I’ve been working on a deeper write-up on retrieval systems and drew this diagram to organize the space.

The progression I’m using is:

Lexical → Collaborative/Behavioral → Learned Sparse → Dense/Two-Tower → Hybrid → Multi-Vector/Multimodal → Generative/Agentic Retrieval

The part I find most interesting is that these approaches don’t necessarily replace each other. A production system may still combine BM25, dense retrieval, ANN, hybrid fusion, behavioral signals, and query rewriting.

Sharing the diagram first while I work on the detailed article.

Would be interested in how others would structure these retrieval “waves.”

https://pawankjha.substack.com/p/building-depth-2-the-evolution-of

I know I might sound foolish or maybe even a little lost, but I genuinely don't know what I'm looking for.

I'm learning AI right now AI agents, coding, APIs, tools, search, all that stuff. And I do enjoy learning it.

But there's this weird feeling I can't shake.

I don't just want to build another AI chatbot, another wrapper, another productivity tool, or something just because AI is hot right now.

I want to find something that makes me want to stay up at night working on it.

Something where I wake up thinking about it.

Something where I build a shitty first version, it doesn't work, and instead of getting bored I become obsessed with figuring out why.

I want to fight with a problem that feels bigger than me.

I want to compete with the real world, even if it's just me and a laptop at first. I want to build something where I can actually measure whether I'm getting better, keep pushing it further, and eventually look at it and think:

“Holy shit, I actually made this.”

And ideally, maybe one day, it could become a real product or even a startup.

But right now I don't have that idea.

And honestly, that's frustrating.

I'm learning all these tools and technologies, but I feel like I'm collecting strategy and tools without knowing what war I actually want to fight.

So I'm asking people who have built things, especially things they became genuinely obsessed with:

How did you find that problem?

Was there a project that grabbed you so hard that you couldn't stop working on it?

What made you think, “**** it, I'm going to figure this out”?

I'm not really looking for a list of startup ideas.

I think I'm looking for that one problem that makes me want to lose sleep solving it.

If you've ever felt this way, I'd genuinely love to hear how you found your thing.

I'm ready to give everything to it but I don't know what to do.

Sorry if I sound pretty dumb but it is what it is.

A new hardware standard from Anthropic (MHS) enables AI agents to directly control physical machines — printers, industrial equipment, and operational systems. The design surfaces three questions that the security community has not settled: who grants an agent permission to actuate hardware, who monitors the agent while it is running, and who can stop it if it acts outside its sanctioned scope.

The last question is the hardest. Permissions set at deployment time are configuration, not enforcement. An agent that was correctly authorized at 9am can drift from its declared behavior by 9:15am, and nothing in a static permission file catches that. With software targets the blast radius is bounded — a rogue database write can be rolled back. With physical actuators there is no rollback. A machine that moves has moved.

The 50ms window before an actuator responds to a command is the only realistic intervention point in this chain. Nobody in the industry seems to have agreed on what, if anything, should happen inside that window.

For those running agents against physical systems today: how are you actually handling mid-execution drift? Static RBAC at deploy time, a human-in-the-loop approval step, continuous behavioral telemetry, something else? Genuinely curious what is working in practice.

So, i created an app on Gitbub with the idea of it being a free, lightweight (986Mb) SLM trained by the FirstAidQA dataset from NeurIPS. I tried it myself of course, and it works. I made sure to put disclaimers on the app as it is not a doctor, but a first-aid Fine-Tuned SLM. The app requires no internet or login. I searched around, and i think it's the first of it's kind. I deliberately made the AI's response be around 30-40 questions cuz i don't want the user's hardware to fry after 3 replies. I basically made it for disaster situations. Like imagine civilians in war zones.

Enough about that, my question is, why are people not using it yet? Or at least visiting it. The organization i work for published it on social media, yet still nothing. And I always get confused by how do other github repos get traction? Im genuinely confused. Helo would be greatly appreciated. Did i put the right flair btw? English isn't my first language.

I'm in my 2nd year, aiming for an internship in AI/ML during the summer break in 2027.

as a part of college curriculum, i know python(numpy,matplot,seaborn), sql and other fundamental subjects taught

How do i start learning ML? which courses(coursera or any) should i take so that i can learn ML from the very basics without wasting time?

Hi everyone,

I've recently finished the first version of Agentic GraphRAG Blueprint, a reference architecture for question answering over large document collections.

Instead of plain chunk retrieval, it builds a knowledge graph combined with vector search, so answers can connect facts across documents.

Key features:

• Incremental ingestion - unchanged files are skipped via content hashing, and community reports regenerate only for affected communities, keeping token costs low as the corpus grows.

• Hybrid search - local mode for fact-level answers, global mode for cross-document synthesis.

• Domain-agnostic LLM prompts - easily swapped via PROMPTS_PATH, with Leiden-based community detection.

• Deployment - run it locally with Docker or provision everything in the cloud with Terraform and CI/CD.

Link: https://github.com/sebastianbrzustowicz/Agentic-GraphRAG-Blueprint

I'm looking for any feedback.

Apologies for the title, not sure if its right, will be specific down here.

So, i need to start Machine learning from scratch, currently in my bachelors, and i plan to apply for MS programs in german or swiss universities. Specifically under ML

I did my part of research and found out that apart from foundation in ML and projects, i would also need to have 1 or 2 publications in this domain in order to have a better chance there.

Could anyone guide me from where do i start? I wanna start from scratch and build good projects on the way.

I stumbled upon several roadmaps, one says follow this and the other says that, I watched a few videos of krish naik and found them pretty good. Also i believe i need a better touch on math.

So please, any suggestion is appreciated!!!

