AI - Artificial intelligence

You’re using AI to be more productive. So why are you more exhausted than ever? The paradox every engineer needs to confront.

Unlike many startups producing nothing but vaporware, Jim Keller’s TensTorrent has actually delivered impressive-looking RISC-V AI accelerators, but there could be some trouble brewing. Starting with firmware version 19.5.0, the firm has now chopped the tensor core count on Blackhole p150 cards from 140 to 120, affecting both new cards and existing units already in customers’ hands.

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Currently uses gpt5.2 for script generation, but the architecture is modular – you can swap in any local LLM (Qwen, Llama, etc.) if you want fully local.

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I needed to download a 3k+ playlist from youtube. It pissed me off how often the music would get deleted from there and the ads…

If you’ve ever trained or fine-tuned an LLM, you’ve likely hit a wall at the very last step: the Cross-Entropy Loss.

The paper argues that we have been wasting a lot of expensive GPU cycles by forcing transformers to relearn static things like names or common phrases through deep computation. Standard models do not have a way to just look something up so they end up simulating memory by passing tokens through layer after layer of feed forward networks. DeepSeek introduced a module called Engram which adds a dedicated lookup step for local N-gram patterns. It acts like a new way to scale a model that is separate from the usual compute heavy Mixture of Experts approach.

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Crossposted from https://fedia.io/m/pcmasterrace@lemmit.online/t/3295923

This paper is one of the more interesting takes on context extension I have seen in a while because it challenges the assumption that we need explicit positional encodings during inference. The authors make a case that embeddings like RoPE act more like scaffolding during construction rather than a permanent load bearing wall. The idea is that these embeddings are crucial for getting the model to converge and learn language structure initially, but they eventually turn into a hard constraint that prevents the model from generalizing to sequence lengths it has never seen before.