GoalOS AGIALPHA Ascension is an experimental framework for a persistent, goal-oriented, self-improving intelligence system that accumulates capabilities, evidence, and economic value over time. GitHub: https://
github.com/MontrealAI/goa
los-agialpha-ascension
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AGENTS
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GoalOS AGIALPHA Ascension: Experimental Framework for Persistent and Self-Improving AI
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AA methodology and Nvidia technical blog on agentic coding performance
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AA methodology: https://
artificialanalysis.ai/methodology/ag
entperf
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Nvidia technical blog: https://
developer.nvidia.com/blog/nvidia-ac
hieves-leading-agentic-coding-performance-on-first-agentic-ai-benchmark/
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NVIDIA GB300 outperforms H200 by 20x in agents per megawatt
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Among other findings the benchmarks shows that NVIDIA's GB300 outperforms H200 by a whopping factor of 20 in terms of running concurrent agents per megawatt. 3/4
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DeepSeek, GLM, Kimi solve real code issues with reasoning and tools
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(DeepSeek V3.2, GLM 4.7, and Kimi K2.5) prompted to resolve issues in real public code repositories. All trajectories include interleaved reasoning and tool calls. 2/4
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Artificial Analysis launches AgentPerf, first agentic AI benchmark
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Artificial Analysis just announced AgentPerf, the industry’s first agentic AI benchmark. The benchmark uses several real-world agentic use trajectories and employs OpenCode agentic harness using three top open-source models with reasoning enabled 1/4
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8 Steps to Deploy AI Agents by 2026
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The 8-step order I use for shipping AI agents in 2026: 1. Filter noisy tool outputs
2. Load tools only when needed
3. Clean cached history before reusing it
4. Compress long logs and terminal outputs
5. Store memory outside the context window
6. Compact manually around 40%
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Turn Claude into 20+ marketing and business specialists
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Turn Claude into 20+ different marketing and business specialists. Install real expertise, not just prompts. Get my Claude skills bundle.
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Diversify AI providers and host a local AI
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All AI agent architects must absolutely connect to multiple providers to dilute risks and especially have an AI on a machine at home.
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Claude Code: no visibility into production issues
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You'll regret using Claude Code without this.
— Akshay 🚀 (@akshay_pachaar) 13 juin 2026
Claude Code stops at the boundary of your terminal.
It has no visibility into Datadog traces, Jira tickets, Slack decision history, or cloud infra state.
So when something breaks in production, Claude Code isn't there. When the… pic.twitter.com/igWgs8AhQlYou'll regret using Claude Code without this. Claude Code stops at the boundary of your terminal. It has no visibility into Datadog traces, Jira tickets, Slack decision history, or cloud infra state. So when something breaks in production, Claude Code isn't there. When the
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AI reads 30,000 X posts daily to write a report
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I built you an AI to read 30,000 posts here on X every day and write you a report. https://
alignednews.com/ai It's how you can keep up better. But yeah, it's moving fast. It's moving faster than I've ever seen in my life, and I've covered technology for decades.