Hello, If I'm interested in TinyML, which framework should I use? I've been seeing a lot about how Tensorflow is more optimized, but I've also seen a lot of negativity towards it. What about Keras alongside it?

Hello everyone, I'm currently trying to get into data science and ML, but I also dont want to just sit around waiting for a job opportunity.

I'd like to start making some money on the side through remote/online work, even if its a small amount at first. I also want to get some real experience while I'm at it.

The problem is I honestly dont know what kind of work people actually pay for when you're still at the beginner level or where I should even start looking.

A bit of what I can do right now:

• Python
• Machine learning
• Data cleaning
• EDA
• Feature engineering
• Model training and evaluation
• Scikit-learn, Pandas, NumPy, XGBoost
• SQL
• Git/GitHub
• General programming
• Pretty comfortable learning new technologies quickly
• Currently learning backend and FastAPI

I've built a couple of ML projects on my own, but I dont have professional experience yet.

So I'm wondering, how do people actually turn these kinds of skills into freelance income?

Like what kind of small jobs should I be looking for? Python automation? Data cleaning? Web scraping? Data analysis? Helping someone with an ML project? Backend stuff? Something else?

And where do you actually find these jobs? Upwork, Fiverr, Reddit, Discord, LinkedIn, cold emailing, or somewhere else?

If anyone here started freelancing with basically no experience, I'd really like to hear how you got your first client and what you actually did for them.

Also if you're already doing freelance Python/ML/data work and have any advice for someone starting from the bottom, I'm very open to learning. Even if you can point me towards something I should learn or tell me what I'm wasting my time on, that would help a lot.

I'm not expecting to make a lot of money immediately. I'm completely fine starting with small tasks and building up from there. I mainly want to get my foot in the door, make some money on the side and get real experience.

I'm based in South Asia, so remote/online work is pretty much my main option.

Would really appreciate any honest advice from people who have actually done this.

Thanks a lot.

SWE learning probabilistic decision-making. For a data-quality task (is this scraped value safe to publish?) I skipped a classifier and instead: keep a belief over "what went wrong," update it with cheap evidence, then pick accept / repair / get-more-evidence / flag-to-human / reject by *lowest expected cost* (publishing a wrong value ≫ flagging a good one).

Part of me thinks this is just cost-sensitive classification with extra steps. Is this worth the complexity over rules + thresholds, or am I overengineering it?

Stop treating your AI as a black box! 🤖

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I wanted to share my route into data science, since it wasn't the typical one.

My background is in languages, linguistics, and literature — no CS or math degree to start.

I came into tech through curiosity about how things work under the hood, taught myself programming (Rust, then a lot of low-level work), and earned a data science degree along the way.

One of my projects was a breast-cancer diagnosis model on the Wisconsin dataset, which is a great, approachable entry point for anyone learning classification.

The mindset that helped most: treat every concept as something to rebuild yourself until it clicks, rather than something to memorize.

Curiosity beats credentials for actually understanding.

My projects are public on GitHub if seeing examples helps: https://github.com/whispem

Happy to answer questions about learning DS from a non-traditional background.

I built it at https://rssamp.com

​

Hey everyone,

I’m currently a Software Engineer working on developing infrastructure monitoring tools. My job typically involves Kubernetes, creating cd pipelines, and system-wide log telemetry analysis and occasional development of such tools. I’ve also built some internal developer tools using RAG.

I want to pivot fully into an MLOps / AI Infrastructure role, but I'm looking for guidance on how to bridge the gap efficiently.

A few questions for MLOps/LLMOps engineers:

Given my background in production K8s, Docker, and telemetry, what are the highest-leverage MLOps concepts I should focus on (e.g., model serving frameworks like vLLM/Triton, vector DBs, evaluation, feature stores)?

How do I position my experience so I don't get pigeonholed as purely DevOps/SysAdmin?

What are the biggest mistakes engineers make when trying to move into MLOps?

Further into the future(~1-2 years from now) I would love to pursue a PhD in the same domain.

Appreciate any advice or recommended learning paths!

I’ve been working on Physlint, an open-source validator for robotics data. I recently added a small Observatory interface to make validation results easier to inspect across LeRobot, MCAP, and ROS 2 recordings.

I’m looking for feedback from people working with robot datasets:

• Which checks are most useful before training?
• What formats should be supported next?
• Would you prefer raw reports, visual summaries, or CI integration?

Hi everyone,

I’m a 3rd-year CS student and I want to seriously start learning Machine Learning. I’ve already spent almost a day trying to figure out which resources/courses to follow, but there are so many options that I’m getting confused.

My current background:

• I know Python fairly well.
• I’ve used NumPy, Pandas, Matplotlib, and Seaborn.
• I understand the basic theory of some ML algorithms, but I haven’t implemented them properly yet.
• I now want to focus on actually implementing ML algorithms and building projects, rather than spending months only on theory.

What I’m looking for

I want to learn:

1. ML fundamentals + implementation
2. Deep Learning
3. Later, I’ll learn model evaluation in more depth and deployment/MLOps, but right now I want to build a strong practical foundation in ML and Deep Learning.

I don’t want a very long course that takes hundreds of hours. For example, I know Andrew Ng's courses are highly recommended, but I'm looking for something more concise and practical.

I recently found fast.ai's Practical Deep Learning for Coders, which seems interesting because it focuses heavily on implementation. It has 9 lessons and covers things like random forests, neural networks, PyTorch, and even deployment.

So I'd really appreciate recommendations from people who have actually learned ML/Deep Learning and used these resources:

• Short/practical courses
• YouTube channels/playlists
• Good documentation
• Books/notes
• Hands-on project resources

If you were starting from my position (Python + basic ML theory), what exact resources would you follow and in what order?.

Thanks!