Dr. Sambit Praharaj

Assistant Professor in CSE | 📚📝Hybrid Human-AI | Postdoc Research - AI in EdTech | 🇮🇳 🇳🇱 🇩🇪 | | 🎥

[Dr. Sambit Praharaj @SambitPhD]

An experience of a lifetime when you end something significant! PhD done :)

Thank you everyone, thanks to my supervisors and the committee for this awesome experience! Especially, my supervisors made this time period a memorable one which will be etched in my memory forever.

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

Transformer Architecture

Explained in 3 ways here:

- Encoder only
- Deconder only
- Encoder - Decoder both

[Anthropic @AnthropicAI]

We're publishing our most detailed threat intelligence report to date.

It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.

We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.

These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.

We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.

Read the report: https://t.co/0EJUnYEgfz

[Dhanian 🗯️ @e_opore]

🤖 Day 1/30 - AI Agents Series

In this first post, we'll understand what AI Agents are, how they differ from traditional LLM applications, and why they are becoming the foundation of modern AI systems.

What Are AI Agents?

An AI Agent is a software system that can perceive information, reason about a task, make decisions, and perform actions to achieve a specific goal with minimal human intervention.

Unlike a traditional chatbot that simply responds to prompts, an AI Agent can plan, use tools, remember previous interactions, retrieve information, and execute multi-step workflows.

LLMs vs AI Agents

A Large Language Model (LLM) generates text based on the prompt it receives.

An AI Agent uses an LLM as its "brain" but combines it with additional capabilities such as:

Memory
Planning
Tool usage
Decision making
External APIs
Knowledge retrieval
Multi-step reasoning

Think of it this way:

LLM = Thinks and generates responses.
AI Agent = Thinks, decides, and takes actions.

What Is an AI Workflow?

An AI workflow is a predefined sequence of steps.

For example:

User uploads a PDF.
System extracts the text.
LLM summarizes the content.
Summary is emailed back to the user.

The flow is fixed and predictable.

What Is an Autonomous AI Agent?

An autonomous AI Agent decides what to do next without requiring instructions for every step.

For example, a coding agent can:

Read a GitHub repository.
Analyze the codebase.
Find bugs.
Generate fixes.
Run tests.
Repeat until the task is complete.

Instead of following a rigid workflow, it adapts its actions based on the current situation.

Building AI Agents this way makes applications more scalable, flexible, and capable of solving complex real-world problems.

Project: Build Your First AI Chatbot Agent

Today we'll build a simple AI chatbot agent.

The chatbot will:

Accept user questions.
Send them to an LLM.
Generate intelligent responses.
Maintain conversation history.
Remember previous messages during the session.

This project introduces the fundamental architecture behind AI agents and serves as the foundation for every project we'll build throughout this series. In the coming days, we'll continue expanding it with memory, tools, retrieval, APIs, planning, and autonomous capabilities.

Grab the Building AI Agents from Scratch Ebook: https://t.co/SWCKKtbPNm

#AIAgents #LLM #GenerativeAI #ArtificialIntelligence

[divyansh tiwari @DivyanshT91162]

10 GitHub repos so good they probably shouldn't be free.

1. OmniRoute
One endpoint. 231 AI providers. 50+ free-tier providers.

Connect Claude Code, Codex, Cursor, and Cline to Claude, GPT, and Gemini for free.

Compresses tokens by 15–95% with automatic fallback if a provider goes down.

GitHub:
https://t.co/DyQkj165aY

2. OfficeCLI
The first Office suite built for AI agents.

Control Word, Excel, and PowerPoint from a single command line.

No Microsoft Office installation. No dependencies. Just one binary.

GitHub:
https://t.co/q2D8nVPj9u

3. System Prompts Leaks
Leaked system prompts from Claude, GPT, Gemini, Grok, Cursor, Copilot, and more.

Regularly updated.

GitHub:
https://t.co/HZpSfXMBcg

4. OpenCut
An open-source CapCut alternative that runs in your browser.

No account. No telemetry. No subscription.

GitHub:
https://t.co/QniZNYr9g3

5. AI Job Search
A Claude Code-powered agent that applies for jobs for you.

Analyzes listings, tailors your resume, writes cover letters, and prepares interview questions.

GitHub:
https://t.co/fvIeDOpg34

6. Meetily
A fully local meeting transcription and summarization tool powered by Whisper and Ollama.

Built in Rust.

GitHub:
https://t.co/6iZOAoFA5o

7. Vibe-Trading
Use natural language and AI agents to build and execute trading strategies.

GitHub:
https://t.co/f3vWfJorhS

8. Strix
An AI-powered open-source vulnerability scanner.

Finds and helps fix security issues before attackers do.

