A coding agent gets stuck in a retry loop during the night. By morning, it has made 10,000 LLM calls. You now have a four-digit bill. Observability tells you what happened, but stopping these problems before they
AI
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Gary Marcus: AI investors bad at math or blind to risk
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further evidence that people shoveling money into AI are either bad at math or blind to risk
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SambaNova at Avnet SKO: AI infrastructure and superhero comics
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Premium inference, but make it comics We had a great time at the @Avnet SKO with Harry Ault talking about the future of AI infrastructure and agentic workloads, plus a few SambaNova superheroes making an appearance. Always a fun time partnering with Avnet!
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From Prompt Optimization to Agent Loop with Memory
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Peter put it well. The next stage is not "writing better prompts," but designing a loop that can continuously drive an agent. Let the agent observe, remember, retrieve, act, and improve with each run. But the loop needs memory. Without persistent, retrievable, and auditable memory, the so-called agent loop will eventually fall back to "cramming more context into the prompt."
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Anthropic’s internal contradiction: AI job impact vs Pope warning
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A walking contradiction: On the one hand, Daniela from Anthropic says that AI has hardly replaced any jobs so far, on the other hand, co-founder Olah warns the Pope about the disruptive effect of AI on the labor market and society. pic.twitter.com/kprlE7N7B0
— Chubby♨️ (@kimmonismus) 8 juin 2026A walking contradiction: On the one hand, Daniela from Anthropic says that AI has hardly replaced any jobs so far, on the other hand, co-founder Olah warns the Pope about the disruptive effect of AI on the labor market and society.
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Anthropic reports 8x code, 52x optimization, and 64% better decisions
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Recursive self-improvement may no longer be just a theory.
Anthropic reports: 8x more code per engineer 76% success on open-ended coding tasks 52x training optimization Better research decisions than humans 64% of the time
The feedback loop is getting tighter.
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800K model aces impossible Sudoku in 15 minutes using Lattice Deduction Transformer
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An 800K model just aced impossible Sudoku in 15 minutes. Most reasoning models scale up to get smarter. This one goes the other way. A new paper introduces the Lattice Deduction Transformer. It is a tiny looped model that reasons like a SAT solver. Instead of guessing
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Concern over AI surpassing human intelligence in profession
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And maybe even worried about their profession and an artificial intelligence becoming more intelligent than they are
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68% of AI code contains errors, No-mistakes stops them
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EL MEME DE “CLAUDE, MAKE NO MISTAKES” SE HIZO REALIDAD.
— Nico (@nicos_ai) 8 juin 2026
El 68% del código que genera la IA tiene errores, según las stats reales del creador de esta Skill.
No-mistakes los para antes de que lleguen a producción.
Cómo funciona:
→ git push no-mistakes en vez de git push origin… pic.twitter.com/LzT5XYPFftThe meme of 'Claude, don't make mistakes' has become reality.
68% of AI-generated code contains errors, according to the real stats from the creator of this Skill.
No-mistakes stops them before they reach production.
How it works:
→ git push no-mistakes au -
High-level notes and comparisons on PivotRL paper
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Anyways, that was a cool paper. I took some high-level notes and comparisons here. PivotRL all the things! https://
maximelabonne.substack.com/p/nemotron-3-u
ltra-what-distillation
…