Early days, nobody wants to over-invest in side-quests and non-standardized concepts that might end up being swallowed by the models themselves. The focus is now on building a strong foundation rather than anything else.
LLMS
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Best Platforms for AI Agents
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Best Platforms for #AIAgents
by @Python_Dv #AI #LLM #ArtificialIntelligence #MachineLearning #ML -
AbacusClaw Outperforms MyClaw with Included ChatLLM
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I compared AbacusClaw to MyClaw today. MyClaw is crap, Abacus is better in every way! Plus you get ChatLLM included @bindureddy cooked with this and I’m going to become her biggest evangelist!
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Top AI News: White House, Gemini, Perplexity, OpenAI, and New Tools
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I took it down since submissions closed
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Mind Meld Bottleneck: Knowledge Gap Between Humans and LLMs
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Banger tweet, i think about it often because i feel it becoming a bottleneck. what's in my brain is "private" information for the LLM – it doesn't know what I know or don't know and I don't know what I don't know… The mind meld is only medium effective.
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Structured AI reasoning over raw scale
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The takeaway for builders:
The future of reliable AI isn’t just bigger parameters. It’s giving models structured, verifiable environments to reason in. Typed control flow > open-ended code generation. Paper: http://
arxiv.org/abs/2603.20105
Code: http://
github.com/lambda-calculu
s-LLM/lambda-RLM
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LLMs struggle with long inputs
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The problem: LLMs choke on long inputs. The usual fix? Bigger context windows. More parameters. More RAM. But there’s a deeper issue. When you let a model write its own recursive code to manage memory, you get infinite loops, broken outputs, and unpredictable costs. Brute
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Anthropic Launches Claude Security for Enterprise Users
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That’s the one I am still finalizing, hopefully will have emails sent tomorrow for attaching proofs and a few days after will have a randomly picked name announced
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Memory Sparse Attention Scales Models to 100M Token Context
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Scaling Attention to 100M context!? Memory Sparse Attention introduces an idea where instead of rereading an entire 100M-token entry, it learns to jump straight into the relevant memories and reason from them end-to-end. More specifically, it first encodes documents into
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FinQA Open-Sourced: RL Environment for Financial Reasoning Agents
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We just open-sourced FinQA — an #RL environment for financial reasoning agents. Real SEC 10-K data, multi-step reasoning + tool use, constrained SQL, binary rewards. The whole 9 yards! The kicker: a 4B model fine-tuned with FinQA outperformed a 235B model from the same family on finance reasoning: 58x smaller!
→ View original post on X — @snorkelai, 2026-03-31 00:19 UTC