Small actions with consistancy makes big difference:
Be conscious of what we are doing,
Wake up early and plan our day
Exercise and meditate
Close to nature
Deep breathing
@dominos_india This is the 4th time this month the Guntur outlet has taken my order (#200) and failed to deliver, citing 'out of stock.' The store phone number given by support doesn't work. If they can't fulfill orders, they shouldn't be accepting new ones. Need accountability.
If you want to become good at AI engineering (in 3 weeks), then learn these 15 concepts:
1 AI Agents: Memory, State & Consistency
→ https://t.co/v8H7O00jub
2 Machine Learning System Design 101
→ https://t.co/9MkHcLb5e0
3 Design Personal AI Chat Assistant
→ https://t.co/nNWq3onTnW
4 How RAG Works
→ https://t.co/cGmunPTUlb
5 LLM Concepts - A Deep Dive
→ https://t.co/5lCKxq2g4N
6 How to Design an AI Agent
→ https://t.co/JvnPd9773A
7 What is Reinforcement Learning
→ https://t.co/AVpl9j1oit
8 How Vector Databases Work
→ https://t.co/FVxan8xHH3
9 Context Engineering 101
→ https://t.co/OMkiZhkODL
10 AI Coding Workflow 101
→ https://t.co/paIf9ksIU9
11 LLM Evals Explained
→ https://t.co/nv3Ol8W53p
12 How AI Agents Work
→ https://t.co/tk3zkCjRvg
13 How MCP Works
→ https://t.co/wgf8gHnnkn
14 Agentic Patterns Explained
→ https://t.co/8YdBBWvTj1
15 Multi-Agent Architecture Explained
→ https://t.co/rS5QQS7Jln
What else should make this list?
===
👋 PS - Want my System Design Playbook for FREE?
Join my newsletter with 210K+ software engineers right now:
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💾 Save & RT to help others ace AI engineering.
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“design a RAG pipeline for 10M docs with zero hallucination”
apparently this was asked in a Google L5 interview round. came across it somewhere on the internet and honestly it’s a way more interesting system design problem than most classic distributed systems questions
1. ingest + normalize docs
- remove duplicates, standardize formats, extract metadata, maintain version history
2. hybrid retrieval (BM25 + embeddings)
- BM25 handles exact keyword matching while embeddings capture semantic meaning
- semantic search alone usually struggles with precision at massive scale
3. ANN retrieval + reranking
- ANN (Approximate nearest neighbor ) quickly pulls top candidate chunks from millions of docs
- then a reranker rescoring step improves relevance by deeply comparing query vs retrieved chunks
4. source confidence scoring
- every retrieved chunk gets scored based on freshness, trust level, overlap and retrieval consistency
- low-confidence context should never heavily influence generation
5. constrained generation
- the model is only allowed to answer using retrieved context (nothing new to be invented outside of the retrieved context)
6. citation-backed responses
- every major claim links back to exact chunks, documents or timestamps
7. hallucination fallback layer
- if retrieval confidence drops below a threshold: “insufficient evidence found”
8. continuous evals
- run adversarial queries, retrieval recall benchmarks and hallucination tests continuously
9. caching + memory layer
- cache high-frequency enterprise queries and retrieval paths (improves latency and output)
10. observability everywhere
- trace retrieval paths, chunk rankings, token attribution and failure points
Also at 10M docs, retrieval quality matters more than the frontier model itself.
🚨 Karpathy was right. He warned that 90% of AI advice dies in 6 months.
spoiler: most tools will not even survive 90 days.
this guy is literally giving away the exact 2026 playbook for AI Agents.
he covers what to learn, what to build, and what to skip 👀
↓ read this today
DPIIT releases operational guidelines for ₹10,000 crore Startup India Fund of Funds 2.0—a major push to deepen India’s startup ecosystem.
#StartupIndia
Read more: https://t.co/OXd1l3ZXhi
Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.
Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day.
Attention Stakeholders!
If you are facing the error “DSC is not registered with the Director DIN” while filing DIR-3 KYC Web, you are advised to ensure the following:
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🔹If business user account exists but registered email ID needs to be changed, you may log in using Mobile OTP on the MCA portal and proceed with DSC registration (or vice versa).
🔹 If both registered email ID and mobile number are inactive, or if you are unable to associate DSC due to any other issue, you are requested to raise a service-related complaint on the MCA Portal. Kindly mention the SRN of DIR-3 KYC Web which is pending for DSC upload and payment.
Note:
DIN holder’s DSC will not be validated if the DIN is deactivated due to non-filing of DIR-3 KYC, and the purpose selected is “Reactivation of DIN”, and DIN holder has not filed DIR-3 KYC eForm at least once where the DIN allotment date is on or before 31 March 2025.
#MCA #MCAUpdate #DIR3KYCWEB
₹10,000 crore Startup India Fund of Funds 2.0 notified.
Driving deep tech, manufacturing, and early-stage innovation through AIFs—backing startups with capital, mentorship & scale.
Read more here: https://t.co/CRa1zUCClJ
📢Attention Stakeholders!
Stakeholders are advised to ensure timely compliance by filing MSME Form I for the half-yearly period from October 2025 to March 2026 on the MCA Portal.
🗓 Last date for filing: 30th April, 2026
👉 Navigation Path for Filing MSME Form I:
✅ Visit https://t.co/A6LwlU5CNP, log in using your MCA V3 Business User credentials
✅ MCA Services → Company e-Filing → Compliance Services → MSME- Half yearly return of Micro or Small Enterprise.