We will be developing more and more software for agents to use, most companies will be building for agent companies rather than direct to user. Onboarding will be seamless
@trq212 I’ve used html outputs for almost a year and then reverted to MD. Models (gemini’s at least) tend to default MD for tables or mixed outputs(even when agressively prompt to output html); breaking rendering like <br> inside a markdown cell etc. Now using tools for inline html instd
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This paper introduces a new method called Agentic Context Engineering (ACE).
It helps language models improve by updating what they read and remember, instead of changing their core weights.
Normal methods that edit prompts tend to make them too short and lose important details over time.
ACE fixes this by treating the model’s context like a growing notebook that keeps and organizes useful strategies.
It has three parts: a Generator that works on tasks, a Reflector that learns from mistakes, and a Curator that updates the notebook with helpful lessons.
Instead of rewriting everything each time, ACE makes small, local edits, which keeps learning fast and preserves past knowledge.
It also trims repeated or useless points so the context stays clean and focused.
In tests with coding agents and financial reasoning, ACE gave about 10% higher accuracy and cut learning time by about 87%.
It even worked well without labeled training data, using only real-time feedback from how tasks ran.
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Paper – arxiv. org/abs/2510.04618
Paper Title: "Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models"