GitHub:
https://t.co/OfaDzLxML7

9. Superpower
A self-hosted AI workspace with 250k+ GitHub stars.

GitHub:
https://t.co/f0gC0yxjrQ

10. Firecrawl
Turn any website into clean, LLM-ready data in seconds.

The industry standard for RAG and data pipelines.

GitHub:
https://t.co/onZ9Vi2dsn

100% Open Source.
All of them are free.
All of them are worth bookmarking.

[Indu Tripathi @InduTripat82427]

An asian guy has discovered a method to learn anything ten times faster using AI!

It just involves the Claude + Obsidian.

Most people learn the slow way: read, forget, re-read, forget again.

His flip: use Claude to turn anything you're learning into small, connected notes. Use Obsidian to link them so nothing you learn ever sits alone.

The slow way: highlight a book, move on, forget it in a week.
The fast way: Claude breaks it into atomic notes, and Obsidian links them into a growing web of knowledge.

Six months in, one new idea instantly connects to twenty things you already know.

[Md Ismail Šojal 🕷️ @0x0SojalSec]

Train your own language model from scratch in 5 minutes. 🥹

Most people think training LLMs is rocket science.
it ridiculously simple:

No PhD, No massive cluster, you can Runs in browser,
Just one Colab notebook The entire pipeline data gen, tokenizer, training, inference,

This is the best "how LLMs actually work" demo I've seen. Perfect for beginners

- https://t.co/S0D2NOCyET

[Amit Shekhar @amitiitbhu]

https://t.co/NqwRD0HP7N

[Oliver @oliviscusAI]

Turn Claude Code into a full academic research team with this guide.

> Deep Research: 13 agents, 8 modes
> Academic Paper: 12 agents, 11 modes
> Academic Paper Reviewer: 7 agents
> Academic Pipeline: 10-stage orchestrator with citation verification

Free for non-commercial use.

[Dharmik Harinkhede @AiwithDharmik]

🚨 BREAKING: Research just became 10x faster.

Claude can now turn dozens of academic papers into clear, structured insights — like a top-tier researcher.

No more overwhelm. Just clarity.

Here are 9 prompts to get straight to the point 👇
Bookmark this 🔖

[Yarchi @undefinedKi]

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC

Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team

Repo: affaan-m/ecc

This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products

What's inside:

A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review

Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work

The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed

This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to

Bookmark this

[How To Prompt @HowToPrompt__]

Claude Code can now run an entire PhD-level research pipeline by itself.

it runs a 10-stage workflow from blank page to publication-ready PDF, replacing the work of a PhD advisor, three peer reviewers, and a copy editor in one repo.

→ Deep research with 13 agents (PRISMA + systematic review)
→ 12 agents write the paper section by section
→ 5-person peer review (Editor + 3 Reviewers + Devil's Advocate)
→ Integrity agent catches fabricated citations + stat errors
→ Final output: LaTeX → PDF, ready to submit

After the paper is finalized, it runs a Collaboration Quality Evaluation that scores YOU, across 6 dimensions, 1–100. Direction setting, intellectual contribution, quality gatekeeping.

It tells you exactly where you were the bottleneck.

Drop it into .claude/skills/ and the whole pipeline auto-loads. Works in Claude Code, Cowork, and as a Claude Project.

100% open source. CC-BY-NC 4.0.

[Ihtesham Ali @ihteshamali]

10 WEBSITES EVERY STUDENT SHOULD USE BEFORE GRADUATION.

Bookmark every single one. Your university will never tell you about most of these.

1. https://t.co/Sw42cy7v6U
Upload every textbook, lecture, and PDF for a course. Ask questions across all of them. Built by Google DeepMind.

2. https://t.co/6w0IBtOEYB
Search scientific research fast. Ask any question and get answers with links to relevant papers in seconds.

3. https://t.co/OAhaYocdxH
The world's largest open library. Almost any textbook your professor assigned is on here for free.

4. https://t.co/Y3KvNzZ11m
Research assistant that cites every source. Replaces 90% of Google searches for academic work.

5. https://t.co/Po8bWb4d0Z
Free reference manager that builds your bibliography automatically. Saves 20+ hours per semester.

6. https://t.co/9kI8wuIEk1
Solves math, physics, chemistry, and engineering problems step by step. Shows the full working.

7. https://t.co/7rJwkujVhX
The job platform built specifically for students. 1.4 million employers actively recruiting on it right now.

8. https://t.co/0FjarSEgUe
Matches you to scholarships you actually qualify for. Over $3.4 billion awarded to students every year.

9. https://t.co/Gv2m8XhUiP
Free Barbara Oakley course taken by 4 million people. The science of how to actually study and remember.

