Pankaj @javaamtho

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Vala Afshar @ValaAfshar

a week ago

The best gift you can give your parents is time. Be patient. Be present. Be grateful. Be kind and interested. Be loving. Be proud.

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Devendra Fadnavis @Dev_Fadnavis

2 weeks ago

On the auspicious occasion of Vishwakarma Jayanti, we honour the divine architect and the craftsman of the universe, Lord Vishwakarma! आपल्या दिव्य सृजनशीलतेने सृष्टीला आकार देणारे, वास्तू व शिल्पकलेचे अधिष्ठाता 'भगवान विश्वकर्मा' यांना जयंतीदिनी कोटी कोटी नमन! #विश्वकर्मा #विश्वकर्मा_जयंती #Vishwakarma #VishwakarmaJayanti #Maharashtra

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Harsh Sanghavi @sanghaviharsh

2 weeks ago

A journey that began with a dream… and became a movement that inspires a nation. From the soil of Gujarat to the global stage, your life continues to embody seva, sankalp and Rastranirman. On your birthday, heartfelt wishes to Hon’ble PM Shri @narendramodi Ji. May your vision continue to give India new horizons, your resolve new strength, and your leadership inspire generations to dream bigger for Viksit Bharat. Wishing you a very Happy Birthday, Modi Ji! #HappyBdayPMModi

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नंदिता ठाकुर 🇮🇳 @nanditathhakur

3 weeks ago

7 lessons from Jain Way of living ..

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Pushpa🍁🍂 @Real_Pushpa

4 weeks ago

Virat Kohli, Anushka Sharma, Steve Jobs, Julia Roberts and Mark Zuckerberg are among the devotees of Neem Karoli Baba, whose life is now the subject of the film #Hanuman_Ansh. But who was the spiritual guru who inspired them?

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Incredible!ndia @incredibleindia

2 months ago

The Ministry of Tourism, in collaboration with Netflix, invites you on a visual journey through India's extraordinary wildlife and breathtaking natural landscapes. From tranquil forests and majestic mountains to timeless wilderness and vibrant biodiversity, discover the many shades of Incredible India—where every journey is a story waiting to unfold. #IncredibleIndiaxNetflix #VisitIndia #ExploreIndia #WildlifeTourism #TravelIndia #IncredibleIndia #Netflix @gssjodhpur @tourismgoi @netflix @netflixIndia

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श्री विठ्ठल रुक्मिणी मंदिरे समिती, पंढरपूर अधिकृत @Vitthal_Rukmini

2 months ago

राजा पंढरीचा 🙏🏻 #आषाढी_एकादशी #वारी

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Puneet Singh @xhawkCEO

3 months ago

x.com/i/article/2071…

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Pankaj @javaamtho

3 months ago

@beastoftraal my favourite was pune bcos no ads :)

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Vineeth K @DealsDhamaka

3 months ago

Many are wasting time watching reels in India Time is money 💰

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Devendra Fadnavis @Dev_Fadnavis

5 months ago

'मिसिंग' नव्हे तर हा आहे प्रगतीचा 'कनेक्टिंग' मार्ग! #Maharashtra #MissingLink

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Pankaj @javaamtho

5 months ago

@nitinvsharma26 Still there?

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Pankaj @javaamtho

5 months ago

@aadit_palicha @ZeptoNow Kindly change your 10 mins commitment to 25 mins 🙏🙏🙏👍

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Langflow @langflow_ai

6 months ago

How to Build a Research Agent with Multi-Step Reasoning in Langflow A research agent goes beyond simple responses. Instead of answering directly, it plans, searches, and synthesizes information before generating an output. This workflow shows how to structure a multi-step research process using tools and reasoning. What this flow solves Single-step agents are limited. They often generate shallow responses and fail to explore external information properly. With this approach, you can: • Plan how to answer a question • Search external sources • Structure retrieved information • Generate more complete and reliable outputs Step-by-step Setup Chat Input Receives the initial research question. Prompt Template (Planning) Defines how the model should create a research plan. Language Model Generates a structured plan based on the user input. Prompt Template (Plan Structuring) Formats the previous output into a clearer research plan. Tavily AI Search Provides access to external information through search. Agent Uses the search tool to retrieve relevant information. Prompt Template (Context Structuring) Organizes the retrieved search results into structured context. Prompt Template (Final Synthesis) Defines how the final response should be generated using the query and results. Language Model Generates the final synthesized answer. Chat Output Returns the final response. How It Works Instead of answering immediately, the system follows multiple steps. It first plans what to search, retrieves external data using tools, organizes that information, and then generates the final answer. This structured approach improves both reasoning and output quality. How to get started This template is already available inside Langflow. Simply click New Flow, select the Research Agent template, and follow the same structure shown above. Learn more about Langflow: langflow.org/?utm_source=x&…

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Mufaddal Vohra @mufaddal_vohra

7 months ago

ICC EDIT ON THE T20 WORLD CUP FINAL. 🏆

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Langflow @langflow_ai

7 months ago

Global Model Provider - Langflow 1.8 Configure model providers once reuse across your canvas. Configuring AI models shouldn’t require repeating API keys and provider settings across every component in a workflow. With the launch of Langflow 1.8, model provider configuration becomes centralized and reusable: - Providers are configured once at the platform level; - Smart components reference a shared provider instead of raw credentials; - Updating credentials or switching providers becomes a single change; Example: Rotating an API key or switching providers can now be done in one place, without reconfiguring credentials across multiple components. Why it matters: - Reduces setup time - Eliminates configuration drift - Improves security by centralizing secret management Learn more about this release: langflow.org/blog/langflow-…

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Gaurav Sen @gkcs_

8 months ago

A very interesting guide to AI Agents. services.google.com/fh/files/misc/…

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Pankaj @javaamtho

10 months ago

@siddharthb_ 9.95L ticket?

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ℏεsam @Hesamation

a year ago

McKinsey studied 50 agentic AI builds and where they fail the most, and boiled it down to 6 key factors—essential for AI engineers: 1. It’s not about the agent, it’s about the workflow. don't obsess over building "impressive" agents. think about the whole system, not fun toys. 2. Agents aren’t always the answer. Not every workflow needs a multi-agent system. Low-variance, predictable tasks are best handled with rules or ML, LLMs add complexity . The big wins for agents come in high-variance, messy processes (e.g. extract complex financial information) 3. Avoid "AI Slop". (common) Focus on long-term development of agents, as you would with the development of an employee. Forget impressive demos. Double down on benchmarks. Agents should be given clear job descriptions, onboarded, and feedback so they improve regularly. 4. Track every step, not just outcomes. Scaling agents up without visibility is asking for silent failures. Think about monitoring every stage of the workflow. This way teams detect errors early, refine logic quickly, and avoid total breakdowns. When mistakes happen (and they will), you can track where things went wrong and why. Don't skip this. 5. Reuse agents when you can. Many companies waste time building one-off agents for each task. The smarter play is creating modular agent components (ingest, extract, verify, analyze) that can be reused for other workflows. Centralizing validated tools and prompts cuts 30–50% of redundant work, this number is no joke. 6. Humans remain essential, but in new roles. Agents can parse, automate, and scale. But humans provide judgment, edge-case handling, and creative problem-solving. The future isn’t agent vs. human, but agent + human. These are the mistakes startups and established companies make at scale. They cause massive damage to reputation and resources. And now you know how to avoid this.