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Overview INTELLECT-3.1 is a 106 billion parameter Mixture-of-Experts reasoning model built by PrimeIntellect as continued training of the base INTELLECT-3 model. The model receives additional reinforcement learning optimization for math, coding, software engineering, and agentic tasks using the prime-rl framework and verifiers library. This is a text-to-text generation model
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Someone asked me for a practical example of context engineering. Here is one pattern I use: keep a separate context layer for product memory, and give agents clear rules for maintaining it. This is different from putting another AGENTS.md into the product codebase. In this pattern, the context layer
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Encrypting the private key still leaves one decryptable key behind one server — the honeypot that MPC later removes. Encrypting the private key still leaves one decryptable key behind one server — the honeypot that MPC later removes. Your backend can decrypt every user's private key. So can your ENCRYPTION_
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A working tutorial for building a character bible that survives a stateless pipeline, from someone who lost a whole afternoon to a heroine who would not stay the same person. The first time I tried to cut a real scene of Lost Garden, my dark-fantasy anime series, my lead character
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This article will sum up the past 4 years of my life. I remember someone telling me “It’s not what you know but who you know”. This stayed with me for as long as i can remember. This is not absolute but does make up a huge part of
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Quasar Models announced that its Subnet 56, which is part of the Gradients project, has joined Subnets 24 and 3 to train its long-context AI models on the Bittensor network. Quasar Models And so we have three subnets working together in a common training effort to push decentralized AI forward.
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The dirty secret is not that AI automation tools are bad. It is that most of them assume cleaner data, simpler workflows, and more internal ownership than startups actually have. Use Zapier, Make, or n8n for low-risk, rules-based workflows. Move to custom AI when the workflow touches customer experience, proprietary
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Foreword: Why This Knowledge System is Needed In the field of image engineering, every engineer faces a common challenge: how to translate perfect designs from the laboratory into consistent quality on the mass production line, ultimately delivering a reliable product to consumers. This is not a single technical issue, but
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Every team I talk to has the same drawer of half-finished AI experiments. A bot that answers questions about the employee handbook. A "chat with your PDFs" tab nobody opens anymore. A Slack assistant that summarizes threads. They were all genuinely useful for about a week, and then
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Bittensor co-founder Const announced that the network now pauses weekly emissions to subnets that have no miner distribution through active mechanisms or lack functional code. The review runs every Monday on an ongoing basis. Subnets can be reinstated on a case-by-case basis once the issues that caused their removal are
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Danish startup achieves 99.3% compression on 1Hz smart meter data, powering real-time appliance-level energy analytics for hundreds of homes. Float is a Danish AI and energy startup that collects 1Hz smart meter data from hundreds of homes and disaggregates it into per-appliance consumption using a proprietary ML model. The
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Prediction markets closed out June with the biggest month in the sector's history. Combined trading volume across Kalshi, Polymarket, and Polymarket US reached $44.8 billion, up 75% from May's $25.66 billion, according to The Block, with the FIFA World Cup doing most of the
