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

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

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…

Your GPU is 99% idle while generating tokens. Yes, really 😳 LLM decode is stuck waiting on memory, not math. The fix is batching: 🚌 Static: waits for the slowest request, one 500-token reply holds up everyone ⏳ Dynamic: starts sooner, still waits to finish ⚡ Continuous: swaps in new requests every single token Our simulator: continuous batching finished the same work in 41% fewer steps. A 30-token request waited 320 steps on static, exactly 30 on continuous. Full breakdown 👇 analyticsvidhya.com/blog/202…

Your RAG system confidently answers from the wrong documents. Here's the fix 🔧 Agentic Corrective RAG adds a safety net: 🧐 An LLM grades every retrieved chunk: relevant or not 🔁 Irrelevant? It rewrites the query and searches the web for live info 🧩 Built in LangGraph with Chroma + Tavily, nodes and edges you can follow Fewer hallucinations, fresher answers. Full guide 👇 analyticsvidhya.com/blog/202…

Nano Banana 2.1 is here and free for everyone 🍌 We threw 4 tests at it: 📊 Bar chart with exact values: proportions came out spot on 🧱 Clay-style RAG explainer: clean, though one of the "two books" went missing 🖼️ Hawa Mahal as a linocut print: gorgeous style, but it deleted the busy street 💻 Debugging comic: consistent character, unreadable typo Great for charts and comics. Always check the details you can't afford to lose. Full review 👇 analyticsvidhya.com/blog/202…

Your LLM judge says "0.995 confident" on every single answer. Useful? Not really 😬 Jev is a tiny judge model with honest probabilities: 💸 277x cheaper and 13x faster than GPT-6 Astra on the CMU study 🎯 Matches it on fact-checking, falls apart on math, code and logic 🪜 Smart trick: trust Jev when it's confident, send only the unsure cases to a bigger LLM. 93.4% accuracy at 41% of the cost In our test, the one case Jev got wrong was also its lowest-confidence call. Full comparison 👇 analyticsvidhya.com/blog/202…

Most agent bugs aren't in the answer, they're in the dozens of tiny decisions before it 🎯 Jev turns those into typed choices with probabilities. 10 GitHub repos show what you can build with it: 🌐 Browser agents that pick actions from a table ✂️ Context pruning for coding agents 🔍 A code reviewer that scores severity 🧭 A router that sends easy prompts to cheaper models Full list + repos 👇 analyticsvidhya.com/blog/202…

We built a real enterprise support agent with Claude Fable 5.1 🏗️ 🕵️ 8 prompts: investigation layer, policy RAG, human approval gates, security attacks, multi-tenancy 🚫 Refund limits stayed hardcoded, never an LLM call 💉 Tried a prompt injection hidden in customer text, it stayed just text 🐛 Planted a bug, Claude Code couldn't reproduce it and said so Full build 👇 analyticsvidhya.com/blog/202…

Tired of retyping the same setup instructions into Claude Code? Turn them into a slash command 🪄 🔒 Mark a skill disable-model-invocation: true so only you can trigger it, never Claude 📁 Scope it personal, project, or just one subfolder 🧩 Example: /new_repo scaffolds backend/frontend/README in one shot One markdown file, one word, done. Full guide 👇 analyticsvidhya.com/blog/202…