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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AI
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Structured AI reasoning over raw scale
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Lambda Calculus for Safe Code Generation
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λ-RLM flips the approach.
Instead of letting the model generate arbitrary code, it runs a typed functional runtime grounded in lambda calculus. Think: a fixed library of pre-verified operations (SPLIT, MAP, REDUCE). The model only handles small, bounded leaf problems. -
Optimize AI inference with smaller models
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You’re probably overpaying for AI inference. Researchers just proved a small model with the right scaffolding crushes a model 50x its size on long-context tasks. Read this thread and you’ll know how to get better results with fewer resources:
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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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T-Mobile and NVIDIA Explore Edge AI for Reduced Latency Performance
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From #GTC26: AI performance depends heavily on architecture. @TMobileBusiness and NVIDIA are exploring how edge compute can reduce latency by processing data closer to where it’s created. ⚡ fierce-network.com/broadband… T-Mobile for Business Partner #EdgeAI
→ View original post on X — @haroldsinnott, 2026-03-31 00:42 UTC
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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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Shanda’s EverMind Team Hiring: AI Tools and Benefits Package
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Recruitment post: @shanda_group's @EverMind team is hiring. See job description below. DMs always open, feel free to ask any questions. • Engineering Team Lead
• Senior Test Development Engineer
• Also hiring for Product Manager, DevOps, Algorithm, Agent Strategy and other positions Let me share some pros and cons Pros
1. Location is in a separate park in Zhangjiang, quiet, few people, plenty of parking
2. Unlimited Claude Code Opus usage
3. All other AI tools with no limits, available upon request
4. Free canteen with good food
5. Company VPN is very stable, no need to figure out any workarounds for internet access
6. Lots of benefits: transportation allowance, meal allowance, supplemental provident fund, supplemental medical insurance, employee apartments, children's school support, etc. (I had 12 days annual leave upon joining) Cons
1. No convenience store downstairs, takes 5 minutes to walk to buy a can of soda 😅 [Translated from EN to English]→ View original post on X — @elliotchen100, 2026-03-31 00:37 UTC
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AI Systems Still Struggling With Basic Visual Tasks
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amazing. i was giving examples like this for DALL-E three years ago. systems are still struggling with some basics.
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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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SquareMind Raises $18M for AI Skin Cancer Detection Robotic Platform
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This goal hasn’t changed https://
x.com/theahmadosman/
status/1978790002835267707?s=46
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