Get live statistics and analysis of Akshay ๐'s profile on X / Twitter
Simplifying LLMs, AI Agents, RAG, and Machine Learning for you! โข Co-founder @dailydoseofds_โข BITS Pilani โข 3 Patents โข ex-AI Engineer @ LightningAI
476following261kfollowers
The Educator
Akshay is a passionate AI and machine learning enthusiast dedicated to demystifying complex technologies like LLMs and AI agents. With hands-on experience and a background rooted in innovation, he shares clear, actionable roadmaps to empower his audience. His profile is a goldmine for anyone eager to dive into AI careers and stay updated with industry breakthroughs.
Akshay tweets so much and so fast, if you blink you might miss a patent application or three buried in his timeline, he's basically turning X into a live AI textbook, whether you're ready or not.
Achieving viral reach with multiple tweets surpassing 1 million views and thousands of likes, Akshay has successfully built authority and trust within the AI and machine learning community.
To simplify advanced AI concepts and make cutting-edge machine learning knowledge accessible and actionable, empowering aspiring tech enthusiasts to navigate and thrive in the AI landscape.
Akshay believes that education should be open, clear, and practical; that sharing knowledge fuels innovation; and that anyone with curiosity and determination can master AI, no matter where they start.
His strongest asset is his ability to translate complex AI and machine learning concepts into digestible, engaging content that resonates widely, combined with an impressive consistency, heโs tweeted nearly 20,000 times!
Akshayโs focus on depth and technical detail might occasionally overwhelm beginners looking for lighter content, and his intense posting schedule may sacrifice moments for broader engagement or personal connection.
To grow his audience on X, Akshay should experiment with interactive content like polls or Q&A sessions to boost direct engagement, and share more personal stories or simplified beginner tips to widen his appeal beyond hardcore tech followers.
Fun fact: Akshay has not only co-founded an educational platform but also holds 3 patents in AI, showcasing his deep technical prowess alongside his teaching passion.
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How do you make it 5x faster?\"\n\nYou: \"I'll optimize the model architecture and use a better GPU.\"\n\nInterview over.\n\nHere's what you missed:","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,208],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1980165776847102276","view_count":991,"bookmark_count":0,"created_at":1760943285000,"favorite_count":5,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1980159925109309799","full_text":"@_avichawla ๐ฏ\n\nI have experienced the exact issues you described here. \n\nAnd this new approach does make a lot of sense.\n\nI am surprised that why didn't we think about this before. Trying this out right away.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1980159925109309799","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,14],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1286074924591874048","name":"Rodrigo ๐๐","screen_name":"mathsppblog","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"mathsppblog","lang":"qme","retweeted":false,"fact_check":null,"id":"1980279797457780903","view_count":192,"bookmark_count":0,"created_at":1760970470000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1979598428511568243","full_text":"@mathsppblog 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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They're layers of the same stack.\n\nAG-UI can handshake with both MCP and A2A, meaning tool outputs and multi-agent collaboration can flow seamlessly to your user interface. \n\nYour frontend stays connected to the entire agent ecosystem through one unified protocol layer.\n\nCopilotKit sits above all three as the Agentic Application Framework.\n\nIt acts as the practical layer that lets you actually build with these protocols without dealing with the complexity.\n\nSo you get all three protocols, generative UI support, and production-ready infrastructure in one framework.\n\nThe best part: All of this is 100% open-source!\n\nIf you want to understand where agent development is heading in 2025, I have shared a free PDF on Agent Protocol Landscape in the replies!\n\nIt breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocolsPDF","expanded_url":"http://go.copilotkit.ai/protocolsPDF","url":"https://t.co/UJdsU4qFVO","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988951623666913318","view_count":930,"bookmark_count":6,"created_at":1763037994000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":1,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: https://t.co/UJdsU4qFVO","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1988951499989455097","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,23],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1988955937340813616","view_count":354,"bookmark_count":0,"created_at":1763039023000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@_avichawla Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: https://t.co/aWWztBikk7","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1988951499989455097","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1988951499989455097?s=20","url":"https://t.co/d1tM1LBAke","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: 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above insight suggests that to generate a new token, every attention operation in the network only needs:\n\n- query vector of the last token.\n- all key & value vectors.\n\nBut, there's one more key insight here. 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duct-taping your AI pipeline together.\n\nPixeltable is a unified, declarative framework that handles your entire multimodal pipeline, from data storage to model execution.\n\nSeamlessly manage images, videos, text, and tabular data all in one place.