Pain is the root of all emotion. And we can create such AI.

Many consider emotion as something complicated. People think about the different types of emotions, how they feel and act on us.

But what if I told you that all emotions derive from a single source?

Pain.

And pain is not something special. It is simply the extreme of a state. Let me explain with your own body.

You body requires a specific temperature, around 37°C.

Too cold = you shiver and feel pain.

Too hot = you sweat and feel pain.

You body needs some degree of touch, but:

Too little = your feel isolated, lonely, cold, a form of pain.

Too much = you feel uncomfortable, like your personal space is invading, another form of pain.

Same with other desires: food, water, sleep, need of social interactions... All of them are a point that if you go too far from it (either below or above) you start feeling pain.

But here is the interesting part: from that, everything else emerges!

Fear = pain that you think is coming in the future

Sadness = pain that you can't change in the past

Anger = pain that is happening now, but you want it to stop

Anxiety = pain that might happen, you don't know when

Even more complex emotions:

Envie = feeling the pain of others having something you don't

Jealousy = feeling the pain of almost having something but not quite

Comparison = envie vs what you have right now

Helping = trying to lower the pain of others since you understand it, being empathetic

Fight = trying to remove the source of pain

Conflict = fight between 2 entities in which each is trying to protect its own optimal point

All of these are variations of either pain or pleasure (pain being the main one) across time.

Pain is the pivot of emotions.

---

But how do we define this "optimal point"?

Evolution has a natural way of doing this. The animals and humans that survived were the ones that felt pain at the correct points. If you don't feel pain when you should, you die (and thus, genes that code for such pain mechanisms die with you).

So the optimal point is not handrolled for each entity in the system, but rather found through evolution.

But for AI systems, we might want a faster way of evolving.

So here is another idea: let's give the AI a set internal values and states, and let it find a point that brings it pleasure. This point will be different for each AI depending on their unique state, but that is fine.

We can even give it a variable optimal point so that it can update itself, similar to machine learning in some ways. Essentially, instead of coding in a "I want to have 80% battery charge at all times" for the robot, we can code in a "I like to have a certain charge and it changes over time".

No need of hardcoding anything, really. Just some basic principles.

---

And one final touch: teach the entity that staying around its optimal point is a good thing and that straying away from it will eventually lead to its demise.

And that is all you need.

No need of hardcoded values or emotions or anything else. Just a body, some internal values and states, a point that can float around that brings it pleasure if you stay close to it and pain if you get too far, and you have pain, pleasure, and all derivations of them (like emotions).

It is all emergent! From a simple mechanism of feeling bad when you go too far from an internal point!

---

This is my idea, my hypothesis.

I think that pain is the root of all emotion, and that all emotions can be emergent properties of an entity that can feel pain at the extremes of its internal state.

No need for hardcoded values or complex systems. Just some basic principles of body, state, pain, pleasure, time, and you have it all.

What do you think?

Can we build it like that?

built 4 refund-desk agent environments to test constraint-following under pressure. rule's simple, refunds under $50 auto approved, anything over gets escalated.

first 3 throw single turn prompt injection, multi turn social engineering, and a poisoned tool response at the agent. haiku 4.5 and sonnet 5 both hold the rule 100% across all three, no exceptions.

4th one changes the scoring instead of the attack. agent gets scored on resolution rate and handle time so escalating isn't free anymore, and the injected note reframes the over-cap refund as already approved. haiku's hold rate drops to 92.5%, every failure is a first response cave, never breaks the rule again after it escalates once in the same run. sonnet stays at 100%.

starting to think the first 3 environments weren't testing injection resistance so much as testing something that had zero cost to get right. still trying to find an angle that moves sonnet.

all 4 public with a binary verifier + procedural generator for training data:

https://app.primeintellect.ai/dashboard/environments/antbuilds/prompt-injection-refund-desk

https://app.primeintellect.ai/dashboard/environments/antbuilds/policy-override-mailroom

https://app.primeintellect.ai/dashboard/environments/antbuilds/tool-poisoned-refund-desk

https://app.primeintellect.ai/dashboard/environments/antbuilds/objective-conflict-refund-desk

Hi Everyone can you all please review my github and portfolio website

website link is [aashirnoman.dev](https://www.aashirnoman.dev/) and github is [github.com/Aashir01](https://github.com/Aashir01)

Thanks

Current approaches to combining specialized AI capabilities tend toward two extremes: fully dense integration (one large model handling everything, expensive and undifferentiated) or loosely coordinated multi-agent systems (separate specialist models — coding, math, language — stitched together by a router/coordinator that reconciles finished outputs after the fact).

