Latent Space interviewed Supabase’s founders about agent-friendly backends.
Local runtime and compute features discussed include alpha releases.
For an agent product, inspectability matters as much as generation. An agent that changes a schema also needs a clear failure and rollback path.
Use a disposable database. Ask an agent to explain one query and one migration; require a human-reviewed rollback before granting write access.
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Sign in to join the conversationOpen-source Bark can generate speech, music, laughter, and sound effects from a single text prompt. The source says it supports 13 languages and more than 100 voice presets, but it may alter wording or add unexpected sounds. Bark generates audio clips of about 13–14 seconds and is released under the MIT license. The source reports about 12 GB of GPU memory for the full models, with smaller variants available for lower-memory use.
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AutoTrust Labs released JEV-27B v1.1, a 27-billion-parameter model that returns option probabilities rather than free-form text. The company reports a score of 67.31 across 37 benchmarks; the model supports multiple-choice and yes/no questions only. The model selects among supplied options and returns a probability distribution. It stands out on tool selection and intent classification, but is weaker on open-ended reasoning and some other tests.
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A review of 28 studies reports that agricultural TinyML deployments largely rely on ESP32, STM32, and ATMega microcontrollers. Some plant-disease detection systems report 90–97% accuracy and latency as low as 7.6 ms, but memory and energy metrics are often missing. The review says models typically run inference on-device in the field while training remains in the cloud. Quantization appears in over half of deployments, whereas structured pruning and neural architecture search are rare.
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