It’s lighting my home right now. Looks like clear skies today.

I wouldn't have a career in IT if I hadn't spent many hours at ages 11 to 15 trying to get round my schools network security. My logon was frequently disabled for misuse and I was even suspended for a couple of days once but I learnt more that way than in any class I've ever taken.

That 9-hop shortening example is disgusting, I wonder if that could be alleviated by introducing some protocol:

1. Make all shortening services append a `This-is-a-shortening-service: true` header to all the responses they send.

2. When a link is added to a shortening service, check if the response from the link has the header above and resolve the destination, recursively.

I don't really understand why this approached is pushed for music. You can overpaint an image, but you can't do that with a song. Cutting an image to reintroduce coherence is easy too. For a song you need midi, or another symbolic representation. That was the approach of pop2piano (unfortunately it is limited to covers, not generating from scratch). And even if a song generated this is OK, listening to half an hour full of AI mistakes is really tiring. With a symbolic representation you could at least fix the mistakes if there is one good output.

I would urge people who have had issues with the documentation to give the 2.0 documentation a try. Many aspects of it have been completely rewritten, both to correctly describe things in terms of the new APIs as well as to modernize a lot of old documentation that was written many years ago.

First off, SQLAlchemy's docs are pretty easy to get to, for a direct link just go to:

https://docs.sqlalchemy.org/

It's an esoteric URL I know! ;)

from there, docs that are new include:

- the Quickstart, so one can see in one quick page what SQLAlchemy usually looks like: https://docs.sqlalchemy.org/en/20/orm/quickstart.html

- the Unified Tutorial, which is a dive into basically every important concept across Core / ORM : https://docs.sqlalchemy.org/en/20/tutorial/index.html

- the ORM Querying guide, which is a "how to" for a full range of SQL generation: https://docs.sqlalchemy.org/en/20/orm/queryguide/index.html

it's still a lot to read of course but part of the idea of SQLAlchemy 2.0 was to create a straighter and more consistent narrative, while it continues to take on a very broad-based problem space. If you compare the docs to those of like, PostgreSQL or MySQL, those docs have a lot of sections and text too (vastly more). It's a big library.

> Published content will be later used to train subsequent models, and being able to distinguish AI from human input may be very valuable going forward

I find this to be a particularly interesting problem in this whole debacle.

Could we end up having AI quality trend downwards due to AI ingesting its own old outputs and reinforcing bad habits? I think it's a particular risk for text generation.

I've already run into scenarios where ChatGPT generated code that looked perfectly plausible, except for that the actual API used didn't really exist.

Now imagine a myriad fake blogs using ChatGPT under the hood to generate blog entries explaining how to solve often wanted problems, and that then being spidered and fed into ChatGPT 2.0. Such things could end up creating a downwards trend in quality, as more and more of such junk gets posted, absorbed into the model and amplified further.

I think image generation should be less vulnerable to this since all images need tagging to be useful, "ai generated" is a common tag that can be used to exclude reingesting old outputs, and also because with artwork precision doesn't matter so much. If people like the results, then it doesn't matter that much that something isn't drawn realistically.

> Basic stealth addresses can be implemented fairly quickly today, and could be a significant boost to practical user privacy on Ethereum. They do require some work on the wallet side to support them

So how easy is it realistically? I hope it's not going to un-ergonomic like PGP where novices are sometimes seeing to be pasting their private key into e-mails and sending things in plaintext which should have been ciphertext, or otherwise leaking info.

I imagine you have to be really careful not to mess things up here.

This is a great writeup by a central figure in Python packaging, and gets to the core of one of Python packaging's biggest strengths (and weaknesses): the PyPA is primarily an "open tent," with mostly independent (but somewhat standards-driven) development within it.

Pradyun's point about unnecessary competition rings especially true to me, and points to (IMO) a hard reality about where the ecosystem needs to go: at some point, there need to be some prescriptions about the one good tool to use for 99.9% of use cases, and with that will probably come some hurt feelings and disregarded technical opinions (including possibly mine!). But that's what needs to happen in order to produce a uniform tooling environment and UX.

It’s interesting that Google—the inventor of the “T” in GPT—is on red alert due to ChatGPT. And Google has a bunch of research in AI (see Dean’s recent blogpost [1]), so what gives with their lack of AI product/execution/strategy? Too scared to upend their existing cash cow maybe, aka innovator’s dilemma?

Also, it doesn’t inspire confidence in Pichai that Sergei and Brin were consulted about something that should be on the forefront of Google’s strategy. Maybe that’s too harsh but I just find all this surprising.

[1] https://ai.googleblog.com/2023/01/google-research-2022-beyon...

The trick of performing binary addition by using analog voltages was used in the IAS family of computers (1952), designed by John von Neumann. It implemented a full adder by converting two input bits and a carry in bit into voltages that were summed. Vacuum tubes converted the analog voltage back into bits by using a threshold to generate the carry-out and more complex thresholds to generate the sum-out bit.

In a house I own in Melbourne, Australia, I just replaced an old gas central heating system with 3 new top of the line Daikin mini split heat pumps. The new units can heat the entire house for the same amount of electrical energy that was used to run the FAN in the old gas unit. They are crazy efficient.

Ducts are dead.

The Daikin Alira X is the gold-plated option and cost $8k AUD for 2x2.5kw and 1x7.1kw units including installation. Payback time is about 3 years. The system is oversized, but enables excellent zoning and of course provides cooling which is a must on 40C/104F days.

Why do they seem to be so much more expensive in the US?

It is quite weird to talk about all the frameworks which are built on top of CUDA eventually, but not talking about Rocm or OpenCL.

OpenAI's Triton compiles down to CUDA atm (if I read their github right), and only supports Nvidia GPUs.

PyTorch 2.0's installation page only mentions CPU and CUDA targets, therefor it's effectively all Nvidia GPUs.

While all the frameworks and abstractions could offer other back-ends in theory the story of anything ML related on the other big name in the industry, AMD, is still poor.

If anybody looses business because of bad decisions it is AMD, not Nvidia, who lead the whole industry. I am not convinced that anything will change in the near future.

I love it. Instead of (a) running mathematical experiments that model human beings as utility-maximizing agents in a highly-simplified toy economy (easy and cheap, but unrealistic), or (b) running large-scale social experiments on actual human beings (more realistic, but hard and expensive), the authors propose (c) running large-scale experiments on large language models (LLMs) trained to respond, i.e., behave, like human beings. Recent LLMs seem to model human beings well enough for it!

Abstract:

> Newly-developed large language models (LLM)—because of how they are trained and designed—are implicit computational models of humans—a homo silicus. These models can be used the same way economists use homo economicus: they can be given endowments, information, preferences, and so on and then their behavior can be explored in scenarios via simulation. I demonstrate this approach using OpenAI’s GPT3 with experiments derived from Charness and Rabin (2002), Kahneman, Knetsch and Thaler (1986) and Samuelson and Zeckhauser (1988). The findings are qualitatively similar to the original results, but it is also trivially easy to try variations that offer fresh insights. Departing from the traditional laboratory paradigm, I also create a hiring scenario where an employer faces applicants that differ in experience and wage ask and then analyze how a minimum wage affects realized wages and the extent of labor-labor substitution.

You might want to consider looking at Asterisk, the open source PBX. They have a list of analog cards that they support.