Cule4Life

Cule for Life

[Cule4Life @ChinksSon]

@BraDepay @JusticeSremSai @cdzas In what? Are you serious

[Cule4Life @ChinksSon]

@BraDepay @JusticeSremSai @cdzas Whats your educational background?

[Michael Bajwa @michael_baj]

We launched Zaro AI - The future of workflows. AI Agents and AI Apps in one place.

[Tom Dörr @tom_doerr]

Converts YouTube videos into structured Markdown without API keys

https://t.co/eoLcECFcDN

[Tom Dörr @tom_doerr]

AI agent to validate startup ideas before building

https://t.co/vg5R6XVrDP

[Max Mynter @MaxMynter]

You asked, I deliver (2 weeks late).

A blogpost about what I did over the last 18 months to sign as a research engineer at a foundation model lab.

Link and summary below

1/n

[Krishna Agrawal @Krishnasagrawal]

Germany remote based Companies for job seekers!

https://t.co/wVWBOpdcvQ

Bookmark it ✅️

Cc @AvinashSingh_20

[Avinash Singh @AvinashSingh_20]

US tech companies that provide visa sponsorship for job seekers!
https://t.co/o1MCBsjmdr

Get startup bundle for more lists - https://t.co/gFxeLKc4QY

Bookmark it!

[Avinash Singh @AvinashSingh_20]

European Tech Companies That Sponsor Work Visas:
https://t.co/QxcaNxpWdo

Bookmark it!

[Rohit Kumar Tiwari @_rohit_tiwari_]

AI Engineering from Scratch.

503 lessons. 20 phases. 320 hours.

https://t.co/UuX9N62nNm

Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)

[Cule4Life @ChinksSon]

@thevibes_gh You are a paedophile.

[Tom Dörr @tom_doerr]

Curated technical interview prep handbook for developers

https://t.co/og62R1t2mE

[Cule4Life @ChinksSon]

@kurowura2 Post reported. We can't be making jokes about child sexual exploitation!

[Tom Dörr @tom_doerr]

Agentic SOC platform automating security investigations

https://t.co/JeigtSBwLG

[Tom Dörr @tom_doerr]

Hands-on notebooks for LLM and RAG evaluation

https://t.co/E1EBXOp4ws

[Tom Dörr @tom_doerr]

Collection of patterns for building software with AI assistance

https://t.co/B2VUWUh260

[Manning Publications @ManningBooks]

Most interviewers don't expect you to know everything.

What they do want to see is how you think when you don't know the answer.

Interview Speak by Barbara Limmer and @LauraBrowne7 teaches you how to break down tough questions, communicate clearly, and answer in a way that helps interviewers understand your strengths.

There's now a companion course, too: https://t.co/J4VUs99N8V

Book: https://t.co/taz41fLmIe

[Sydney Runkle @sydneyrunkle]

learn about how to build agents that interact w/ the browser!

[Thariq @trq212]

Lots of people asked how I used Fable to edit its own launch video so I made a video about that!

TLDR it wrote a lot of code & tool calls to use transcription services, ffmpeg, do colorgrading, use the figma mcp, make remotion UI and render it.

I didn't touch a video editor.

[Markus J. Buehler @ProfBuehlerMIT]

We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition.

We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta.

Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include:
1⃣Builder/Breaker model that discovers mode-conditioned compliance in proteins;
2⃣CategoryScienceClaw that finds anisotropic fiber-network stiffness rules.

Great work in collaboration with my graduate student @fwang108_ @MITdeptofBE

F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026