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  1. I think most people are probably compacting conversations wrong in AI agent context management.

    Because the naive way is to tell it to read the history message by message and summarize it.

    From the start to the end. One chunk at a time, always updating the summarization.

    But you really need to understand what you're doing here. Starting from the beginning, the agent cannot really understand what is important yet. What is important is not in the historical side, it is in the current side.

    It should be given the context from the most recent message backwards, because then it can see what the current stuff is about, and then from the past focus on the information which is still relevant for the current stuff.

    Of course if you give it the whole context one-shot, then you should give it the right way around, because that is the typical order of conversations. But chunk-wise the order should be from the present to the past, so that the summary is updated with the knowledge of what is relevant *now* in mind.

    Also in the instructions one should be mindful to not overwrite the current moment focus, like one might do if one were to process the chunks from the past to the future.

    When designing these sorts of things, one needs to be super cognizant of the information and its relations, in the processes used to refine and compress it.

    #AgenticAI #AI #GenAI

  2. I think most people are probably compacting conversations wrong in AI agent context management.

    Because the naive way is to tell it to read the history message by message and summarize it.

    From the start to the end. One chunk at a time, always updating the summarization.

    But you really need to understand what you're doing here. Starting from the beginning, the agent cannot really understand what is important yet. What is important is not in the historical side, it is in the current side.

    It should be given the context from the most recent message backwards, because then it can see what the current stuff is about, and then from the past focus on the information which is still relevant for the current stuff.

    Of course if you give it the whole context one-shot, then you should give it the right way around, because that is the typical order of conversations. But chunk-wise the order should be from the present to the past, so that the summary is updated with the knowledge of what is relevant *now* in mind.

    Also in the instructions one should be mindful to not overwrite the current moment focus, like one might do if one were to process the chunks from the past to the future.

    When designing these sorts of things, one needs to be super cognizant of the information and its relations, in the processes used to refine and compress it.

    #AgenticAI #AI #GenAI

  3. Two and a half days of light messing around and I have a functioning home-made #agenticAI coding assistant. Not as polished as Claude Code et al, but does a perfectly adequate job of RNGing valid code to a prompt and writing it to disk. Affords me the chance to talk about agentic workflows in interviews from a position of non-total-ignorance. Also runs against a local model and is free (as in beer) to use. 1/

  4. What if we used specific first- and second-person pronouns for non-conscious entities? I think this might help avoid #confusion and #cognitiveload when interacting with #bots, etc. Any #linguists or #conlangers here who have seen attempted solutions to this problem (or who have a good idea of how it might be solved)?

    #AI #LLM #LLMs #bot #chatbot #ChatGPT #Claude #pronoun #pronouns #linguistics #linguist #conlang #conlanger #agenticAI

  5. What if we used specific first- and second-person pronouns for non-conscious entities? I think this might help avoid #confusion and #cognitiveload when interacting with #bots, etc. Any #linguists or #conlangers here who have seen attempted solutions to this problem (or who have a good idea of how it might be solved)?

    #AI #LLM #LLMs #bot #chatbot #ChatGPT #Claude #pronoun #pronouns #linguistics #linguist #conlang #conlanger #agenticAI

  6. Zuckerberg admits Meta AI agent development is slowing yayafa.com/2838561/ #AgenticAi #AI #ArtificialGeneralIntelligence #ArtificialIntelligence #LLAMA #Meta #MetaAI #インドネシア証券取引所、ジャカルタ証券取引所、インドネシア金融、株式、株主、取引所、株式、株主、金融、企業、総合、ジャカルタ総合指数 #エージェント型AI #人工知能 #汎用人工知能

  7. Organizational learning and retrospectives as a model for software agent learning:

    Many companies collect agentic traces of software engineering, and aim to use these as training materials for next generation of the models. But how to do it? The naive, classical method is to use rejection sampling and only leave in successful engineering examples, removing the failed ones.

    But is these a better way?

    Yes there is.

    You really have to think about these things in an epistemological frame — where the new knowledge and informative signal comes from for the next generation of models.

    It comes from not blind trial and error, but from learning about what worked and what did not, with hindsight.

    We can take an agentic trace, and then do a retrospective on it with AI agents. They can determine what worked, what did not, and they can rewrite the trace removing the bad choices, removing bad exploration decisions, rewriting the trace with hindsight, to produce an agentic trace which is superior to the capabilities of any real existing AI model.

    And if you collect enough of these superior traces, what do you do with them? Train the next generation of models with it, and rinse and repeat.

    #AgenticAI #AI #RecursiveSelfImprovement #GenAI