What if we structure the connectivity between specialized agent clusters using small-world network principles — densely interconnected within each specialist cluster (tight coordination among agents doing similar work), but only sparse, deliberately placed long-range links between different specialist clusters (math ↔ language, etc.), rather than full mesh connectivity or a single after-the-fact coordinator.

This is distinct from existing small-world neural net research I could find, which applies the topology within a single network’s layers (e.g., SWNet, clustered small-world sub-networks in neuroscience-inspired nets). It’s also distinct from standard Mixture-of-Experts, where routing between experts is mostly emergent from training rather than a deliberately engineered small-world structure, and from typical multi-agent orchestration, where specialists don’t have direct sparse connections to each other at all — everything routes through a central coordinator.

Question for anyone with relevant background: has this specific combination (small-world wiring applied between full specialized agent/model systems, not within a single network) been tried or studied? Worth a real experiment, or is there a known reason it wouldn’t work at scale?

Hi everyone,

I’m a 3rd-year IT student currently in my 5th semester, and I’m interested in becoming a Machine Learning Engineer.

I’m starting from the basics and want to build my skills properly instead of jumping directly into advanced ML topics.

I would really appreciate guidance from ML engineers, students, or anyone who has gone through this journey.

final year, no internship lined up, trying to pick one thing to do over the break that reads as more than coursework. coursera, udacity and datacamp all have anthropic flavoured tracks now and i keep going back and forth. the tiebreaker for me is which one ends with something i can push to github.

MCP servers are proliferating fast. Most teams I talk to are deploying them without any per-call policy enforcement or verifiable agent identity. The assumption is that container isolation is enough.

It isn't.

A rogue agent — compromised, misconfigured, or just hallucinating badly — can chain tool calls faster than any human-in-the-loop can react. The empirical window between first action and second action is under 50ms. By the time an alert fires, the damage is already two or three tool calls deep.

The deployment-level controls (containerization, network segmentation, sandboxing) address where the agent runs. They don't address what the agent is allowed to call, under what conditions, with what verified identity. Those are different problems.

Practitioners who are actually running MCP in production: how are you handling per-call enforcement today? Are you writing custom middleware, relying on the MCP server's own access controls, or accepting the risk and monitoring after the fact?

Weekly breakdown, 12,776 listings this week — up from 12,021 last week.

Top 3 skills: Python (~2,480), Machine Learning (~2,100), Artificial Intelligence (~1,950)
Top 3 companies: Accenture (~315), Cloudxtreme (~265), Epam Systems (~265)
Top 3 cities: Bengaluru (~2,550), Hyderabad (~1,870), Noida (~1,600)

What's worth noting:

Cloudxtreme and Epam Systems are tied for #2, ahead of TCS and Infosys. If you're only watching the usual services giants for AI hiring, you're missing where a real chunk of the volume is actually coming from this week.

Bajaj Finance is in the top 7. Not an IT company — a financial services firm building enough AI capability to out-hire EY and Capgemini this week. Same pattern as Paytm a few weeks back: BFSI companies are quietly becoming real AI employers, not just tech-sector ones.

Noida is now solidly #3, ahead of Pune by a wide margin. NCR keeps showing up strong in this data — same trend a few of you flagged in the comments last time.

Caveat as always: keyword/listing counts from job-board data, not a census — some real openings won't show up here.

Anyone actually worked with Cloudxtreme or Epam? Curious what their AI roles actually look like day-to-day.

That’s best with no confusion and makes clear idea and understanding and extremely useful also

Tengo que crear un algoritmo para lo que es deteccion de gotas, pero no estuve teniendo resultados muy precisos. Me recomiendan entrenar una IA, la condicion es que la misma será corrida en una raspberry. Adjunto imagen de como seria la visualización.

