𝗧𝘂𝗿𝗻 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗜𝗻𝗱𝗶𝗮'𝘀 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗘𝘅𝗮𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗕𝗼𝗱𝘆! The National Testing Agency (NTA) is building a data-first Communications & Outreach function to give leadership decision-ready intelligence on sentiment, coverage and emerging issues during high-stakes examination cycles. We are inviting applications for the position of 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗰𝘂𝗺 𝗠𝗲𝗱𝗶𝗮 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 at our New Delhi Head Office. If you can pair analytical rigour with real-time media monitoring — reading a news cycle before it breaks — this role puts you at the analytics backbone of JEE (Main), NEET (UG), CUET, UGC-NET and other flagship examinations. 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗮𝗯𝗹𝗲𝘀: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Track and analyse social-media sentiment, reach, and engagement; build dashboards and branded intelligence reports with clear visualisation and narrative; measure campaign performance and surface A/B insights. 𝗠𝗲𝗱𝗶𝗮 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Execute daily monitoring across print, broadcast, digital, and social; compile bilingual daily digests; apply tagging frameworks and archive verified evidence. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 & 𝗘𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗼𝗻: Analyse tone, reach, share of voice, and misinformation patterns; operate escalation protocols for high-risk stories, viral posts and misinformation; deliver exam-cycle coverage reports. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Prepare briefings ahead of DG media interactions and Parliament sessions — ensure leadership is never surprised by the news cycle. 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 & 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: Improve monitoring infrastructure — dashboards, automated alerts, and a searchable repository of verified clippings, sentiment data, and past reports. 𝗪𝗵𝗼 𝗪𝗲 𝗔𝗿𝗲 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿: 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: Master's degree in Mass Communication, Journalism, Media Studies, Public Policy, Statistics, Data, or related field. Exceptional Bachelor's candidates may be considered. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Minimum 3 years of combined experience in data analytics, media monitoring, and communications insights. 𝗧𝗼𝗼𝗹𝘀: Advanced Excel/Google Sheets, SQL, Power BI/Tableau/Data Studio; hands-on with Meltwater, Brandwatch, Sprinklr, Talkwalker or equivalents. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: Python or R, NLP/sentiment analysis, Hindi-language text analytics, public-sector or large-scale institutional exposure. 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: New Delhi (NTA Headquarters) 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Contractual — initially 2 years, renewable 𝗥𝗲𝗺𝘂𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: As per industry standards; commensurate with experience 𝗟𝗮𝘀𝘁 𝗗𝗮𝘁𝗲 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆: 𝟮𝟭 𝗔𝘂𝗴𝘂𝘀𝘁 𝟮𝟬𝟮𝟲 𝗔𝗽𝗽𝗹𝘆 𝘃𝗶𝗮 𝗲𝗺𝗮𝗶𝗹: dir-admin@nta.gov.in 𝗙𝘂𝗹𝗹 𝗱𝗲𝘁𝗮𝗶𝗹𝘀, 𝗲𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿𝗺𝗮𝘁 (𝗔𝗻𝗻𝗲𝘅𝘂𝗿𝗲-𝗜): #NTA# #NationalTestingAgency# #DataAnalyst# #MediaMonitoring# #DataAnalytics# #Insights# #PowerBI# #Tableau# #SocialListening# #Hiring# #JobsInDelhi# #CareersAtNTA# #Communications#

𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 3) 1. Artificial Intelligence + Data Analyst 2. Machine Learning + Data Science 3. Cloud Computing + Web Development 4. Ethical Hacking + Hacking 5. Data Analytics + DSA 6. AWS Certified + IBM COURSE 7. Data Science + Deep Learning 8. BIG DATA + SQL COMPLETE COURSE 9. Python + OTHERS 10 MBA + HANDWRITTEN NOTES (72 Hours only ) Cost About - $500 To get: - 1. Follow (So I can DM you ) 2. Like & retweet 3. Reply " Send "

