What a trip that every 1-2 months the most powerful models on the planet, used by everyone, get even more powerful.
AGI
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The Age of Async Agents: Devin’s Growth, AI Commits, and Cloud Engineering
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🆕The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect https://t.co/x5Hw5S3egc@cognition cofounder + CPO @walden_yan and Open-Inspect creator @_colemurray explain why engineering is moving from local IDEs to cloud… pic.twitter.com/fciT77nJNI
— Latent.Space (@latentspacepod) 28 mai 2026The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect https://
latent.space/p/cognition @cognition cofounder + CPO @walden_yan and Open-Inspect creator @_colemurray explain why engineering is moving from local IDEs to cloud -
AI Model Comparison: Opus 4.8 vs. GPT-5.5 Trajectory
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Opus 4.8 is clearly a strong model, but my impression is that Anthropic is increasingly playing catch-up with OpenAI rather than setting the pace. It feels like GPT-5.5 has shifted the benchmark again, and if OpenAI keeps this trajectory, GPT-5.6 could very plausibly become the
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Deep Agents v0.6 Introduces ContextHubBackend for Agent File Management
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New in Deep Agents v0.6: ContextHubBackend A versioned home for the files that power agent behavior, backed by LangSmith Context Hub, enabling context improvements from one run to the next.
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Anthropic Releases Claude Opus 4.8: Faster, Cheaper, and Smarter
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ANTHROPIC ACABA DE LANZAR CLAUDE OPUS 4.8
— Nico (@nicos_ai) 28 mai 2026
Y puede que sea su mejor modelo hasta la fecha:
→ 2.5x más rápido y 3x más barato con modo /fast
→ Trabaja SOLO como un ingeniero senior, sin que lo supervises
→ Lanza cientos de subagentes en paralelo para completar tareas complejas… https://t.co/QSIQ1rvfpo pic.twitter.com/2cNoRMTRDgANTHROPIC JUST RELEASED CLAUDE OPUS 4.8 And it might just be their best model to date: → 2.5x faster and 3x cheaper with /fast mode
→ Works ONLY like a senior engineer, no supervision needed
→ Launches hundreds of subagents in parallel to handle complex tasks on its own And -
Opus 4.8 AI Model Release: Agentic Coding Boost, Faster, Cheaper Modes
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Opus 4.8 is live. Benchmarks especially significant jump in Agentic coding, but more important: „Fast mode is available for Opus 4.8. It's the same model at roughly 2.5x the speed, and we've made it three times cheaper than before.“
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AI Agents Need to Learn from Executions, Not Just Complexity
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Exacto. Ese es el problema que nadie estaba atacando. Todos haciendo agentes más complejos pero ninguno que realmente aprenda de sus propias ejecuciones
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AI Self-Improving Agent Outperforms Karpathy’s Autoresearcher
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Esa gráfica es clave. Se ve claramente el momento en el que el self-improving agent se despega del autoresearcher de Karpathy y ya no baja
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AI Agents Need to Learn from Executions for Meaningful Functionality
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I've been waiting for something like this for a while. Agents that don't learn from their executions make no sense.
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Self Improving AI beats Karpathy’s autoresearcher by improving itself
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Self Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itself!
— Sumanth (@Sumanth_077) 28 mai 2026
SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task.
Most agent frameworks are static. Fixed harness, fixed model… https://t.co/bk4BaLqsCE pic.twitter.com/kiByM7J0KvSelf Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itself! SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task. Most agent frameworks are static. Fixed harness, fixed model