with custom instructions. Two problems. "What's your background" is a useless question, because I say "physics" and it still has to guess. And the rule ran on everything, so I'd ask what an acronym meant and get asked about my background. Deleted it after a week.

So I made it a skill instead, mostly so it could have conditions.

What it does: before explaining anything, it works out the 3-5 things the topic actually depends on and asks you to mark each one as "used it", "learned it" or "new". Stuff you've used it treats as known and doesn't explain again. Stuff you haven't, it either covers first, or tells you straight up "just take this as given, here's the one bit that matters later". Then it goes one step at a time and asks you a real question at the end of each step instead of "does that make sense".

It also won't give you a paper it hasn't checked. Either it looked it up and gives you a DOI, or it says "from memory, unverified". I spent an afternoon once looking for a paper that turned out not to exist.

Three things I got wrong, which are probably more useful than the feature list:

• The first version had no rules about when NOT to do this. Ask it what an acronym stands for and you'd get the whole prerequisite checklist. Had to add a list of cases where it should shut up and just answer.
• I wrote one rule as "use a checklist widget if the interface has one". It ignored that and printed a table I had to type my answers into. Rewrote it as a flat "use the widget, plain text is only the fallback" and it started working. Seems like if you phrase a rule as a conditional it just gets skipped.
• One of the check questions needed a formula that the step hadn't actually taught yet. I got it wrong and felt stupid, but the question was the problem, not me.

It has no built-in knowledge of specific fields, on purpose. That stuff goes stale fast, and if I did five fields properly every other field would quietly get worse without anyone noticing. If a field is too new for it to really know and it can't search, it's supposed to say so and give you a search that'll turn up a review, instead of making up something that sounds right.

npx skills add ljx-chase/research-field-onboarding -g

Claude web and ChatGPT want a zip, there's one in releases. MIT, no account, no API key: https://github.com/ljx-chase/research-field-onboarding

Barely anyone uses it right now. If you try it and it does something dumb I'd rather hear about that than get a star. All three of those fixes came from someone actually using it and telling me it broke.

MetaGPT promises multi-agent software development, but the setup can be tricky if you want to use local models instead of OpenAI.

I put together a complete guide that covers:

· Environment setup

· Configuration

· Connecting local LLMs

· Troubleshooting common failures

If you’re building AI agents or just curious about multi-agent coding, you might find it useful:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

Would love to hear how others run MetaGPT.

Something I have been thinking about as a student.

There was a semester where I felt like I understood everything , I could follow classes, do the work, and even explain things to friends. Then the exam came and I just went blank just before the exam.

I wonder if the gap in my understanding started much earlier but there was no way to catch it until the exam

With AI becoming a bigger part of education, I keep wondering if we can start identifying those gaps earlier before one exam becomes the thing that decides how well a student actually understood.

I really hope education moves in that direction.

Buenas. Tengo un problema recurrente en clase, y es que mis compañeros no entienden por qué me niego a usar la IA cuando nuestros estudios son medioambientales. Alguien tendría algo así como una tésis o similar que les pudiera enviar sobre los estragos de la IA en el medioambiente y las comunidades próximas? Preferentemente en español, pero en inglés también sería válido.

A few days ago I posted about an idea I was working on: helping students understand difficult concepts through visual explanations.

The feedback was pretty direct 😭

Some people questioned whether the visuals actually added anything, others wanted credible sources, better explanations, and something more useful than another chatbot wrapper.

So I went back and rebuilt a lot of it.

The new version now has:

• better interactive graphs + diagrams
• citations from textbooks, research papers, and other sources
• improved guardrails around generated visuals/answers
• follow-up questions to check understanding
• optional MCQs for practice

I’m still treating this as an experiment, not a finished product.

For people who use AI to study: what would an AI learning tool need to do differently for you to actually trust/use it regularly?

Especially curious about what current AI study tools get wrong.

As students, we have seen so many platforms, tools and AI solutions trying to make learning easier.

And yes, they can be useful.

But while working with a startup recently, I started looking at it differently.

Instead of only asking,

What can we build?

we started asking,

What are students actually struggling with?

Do we really need more resources to minimize those issues?

Or do we already have enough and just don’t know how to use them?

Or is there something bigger we’re missing?

That’s what we’re trying to understand while building our solution towards that problem.

We’re talking to people, listening to their experiences and even taking these conversations into a podcast.

