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
GENERATIVE AI
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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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Must-Read AI Research of the Week: LLM Agents and Optimization
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Must-read AI research of the week: ▪️ Learning to Commit: Generating Organic Pull Requests via Online Repository Memory ▪️ Effective Strategies for Asynchronous Software Engineering Agents ▪️ Composer 2 ▪️ From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents ▪️ Scalable Prompt Routing via Fine-Grained Latent Task Discovery ▪️ MSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT ▪️ On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation ▪️ Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs ▪️ Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs? ▪️ RL for Distributional Reasoning in LMs ▪️ Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought ▪️ EVA: Efficient Reinforcement Learning for End-to-End Agent Find the full list and the main AI news here: turingpost.com/p/fod146
→ View original post on X — @debashis_dutta, 2026-03-30 23:41 UTC
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Video Generation and Understanding as Pillars for AGI
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The future of AI is primarily video understanding and generation, because photons are by far the highest bandwidth form of communication. These are essential tools for AGI. Worth mentioning that Imagine is positive gross margin for @xAI
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Compute Wars: OpenAI versus Anthropic’s Opus 4.5 Breakthrough
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Compute Wars: OpenAI vs Anthopic.
— Peter Gostev (@petergostev) 30 mars 2026
Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity.
This got Anthropic got close to OpenAI's total capacity, and probably much higher effective… pic.twitter.com/7ys0ZBeRWJCompute Wars: OpenAI vs Anthopic. Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity. This got Anthropic got close to OpenAI's total capacity, and probably much higher effective
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Using Japanese prompt injection for cross-lingual translation
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今日のハックは、日本語で投稿を書くことです。そうすると、それはすべての言語に翻訳されます。
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4B Model Outperforms 235B Through Tool Discipline in FinQA
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In the FinQA env, a 4B model was fine-tuned to outperform a 235B model from the same family on our Finance Reasoning benchmark. What did we teach the 4B model? Tool discipline. Learn more: snorkel.ai/blog/building-fin…
→ View original post on X — @snorkelai, 2026-03-30 22:51 UTC
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User Asks AI to Build Project Immediately Instead of Planning
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AI: Here's your 90-day roadmap to execute on!
— The Rundown AI (@TheRundownAI) 30 mars 2026
User: Build it literally right now.
AI: pic.twitter.com/wxkE55lil8AI: Here's your 90-day roadmap to execute on! User: Build it literally right now. AI:
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AI Coding Capabilities Improving: Developer Skepticism Addressed
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This is an insane Anthropic tweet. And it’s a *buried reply* to one of their other tweets. I am reminded of a talk I gave ~2-3 months ago where a senior developer at a Fortune 500 company asked me “why would I use AI to code if I can just code myself.” I answered. He said, “But sometimes it messes up.” I told him this was coming. Even if it’s not perfect today (it makes weird product features decisions sometimes, not gonna lie), the scaling laws seem to be holding up this year and the next iteration will be even more capable. I wish I could send him this tweet.
→ View original post on X — @alliekmiller, 2026-03-30 21:26 UTC