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Monday

  2. Unite.AI

    Interview with 0G Co-founder and CEO Michael Heinrich: Building a Decentralized Trust Layer for AI

    In the interview, 0G Co-founder and CEO Michael Heinrich explained that 0G is building a decentralized AI infrastructure stack that integrates verifiable computing, encrypted decentralized storage, private AI inference, and an AI-native EVM-compatible Layer 1 blockchain into a trust layer for AI. He noted that compute has become a strategic resource as important as capital and energy; the four hyperscale cloud providers' combined AI capital expenditure in 2025 exceeded $500 billion and is expected to double this year, yet compute itself still lacks a transparent market and independent verification.

    In the interview, 0G Co-founder and CEO Michael Heinrich explained that 0G is building a decentralized AI infrastructure stack that integrates verifiable computing, encrypted decentralized storage, private AI inference, and an AI-native EVM-compatible Layer 1 blockchain into a trust layer for AI. He noted that compute has become a strategic resource as important as capital and energy; the four hyperscale cloud providers' combined AI capital expenditure in 2025 exceeded $500 billion and is expect

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    #hot-signal#English text is being prepared.#Industry news#English text is being prepared.
  5. CTech

    Shashua's new company doubleAI releases Maxwell code execution model

    Amnon Shashua's new company doubleAI has released its first product, Maxwell, which it says belongs to a new category called Code Execution Models (CEM), optimizing software execution efficiency between code and chips without modifying source code. doubleAI says Maxwell continuously analyzes running programs, hardware, and data to optimize underlying machine instructions, while using formal mathematical verification to ensure each optimization does not change the program's original behavior, with support for CPUs and GPUs. The company says Maxwell speeds up data pipelines by more than 38x and AI inference kernels by more than 4.5x on real workloads; these figures have not been independently verified. Maxwell is now available for early access through the official website, and doubleAI plans to charge customers based on the compute costs saved.

    Amnon Shashua's new company doubleAI has released its first product, Maxwell, which it says belongs to a new category called Code Execution Models (CEM), optimizing software execution efficiency between code and chips without modifying source code. doubleAI says Maxwell continuously analyzes running programs, hardware, and data to optimize underlying machine instructions, while using formal mathematical verification to ensure each optimization does not change the program's original behavior, wit

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    #tech-media#Model releases#Reasoning#English text is being prepared.
  6. AYi AI Notes

    MiniMax Claims Blind-Test Model Space Bunny, Confirms It as MiniMax-M3.1-Flash

    MiniMax officially claimed Space Bunny, a model tested anonymously on an overseas blind-test platform, confirming it as MiniMax-M3.1-Flash. The model supports a generation speed on the order of one hundred words per second and a one-million-token context; community tests measured generation speed steady at 85 to 112 words per second, capable of running through complex 3D scenes and code builds in a single pass within three minutes, and achieving near-imperceptible response in multi-file project repairs. The author believes this approach of anonymous blind testing first and public claiming afterward turns inference speed and million-token context into efficiency tools usable for everyday coding.

    MiniMax officially claimed Space Bunny, a model tested anonymously on an overseas blind-test platform, confirming it as MiniMax-M3.1-Flash. The model supports a generation speed on the order of one hundred words per second and a one-million-token context; community tests measured generation speed steady at 85 to 112 words per second, capable of running through complex 3D scenes and code builds in a single pass within three minutes, and achieving near-imperceptible response in multi-file project

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    #hot-signal#English text is being prepared.#Model releases#Reasoning
  7. Analytics India Magazine

    Utho Cloud Opens NVIDIA B300 GPU Pre-Orders, Planned Launch in February 2027

    Indian cloud service provider Utho Cloud has opened pre-orders for NVIDIA B300 GPUs, with deployment planned for February 2027, offering rupee billing and local hosting in India. Customers can reserve compute for 1, 2, or 3 years, with allocation following the order of pre-orders. The B300 is based on the Blackwell Ultra architecture, features 288GB of HBM3e memory, and supports large language model training, inference, and AI agent workloads.

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    #hot-signal#Industry news#English text is being prepared.#English text is being prepared.
  8. MarkTechPost

    TinyFish Makes Search Free: Breaking Down the Real Cost of AI Agents Reading Web Pages

    TinyFish announced that, within limits, it will waive retrieval fees for Search and Fetch: Search is limited to 30 requests per minute and 500 per hour, while Fetch is limited to 150 URLs per minute and 1,000 per day. The article notes that free retrieval does not eliminate the inference costs of downstream models; the same article of about 4,300 characters returns an estimated 1,184 to 42,774 tokens across different providers, and three pages of unfiltered content can fill a 128K context window. In the September 28, 2026 Artificial Analysis snapshot, TinyFish Search scored 71 at $9.30 per 1,000 tasks, the lowest total cost among the displayed configurations but also the lowest score, with about 59 seconds per task.

