Zubaydullo Abdirakhmonov

- AI & Software Engineer 👨‍💻
- 10+ Hackathon Winner 🏆
- Sharing tech, startups & lessons from shipping fast.

[ClaudeDevs @ClaudeDevs]

Claude Fable 5 changed how we work on the Claude Code team day to day.

We used to verify that Claude did the work right. Now we verify that it's doing the right work.

Here’s the 3 biggest changes:

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

Welcome Claude Fable 5: a Mythos-class model 🔥

https://t.co/0jq7w8FbFC

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

Worth reading this!

“i’m going back to writing code by hand”

https://t.co/kw6e07rSY7

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

Demo Video: https://t.co/rNLMTMcsEr

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

1st place at the Sharjah Int’l Conf. for Arabic Language & AI 🇦🇪

100+ teams | 30+ countries

We built Bayan AI : Arabic grammar correction, tashkīl, classical transformation & rhetorical refinement.

PS: check comments for demo.

[Charly Wargnier @DataChaz]

this guy literally breaks down the exact Claude + Obsidian playbook to build your own AI employee in a single afternoon 🤯

[Vasu-Devs @Vasu_Devs]

I’m open sourcing JustHireMe 🚀

A local-first Agentic AI desktop app I’ve been building to make job searching more intelligent, transparent, and user-controlled.

GitHub: https://t.co/5R8mxCDSiR
The current job search process is broken.

Candidates spend hours scrolling through:

stale job posts

irrelevant roles
spammy listings
senior-only positions
repeated listings across platforms
jobs with almost no useful context
And most AI job tools either scrape too broadly, rank opportunities like a black box, or try to automate applications without giving the user enough control.
I wanted to build something different.

JustHireMe is designed as a personal job intelligence workbench.

Instead of blindly applying everywhere, it helps users discover better opportunities, evaluate them against their real profile, and generate tailored application materials while keeping sensitive career data local.

What it can do:

Ingest resume/profile data

Build a local professional profile graph
Discover job leads from multiple sources
Filter out low-quality or irrelevant postings
Score roles based on explainable fit
Match jobs using graph + vector search
Generate tailored resumes
Generate cover letters
Draft cold emails
Draft LinkedIn outreach messages
Track leads in a local CRM-style pipeline
Keep the user in control through a human-in-the-loop workflow
The main principle behind the project is:
More signal.
More explanation.
More local control.
Less blind automation.

The tech stack:
Tauri for the desktop shell
React + TypeScript for the frontend
Python + FastAPI for the backend sidecar

SQLite for local lead tracking

KuzuDB for graph-based profile modeling
LanceDB for vector search and semantic matching
Playwright for experimental browser automation
One of the biggest goals is privacy.
Your resume, career history, generated documents, job leads, application notes, and API keys should not have to live on someone else’s server by default.
JustHireMe is built around a local-first architecture so users can keep ownership of their data while still benefiting from modern AI workflows.
Another major goal is explainability.
I don’t want an AI system that just says:

“This job is a good match.”

I want it to explain:

which skills matched

which projects support the application

what gaps exist

why a role was filtered out

why a role deserves attention
what to highlight in the resume or cover letter
That matters because job search is not just a productivity problem.
It is personal.
It affects confidence.
It affects opportunity.
It affects people’s careers.
The project is currently in alpha, but the foundation is in place.
I’m looking for contributors interested in:
Agentic AI

AI agents
workflow automation
job source adapters
web scraping

ranking algorithms

GraphRAG

vector databases
semantic search
resume parsing
document generation
local-first software
privacy-first AI
UI/UX
testing and documentation
If you’re a developer, designer, AI engineer, student, or someone who has felt the pain of modern job searching, I’d love your feedback, ideas, issues, PRs, or even just a star ⭐
Repo: https://t.co/5R8mxCDSiR
Let’s build a better, more transparent job search system together.
#OpenSource #AgenticAI #AIAgents #RAG #GraphRAG #Python #FastAPI #ReactJS #TypeScript #Tauri #VectorDatabases #JobSearch #CareerTech #Automation #PrivacyFirst

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

@borjafat This is cool!

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

@ay_140 @sironyeka 😂😂

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

Cursor AI Hackathon — Hamburg 🇩🇪

48 hours.
400+ builders.
First international hackathon.

Placed 30/105.

“Good… but not dangerous yet.”

Hard truth:

It’s not about ideas anymore.
It’s execution under chaos.
Speed > perfection.

We didn’t win.
But we leveled up.
Next one? 👀

[Hasan Toor @hasantoxr]

5 AI projects that will get you hired in 2026:

save & retweet it.❤️

1. RAG from Scratch

GitHub: https://t.co/11KsgR4k35

2. Al Social Media Agent

GitHub: https://t.co/GYHcOclzV8

3. Medical Image Analysis

GitHub: https://t.co/XkWsS6NbBk

4. MCP Tool-Calling Agents

Notebook: https://t.co/MUjZUE5Qll

5. Al Assistant with Memory

GitHub: https://t.co/TPuGq8IOIF

[Krishna Agrawal @Krishnasagrawal]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/Avq3pNZxWY

