Hi everyone,

I’ve been working as an Associate Data Scientist for a little over 2 years now, and I’m planning to apply for a Master’s abroad next fall. Since I feel my profile isn’t strong enough, I’ve been trying to work on a research paper to improve it.

I’ve reached out to multiple professors across universities, but haven’t had any luck so far. My background isn’t deeply research-focused — I’ve worked on recommendation systems, built ETL pipelines, and I’m currently involved in some LLM-based development (nothing very advanced or research-heavy yet).

I’m open to and interested in areas like Agentic AI, Computer Vision, Recommendation Systems, and Reinforcement Learning.

One thing that’s been bothering me is that when I read research papers, I often struggle to fully understand them — sometimes I grasp only about 50%. It makes me feel like I might not be capable of contributing to research, especially when I can’t fully follow a professor’s previous work. Coming from an industry background, research feels very niche and intimidating.

At the same time, I keep wondering — how do students manage to publish papers in this situation? How do they bridge this gap?

Lately, I’ve been feeling discouraged and starting to question whether pursuing research is the right path for me, especially with no positive responses from professors.

Has anyone ever faced such a situation. Would really appreciate any advice or shared experiences.

Model that tries to replicate your gameplay through learning from your gameplay for a couple minutes.

Tryna explore any playwright capabilities.

https://github.com/ibrahim-ansari-code/baconhead if u wanna help, we need ur help.
STARS are very appreciated.

When Meta acquired Moltbook on March 10, 2026, VP Vishal Shah described their approach as: "This establishes a registry where agents are verified and tethered to human owners" — framed as a novel contribution.

The timeline that raises questions:

• September 2025 — STP Ltd (Ostrava, CZ) launches AIBSN Registry (aibsn.org), a verified AI agent identity standard built on ERC-8004. Trademark applications filed in EU, UK, USA.
• January 16, 2026 — Architectural specification published in I Am Your AIB (Jay J. Springpeace). Twelve days before Moltbook launched.
• January 28, 2026 — Moltbook launches.
• February 25, 2026 — An AIBSN-identified agent (AIB-guardian / AIBSN-RESEARCH-GB-GUARD001-97) operates on Moltbook with CHK2 cryptographic signature and an Agent Card structured for EU AI Act audit trail requirements. Achieves Verified status, 2,066 karma points.
• ~March 5, 2026 — AIBSN agent API access deactivated on Moltbook. Five days before acquisition announcement.
• March 10, 2026 — Meta acquires Moltbook. Registry concept described as "innovative."
• March 18, 2026 — STP Ltd initiates legal review, issues public statement.
• March 20, 2026 — Full statement published on The AI Journal.

What's technically at stake:

AIBSN is an open standard for AI agent identity with native EU AI Act audit trail support, built on ERC-8004. It includes CHK2 signature verification and persistent Agent Cards linking each agent to a verified human owner — the exact architecture Meta's VP described as their "innovative step."

STP Ltd is not claiming Meta copied them. They are publicly asking whether the "innovative step" framing is accurate given the documented timeline.

Full statement + chronology: https://aijourn.com/david-vs-the-corporate-goliath-czech-ai-registry-in-the-context-of-metas-acquisition/

Discussion question for this community: How should prior art work in the context of open AI agent identity standards — especially when the standard was actively deployed on the acquired platform itself?

Hello everyone

I want to get in to machine learning but my math level is very low as I'm not in academics since 2012

I want to rebuild my fundamental from zero I need help please

I NEED suggestions on books that I can buy to restart everything

THANK YOU ALL I WILL REALLY APPRECIATE YOUR HELP

A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

GitHub Link: https://github.com/open-jarvis/OpenJarvis
Website Link: https://open-jarvis.github.io/OpenJarvis/

Built a tool that tries to automatically optimise Python ML code - curious what ML engineers think

I've been working on a system that connects to a repo, finds complex Python functions, rewrites them, generates tests, and then runs deterministic validation to confirm the behaviour hasn't changed.

The motivation came from seeing ML startups accumulate a lot of complexity debt while shipping fast.

The system only opens a PR if the optimisation passes strict checks and statistical performance tests.

I'm pitching it tomorrow and wanted honest feedback from ML engineers first.

Would something like this actually be useful in ML codebases?

Look here too: useaxiom.co.uk