India Unveils MCP Server for eSankhyiki with Seamless AI Integration

So we now have an official MCP setup for India's government statistics thanks to the MoSPI MCP. Quite easy to setup on Claude or ChatGPT and start making queries - economic indicators, employment data, price indices, and health statistics using natural language. https://lnkd.in/dZPfQWNy #India #MCP #IndiaStack #Statistics

Are we about to train frontier LLMs on the web… or on their own exhaust? Scaling laws are rapidly consuming high-quality human text. At the same time, online content is increasingly machine-generated. This creates a feedback loop: replay = training on a model’s own past outputs; In that regime, model collapse becomes a risk you can analyze and prove! We study it with a replay adversary that reinjects past generations into the stream. We characterize when replay is benign, and when fundamentally limits weaker generation guarantees. Practical mitigations like data cleaning, watermarking, and output filtering can help—while our separations show regimes where they can still fail - arXiv soon! Huge credit to the amazing people that our Isara Labs collaborares with: the up-and-running genius Giorgio and his supervisor Amartya from Department of Computer Science, University of Copenhagen - DIKU (cc Serge)!

𝐒𝐩𝐞𝐞𝐝 𝐮𝐧𝐥𝐢𝐤𝐞 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠 𝐈 𝐡𝐚𝐯𝐞 𝐞𝐯𝐞𝐫 𝐬𝐞𝐞𝐧. ⚡ Meet chatjimmy.ai. It’s worth a few minutes of your time. The speed is unlike anything I’ve seen—responses feel instantaneous. Taalas (taalas.com) has hardwired Llama 3.1 8B into its HC1 chip, achieving over 16,000 tokens per second. The chip also has a small SRAM which can be used to store fine-tuned weights and a KV cache. For comparison, a 4-bit quantized Llama 3.1 8B model typically runs at around 20–50 tokens per second on a Mac with M4 CPU. That’s not just faster—it’s an entirely different category of performance. It’s worth exploring where this kind of speed could take AI and what new kinds of applications become possible when latency nearly disappears. Try it: chatjimmy.ai -------------------------- Disclaimer: I have no affiliation with Taalas or chatjimmy.ai #Innovation #Technology #AI #Semiconductors #Llama3

🟢 ⚪ 🔴 DSA Practice Update: Dutch National Flag Algorithm Today I implemented the classic Dutch National Flag problem — sorting an array of 0s, 1s, and 2s in-place. Example input: [1,0,2,0,1,0,2,0,1,0,2,0,1] 🧠 The Strategy (Three Pointers) Instead of sorting traditionally: ✔ Use low, mid, high ✔ Partition array into regions ✔ Swap based on value at mid Rules: • 0 → swap with low, move low++, mid++ • 1 → just mid++ • 2 → swap with high, move high-- ⚡ Why This Algorithm is Beautiful ✅ One pass → O(n) ✅ In-place → O(1) space ✅ No extra arrays ✅ Clean pointer logic 🎯 Output Perfectly grouped: [0s | 1s | 2s] This problem is a great reminder: 👉 Smart pointer movement > brute-force sorting What DSA algorithm impressed you recently? 👇 #DSA #DutchNationalFlag #Algorithms #TwoPointers #CodingJourney #ProblemSolving

Unsloth Fixes Qwen3.5-35B-A3B GGUF Quantization for Superior Research Performance 📌 Unsloth’s fixed GGUF quantization of Qwen3.5-35B-A3B unlocks unprecedented research performance, solving critical tool-calling issues and enabling seamless web search and document fetching. With speeds over 600 tokens per second and support for 262,144-token contexts, the model autonomously handles complex tasks like evaluating remote desktop solutions-outperforming giants like GPT-OSS-120B in instruction following and agent tasks. 🔗 Read more: https://lnkd.in/es77cK9b #Unsloth #Qwen3535ba3b #Ggufquantization #Llamacpprocm #Researchperformance