2. LLMs from Scratch: https://t.co/nGJGCQYi89

3. Agentic AI Overview (Stanford): https://t.co/1JbA2JypnJ

4. Building and Evaluating Agents: https://t.co/02o8b7RAS2

5. Building Effective Agents: https://t.co/Jw0cd23A3K

6. Building Agents with MCP: https://t.co/9uvcpin1j9

7. Building an Agent from Scratch: https://t.co/QiYCLK48tn

8. Philo Agents: https://t.co/TCSX4hnKRf

🗂️ Repos

1. GenAI Agents: https://t.co/VdfLWAA3wW

2. Microsoft's AI Agents for Beginners: https://t.co/3SpFdnMcQa

3. Prompt Engineering Guide: https://t.co/4W7Eh6NKaE

4. Hands-On Large Language Models: https://t.co/LEtKGYBdgU

5. AI Agents for Beginners: https://t.co/3SpFdnMcQa

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/sL3gbmGUky

8. Hands-On AI Engineering:https://t.co/J79go1Ivlo

9. Awesome Generative AI Guide: https://t.co/xXF08rY8zP

10. Designing Machine Learning Systems: https://t.co/Q39XZLn50b

11. Machine Learning for Beginners from Microsoft: https://t.co/vfkyHL1Gnx

12. LLM Course: https://t.co/wuDkhQ0572

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/BSjthrG81u

2. Google's Agent Companion: https://t.co/wdzh5zuSvp

3. Building Effective Agents by Anthropic: https://t.co/HmYVgMIA7l.

4. Claude Code Best Agentic Coding practices: https://t.co/H3JpJWlpp3

5. OpenAI's Practical Guide to Building Agents: https://t.co/G1TL1Z2TR3

📚Books:

1. Understanding Deep Learning: https://t.co/EEqkGL7lHe

2. Building an LLM from Scratch: https://t.co/8Ehn91NNxE

3. The LLM Engineering Handbook: https://t.co/yzvXeQAgtV

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/FKEzpBewe4

5. Building Applications with AI Agents - Michael Albada: https://t.co/nvBaoR6FxZ

6. AI Agents with MCP - Kyle Stratis: https://t.co/u0M1GfBlsL

7. AI Engineering: https://t.co/A2QxhrpmDc

📜 Papers

1. ReAct: https://t.co/Vbc3vWxQHO

2. Generative Agents: https://t.co/PvNHYnqsOD.

3. Toolformer: https://t.co/HFdds582DI

4. Chain-of-Thought Prompting: https://t.co/GxX5BxXLzV.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/zhizqjunzS

2. MCP with Anthropic: https://t.co/LqDZ0oCRfj

3. Building Vector Databases with Pinecone: https://t.co/ncqSVaVHqH

4. Vector Databases from Embeddings to Apps: https://t.co/JOl7SIlguq

5. Agent Memory: https://t.co/5m0UrGwDM9

Repost for your network ♻️
&follow for more stuff on building AI Agents.

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

@swapnakpanda Need it please:)

[Dan Kornas @DanKornas]

If you’re serious about understanding AI models - not just running them - mastering the math is non-negotiable.

This book gives you that mastery: it builds the mental model of how learning actually happens - from vector spaces and eigenvalues to gradients, loss landscapes, and uncertainty - with Python implementations at every step.

It’s the fastest path from “black box” to “I can explain, debug, and improve this.”

What you’ll learn (and actually apply):

🔹Linear Algebra that powers ML: vectors, norms & inner products, eigenvalues/eigenvectors, LU/QR/SVD factorizations, matrices ↔ graphs → the backbone of PCA, embeddings, attention, and graph methods.

🔹Calculus for learning dynamics: limits/continuity, differentiation & integration, numerical methods → why gradient descent works, how backprop computes updates, and what your loss landscape looks like.

🔹Multivariable optimization: partials, Jacobians, Hessians, total derivatives, high-dimensional geometry → diagnosing plateaus, curvature-aware intuition, and safer optimization choices.

🔹Probability & information theory: random variables, common distributions, expectation/variance, Bayes’ theorem, LLN, entropy & MLE → modeling uncertainty, likelihood-based training, and data-driven reasoning.

🔹Hands-on code, not just theory: every concept tied to practical Python so you can implement gradient descent, decompositions, and probabilistic modeling—not just read about them.

Bottom line: Mathematics of Machine Learning is a blueprint for moving from cargo-cult ML to engineer-level understanding—the kind that lets you read papers, reproduce results, pick the right tools, and ship reliable systems.

Thanks to @Packt and @TivadarDanka for making high-level math genuinely usable for ML engineers.

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

@iam_elias1 SEND

[Zubaydullo Abdirakhmonov @zubaydullo_dev]

@damianplayer VAULT

[Damian Player @damianplayer]

LEAKED: 100s of premium AI Agents EXPOSED...

These Agents sell for $5,000+ per build, easily...

Inside the file you’ll get:

→ Lead qualification agents
→ Content generation pipelines
→ Appointment booking automation
→ Cold outreach sequences
→ Data extraction & web scrapers
→ Customer support agents
→ +100s more plug & play systems

BONUS: An n8n Masterclass, so you know how to run, customize, and scale every workflow.

These are the same systems 6-figure agencies use to deliver high-ticket builds.

Follow + RT + Comment “VAULT” and I’ll send you the drive for FREE!

PS: If you’re still building agents manually… Consider this your early retirement package.