    TinyFish announced that, within limits, it will waive retrieval fees for Search and Fetch: Search is limited to 30 requests per minute and 500 per hour, while Fetch is limited to 150 URLs per minute and 1,000 per day. The article notes that free retrieval does not eliminate the inference costs of downstream models; the same article of about 4,300 characters returns an estimated 1,184 to 42,774 tokens across different providers, and three pages of unfiltered content can fill a 128K context window

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    #ai-vertical#Product updates#Search#English text is being prepared.

Tuesday

  6. Fireworks AI

    Fireworks Launches Free Weights Club, Free SFT Training for Muse Glimmer 30B

    Fireworks AI launches the Free Weights Club: SFT training for Muse Glimmer 30B is free until November 30, with no credit card required; inference and evaluation calls are billed separately. The tuned Glimmer trained on Kalshi market data achieved 58.3% prediction accuracy on unseen topics, 11.2 percentage points higher than Jev's 47.1%; in a historical backtest, a hypothetical $1,000 grew to $1,071 after simulated fees, while both base Glimmer and Jev posted losses. In another synthetic benchmark, the System One serving stack achieved a median server-side processing speed 2-3 times faster than standard BF16 chat completions.

    Fireworks AI launches the Free Weights Club: SFT training for Muse Glimmer 30B is free until November 30, with no credit card required; inference and evaluation calls are billed separately. The tuned Glimmer trained on Kalshi market data achieved 58.3% prediction accuracy on unseen topics, 11.2 percentage points higher than Jev's 47.1%; in a historical backtest, a hypothetical $1,000 grew to $1,071 after simulated fees, while both base Glimmer and Jev posted losses. In another synthetic benchmar

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    #editorial#Product updates#English text is being prepared.#Open-source ecosystem

Sunday

  3. Tencent Cloud Developer Community

    A Guide to "Agent Engineering in Practice: Eight Lectures": Engineering Methods from Working Prototype to Production-Ready

    The Tencent Cloud Developer Community has published a guide to "Agent Engineering in Practice: Eight Lectures," centered on how to upgrade a prototype capable of calling models and tools into a system that can complete business tasks, explain failures, control costs, and iterate continuously. The series runs through two fictional teaching cases—a case-filing assistant Agent and a postoperative follow-up Agent—covering eight topics: task contracts, reasoning and collaboration, context and memory, reliable tool execution, observability and replay, evaluation and calibration, security boundaries, and production operations. The article emphasizes that a model being able to suggest the next step does not mean the system can deliver business results; the example fields, budgets, and figures are all teaching calculations and do not constitute legal advice.

    The Tencent Cloud Developer Community has published a guide to "Agent Engineering in Practice: Eight Lectures," centered on how to upgrade a prototype capable of calling models and tools into a system that can complete business tasks, explain failures, control costs, and iterate continuously. The series runs through two fictional teaching cases—a case-filing assistant Agent and a postoperative follow-up Agent—covering eight topics: task contracts, reasoning and collaboration, context and memory,

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.
  4. The Decoder

    Microsoft and CityU Hong Kong propose Taste-Bench to evaluate AI agents' long-term decision-making capabilities

    Researchers from Microsoft and City University of Hong Kong propose Taste-Bench, using 502 binary-choice questions to evaluate AI agents' ability to make long-term decisions in long-horizon tasks. The best-performing model, GPT-5.6 Sol, achieves only 59.7% average accuracy, while GPT-5.5 reaches 59.5%. When key clues only become apparent after substantial subsequent work, the average accuracy of all models drops to 21%, close to random guessing, and increasing the reasoning budget brings almost no improvement. The researchers also distilled the teacher model's judgments using Qwen3.6-27B as the base model; the student's accuracy on unseen tasks rose from 30% to 47.9%, and its success rate on 41 SWE-bench Pro tasks increased from 14.6% to 33.7%.

    Researchers from Microsoft and City University of Hong Kong propose Taste-Bench, using 502 binary-choice questions to evaluate AI agents' ability to make long-term decisions in long-horizon tasks. The best-performing model, GPT-5.6 Sol, achieves only 59.7% average accuracy, while GPT-5.5 reaches 59.5%. When key clues only become apparent after substantial subsequent work, the average accuracy of all models drops to 21%, close to random guessing, and increasing the reasoning budget brings almost

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    #ai-vertical#Papers/Research#Evaluations / benchmarks#Agents