I've low key invented a type of system category, based on a "behavioural inference engine". ALL SORTS of possiblities for new systems, Spam, Segmentation, auto adjustng rate limits and accessible apis to clients etc... So now the 'poor cousin of OSS' .NET has a UNQUE application class 🤓 Think Audience Segmentation and analysis only...for web requests and in sub-millisecond timeframes for every request over MONTHS with hundreds of signals over 28 detectors, everything from UA to markov chain path analysis and behavioural drift detection. TINY (0.6b class...runs in CPU in 200ms!) LLM optional to describe stuff and handle edge cases. Free & Open Source Software https://www.stylobot.net #netcore #aspnetcore #aspnet #botdetection #behaviouralinference #foss

We talk about "The Algorithm" like it’s a modern mystery, but it started over 1,000 years ago with a man named Al-Khwarizmi. The word "Algorithm" is actually the Latinized version of his name. He didn't just invent math; he perfected the art of the step-by-step process. Think algorithms are just for computers, laptops,tabs, mobiles, Google search, coders? Think again. We all use them every day: 🍳 In the Kitchen: A recipe is a culinary algorithm. Follow the steps in order, and you get the same result every time. 🗺️ On the Road: Your GPS uses Al-Khwarizmi’s logic to find the fastest route to your destination. 🛍️ Online Shopping: When a site "recommends" a product you actually like, that’s his legacy at work. ☕ Morning Routine: Your specific steps to make the perfect coffee? That’s your personal algorithm. From the House of Wisdom in Baghdad to the smartphone in your hand—his logic is the heartbeat of our modern world. 🌍 #AlKhwarizmi #HistoryOfTech #Algorithms #Logic #Innovation #DigitalLife #DailyTech

China’s AI trajectory—from Kimi K2.5’s 15T token training/agentic swarm capabilities outpacing GPT-5.2, to overtaking U.S. invocation volumes in Feb 2026—signals a paradigm shift that’s both humbling and ripe with opportunity. This isn’t hype; it’s evidenced by 30 major updates in 47 days and policy-driven scaling to 500+ real-world apps by 2027, positioning China at the AGI frontier with a 20% shot at global primacy via algorithmic edge over raw compute. In talent terms, demand for AI Infra, VLLM, ML RL AGI Engineers likes those from Moonshot or DeepSeek will skyrocket—especially for Asia - Singapore and Hong Kong firms navigating this shift.

Few weeks ago, Liquid AI released a 1.2B-parameter reasoning LLM capable of running directly in the browser at 200+ tokens/sec via WebGPU. No installation. No server-side inference. Fully local execution. This is a meaningful step forward for edge AI. If reasoning models can run natively in the browser, AI becomes part of the front-end architecture — not just a backend service. You can try it here: Multi-tool demo: https://lnkd.in/dcK4fWWM Real-time video captioning: https://lnkd.in/daPjT4iT The shift toward browser-native AI inference is accelerating.

We’ve all struggled with long-context inference in LLMs — slow attention, huge KV-cache, and latency that kills interactivity. Is there a way to internalize context instantly instead of continually re-feeding it? In a new paper, researchers introduce Doc-to-LoRA (D2L), a lightweight hypernetwork that learns to convert a full context into a LoRA adapter in a single forward pass. Here’s what’s compelling about it: - Traditional context distillation can internalize information into parameters, but it’s slow and expensive. D2L meta-learns this process so that the hypernetwork can generate an adapter tailored to a context with minimal compute. - Once internalized, the model can answer queries without re-reading the original context, dramatically reducing inference latency and KV-cache memory use. - On long-document QA tasks and synthetic “needle-in-a-haystack” setups, D2L generalizes beyond the base LLM’s native context window, retaining performance with far less overhead. This work points toward a future where models can absorb context into weights on the fly, enabling faster interactive systems and more efficient long-form understanding. We’d love to hear your thoughts on whether context internalization will become a standard part of LLM workflows. Link in the comments. #InContextLearning #LLMResearch #AIInfrastructure #ModelEfficiency