GoalOS is architecturally pointed at one of the Holy Grails: proof-gated, economically coordinated, open-ended autonomous work that can generate verified experience and reusable capability. #AGIALPHA
AUTOMATION
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GoalOS provides bounded, evidenced, validated, governed AI work as default unit
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The industry is producing increasingly capable systems, but capability alone does not solve authorization. In high-trust environments, work must be bounded, evidenced, validated, governed, and reusable. GoalOS makes that the default unit of AI work.
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GoalOS converts autonomous AI output into evidence-bearing institutional capability
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GoalOS turns autonomous AI output into evidence-bearing institutional capability. #AGIALPHA
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AI detects attention drops and regenerates engaging lecture clips
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5. Retention: Finds the moments in a recorded lecture where attention drops and uses Pika to automatically regenerate more engaging clips—using a brain simulation model as the judge.
— Pika (@pika_labs) 26 juin 2026
Team: Marcos Arnold, John Connell, Eric Fu, Ava Gonick pic.twitter.com/n4yspYMval5. Retention: Finds the moments in a recorded lecture where attention drops and uses Pika to automatically regenerate more engaging clips—using a brain simulation model as the judge. Team: Marcos Arnold, John Connell, Eric Fu, Ava Gonick
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PantryAgent connects pantry, meals, and grocery list to reduce waste
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4. PantryAgent: Helps you connect your pantry, meals, and grocery list into one system so all food gets used before it gets thrown out. The team generated their entire demo video with the Pika MCP.
— Pika (@pika_labs) 26 juin 2026
Team: Paul Jiang, Yuxuan Jiang, Tianjun Li, Elaine Zhu pic.twitter.com/AEQTR7AXBT4. PantryAgent: Helps you connect your pantry, meals, and grocery list into one system so all food gets used before it gets thrown out. The team generated their entire demo video with the Pika MCP. Team: Paul Jiang, Yuxuan Jiang, Tianjun Li, Elaine Zhu
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Book on ML for Time Series Forecasting and Anomaly Detection
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Machine Learning for Time Series with Python — Forecast, predict, and detect anomalies with state-of-the-art ML methods: http://
amzn.to/3wrjXVv by @benji1a —————
#DataScience #AI #Forecasting #PredictiveAnaytics #AnomalyDetection #IoT #IIoT #EdgeAI #EdgeComputing -
Designing Machine Learning Systems – Iterative Process for Production
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Designing #MachineLearning Systems — An Iterative Process for Production-Ready Applications: http://
amzn.to/46epLSi by @chipro — 𝓣𝓸𝓹𝓲𝓬𝓼:
Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, -
MCPs vs CLIs: Which to Use When Building Agents?
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Should you use MCPs or CLIs when building agents? @BraceSproul + @jakebroekhuizen spill the tea. pic.twitter.com/BjeEvzgjiU
— LangChain (@LangChain) 26 juin 2026Should you use MCPs or CLIs when building agents? @BraceSproul + @jakebroekhuizen spill the tea.
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Anthropic revolutionizes frontend design with AI
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ANTHROPIC ACABA DE CAMBIAR CÓMO SE DISEÑA FRONTEND CON IA
— Nico (@nicos_ai) 26 juin 2026
Se llama frontend design, una skill oficial de anthropic.
El problema que resuelve es simple.
Cada vez que le pedías una web a la ia, salía lo mismo:
→ inter como fuente
→ gradiente morado
→ layout genérico de… pic.twitter.com/joapURSeKAANTHROPIC HAS JUST CHANGED THE WAY WE DESIGN FRONTEND WITH AI It's called frontend design, an official Anthropic skill. The problem it solves is simple. Every time you asked an AI for a website, it was always the same: → Inter like
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AI agent delays shift from code to coordination as platforms converge
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Enterprise platforms converge on data, models and orchestration layers. Choices on architecture now steer how fast AI agents act across processes, so delays shift from code to coordination. Source @Gartner_inc Link https://
gtnr.it/4tKg5vC via @antgrasso