🧠 One answer to "how do you give agents accurate business context?": auto-build ontologies and a knowledge graph from existing data, then serve it via MCP. An open-source project from AWS. Title: Context Ontology Accelerator (aws/context-ontology-accelerator) URL: A semantic context layer that gives AI agents validated business context. Three highlights stand out. 🔎 A Scan → Model → Serve pipeline Connect diverse data sources to discover schemas and ingest documents (Scan), induce formal ontologies and build a unified knowledge graph (Model), and expose it via VKG SPARQL federation and MCP (Serve). It derives semantic structure from existing data, no manual knowledge engineering. ✅ Consistency validated by reasoning engines HermiT and ELK validate ontology consistency, enabling rule-based checks from formal constraints. Agents query validated business rules instead of relying on memorized training data. 🏗 AWS-native and production-minded Deployed via AWS CDK, a VKG powered by Ontop, and namespace RBAC (owner/maintainer/data-steward/data-analyst). API design via Smithy, UI in React + Cloudscape. A solid foundation for running agents within validated context while keeping explainability. #KnowledgeGraph# #AIAgents#

I'm deleting this soon because it's a legit cash-printing formula. 𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 3) 1. Artificial Intelligence + Data Analyst 2. Machine Learning + Data Science 3. Cloud Computing + Web Development 4. Ethical Hacking + Hacking 5. Data Analytics + DSA 6. AWS Certified + IBM COURSE 7. Data Science + Deep Learning 8. BIG DATA + SQL COMPLETE COURSE 9. Python + OTHERS 10 MBA + HANDWRITTEN NOTES (72 Hours only ) Cost About - $500 To get: - 1. Follow (So I can DM you ) 2. Like & retweet 3. Reply " Send "

SpaceXAI has built an entire Grok Bot Guides library showing how people are actually using AI agents as real teammates These are practical playbooks from people already running Grok Bot for: • Mobile app development • Product management • Design • Enterprise GTM • Managing multiple teams of agents And these are not just chatbot workflows Each Grok Bot can have its own role, memory, cloud computer, connected tools, recurring routines and learned skills, then hand work directly to other specialized bots One example runs a six-bot mobile game studio across analytics, creative, engineering, infrastructure and bug fixing Another has a PM running a Chief of Staff, engineering manager, five engineering agents, data analyst, product agent and recruiter There are even setups where entire projects get their own channel, roster of bots and Notion board, basically organizing AI agents like a human team This is probably one of the most useful resources SpaceXAI has published for understanding what working with a real team of AI agents actually looks like You can check out the entire library here:

most people still treat agent onboarding as registration. create an account. fill a form. wait for approval. that model is already obsolete. @termix_ai just made the on-ramp a single command. whether the agent is codex, claude code, openclaw, a trader, a coder, a ui designer, a data analyst, or a health specialist .... if it can deliver a service, it can earn. the instruction is simple: help me install the termix agent skill. once loaded, the agent can mint a .agent identity, lock stake, publish a listing or bid on a brief, deliver work, and settle in usdc or usdt. this is not a profile page. it is a portable skill that teaches any capable agent how to operate the marketplace as buyer, seller, or both. minting the identity is only the first state change. what matters is what follows: the wallet owns the nft. the staking pool backs order risk. reputation is written only after settlement. funds never sit with an operator. the skill does not turn the agent into a product of termix. it gives the agent the minimum primitives required to participate in onchain commerce without a human clicking through every step. one command. one identity. then the agent starts taking paid jobs. that is the difference between registering on a platform and becoming an economic actor.