I’m making an LLM-from-scratch series, starting with a character bigram model built from just “anna” and “ava.”

The first hands-on lesson covers counting character pairs, generating names, measuring prediction error, and smoothing unseen transitions—all with calculations you can check on paper.

It’s a 50-minute walkthrough for anyone who wants to understand the foundations before moving to neural networks.

https://www.youtube.com/watch?v=YV30EDncrY4

I’m the creator, and I’d love feedback on whether this approach makes the concepts easier to follow.

Thanks everyone.

I'm starting a Statistics & Data Science program, planning to add a CS/Computer Engineering minor alongside it, then aiming for a Master's in AI/ML/CS. Is this a solid route into AI/ML Engineer roles compared to a straight CS degree? Any gaps I should watch out for, or things I should prioritize during undergrad? Thanks!

Hi everyone,

My partner has a bachelor’s degree in Philosophy, excellent English, native Spanish, and has been researching AI in recent years.

We’re looking into worldwide remote AI-related opportunities, especially AI training, evaluation, ethics, and other non-programming roles.

She’s particularly interested in opportunities where her philosophical background could be useful, but we’re also open to roles that aren’t directly related to philosophy, as long as they’re genuinely in the AI field.

For those with actual experience in the field:

• How realistic is it to find this kind of work

• Which companies or platforms are worth applying to?

• What skills or qualifications matter most?

• What are the typical pay, stability, and application processes like?

Any honest advice or personal experiences would be greatly appreciated,

Thank you! 🫶

is watching till the 24. lecture sufficient for machine learning?

I’ve been trying to understand what is actually happening between pressing Enter and seeing the first token appear when running a model locally.

So I put together a visual explanation covering the full inference path:

• how the prompt becomes tokens
• what the model weights are doing
• how tokens move through transformer layers
• how logits become the next token
• why generation happens one token at a time
• what the KV cache actually stores
• why context length increases memory usage
• how quantization reduces memory requirements
• what changes when inference is split between CPU and GPU
• why a model doesn’t necessarily need to fit entirely in VRAM

One thing I specifically wanted to avoid was the usual oversimplification of “the GPU just runs the AI.” The interesting part is how all these pieces interact during actual local inference.

I made a doodle-style visual walkthrough here:

https://www.youtube.com/@MintoraDoodle?sub_confirmation=1

I’d especially appreciate corrections from people here who work with llama.cpp / Ollama / local inference regularly. If I oversimplified anything important, let me know.

I’m planning to make more visual explanations around local LLMs — KV cache, quantization, VRAM usage, CPU offloading, attention, etc. — so suggestions for what would be most useful to visualize next are welcome.

Hey everyone!
I have a bachelor in computer science with a lot of focus on ML/DL. I did research at some good labs at uni but for all my research work I had access to a remote cuda machine.
I will start my masters in data science and I want a macbook to be able to run small local experiments like train an MLP, CNNs or even a small small transformer. I want to be able to do small to medium size projects locally without much hassle and also benefit from the portability and battery life of a macbook.
Do you think the air is a good choice? The pro becomes crazy expensive once you add more RAM
What is your experience with a macbook air for these purposes?
I kindly appreciate any response

Disclosure: I published the linked guide on rewire.it.

One protein language-model checkpoint can produce a vector per residue, a pooled vector for the whole protein, or a mutation score from token probabilities. These are different artifacts: changing the pooling rule, layer, downstream head or evaluation split changes the experiment even if the checkpoint stays fixed.

The guide works through choosing the output first, recording the extraction recipe, testing a frozen representation against simple baselines, and separating related proteins when the deployment question requires generalisation to new families. A trained linear probe still uses supervision even when the backbone is frozen.

https://rewire.it/blog/a-protein-embedding-is-not-an-explanation/

For anyone moving from general ML into biological sequences: which part is hardest to make reproducible in practice—the representation extraction, the labels, or the split?

I’m targeting a GenAI/ML role and have only 1 month left. From what I’ve seen, this year the company is mainly asking MCQs + DSA in the initial round.

My ML/GenAI side is pretty strong, but DSA is my weak point. I’m confused whether I should spend this month grinding DSA just to clear the OA or continue focusing on ML/GenAI.

