Analytics Vidhya

Your AI agent fixed an incident on Monday. On Tuesday, a fresh chat has no idea 🧠💨 The lessons were only ever in the chat: keep event IDs for 30 days, reply 200 (never 409) to duplicates, run the reconcile script. TencentDB Agent Memory turns them into shared, team-owned assets: 🛠️ Reusable skills with steps and checks 🕸️ A CodeGraph of files, symbols and call paths 👥 Every item has an owner, status and visibility Its README reports a score of 48% → 76% without and with the memory. That’s the project’s own number, not an independent test. Full hands-on 👇 analyticsvidhya.com/blog/202…

"On average, how many hours does Cam work per day?" Sounds simple. It isn't 🤔 What's a "day"? Every calendar day? Each roster record? Only days Cam actually worked? Each gives a different, defensible number. Enterprise LLMs often fail before they calculate, at the question itself: 🗺️ Give the model a path, not a pile of context ✅ Resolve intent first, then pick valid evidence 🧪 Evaluate the route, not just the final answer ⚠️ A structured path can still be wrong Full read 👇 analyticsvidhya.com/blog/202…

What if your AI assistant didn’t need the cloud, a subscription, or even an internet connection? 👀 Yep, you can run powerful AI models right on your own machine! 💻 From Llama and Qwen to Mistral and Gemma, here are 5 local AI models worth exploring in 2026, plus how to get them running with Ollama. Less cloud dependency. More control. Your machine, your AI. 🔥 Explore the list 👇 analyticsvidhya.com/blog/202… #LocalAI #LLMs #OpenSourceAI #GenerativeAI #Ollama

Kimi K3 has 2.8 trillion parameters but only runs ~104 billion per token 🤯 How it stays cheap: 📖 Reading: linear attention in most layers, only 16 of 896 experts fire per token ✍️ Writing: KV cache cut by up to 75%, 4-bit weights, and speculative decoding for several tokens per pass 🚦 Stable LatentMoE halves GPU-to-GPU traffic Result: over 3x cheaper per token than Claude Fable 5. Full breakdown 👇 analyticsvidhya.com/blog/202…

Why does BERT use GELU instead of ReLU? ReLU works like a switch: negative inputs become zero, positive ones pass. GELU works like a dial. It multiplies each input by a value between 0 and 1, so strong signals pass almost fully, weaker ones are dialed down, and small negative signals still survive. No hard cutoff means smoother gradients, which is why GELU became a default in Transformer feed-forward layers. It isn't universal, though. Many newer LLMs use SwiGLU. 👉 Read the full explainer, with PyTorch code: analyticsvidhya.com/blog/202…

Building with LLMs without reinventing the wheel every time? That's LangChain's whole job 🔗 It gives you 4 building blocks: 🔌 LLMs: swap OpenAI, Anthropic, Google with minimal rewrites ⛓️ Chains: connect steps into workflows 🤖 Agents: let the model pick its own next move 🧠 Memory: remember what happened earlier Includes a hands-on build, from loading data to a working agent 👇 analyticsvidhya.com/blog/202…

Same skills on the job description. Very different Tuesday 👀 🤝 FDE: interviews customers, scopes a first release, ships it into messy real-world systems 🔧 AI Engineer: owns one product across releases, catches regressions, decides if a model upgrade is worth shipping Not sure which fits? Look at the working day, not the title. Full comparison 👇 analyticsvidhya.com/blog/202…

You can make 3D projects with Kimi without learning Blender, Unreal, or Three.js 🎮 Kimi K3.1 Pro builds models with code or calls tools like Tripo, then assembles scenes in game engines. 🏯 A celestial palace in Unreal Engine 💡 A 3D-printable Fujian tulou desk lamp 🏙️ A miniature hillside city from Blender + Tripo 🏢 A Zaha Hadid-style building from one prompt Best tips: start with reference images, block out the scene first, and save what works as a skill. Read here:👇 kimi.ai/academy/3d-tutorial

How does SVM draw the perfect line between two classes? 📏 It doesn't just pick any line that works. It picks the one with the widest margin, so new data is less likely to be misclassified. 🔀 Data not separable? Kernels lift it into higher dimensions 🧰 Linear, Polynomial, RBF, Sigmoid kernels 🐍 Python + R code included ⚠️ Not great for big or noisy datasets Full guide 👇 analyticsvidhya.com/blog/201…