10. https://t.co/GdrQtY6qs6
Free with most university logins. 16,000+ courses on everything from Excel to AI engineering.

The students who graduate with the biggest head start are not smarter. They just found the right tools earlier.

[Rishabh @Rixhabh__]

Anthropic Academy just dropped FREE AI courses that could replace a $10,000 degree.

$0. No catch. No gatekeeping.

Here are 6 AI courses that could separate you from everyone else in 2026:

[Nainsi Dwivedi @NainsiDwiv50980]

🚨Breaking: Someone open sourced a knowledge graph engine for your codebase and it's terrifying how good it is.

It's called GitNexus. And it's not a documentation tool.

It's a full code intelligence layer that maps every dependency, call chain, and execution flow in your repo -- then plugs directly into Claude Code, Cursor, and Windsurf via MCP.

Here's what this thing does autonomously:

→ Indexes your entire codebase into a graph with Tree-sitter AST parsing
→ Maps every function call, import, class inheritance, and interface
→ Groups related code into functional clusters with cohesion scores
→ Traces execution flows from entry points through full call chains
→ Runs blast radius analysis before you change a single line
→ Detects which processes break when you touch a specific function
→ Renames symbols across 5+ files in one coordinated operation
→ Generates a full codebase wiki from the knowledge graph automatically

Here's the wildest part:

Your AI agent edits UserService.validate().

It doesn't know 47 functions depend on its return type.

Breaking changes ship.

GitNexus pre-computes the entire dependency structure at index time -- so when Claude Code asks "what depends on this?", it gets a complete answer in 1 query instead of 10.

Smaller models get full architectural clarity. Even GPT-4o-mini stops breaking call chains.

One command to set it up:
`npx gitnexus analyze`

That's it. MCP registers automatically. Claude Code hooks install themselves.

Your AI agent has been coding blind. This fixes that.

9.4K GitHub stars. 1.2K forks. Already trending.

100% Open Source.

(Link in the comments)

[Mustafa. @oprydai]

the polymath is not a person who collects hobbies.

it is a person who refuses artificial borders.

• art and science
• engineering and philosophy
• history and design

for most of history, these were not separate worlds.

the polymath by peter burke shows that broad thinkers shaped culture by moving between domains.

they borrowed methods
translated ideas
connected distant fields

specialists optimize inside systems.

polymaths redesign the map itself.
in an age of narrow expertise, synthesis becomes rare leverage.

[Allen Braden @allen_explains]

🚨 The creator of Claude Code just shared a full walkthrough on how to actually use it the right way.

30 minutes. Free. Straight from the person who built it.

Watch the workshop and save it for later.

You’ll likely get more practical value from this than from most expensive coding courses online.

Most people are only scratching the surface of what Claude Code can do.

Then check out the guide below.

[ZARA @HeyZaraKhan]

100 Claude Repos that will completely change your life:
(save this)

1. Terminal AI coding agent https://t.co/gkTwIb7T9s
2. Ready-to-use starter apps https://t.co/PtEclReNCv
3. Official agent skills https://t.co/1RZ7lpP3xB
4. Plugin marketplace https://t.co/GVeknTu56n
5. Full ecosystem https://t.co/HOkmGrvrvL
6. Master list https://t.co/BVrGRmcQdV
7. 1000+ plugins https://t.co/8lzXVbeps2
8. Huge skills library https://t.co/wnArbr7a7N
9. Curated skills https://t.co/apIhhs1UEG
10. Cross-platform skills https://t.co/7wcdPfFLSh
11. LLM pipelines https://t.co/iqzPWyUrXk
12. Agent workflows https://t.co/bXPebvjN4v
13. Multi-agent systems https://t.co/o6NGMPl9h7
14. Team-based agents https://t.co/Ay1jn8wFpZ
15. AI dev team https://t.co/IXZNK0ym7r
16. Code agents https://t.co/Uq0aHekO6Y
17. Auto PR fixes https://t.co/zbCEAnFHlD
18. AI coding assistant https://t.co/rAeiD6uphU
19. Code search https://t.co/gDIpT7KMs3
20. Agent standards https://t.co/S6YyQF6v8v
21. Productivity plugins https://t.co/xOowIKylVb
22. AI SDK https://t.co/eIYxaxaDLm
23. Memory layer https://t.co/mXiinRZafi
24. Voice agents https://t.co/mWSwV3YifX
25. Deploy agents https://t.co/NtTWJdcqx4
26. Web agents https://t.co/b8HiXRrI7G
27. Reasoning agents https://t.co/fMvI18BxYn
28. Long-term memory https://t.co/sJK9vrePQI
29. AI apps infra https://t.co/RYXlG8O4xD
30. API layer https://t.co/i7JfKyvYy1
31. Clean UI https://t.co/vqyN30JVso
32. Self-hosted UI https://t.co/mi8adckJBZ
33. Modern UI https://t.co/auzUkeggzf
34. Desktop app https://t.co/8yK33a3izg
35. Next.js clone https://t.co/9zpY0oN5Qv
36. Template https://t.co/2kkhJIIafG
37. Claude-ready UI https://t.co/yoaCZQco7W
38. Minimal UI https://t.co/dh9HOubWu8
39. Lightweight UI https://t.co/lr4f3N8CKv
40. Adaptable UI https://t.co/f6ekpq25Ik
👇