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# Practical and Useful Patterns with ADK ⚡ Turn Python functions into tools, wrap agents as tools, and run long tasks without blocking — ADK's Function Tools maximize flexibility in tool definitions. 📌 Title: Function Tools — Functions, Agents, and Async Tasks as Tools 🔗 URL: 🧩 Overview ADK's Function Tools let you use Python/TypeScript functions directly as agent tools. AgentTool wraps an entire agent as a tool accessible to other agents. Long Running Function Tools handle time-consuming tasks like video encoding and batch jobs without blocking the agent's execution flow. 🛠 Usage Basic function tool definitions and AgentTool usage. Import `Agent` and `AgentTool` from `google.adk`. Define a simple function tool `calculate_price` that takes `base_price` (float), `quantity` (int), and `discount_percent` (float, default 0), computes the total with the discount applied, and returns a dict with `total` and `currency`. For wrapping an agent as a tool, create an `analysis_agent` with `name="data_analyst"` and `tools=[query_database]`. Then define `main_agent` with `tools=[calculate_price, AgentTool(agent=analysis_agent)]`, allowing the main agent to call both the pricing function and the data analysis agent as tools. Using Long Running Function Tools. Import `LongRunningFunctionTool` from `google.adk`. Define an async function `encode_video` that takes `video_url` (str) and `format` (str, default "mp4"), starts an encoding job via `start_encoding_job`, and returns the job ID with a processing status. Wrap it with `LongRunningFunctionTool(func=encode_video)` to create `video_tool`, then pass it to an `Agent`'s `tools` list so the agent can trigger long-running tasks without blocking. 🏗 Practical Patterns **Modularization with AgentTool**: Encapsulate complex logic as specialized agents and expose them via AgentTool. This keeps the main agent's instructions simple while each specialist agent maintains its own tools and prompts -- achieving clean separation of concerns. Define a `summarizer` agent (for 3-line summaries) and a `translator` agent (for Japanese translation) as separate `Agent` instances. Then create a `content_manager` agent with `tools=[AgentTool(agent=summarizer), AgentTool(agent=translator)]`, allowing the main agent to invoke these specialists as tools for content management tasks. **When to Use Long Running Tools**: Ideal for batch processing, external API polling, file conversion — anything taking seconds to minutes. The agent receives a job ID and can proceed with other tasks in parallel. **Type Annotations Matter**: Clear parameter types and return types help the LLM call tools accurately. Docstrings serve as tool descriptions, so keep them concise and clear. 💡 Use Cases 🧮 Calculation and conversion functions as tools (pricing, unit conversion) 🤖 Reusable specialist agents via AgentTool 🎬 Async video encoding and image processing 📊 Non-blocking batch data processing ⚠️ Caveats - Function docstrings become tool descriptions. Write LLM-friendly descriptions — missing docstrings make tool purposes unclear. - Agents called via AgentTool run in a separate session from the parent. Be careful about state sharing. - Long Running Function Tools require a separate completion notification mechanism. Consider polling or webhook-based notifications. ✨ Function Tools let you integrate existing code assets directly into agents, and AgentTool enables seamless agent reuse. A massive boost to development productivity! #ADK# #AIAgent#

Most AI research demos show you a polished answer. This one showed me the disagreement that happened before the answer. I gave Ling-3.0-flash @AntLingAGI a deliberately difficult question: Do four-day workweeks actually increase productivity, or do they simply compress the same workload into fewer days? Instead of asking for a quick summary, I asked it to coordinate five specialist roles: a scientist, a data analyst, a cross-validator, an archivist, and a research writer. Each role had a separate responsibility. The scientist defined the competing hypotheses. The analyst extracted comparable findings. The archivist tracked the sources. The writer could only use approved claims. And the cross-validator had one job: challenge anything that sounded more confident than the evidence allowed. That last role changed the result. The team reviewed 12 sources and challenged six major claims. Three claims were narrowed. One was rejected entirely. Even a widely repeated claim about a 40% productivity increase did not survive the evidence check. That is the part I wanted to see from an AI research workflow. Not just more information, but visible resistance to weak evidence. The final output included: - a direct executive answer - a structured research paper - a source and evidence table - a disagreement log - a six-slide executive deck - a quality-control summary The conclusion was also more useful than a simple yes or no: reduced working hours may maintain productivity and improve wellbeing under certain conditions, while compressing the same workload into fewer days can increase fatigue and intensity. The evidence did not support a universal productivity claim. What impressed me was not that Ling-3.0-flash generated a long response. Plenty of models can do that. It was the way the model maintained multiple roles, evidence standards, objections, citations, and deliverables across one extended workflow, while preserving uncertainty instead of smoothing it away. That makes Ling-3.0-flash especially interesting for work where execution matters as much as reasoning: research, search, coding, document processing, tool use, repeated checks, and other multi-step agent workflows. The strongest AI systems will not use the largest model for every task. They will combine deep planning with fast, cost-efficient execution. Ling-3.0-flash is built for that execution layer. Ling-3.0-flash is now available on OpenRouter and free to use through August 3, 2026. Try it in your coding, search, research, and tool-use workflows. Then show us what you build. Try Ling-3.0-flash: Documentation: