Shiv 🎥

Data Scientist. Math, Tech, Business.

[Swati Gupta @hrswatigupta]

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free.

Bookmark & watch this today before someone takes it down and read this article below

[Shiv 🎥 @GuyWithStories]

Generational product ! Sad part is big tech will eat this product up . Hope they are fair and acquire this product for a huge price

[Farza 🇵🇰🇺🇸 @FarzaTV]

We built an AI that can draw on your screen.

It's a true personal tutor.

Using Claude Opus we're able to draw polygons, point with pixel perfect accuracy, and walk users through complex steps directly on their screen.

Here's me learning Pythagorean Theorem + FL Studio.

Demo:

[himanshu @retr0sushi_]

anybody with a world models background is not surprised!

pretty neat work

[Suraj Sharma @suraj_sharma14]

If I had to build 5 projects to get hired as an Agentic AI Engineer.

I'd do this.

Project 1: Compliance Sentinel Agent
Scans new regulations daily. Auto-audits code. Generates audit-ready reports.

Saves legal teams 40+ hours/week.
Stack: LangGraph + Playwright + Legal RAG

Project 2: Autonomous Code Reviewer
4 agents debate every PR: Security, Style, Tests, Architecture.

Cuts review time by 70%.
Stack: GitHub Actions + Tree-sitter + LLM-as-a-judge

Project 3: Research-to-Deck Generator
Reads 50+ papers. Synthesizes key points. Exports editable PPT with citations.

Turns 3 days of work into 5 minutes.
Stack: Semantic Scholar API + python-pptx + RAG

Project 4: Recruiting Swarm
Parses resumes. Scores fit. Drafts outreach. Books interviews.

Increases interview show-up rates by 40%.
Stack: NER models + Vector search + SendGrid + Calendar API

Project 5: Cost-Aware Agent Router
Token budgeting. Model routing by cost/latency. Early exit on confidence.

Reduces inference costs by 60%.
Stack: vLLM + Prometheus + Dynamic model switching

Most people build chatbots.
Builders build systems that solve expensive problems.

(Bookmark & Repost)

[Daksh Gupta @dakshgup]

parampara, prathishta, anushasan

pre-training, alignment, post-training

[probiex007 @probiex007]

🤯 This is a website, a simple web-based game built with WebGL and Three.js.
Website: https://t.co/G0cDgaWzKe
It's honestly surprising how far web development has come.

[Shiv 🎥 @GuyWithStories]

TIL it stands for secret key . But in my mind I always read it as Siva Karthikeyan 😭😭😭😭

[Raj @heyraj__]

why all API start with sk 👀

[Shiv 🎥 @GuyWithStories]

Proof that you can just build things. The only moat is can you think of great ideas like this

[Ojasvika Sahu (Oz) @Ozacle23]

we just built what @satyanadella is talking about.

here's the ultimate COMPANY BRAIN, your sovereignty 👇🏼

try here- https://t.co/p4xkDWmefL

minimi feeds Claude every tab, doc, call, and thread on your mac. add your team-mates' links and claude knows their work as well. 💫

[Vaibhav Sisinty @VaibhavSisinty]

A PM with 12 years of experience watched smart people ship products nobody wanted. Over and over again.

So he built a free Claude skill that fixes the thinking before the building starts.

It's called vibe-check. Install it in Claude Code, Codex, or upload it as a project skill in Claude.

One command and Claude stops being your coding partner and becomes your product partner first.

Here's what it does before a single line of code gets written:

→ Grills you on the actual problem. Not features. Not screens. The real pain a real person has right now.

→ Checks your answer against Reddit raw unfiltered complaints people actually post to validate if the pain is real before you build.

→ Generates user flows as mermaid diagrams you can hand directly to your coding agent.

→ Recommends a tech stack with plain-language rationale. Not "use Next.js" but why this stack for this project.

→ Builds a data model derived from the flows, not from guesses.

→ Creates a phased build order with stop-and-validate checkpoints.

→ Designs the growth loop does the app pull in the next user on its own or are you fetching every single one by hand forever?