[Brij Pandey @LearnWithBrij]

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/YkuDFVmW9e

2. LLMs from Scratch: https://t.co/u3kSz5SGuJ

3. Agentic AI Overview (Stanford): https://t.co/W6rzVHGSgC

4. Building and Evaluating Agents: https://t.co/sEl8vVax3F

5. Building Effective Agents: https://t.co/c7fD4aWFYO

6. Building Agents with MCP: https://t.co/GlMdR6htgA

7. Building an Agent from Scratch: https://t.co/kUQ9jPuI0R

8. Philo Agents: https://t.co/8JHvqw0DKn

🗂️ Repos

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

2. Microsoft's AI Agents for Beginners: https://t.co/zFJAN74JQe

3. Prompt Engineering Guide: https://t.co/liUshX2XsP

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

5. AI Agents for Beginners: https://t.co/zFJAN74JQe

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

7. Made with ML: https://t.co/lkXP6itwK0

8. Hands-On AI Engineering:https://t.co/zB8EEctE4Y

9. Awesome Generative AI Guide: https://t.co/lF7CuIQHRw

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

11. Machine Learning for Beginners from Microsoft: https://t.co/hF5UzZoMJB

12. LLM Course: https://t.co/4tLAwy8fOQ

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/0OEKVLgF34

2. Google's Agent Companion: https://t.co/r0Dxe4VvDO

3. Building Effective Agents by Anthropic: https://t.co/I0ZyuwiOS3.

4. Claude Code Best Agentic Coding practices: https://t.co/HIBC2TwwAP

5. OpenAI's Practical Guide to Building Agents: https://t.co/1I8n0wnjHQ

📚Books:

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

2. Building an LLM from Scratch: https://t.co/4sZmBnHPEg

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

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/KsFnET47hx

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

6. AI Agents with MCP - Kyle Stratis: https://t.co/C2lhD8uTDL

7. AI Engineering: https://t.co/34EyUiIVMv

📜 Papers

1. ReAct: https://t.co/kfQ8tWysne

2. Generative Agents: https://t.co/wbfqXq8KZK.

3. Toolformer: https://t.co/OQ7m49YWls

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

🧑🏫 Courses:

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

2. MCP with Anthropic: https://t.co/wx1DAIWis0

3. Building Vector Databases with Pinecone: https://t.co/8XsQzDstTB

4. Vector Databases from Embeddings to Apps: https://t.co/9n6DvZGTMN

5. Agent Memory: https://t.co/OxFAaM0fp7

Repost for your network ♻️

[CODIFY @codi_fyy]

🚨 BREAKING: Someone just built the exact tool Andrej Karpathy said someone should build.

48 hours after Karpathy posted his LLM Knowledge Bases workflow, this showed up on GitHub.

It's called Graphify. One command. Any folder. Full knowledge graph.

Point it at any folder. Run /graphify inside Claude Code. Walk away.

Here is what comes out the other side:

-> A navigable knowledge graph of everything in that folder
-> An Obsidian vault with backlinked articles
-> A wiki that starts at index. md and maps every concept cluster
-> Plain English Q&A over your entire codebase or research folder

You can ask it things like:

"What calls this function?"
"What connects these two concepts?"
"What are the most important nodes in this project?"

No vector database. No setup. No config files.

The token efficiency number is what got me:

71.5x fewer tokens per query compared to reading raw files.

That is not a small improvement. That is a completely different paradigm for how AI agents reason over large codebases.

What it supports:

-> Code in 13 programming languages
-> PDFs
-> Images via Claude Vision
-> Markdown files

Install in one line:

pip install graphify && graphify install

Then type /graphify in Claude Code and point it at anything.

Karpathy asked. Someone delivered in 48 hours.

That is the pace of 2026.

Open Source. Free.

[pdawg @prathamgrv]

I made a Claude Code skill that turns any arxiv paper into working code.

Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed.

open sourcing it -
https://t.co/sSio4JfpIo