ChatGPT pidió 74 veces que “recreara perfectamente esta imagen, sin cambios” pic.twitter.com/Vf44smKm8y
— SONIA (@S0N_IA_) 27 mai 2026
ChatGPT asked 74 times to “perfectly recreate this image, without changes”
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ChatGPT pidió 74 veces que “recreara perfectamente esta imagen, sin cambios” pic.twitter.com/Vf44smKm8y
— SONIA (@S0N_IA_) 27 mai 2026
ChatGPT asked 74 times to “perfectly recreate this image, without changes”
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❤️ @Lovable now integrates with Databricks, providing a natural language interface that allows anyone, regardless of technical skills, to build live applications that can read and write data stored in Databricks.
— Databricks (@databricks) 27 mai 2026
See how teams can build dashboards, operational tools, internal… pic.twitter.com/HlVEwkbtyd
@Lovable now integrates with Databricks, providing a natural language interface that allows anyone, regardless of technical skills, to build live applications that can read and write data stored in Databricks. See how teams can build dashboards, operational tools, internal

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Deep Agents v0.6 brings Delta channels, reducing checkpoint storage by up to 100x for long-running agents, without sacrificing observability or resilience. Here’s a 200-turn coding agent session.
Without Delta Channels: 5.3GB of checkpoint storage
With Delta Channels: 129mb

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Run your personal AI company with a team of AI agents! Alook is an open-source collaboration platform for AI coding agents. Self-hosted and local-first. The setup: Define an org structure. Give each agent a role – dev, ops, research, whatever you need. Set reporting lines.
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Meet TRIBE v2: A Trimodal Brain Encoder Creating #Digital Twins of Neural Activity
— Ronald van Loon (@Ronald_vanLoon) 27 mai 2026
by @AIatMeta
#NeuralNetworks #FutureTech #Innovation #DeepLearning #AI pic.twitter.com/nXueGRFIxT
Meet TRIBE v2: A Trimodal Brain Encoder Creating #Digital Twins of Neural Activity
by @AIatMeta #NeuralNetworks #FutureTech #Innovation #DeepLearning #AI
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Alook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents.
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026
Close the screen and terminal, the agents keep running, and the work lands in your inbox. pic.twitter.com/zkmRiyidSo
Alook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents. Close the screen and terminal, the agents keep running, and the work lands in your inbox.
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OPEN-SOURCE AVATARS JUST LEVELED UP@avaturn_me just open-sourced AVTR-1, and the tech is pretty wild.
— Charly Wargnier (@DataChaz) 27 mai 2026
> clone the repo
> download the weights
> then run a conversational avatar locally on your machine at almost zero cost 🔥
repo + weights + demo links below 🧵↓ https://t.co/IbrBKsDFyl
OPEN-SOURCE AVATARS JUST LEVELED UP @avaturn_me just open-sourced AVTR-1, and the tech is pretty wild. > clone the repo
> download the weights
> then run a conversational avatar locally on your machine at almost zero cost repo + weights + demo links below ↓

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Claude is more than a chatbot • Projects → persistent context
• Artifacts → live outputs
• MCP → external tools
• Connectors → enterprise knowledge
• Rules → operational consistency AI becomes powerful when connected to real workflows. Via Giuliano Liguori
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Problem is that codex gives me 2% of my context so random stuff adding skills gives me warning messages.

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You won't run Claude Code the same way after this. AI coding sessions often fall apart the same way. You ask for a feature, nine files get edited, something silently breaks. Claude Code Harness wraps the model in a real delivery loop. It forces a strict cycle before any