Kinshuk 🇮🇳🇩🇪

Don’t think we’ve seen this before—PM Modi personally denies a CNBC-TV18 story saying the Govt is considering a tax/cess on foreign travel.

NEET UG canceled? Pathetic. This education minister should be held accountable.

2024 NEET Paper Leak 2026 NEET Paper Leak UGC Rules Debacle The Most Incompetent Minister and Ministry of Modi Ji’s Government needs total Overhaul RT if you Agree!

Retrieving the minimum element in constant time shouldn't require a loop. 🧠 Master the two-stack approach to solve LeetCode 155 with O(1) efficiency. Stop scanning and start optimizing! 🚀 youtube.com/watch?v=Ro5MTa… #LeetCode #DataStructures #CodingInterview

Most devs learn Bubble Sort first. It's also O(N²) — one of the slowest. Here's every comparison and swap visualized 🧵👇 #100DaysOfCode #coding #softwareengineering #javascript #python #webdev

📢 Open-sourcing the Sarvam 30B and 105B models! Trained from scratch with all data, model research and inference optimisation done in-house, these models punch above their weight in most global benchmarks plus excel in Indian languages. Get the weights at Hugging Face and AIKosh. Thanks to the good folks at SGLang for day 0 support, vLLM support coming soon. Links, benchmark scores, examples, and more in our blog - sarvam.ai/blogs/sarvam-3…

Sarvam's highly competitive AI models illustrate an important point: we must do catch-up R&D, however un-prestigious or thankless it feels and as we start to catch up, innovative new ideas will emerge. Sarvam is on a great trajectory! This is why we quietly persist in all the efforts we do.

🇮🇳's AI arrives on the world stage, representing a critical milestone in establishing a sovereign AI stack for India (1) @SarvamAI just open-sourced 2 MoE reasoning models - 30B & 105B - trained from scratch entirely in India on IndiaAI Mission compute (2) It's a robust GRPO-based RL pipeline, validating that frontier-tier reasoning, programming & agentic capabilities can be indigenized (3) BrowserComp is a sleeper hit for the 105B - nearly 17x improvement than DeepSeek's R1 suggests their agentic RL pipeline (tool use, search integration) is genuinely differentiated (4) 30B model seems to be the true disruptor - combining Grouped Query Attention (GQA) w/ an ultra-efficient Indic tokenizer (yielding up to a 10x performance delta), the 30B fundamentally alters inference economics for edge & real-time enterprise deployments in the subcontinent (5) 30B's benchmark numbers would have been frontier-class ~12 months ago & the inference optimization story (3-6x throughput on H100) makes it a plausible production model for cost-sensitive Indian enterprise deployments (6) The 105B model demonstrates exceptional depth in tool interaction & environment reasoning. The RL pipeline's use of an asynchronous GRPO architecture (notably bypassing standard KL-divergence constraints against a reference model) explicitly rewards verifiable multi-step execution over mere conversational chattiness. (7) The full-stack inference optimization, achieving 20-40% higher token throughput via custom-shaped MLA optimizations and vocabulary parallelism, creates stickiness at the infrastructure layer that pure model-builders lack. (8) If Sarvam 30B becomes the default Indic voice/conversational model (which the inference economics support), it creates a meaningful wedge in the Indian BFSI conversational AI market. The 2.4B active parameter count at this quality level is a structural cost advantage v/s deploying GPT/Claude for Hindi/Tamil telephonic agents. (9) I see the Indic tokenizer + inference optimization stack as a compounding advantage. Every other model serving Indian languages pays a "tax" in token inefficiency and latency. This compounds across millions of API calls. (10) There are a couple of areas where I'd like to see improvements though: (10.1) SWE-Bench is the elephant in the room. For a model positioned around agentic workflows, this ~20-point gap on real-world software engineering tasks is material. It signals that while the model can reason well in structured settings, it struggles w/ the messy, multi-file, context-heavy nature of real codebases (10.2) In an era where vision-language is table stakes, both models are text-only. They acknowledge this - mentioning future models for "multimodal conversational tasks" - but it's a gap today. (11) For Sarvam as a company, this is a credibility-establishing release. @pratykumar & co have demonstrated they can train competitive models from scratch - a very short list globally. The question is whether the model business itself captures value or whether Sarvam's value creation is upstream (Samvaad platform, enterprise deployments) using these as proprietary infrastructure. (12) If I had a say, I'd suggest the following couple of things: (12.1) They could offer the 30B model completely free (including localized inference hosting) to Indian telcos & financial institutions for edge deployment, explicitly in exchange for federated access to their anonymized customer interaction data. This would create an insurmountable, proprietary data moat for future RLHF. (12.2) I'd think about aggressively commercializing the "romanized colloquial" capability into a proprietary API for WhatsApp/Telegram business layers. Indian commerce runs on WhatsApp in code-mixed "Hinglish" or "Tanglish" - dominating this exact syntactic niche captures the entire B2C transactional layer. (12.3) Voice AI vertical integration - Combining the 30B w/ their existing TTS/STT APIs into an end-to-end voice agent stack purpose-built for Indian BFSI could be a very high ROI product move. Regardless, this is the most credible "sovereign AI" release from India to date - long AI in India.