\n\n100% open-source. https://t.co/OkpUbxHfBk","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"788451688206077952","name":"Aravind 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LangGraph user I know is making the same mistake!\n\nThey all use the popular supervisor pattern to build conversational agents.\n\nThe pattern defines a supervisor agent that analyzes incoming queries and routes them to specialized sub-agents. Each sub-agent handles a specific domain (returns, billing, technical support) with its own system prompt.\n\nThis works beautifully when there's a clear separation of concerns.\n\nThe problem is that it always selects just one route.\n\nFor instance, if a customer asks: \"I need to return this laptop. Also, what's your warranty on replacements?\"\n\nThe supervisor routes this to the Returns Agent, which knows returns perfectly but has no idea about warranties.\n\nSo it either ignores the warranty question, admits it can't help, or even worse, hallucinates an answer. None of these options are desired.\n\nThis gets worse as conversations progress because real users don't think categorically. They mix topics, jump between contexts, and still expect the agent to keep up.\n\nThis isn't a bug you can fix since this is fundamentally how router patterns work.\n\nNow, let's see how we can solve this problem.\n\nInstead of routing between Agents, first, define some Guidelines.\n\nThink of Guidelines as modular pieces of instructions like this:\n\n```\nagent.create_guideline(\n condition=\"Customer asks about refunds\",\n action=\"Check order status first to see if eligible\",\n tools=[check_order_status],\n)\n```\n\nEach guideline has two parts:\n\n- Condition: When it gets activated?\n- Action: What should the agent do?\n\nBased on the user's query, relevant guidelines are dynamically loaded into the Agent's context.\n\nFor instance, when a customer asks about returns AND warranties, both guidelines get loaded into context simultaneously, enabling coherent responses across multiple topics without artificial separation.\n\nThis approach is actually implemented in Parlant - a recently trending open-source framework (15k+ stars).\n\nInstead of routing between specialized agents, Parlant uses dynamic guideline matching. At each turn, it evaluates ALL your guidelines and loads only the relevant ones, maintaining coherent flow across different topics.\n\nYou can see the full implementation and try it yourself.\n\nThat said, LangGraph and Parlant are not competitors.\n\nLangGraph is excellent for workflow automation where you need precise control over execution flow. Parlant is designed for free-form conversation where users don't follow scripts.\n\nThe best part? They work together beautifully. LangGraph can handle complex retrieval workflows inside Parlant tools, giving you conversational coherence from Parlant and powerful orchestration from LangGraph.\n\nI have shared the repo in the replies!","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,70],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"github.com/emcie-co/parlaโฆ","expanded_url":"https://github.com/emcie-co/parlant","url":"https://t.co/78F9MVfSsx","indices":[21,44]}],"user_mentions":[]},"favorited":true,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":true,"fact_check":null,"id":"1983874403046989924","view_count":7910,"bookmark_count":43,"created_at":1761827490000,"favorite_count":38,"quote_count":0,"reply_count":1,"retweet_count":6,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"Parlant GitHub repo: https://t.co/78F9MVfSsx\n\n(don't forget to star ๐)","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1983874390149484881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,177],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1983877554529563062","view_count":951,"bookmark_count":0,"created_at":1761828242000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@_avichawla Wonderful!\n\nAnother key advantage is that dynamic guidelines keep the system prompt clean, ensuring the agent receives only the right instructions at the right time.","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1983876909629919568","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,51],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"4531602133","name":"BadGuyTy ๐ ๐ ๐ฝ (Tyler Hess)","screen_name":"badguyty","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"badguyty","lang":"en","retweeted":false,"fact_check":null,"id":"1983919228060103093","view_count":378,"bookmark_count":0,"created_at":1761838178000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@badguyty Yes the ideas are certainly relatable!\n\n๐ฏ","in_reply_to_user_id_str":"4531602133","in_reply_to_status_id_str":"1983898076239819155","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,88],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1764584809689382912","name":"Ayesha Imran","screen_name":"ayesha_imr","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"ayesha_imr","lang":"en","retweeted":false,"fact_check":null,"id":"1983892221976785375","view_count":334,"bookmark_count":0,"created_at":1761831739000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@ayesha_imr Couldn't agree more! ๐ฏ\n\nSkills : Agent context :: Guidelines : System prompt","in_reply_to_user_id_str":"1764584809689382912","in_reply_to_status_id_str":"1983884825313603881","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,166],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1949049858683445248","name":"Murad Ahmed","screen_name":"devmuradahmed","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"devmuradahmed","lang":"en","retweeted":false,"fact_check":null,"id":"1983919614934278452","view_count":208,"bookmark_count":0,"created_at":1761838270000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983874390149484881","full_text":"@devmuradahmed Exactly!