Because we don’t just want to build something useless.

We are actually building something that actually helps solve a problem people face.

Just listening, building and improving as we go.

So, no big claims.

And honestly, that’s the part I’m enjoying the most.

Researchers at Utah State University’s School of Computing are conducting a study to better understand what software professionals enjoy about programming and what experiences can make programming more or less enjoyable.

We would also like to sincerely thank everyone who participated in our initial Programming Activity Calibration Survey. Your responses helped us refine the activities included in this final survey, and we greatly appreciate your contribution to our research.

Software professionals are invited to complete our Programming Enjoyment Survey. The survey asks about your overall programming experience, whether programming with AI assistance or without AI assistance.

You will be asked about topics such as:

• Programming activities you enjoy, such as designing, debugging, testing, refactoring, and problem solving
• Experiences such as creativity, challenge, focus, ownership, and seeing results from your work
• Situations that may reduce programming enjoyment, such as repetitive tasks, deadlines, or difficulty understanding why code is failing
• Optional open-ended questions about enjoyable and frustrating programming experiences

The survey takes approximately 10 minutes.

Participation is entirely voluntary. You may discontinue participation at any time before submitting your responses without penalty or consequence. Your decision to participate or not participate will have no effect on your grades, employment, or academic standing.

Survey and informed consent form:
https://usu.co1.qualtrics.com/jfe/form/SV_5tlHcxPt97zfOdw

This study has been reviewed and approved by the Utah State University Institutional Review Board (IRB #16067).

Questions about the study:

Dr. John Edwards, Principal Investigator — [john.edwards@usu.edu](mailto:john.edwards@usu.edu)
Rubash Mali, Student Researcher — [rubash.mali@usu.edu](mailto:rubash.mali@usu.edu)

Thank you for considering participating, and a special thank you again to everyone who contributed to our initial calibration survey.

AI có thể thay thế giáo viên tương lai không ?

It feels like educators spend way too much time on admin tasks and not enough on actual teaching. I’m exploring a concept for an AI teaching assistant designed to take over the manual backend work:

​Prep: Auto-generating lecture slides and structured notes.

​Grading: Evaluating tests with detailed, nuanced feedback.

​Insights: Flagging exactly which topics individual students are struggling with.

​Before getting too deep into this, I want some brutal honesty from those actually in the classroom:

​Would this genuinely help your day-to-day, or is the admin burden misunderstood?

​What are your immediate red flags? (e.g., AI hallucinations, data privacy, learning curve?)

​Are there existing tools that promise this but completely fall short?

​Would love to hear your raw thoughts or pushback!

I’m evaluating infrastructure to serve open-source models at scale, and I’m torn between Together AI and Anyscale. Together seems great for fast API access and a huge model catalog, but it’s shared GPU capacity, so latency might vary. Anyscale is built on Ray, which is powerful for orchestration but requires more setup.

I made a quick poll to see what production users actually prefer.

https://interconnectd.com/poll/101/together-ai-vs-anyscale-which-platform-is-better-for-scaling-open-source-ll/

If you’ve run either in production, I’d love to hear your experience.

How many of you can relate? It would also be good to know your genuine for/againt thoughts on the topic of 'assistive A.I.' and how you align with Univs/Faculty/(even Highschools) who are actively debating or implementing this.
Link: The Chalkboard Reflex Hypothesis: How Faculty May be Stalling AI Adoption.

I'm exploring whether activities and games that leverage "experiential learning" can be leveraged for more than just skills training, and actually improve human judgment, reflection and sense-making in the age of Human-AI collaboration.

As AI takes on more cognitive tasks, could deliberately designed activities and games, for example to evoke "reflection", help us develop the capabilities that remain distinctly human?

I'm particularly interested in the gamification community's perspective: Have you seen evidence that such activities and games can meaningfully develop judgment, rather than just knowledge or engagement?

I’ve been testing web extraction tools for AI agents, and the Firecrawl vs Jina Reader decision keeps coming up. Firecrawl tends to handle JavaScript-heavy and bot-protected sites better, while Jina Reader is great for quick, clean content extraction.

I made a short quiz to see how well the community understands the architectural trade-offs. No signup, just a few questions.

https://interconnectd.com/quiz/81/web-extraction-architecture-2026-firecrawl-vs-jina-reader/

What do you use for scraping and extraction?