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Friday26 items

  1. heise onlineAnalysis
    AI score55

    Aleph Alpha Releases Kolibri-1: A 78B-Parameter Open-Weight Model for German-Language Reasoning

    Aleph Alpha has released the open-weight model Kolibri-1, with 78B total parameters. It uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a 256K Token context that can be extrapolated to about 1 million Tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1137 Tokens, whereas the compared DeepSeek-V4.1-Flash requires 1910 Tokens. Pretraining used 20 trillion Tokens, with data current as of June 18, 2026. The author's hands-on test found its answers correct and its German-language reasoning process evident, and stated that the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be verified in real-world use.

    #Model releases#Reasoning#English text is being prepared.

  2. ComputerworldAnalysis
    AI score63

    Surface Laptop Ultra review: NVIDIA RTX Spark's local AI performance

    The author tested Microsoft's Surface Laptop Ultra equipped with the NVIDIA RTX Spark N1X SoC. The top configuration of this machine features a 20-core Grace Arm CPU, a 6144-core Blackwell GPU, and 128GB of unified memory, with a starting price of $2,599.99. In the UL Procyon AI text generation test, it scored 2703, about 33% higher than the IdeaPad Pro 5 with an Intel Core Ultra X9 388H; MLPerf Flux 2 Klein 4B image generation reached 26.97 images per minute, about 2.7 times that of the latter. The author also used a local Qwen3.8-27B to drive the Hermes agent, which completed extracting benchmark charts from Excel and generating a Word report in about five minutes.

    #Evaluations / benchmarks#English text is being prepared.#Agents

  4. shao_meng
    AI score60

    English translation is being prepared.

    Quoted postlauren · @poteto

    perhaps one of the most durable parts of pstack are its engineering principles: very concise skills (~10-20 loc) that distill down good practices into its core. as models get smarter, i expect most of pstack skills to compress into short principles or workflows (a generic list of steps) and that i will be able to delete a few of them. how it works is that /poteto-mode contains a short index of available principles and tells agents "In your reply, name each principle that shaped a decision and the specific choice it changed." this is why you'll often see a Principles Named or Used section at the end of the agent's response many of these principles were extracted from my sessions correcting agents since the start of this year. some are inspired by problems i see people talk about, like Attack the Premise, inspired by @VictorTaelin's post https://t.co/qTGVfoKSyD you can also use the principles as skills if you want greater control, by invoking /principle-<name> https://t.co/wuTWsflVN3

    #Tutorials / practice#Agents#English text is being prepared.

  12. ARC PrizeFirst-partyAnalysis
    AI score81

    ARC Prize announces evaluation results of Gemini 4 Argon on the ARC-AGI series

    ARC Prize announces the validated results of Google Gemini 4 Argon on ARC-AGI: ARC-AGI-3 reaches 97.5% (provider adapter harness, cost $4.8K), and 49.8% under the standard harness (cost $5.8K); ARC-AGI-2 is 91.3%, at $0.32 per task; ARC-AGI-1 is 98.5%, at $0.16 per task. ARC Prize states that Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test failed to complete all 55 games due to API issues, and the high reasoning tier completed only 19 games so no score was published; costs are calculated at the current discounted price of $2 per million input tokens and $10 per million output tokens.

    #Evaluations / benchmarks#Reasoning#Google