\n\nAnd when itโs comes to building conversational AI, humans are tricky and not easy to handle. \n\nYou can not use patterns like a silver bullet.","in_reply_to_user_id_str":"1949049858683445248","in_reply_to_status_id_str":"1983917174918991985","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,96],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1796642877172551682","name":"Firecrawl","screen_name":"firecrawl_dev","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"firecrawl_dev","lang":"en","retweeted":false,"fact_check":null,"id":"1983952346704138660","view_count":318,"bookmark_count":0,"created_at":1761846074000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1983926939921609068","full_text":"@firecrawl_dev The best way to give your AI access to web data! ๐ฅ \n\nCongrats on shipping team! ๐","in_reply_to_user_id_str":"1796642877172551682","in_reply_to_status_id_str":"1983926939921609068","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1983874390149484881","url":"https://t.co/JBB3SHJlDY","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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Even for static information that hasn't changed in months.\n\nThis is expensive, slow, and unnecessary.\n\nCache-Augmented Generation (CAG) addresses this issue by enabling the model to \"remember\" static information directly in its key-value (KV) memory.\n\nEven better? You can combine RAG and CAG for the best of both worlds.\n\nHere's how it works:\n\nRAG + CAG splits your knowledge into two layers:\n\nโณ Static data (policies, documentation) gets cached once in the model's KV memory\n\nโณ Dynamic data (recent updates, live documents) gets fetched via retrieval\n\nThe result? Faster inference, lower costs, less redundancy.\n\nThe trick is being selective about what you cache.\n\nOnly cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating \"cold\" (cacheable) and \"hot\" (retrievable) data keeps this system reliable.\n\nYou can start today. 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They're layers of the same stack.\n\nAG-UI can handshake with both MCP and A2A, meaning tool outputs and multi-agent collaboration can flow seamlessly to your user interface. \n\nYour frontend stays connected to the entire agent ecosystem through one unified protocol layer.\n\nCopilotKit sits above all three as the Agentic Application Framework.\n\nIt acts as the practical layer that lets you actually build with these protocols without dealing with the complexity.\n\nSo you get all three protocols, generative UI support, and production-ready infrastructure in one framework.\n\nThe best part: All of this is 100% open-source!\n\nIf you want to understand where agent development is heading in 2025, I have shared a free PDF on Agent Protocol Landscape in the replies!\n\nIt breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocolsPDF","expanded_url":"http://go.copilotkit.ai/protocolsPDF","url":"https://t.co/UJdsU4qFVO","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988951623666913318","view_count":930,"bookmark_count":6,"created_at":1763037994000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":1,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: https://t.co/UJdsU4qFVO","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1988951499989455097","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,23],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1175166450832687104","name":"Avi Chawla","screen_name":"_avichawla","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"_avichawla","lang":"en","retweeted":false,"fact_check":null,"id":"1988955937340813616","view_count":354,"bookmark_count":0,"created_at":1763039023000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@_avichawla Absolutely!","in_reply_to_user_id_str":"1175166450832687104","in_reply_to_status_id_str":"1988955534381445368","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,31],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1483265343858618368","name":"Shango","screen_name":"shangobashi","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"shangobashi","lang":"en","retweeted":false,"fact_check":null,"id":"1988956672182931464","view_count":89,"bookmark_count":0,"created_at":1763039198000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@shangobashi You're welcome! :)","in_reply_to_user_id_str":"1483265343858618368","in_reply_to_status_id_str":"1988952151025181101","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[15,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1562071816067510272","name":"Kritik","screen_name":"Tech_Next_Gen","indices":[0,14]}]},"favorited":false,"in_reply_to_screen_name":"Tech_Next_Gen","lang":"en","retweeted":false,"fact_check":null,"id":"1988956787316535456","view_count":68,"bookmark_count":0,"created_at":1763039225000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"@Tech_Next_Gen Exactly!\n\nGlad you found it helpful, Kritik!","in_reply_to_user_id_str":"1562071816067510272","in_reply_to_status_id_str":"1988956594797965654","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,61],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"go.copilotkit.ai/protocols","expanded_url":"http://go.copilotkit.ai/protocols","url":"https://t.co/aWWztBikk7","indices":[38,61]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"akshay_pachaar","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1989018624183701583","view_count":3485,"bookmark_count":10,"created_at":1763053968000,"favorite_count":3,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"703601972","conversation_id_str":"1988951499989455097","full_text":"Free PDF on Agent Protocol Landscape: https://t.co/aWWztBikk7","in_reply_to_user_id_str":"703601972","in_reply_to_status_id_str":"1988951499989455097","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,182],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/akshay_pachaarโฆ","expanded_url":"https://x.com/akshay_pachaar/status/1988951499989455097?s=20","url":"https://t.co/d1tM1LBAke","indices":[159,182]}],"user_mentions":[{"id_str":"703601972","name":"Akshay 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