Researchers have prototyped an AI-powered internet worm . The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into. This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

For me AI has been a saviour when it comes to coding as it help me learn at the start. However, AI is still relatively young and stilll in development especially a pain when you working big projects. Now the question is: What is the number 1 most constant thing that you dealt with AI in coding (aside from a colleague using it and make a mess of your code)? submitted by /u/Haunting-Bother7723 [link] [留言]

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Sharing a small CPU inference benchmark for nvidia/parakeet-tdt-0.6b-v3 that turned up a result I didn't expect going in. Setup: 2 x86-64 vCPUs (AVX2/FMA), 7.7GB RAM, no GPU. Test audio: 16.78s Harvard sentences at 16kHz mono. Results: Inference path RTF Peak Memory CPU utilization HF Transformers bfloat16 0.519 ~430MB delta — ONNX Runtime FP32 (onnx-asr) 0.328 2,667MB 49.9% GGUF Q6_K (parakeet.cpp) 0.708 928MB 99.8% ONNX Runtime is 37% faster than HF Transformers bfloat16 on this hardware. The gap comes from operator fusion and AVX2-optimized execution providers in ONNX Runtime that the PyTorch CPU path doesn't exploit as aggressively. Memory cost is the tradeoff — FP32 weights load at ~2.7GB peak. GGUF Q6_K trades throughput for memory efficiency. 928MB peak vs 2.7GB, but RTF doubles and CPU utilization hits 99.8%. For memory-constrained deployments it's the right call. For sustained throughput on a box with headroom, ONNX wins. One methodological note worth flagging for anyone doing ASR benchmarking with synthetic audio: espeak-ng inflated WER to 20.9% on a sentence set where gTTS got 4.65%. Both runtimes got identical WER within each run, confirming it's the TTS distribution mismatch rather than model or quantization quality. NVIDIA reports 1.93% on LibriSpeech — the gTTS number is a much more honest CPU-only proxy. Github repo with code, raw results, and evaluation scripts in comments below. Disclosure: benchmark was run using Neo, a local AI engineering agent inside Claude Code using its MCP. Mentioning because the runtime and audio choices came from its research phase, not prior knowledge on my end. submitted by /u/gvij [link] [留言]

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