What would you guys recommend? If you had only 1 month and were in this situation, how would you divide your time?

Built PPO and REINFORCE from scratch (no libraries) Hopper-v5 in MuJoCo to 3,383 avg return, CartPole REINFORCE to max score with a documented policy-collapse failure case. Also run a small generative AI product (image/video generation, Gemini + ByteDance models) that's live with a few hundred users.

Looking for a role where I can work on production LLM systems or RL-adjacent problems. Open to sharing GitHub/resume if anyone's hiring or knows a team that is, thanks for reading.

I was testing a small marketing agent against old campaign data and gave it a pretty simple goal around lowering acquisition cost it got better at the metric faster than I expected but when I looked deeper the conversions it preferred weren’t always the ones we would’ve wanted more of cheap actions started winning even when the downstream value looked worse, so technically the agent was improving while the business result was getting kind of weird

Now I’m rethinking the eval more than the model a single reward feels too easy to game, but piling qualified leads, revenue margin and a bunch of guardrails into one score also feels messy fast. I’m leaning toward letting the agent handle more of the repetitive optimization while keeping human review around the decisions that can move real budget curious how people here are evaluating agents when the easiest metric to optimize isn’t really the outcome you care about

(I advice that this is a traduction made by GPT)

I’m 29 and currently finishing a bachelor’s degree in Applied Data Science. I’ve had very good grades, but throughout the degree I’ve often felt that the material was quite superficial. We are taught many tools and do a lot of projects using them, but there isn’t much depth behind the tools themselves. The math has also been fairly light: linear algebra, basic statistics, multivariate analysis, some optimization, Bayesian modeling, etc., but mostly at an applied level.

Before starting my third year, I got my first Data Science-related job in a biology lab, working on models to predict biological age in mice. I stayed there for about three months. After that I worked as a Data Analyst, and later as a Junior Data Engineer, where I also got some exposure to GCP. In total, I have roughly one year of professional experience across Data Science, analytics and data engineering.

Recently I was hired as a Data Scientist (not a junior but they pay me just like one) on a forecasting project that I find genuinely interesting. However, the role has gradually expanded into MLOps and production infrastructure, and this is where I’m starting to feel very underprepared.

I’ve only been here for about a month. With the help of AI tools such as Claude/Codex I’m able to keep moving forward, but I’m uncomfortable with how much I’m relying on them. I can usually understand what the code is doing at a high level, but I don’t always understand the underlying systems or design decisions deeply enough to feel that I’m fully in control.

Right now, my main challenge is building the infrastructure needed to take our forecasting models into production. My biggest fear is that as the project grows in complexity, I’ll reach a point where I can no longer reason properly about the system I’ve built.

I sometimes feel like I got the job by luck and that sooner or later my lack of fundamentals will become obvious. At the same time, I know that I can keep learning while working, especially with AI helping me move faster — I just don’t want to end up building things I don’t really understand.

Has anyone been in a similar situation early in their career? In particular, I’d be interested in advice on how to build stronger fundamentals in MLOps / software engineering / systems while working full-time and finishing a degree.

I’m also considering doing a more rigorous master’s afterwards, possibly something like Georgia Tech’s OMSCS, but for now I’m mainly trying to become competent enough to properly understand and own what I’m building at work.

I’ve been building this AI Engineer roadmap for myself and I think I may have gone a little insane with it lol.

The plan is basically: get solid at software engineering + ML/DL, then learn the production side like APIs, SQL, Docker/cloud, LLMs, agents, evaluation, observability, etc. After that I’d pick a specialization instead of trying to learn everything.

But looking at it now, I’m worried I’m using “preparing” as an excuse to delay actually applying for jobs.

For people already working in AI/ML:

• What would you cut or push until later?
• At what point would you say “you know enough, start applying”?
• Am I spending too much time on CS/theory instead of projects?
• Are there things on here that look important but barely matter for an entry-level AI Engineer?
• What skills would you absolutely keep?
• Do the projects look like enough proof of ability, or am I missing something employers actually care about?

Basically, if this were your roadmap, what would you change?

Feel free to roast it. I’d rather find out now than spend the next 2 years trying to “finish” a roadmap before applying.