Instead of treating prompts as static, Agentic Context Engineering (ACE) explores how context can evolve over time through generation, reflection, and curation. Read the paper
SYSTEMS
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RDUs for Continuous Inference and Large-Scale AI Workloads
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Built differently for inference 🦾
— SambaNova (@SambaNovaAI) 11 mai 2026
RDUs are designed to keep execution streaming continuously across the system, from memory to compute to parallel execution at scale.
256 RDUs working together can generate thousands of tokens in parallel for modern AI workloads.
Learn more:… pic.twitter.com/2Rf4GTon3cBuilt differently for inference RDUs are designed to keep execution streaming continuously across the system, from memory to compute to parallel execution at scale. 256 RDUs working together can generate thousands of tokens in parallel for modern AI workloads. Learn more:
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Importance of Data Validation for AI Predictive Maintenance Systems
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The moment document content reaches a digital twin or predictive maintenance algorithm, that system treats the content as fact – validation must happen before.
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Developer burns full transformer into FPGA at 50K tokens/sec
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/1 Developer implements a full transformer model in FPGA hardware, achieving 50,000 tokens per second without a GPU. What if an AI model ran with zero software? No Python, no GPU, no runtime—just logic etched into a chip. That’s exactly what TALOS-V2 does. TALOS-V2 explores what happens when a small…
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Essential Technical Skills for AI Engineers
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As an AI Engineer. Please learn: – Harness engineering, not just prompt engineering
– Prompt caching vs. semantic caching tradeoffs
– KV cache management at scale
– Speculative decoding vs quantization
– Structured output failures & fallback chains
– Evals (LLM-as-judge + human -
Defining the Components of Agentic AI Systems
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Agentic AI = systems, not models • Intelligence (LLMs)
• Memory
• Tools
• Feedback loops That’s how AI moves from thinking → to doing Via Giuliano Liguori (
@ingliguori
) #AI #AIAgents #AgenticAI -
Why Do Large Neural Networks Work?
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Researchers have just explained why massive neural networks work so effectively. Deep networks possess enough capacity to memorize completely random noise. Yet, they still excel at making accurate predictions on unseen data. A new paper finally sheds light on this phenomenon. The empirical Neural Tangent Kernel (NTK) subtly divides the output.
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A 20-year-old earns $37,250 with an AI content factory
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Un jeune de 20 ans a généré 37 250 $ en un seul mois grâce à du contenu YouTube… sans presque jamais ouvrir un logiciel de montage.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 10 mai 2026
Il a construit une véritable “content factory” autonome où Claude agit comme le cerveau et Premiere Pro comme le corps.
Pendant qu’il dort, sort… https://t.co/NRJJAfFlcS pic.twitter.com/stmnqZEnudA 20-year-old generated $37,250 in a single month through YouTube content… without almost ever opening editing software. He built a true autonomous “content factory” where Claude acts as the brain and Premiere Pro as the body. While he sleeps, goes out
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Speculative decoding speeds up LLMs by 6x
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The slowest part of running an LLM just got 6x faster without losing a single token.
— AlphaSignal AI (@AlphaSignalAI) 10 mai 2026
LLMs generate text one token at a time.
That sequential bottleneck wastes GPU power and slows everything down.
Speculative decoding fixes part of this.
A small draft model guesses ahead,… pic.twitter.com/y7YlqTWvrjThe slowest part of running an LLM just got 6x faster without losing a single token. LLMs generate text one token at a time. This sequential bottleneck wastes GPU power and slows everything down. Speculative decoding addresses part of this issue. A small draft model predicts ahead.
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Rediscovering Karpathy’s wikiLLM with Obsidian and Claude as a second brain
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I'd somehow completely forgotten that Karpathy introduced the wikiLLM a while back (obsidian + Claude code/codex). I'm sick in bed and set it up because I have nothing else to do. I love it. I have a second brain now. Amazing.