scw

The Merciless Peppers of Quetzalacatenango… grown deep in the jungle primeval by the inmates of a Guatemalan insane asylum.

As a kid, my first machine was a 486 DX2 66. My dad mandated we buy on value, and I scoured the MicroTimes looking for the best possible deal, finally settling on a machine from a small independent shop off of South Bascom Ave in San Jose. The office smelt like burnt coffee and exhaust from burning-in PCs, but the machine was awesome: a PCI video card, and as we later found out, a built-in SCSI adapter which meant we needed to throw in another $240 for an Adaptec SCSI controller when we later upgraded to Pentium.

They're unlikely to find much unless they instead try “dispositive”.

TurboQuant has a specific benefit by compressing the KV cache at a negligible cost to quality. That mainly means that the context lengths can go up in models for the same amount of memory, however the KV cache only accounts for something like 20% of the overall model size, and this will not dramatically decrease memory demands in the way that some of the more sensationalist reporting has stated.

Still active, but many fewer resources than in the past. Many backends like CUDA for Windows have been dropped and others pushed off to partners with varying levels of support. TensorFlow 2.19 is going to release soon without Python 3.13 support, it's hard not to imagine that resource constraints are at play.

The Microsoft stake finally allowed him to let loose and buy a keyboard with a working shift key.

Exciting concept! Note that the LLM corrected version does drop a full paragraph from the output at the bottom of the second page (starting with an asterisk and "My views regarding inflationary possibilities". I'm not sure if there is a simple way to mitigate this risk but would be nice to fall back on uncorrected text if the LLM can't produce valid results for some region of the document.

I recently had occasion to evaluate a database of 1200+ NVIDIA GPUs and can tell you that the only thing consistent about the model numbers is their inconsistency. For example, what is an RTX 4000? It could be the 2018 Quadro RTX 4000, the Quadro RTX 4000 Max-Q, or Quadro RTX 4000 Mobile (all Turing cards), but it could also be the RTX 4000 Mobile Ada Generation (Ada Lovelace card released 2023).

Endpoints are the machines (desktops, servers, &c) in organizations which have Carbon Black installed. Their client continuously monitors for process executions, network connections, file changes, registry changes, and samples unique files on the machine, and depending on configuration, can upload these contents both within the enterprise and share them with their cloud platform. That's what is meant by "our technology uniquely collects complete, "unfiltered" endpoint data by continuously recording endpoint activity and centrally storing the collected data for advanced analytics".