4/ The workflow also feels way cleaner. The output goes to the filesystem instead of just filling up your context window, and TinyFish says it uses 87% fewer tokens than MCP per operation, with 2x higher task completion on more complex multi-step tasks.
LLMS
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Qwen 3.5’s overthinking helps infer intent, boosting Gemma 4’s performance
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Qwen 3.5 tends to overthink, which ironically helps it better infer intent Gemma 4 is the opposite, you have to spell everything out (probably guardrails) Had Qwen rewrite my prompts, then used those on both models Gemma’s performance jumped noticeably
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Reinforcement Learning Speed Trade-offs in Large Language Models
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Significant downside of larger models is that it is much harder to do RL on them. Smaller model = quicker RL cycles, bigger model = slower RL cycles. So far this matches – GPT-5 is the smallest model with quickest iterations, Gemini 3 is biggest and slowest iterations, Claude is
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Mythos: Teaching AI to Understand Stories and Narratives
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Mythos: Teaching AI to Understand Stories, Not Just Text
— Satya Mallick (@LearnOpenCV) 14 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Mythos, a new approach focused on helping AI systems understand narratives, structure, and meaning within stories. Rather than treating text as… pic.twitter.com/jjAZvymyiuMythos: Teaching AI to Understand Stories, Not Just Text In this episode of Artificial Intelligence: Papers and Concepts, we explore Mythos, a new approach focused on helping AI systems understand narratives, structure, and meaning within stories. Rather than treating text as
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Founder Gets First MCP Request to Connect Claude with SaaS Order History
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Rob, relaying from another founder: Got our first request for an MCP from a customer! They have Claude Cowork setup to draft replies to CS emails, but want those less technical users to be able to check e.g. order history from our SaaS, which requires some connective tissue.
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LLMs Can Grade Every Sales Call for Instant Feedback
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Analysis: Many SaaS capture routine events in people’s workflows. You can potentially have LLMs give ~instant feedback on execution quality of those with less friction and more consistency than traditional human management would ever be able to do. e.g. Grade every sales call.
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Suggest Prompts to Users Instead of Blank LLM Input Box
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Given you know domain better than customers rather than giving blank text box for interaction with LLM over deterministic domain model, give suggestions of what they could ask for.
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LLMs Simplify Search Interfaces Beyond Boolean Logic for All Users
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Rob: Lay users (non-engineers) don't understand boolean logic like AND/OR. We once relied on complex composed interfaces to do filtering/search, and/or pseudo-syntax. LLMs can reinvent some interfaces to make them radically simpler and more effective for users. Everyone can text
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Bootstrappers Gain Edge Designing LLM-Native Solutions From Scratch
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Because boostrappers are much more nimble than existing market, you can reasonably be on leading edge of "What does a solution in X space look like *if it is designed from the ground up in the knowledge that LLMs exist*, as opposed to shoehorning LLMs into a single screen?"
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AI Product Strategy: Bootstrappers Should Build Downstream of Labs
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AI as product: Difficult for bootstrappers to keep up with the labs on LLM/etc development. Very, very tractable to make something downstream of the labs.