Java Concurrency: wait vs sleep vs yield Explained #k5kc #java - Thread.sleep()`: Keeps the lock 🔒 - `object.wait()`: Releases the lock 🔓 (Must be in a synchronized block!) - `Thread.yield()`: A polite hint to the CPU scheduler ⚙️ #coding #programming #k5kc

Valid Sudoku: Validate Sudoku 🧩. Rows, cols, 3x3 boxes must have 1-9 unique. ✅ 📺 Watch now: youtube.com/watch?v=18oaKC… 📜 Read: k5kc.com/cs/problems/va… #algorithms #array #coding #coding-challenge #hash-table #java #k5kc #leetcode #matrix #python

How does AI *really* understand language? 🤔 Discover embeddings – the secret sauce for semantic search & RAG! Get the breakdown in this quick Short. 👇 youtube.com/shorts/bWMgjgi… #shorts #Embeddings

Merge Two Sorted Lists v2: Sorted LL merge! 🔗💡 📺 Watch now: youtube.com/watch?v=UoH_MQ… 📜 Read: k5kc.com/cs/problems/me… #coding #k5kc #computerscience #programming #java #python #leetcode #coding-challenge #algorithms #linked-list #recursion

This is not dirt; scrubbing will not remove it. This condition is called acanthosis nigricans. In most cases, it is a visible warning sign that insulin levels are high, even when blood sugar reports are still normal. Many people also develop skin tags over it. It commonly involves the neck, axillae, under the breasts, and the groin. Acanthosis nigricans is commonly associated with obesity, insulin resistance, prediabetes, PCOS, or early metabolic imbalance. Topical creams can improve the skin’s appearance, but acanthosis nigricans is primarily a sign of an internal metabolic problem. Creams are supportive, not curative. The real treatment lies in lifestyle correction, weight reduction, improved diet, regular physical activity, and timely medical evaluation. Don't ignore it. It's your skin trying to warn you that all is not well inside.

Valid Parentheses: Brackets valid? Type & order rules. ✅ 📺 Watch now: youtube.com/watch?v=6xIQh8… 📜 Read: k5kc.com/cs/problems/va… #coding #k5kc #computerscience #programming #java #python #leetcode #coding-challenge #algorithms #stack #string

Alien Dictionary: Find alien char order from sorted dict! 👽📚 📺 Watch now: youtube.com/watch?v=_kc1Yq… 📜 Read: k5kc.com/cs/problems/al… #coding #k5kc #computerscience #programming #java #python #leetcode #algorithms #bfs #dfs #graph #string #topological-sort #kahns-algorithm

Long live India. Long live the friendship between Europe and India. 🇪🇺🇮🇳

Shubh Ganntantra Diwas Everyone!

Number of Connected Components in an Undirected Graph: Count separate "islands" in an undirected graph. 🏝️🔗 📺 Watch now: youtube.com/watch?v=Wa8LQ-… 📜 Read: k5kc.com/cs/problems/nu… #coding #k5kc #cs #programming #java #python #leetcode #algorithms #BFS #DFS #graph #union-find

Pacific Atlantic Water Flow: Cells 💧➡️🇵 & 🇦? 📺 Watch now: youtube.com/watch?v=ZvCI0Z… 📜 Read: k5kc.com/cs/problems/pa… #coding #k5kc #computerscience #programming #java #python #leetcode #coding-challenge #algorithms #array #breadth-first-search #depth-first-search #matrix

Graph Valid Tree: 🌳? 📺 Watch now: youtube.com/watch?v=1chWSc… 📜 Read: k5kc.com/cs/problems/gr… #coding #k5kc #computerscience #programming #java #python #leetcode #coding-challenge #algorithms #breadth-first-search #depth-first-search #graph #union-find