To try it: install the skill and tell Claude "I have an idea for an app that helps dog owners share walking routes. Pressure-test it."

Free. MIT license. Open-source.

https://t.co/XyooyYDQKp

[Darshil | Data Engineer👨🏻‍🔧 @parmardarshil07]

There is no Leetcode for Data Engineers

Yet.

Here's how we can help you to crack data engineering interviews

-- 515+ Editorial to learn foundations
-- 220+ video lessons
-- 20+ End-To-End Real-World Projects
-- 850+ coding questions (PySpark, SQL, Python, Scala, DBT)
-- 100+ data/dimension modelling design question
-- 50+ data architecture design questions
-- AI data engineer resume review

Finally, a data engineering platform that helps crack real interviews.

[Asmit @coolcoder56]

The best playlist if you want to become a DevOps engineer in 2026

[Tony Chen @tonychenAI]

We're hiring at @KleePay.

We're building payment infrastructure for AI agents and looking for AI-native builders across engineering, product, growth, operations, and internships.

You don't need 10 years of experience, but you do need to move fast, learn fast, and figure things out without being told exactly what to do.

We care more about proof of work than credentials.

Competitive base salary + meaningful equity.

DM me your GitHub, X account, portfolio, side project, or anything you've built.

[Narayani Gurunathan @Narayani07]

🚀 #Hiring a GenAI Engineer

Looking for someone with experience in:

• GPT, Claude, Gemini, Llama, Mistral
• RAG, Prompt Engineering, Embeddings
• LangChain / LangGraph / LlamaIndex
• Python, FastAPI, APIs
• Pinecone, FAISS, Weaviate
• AWS / Azure / GCP

📩 [email protected], if interested or tag someone who should see this.

[Vaishnavi @_vmlops]

While everyone was grinding DSA, Sarvam AI was asking candidates to build a VAD from scratch for an 84 LPA role 🫡

The barrier to entry is becoming a barrier to existence 💀

[Vishal Singh 🥑 @vishalsingh2972]

Ok we begin 🇮🇳,

A project that aims to kill corruption, babu culture, bureaucratic bottlenecks
that are slowing India down.

Made with super powerful Sarvam AI 105B parameter models.

Demo video out soon.

[Movez @0xMovez]

Claude Code creator:

"100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too.

The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops"

in 1-hour interview, Boris reveals his setup, which helps him build the #1 coding tool of this year.

Worth more than a $500 vibe-coding course.

[Rahul @sairahul1]

This is the best site on the internet to learn how LLMs actually work.

Free. Completely.

https://t.co/YOGF6PsmBN

Bookmark this site.

Then read this setup ↓

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an Agentic AI Engineer.

I'd do this.

Stage 1: Python + Async Foundations
asyncio, FastAPI, event-driven architecture, error handling, API integration patterns.

Stage 2: LLM Fundamentals for Agents
Context management, model routing, token economics, latency tradeoffs, failure modes.

Stage 3: Tool Calling + Structured Outputs
Pydantic validation, function calling schemas, error recovery, dynamic tool discovery.

Stage 4: Memory + State Management
Short-term buffers, long-term vector recall, context compression, cross-session sync.

Stage 5: Single Agent Workflows
ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation.

Stage 6: Multi-Agent Orchestration
LangGraph/CrewAI, supervisor patterns, message passing, conflict resolution, handoffs.

Stage 7: Human-in-the-Loop Systems
Uncertainty detection, approval gates, audit trails, resume logic, intervention points.

Stage 8: Evaluation + Quality Assurance
Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics.

Stage 9: Observability + Tracing
Distributed tracing (LangSmith/Arize), cost dashboards, latency monitoring, alerting.

Stage 10: Security + Guardrails
Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance.

Stage 11: Production Deployment
vLLM/SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies.

Stage 12: Open Source + Portfolio
Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs.

Most people stay stuck watching tutorials.

Builders get hired.

(Bookmark it)

[Khushi @khushiirl]

Found the Best Resource to learn Harness Engineering. 😭

https://t.co/3F1rwTCbKZ

[neural nets. @cneuralnetwork]

this is an avg ml interview man 😭