GPT-6.1 Terra matches Astra on a professional-tasks benchmark at $0.29 per task vs $4.82 💸 We gave it 5 real jobs: 📊 Excel audit with live formulas 🐍 Python bug fix, actually ran the edge cases 🎓 3-slide lesson from the Transformer paper 📅 Calendar across 4 time zones, found the provably earliest finish 🎧 Audio file processed, signal matched our check to 0.022% error It also left a missing price flagged instead of guessing. Full review 👇 analyticsvidhya.com/blog/202…

Your GPU isn't slow. It's waiting on memory 🐌 LLM decoding keeps re-reading weights and the KV cache for every single token. The fixes: ⚡ Quantization 🚀 FlashAttention 🔄 Continuous batching 📦 PagedAttention Full guide 👇 analyticsvidhya.com/blog/202…

Kimi K3.1 Pro: 65.7% on Terminal-Bench 4.0. Opus 5.5: 66.4%. GPT-6 Astra: 58.2%. Open-weight just got serious 👇   analyticsvidhya.com/blog/202… #KimiK31Pro

Your subscription list is getting out of hand 💸 Here are 10 free, open-source swaps: 🎨 PhotoCraft → Photoshop 📋 Plane → Jira ⚡ Activepieces → Zapier 📸 Immich → Google One 📄 Stirling PDF → Acrobat Pro 🎬 OpenScreen → Camtasia 🖥️ RustDesk → TeamViewer ✍️ Documenso → DocuSign 📧 Listmonk → Mailchimp 🎙️ OpenWhispr → Wispr Flow Free code isn't free hosting, so each one comes with a "before you switch" checklist. Full list 👇 analyticsvidhya.com/blog/202…

Your RAG bot can still make things up, even with the source text right in front of it 🫠 So let the LLM write 5 answers and let JEV, a tiny, cheap checker, grade each one on 4 yes/no questions. 🚫 Blocked all 16 planted wrong answers 💸 56 checks cost about $0.001 ⚠️ But it also blocked ~1/3 of the good ones Full breakdown 👇 analyticsvidhya.com/blog/202…

Closed-source app. No docs. One AI agent, 7 minutes, $1.83 🕵️ We tested REA, an open-source MCP server for reverse engineering. First we guessed how Obsidian’s quick-switcher ranks results by hand, then let the agent inspect the code. 🎯 It found the scoring function inside a 3.9MB minified file ✅ Our hand-made prediction was right on priority order, even the exact “a word boundary = 10 positions” trade-off 🔍 It also found 4 hidden mechanisms we couldn’t see from outside ⚠️ Its written formula had an off-by-one that only showed up when checked against its own data Lesson: predict first, then verify. Full breakdown 👇 analyticsvidhya.com/blog/202…

FDE or AI Engineer? Both build with AI. The difference: where you sit, and who owns the system after it ships. New to the FDE path? Start with our free course: analyticsvidhya.com/courses/… #AICareers #AIEngineer

Jev costs $0.044 per 1,000 judgements. GPT-6 Astra costs $12.182. But cheaper isn't always better: Jev matches on simple evidence checks and trails on harder reasoning. Best setup? Use both. analyticsvidhya.com/blog/202… #AIEvals #LLM

Your RAG bot can still make things up, even with the source text right in front of it 🫠 Fix: let the LLM write 5 answers, then let JEV (a tiny, cheap checker) ask 4 yes/no questions about each: supported? on-topic? invented details? contradictions? 🚫 Blocked all 16 planted wrong answers 💸 56 checks cost about $0.001 ⚠️ But it also blocked ~1/3 of the good answers 🤷 Writing 5 drafts didn't help, the first one was already right Start with 1 draft + 1 JEV check, then measure before scaling up. Full breakdown 👇 analyticsvidhya.com/blog/202…

We put Gemini 4 Argon through 4 real tasks 🧪 🎬 8-second product video: nailed it 🌐 Working booking website in Canvas: nailed it 📉 "Conversion fell, roll it back": it spotted that both visitor groups actually improved 📄 8 conflicting launch claims: all labeled correctly, but one invented phrase slipped into the summary 👀 Great at catching traps, still needs a proofread. Full review 👇 analyticsvidhya.com/blog/202…

OpenAI's new API doesn't chat, it decides 🎯 ✅ Yes/no with probability 🧭 Pick from options, with odds 📏 Grade on a rubric 10x faster, $0.10 per 1M tokens, text and images. And it knows when it's unsure: a half-markets, half-regulation headline scored just 52% 😅 analyticsvidhya.com/blog/202…