nickpsecurity

I was considering paying someone to build something like this at some point. With two jobs, I eventually had no time to even organize what I find. It's just piles of links in text files.

Can I give your software a huge list of URL's to index? Or do I need to use browser automation to open them a few at a time with it caching and indexing them?

We should always be allowed to read and know the law we're required to follow. Preferrably for free or easily.

It's not. I got articles this year in my feed citing heads of OpenAI and Anthropic about the threat of AI and how they're addressing it.

Cloud companies were made to sell others compute. Now, one is buying billions of compute from SpaceX, a rocket company. That sounds so backwards lol.

Great work by Musk and his companies to be in a position to sell billions to cloud vendors. I'd have probably missed that opportunity while trying to build great rockets or AI models.

People learning Python or searching for it will run into endless answers using PIP. Then, lots of advice on how to work around PIP's problems. Then, multiple alternatives they have to consider. I only recently started using UV after going through all that.

Packaging, concurrency, and type errors had me strongly considering switching to Go or Rust recently. These are such long-solved problems in other languages that I question why we should put up with it in Python. Then, I remember it was the ecosytem, including job market and AI performance, that made me use Python.

So, maybe a Python/Rust combo... There's the extensions the OP article mentioned and a Python interpreter written in Rust.

How so?

I'd normally call that a low-effort, troll comment. But, thinking on it, you may have a great metaphor.

They keep promising great performance out of models whose key ingredient (parameters) they are diluting. Many seem to be in a competition saying they're getting smaller and higher performance at the same time. Then, the homeopathic models don't perform as well as real models when independently tested. Again, spot on.

Good overview except for the last part. I've heard multiple things from people of the time:

1. In "If A1 was the answer, what was the question," thr author pointed out that features and assurance levels were mandated together. Buyers often didn't need specific features which made it more costly and slow to develop for nothing. The festures the market demanded weren't present. So, TCSEC-certified, high security was unmarketable.

2. In a similar vein, Lipner's "Ethics of Perfectiom" talked about how it took two to three quarters to make a significant change to the VAX Security Kernel. The market was wanting major features every quarter. They couldn't afford to lag behind all the competition in velocity.

3. Another person mentioned changes in DOD (other government?) purchasing policy to order COTS products from many vendors. Those vendors were also sometimes paying campaign contributions or hiring ex-Pentagon people to be favored. Their products weren't TCSEC A1. So, corruption and supplier diversity both forced government agencies to use insecure products which made secure products less competitive.

4. Similarly, the NSA started pushing lower-assurance like CC EAL4 and later Commercial Solutions for Classified. They were also selling GOTS gear guaranteed to get their approval. In these ways, they caused a surge of low-assurance competition with high-assurance vendors.

5. They promoted, required expensive certs for, and basically killed the Seperation Kernel Protection Profile. Spending millions on something that ultinately didn't matter to them doesn't inspire more EAL6+ certifications.

So, those are the examples I remember.

This book has many of them:

https://homes.cs.washington.edu/~levy/capabook/

What's the cost of living in that area?

If I understand them correctly, they're saying to use standard, optimization methods after writing a fitness or evaluation function to score your possible solutions. Which is a normal, non-SAT way of doing optimization.

So, you could use it for any application you saw benefit from genetic algorithms, simulated annealing, or tabu search. You can even use those to optimize neural networks without backpropagation and with fewer, local optima. Many papers on this but it's computationally heavier.

They come in mobs or waves, too. Historically, they show up first while the reasonable people show up later, sometimes reversing those votes. Others here have noted that phenomenon.

Is that how the U.S. and U.K. economies grew to be significant? Mostly foreign investment for cheap labor with hardly any innovation or entrepreneurs of their own?

I don't think that's the case for us.

There's also work in polynomial regression as an alternative to regular, neural networks.

Polynomial Regression As an Alternative to Neural Nets (2018) https://arxiv.org/abs/1806.06850

Π-nets: Deep Polynomial Neural Networks (2020) https://arxiv.org/abs/2003.03828

You're thinking in a provably-useful direction:

https://arxiv.org/pdf/2312.11514

Open source for ML is more like Allen Institute's Olmo models.

https://allenai.org/olmo

I'm just giving it as an example. I haven't looked at Granite's repos.

It can be worse in terms of justice. You might be able to charge or win in court against a street hustler. Most people can't beat a big company in court. They usually won't even try.

What needs to happen is for companies (or individuals) tired of that to pool money together to build new, memory products. Then, sell them to consumers first and for non-AI use. If not that, then round-robin scheduling of quantities so the units are spread around more.

If costs are high, they might reserve a certain percentage for big business at market prices (or just under) to cover the chip's mask costs.

After DDR5+ RAM, then GDDR5-6 RAM for use with AI accelerators. They might try to jump right in on a HBM alternative. That could be the percentage for AI buyers I just mentioned. Especially if they could put 40-80GB on accelerators like Intel ARC's.

If successful enough, they license MIPS' gaming GPU's to combine with this stuff with full, open-source stack and RTOS support for military sales.

On changing the training mix, H20 did that with Danube in 2024:

https://arxiv.org/pdf/2401.16818

With those results, I would've already done that in any models I got to train. There's also the principle that the LLM's are often better at what they saw last in their training set. That also justifies putting more logic, code, and math in at the end for an analytical or coding model. So, a few precedents for that technique already.

Don't many already do something similar by insider trading on the stock market?

Maybe they didn't need more uncertainty in their portfolios.

You create a subset or model of what's computable. Then, work in it. You might also prove refinements from high- to low-level forms.

Interestingly, we handle static analysis the same way by using language subsets. The larger chunk is unprovable. So, we just work with what's easy to analyze. Then, wrap it in types or contracts to use it properly.

And plenty of testing for when the specs are wrong.

Ot actually doesn't change their recommendation. There will be less, market share among people really worried about the American government. Others would be happy to pay for a better cloud run locally mostly under local laws.

I appreciate you noting my prior work on that problem. In my original post, I said dissent isn't allowed. I posted a dissenting opinion on this liberal site. It got three dowvotes to censor it followed by your ad hominem. Now, multiply that by almost every person who tries to discuss this in any liberal-leaning venue. That the majority react the same way to support their positions by social dominance, not discussion, led many of us to believe that's how the supposed consensus got there, too.

The other point was that money was driving things on the climate policy side. Al Gore and his partner set up a company to make millions on the off chance that people believe in man-made, climate change and invest in his recommendations. Then, he makes An Inconvenient Truth. Likewise, many of them are trading carbon on the market for profit. Blackrock pushed ESG on businesses everywhere while having billions of investments that will make a huge profit if people do this. Specific billionaires also fund these studies for whatever their purposes are.

Many articles by climate activists mention that money corrupts studies and policies. Yet, they don't mention all the money behind their own, all the people who profit off it, and how they themselves often take money from the same people. The OP article follows this pattern by describing the effects of money and what the other side does but pretends like their side doesn't work that way. They'd leave readers thinking everyone is going broke or operating at a loss promoting climate policy while only oil companies and their agents make money. Which is a bold-faced lie!

So, the general public that previously only saw information from such liberal sources now is seeing all kinds of information. Twitter and Meta are allowing conservative sources to show up in feeds way more than before. People finally saw reports showing all the financial corruption, science that was paid for, how the projects often failed, how many were money laundering, and so on. So, they stopped believing in it... rightly.

The fixes are straight forward. Those who have been doing this need to confess their sins. If they don't, philanthropists need to put money into honest media and scientific institutions that will tell the truth no matter what. They need to find people without conflicts of interest to run these analyses. Dissent must always be allowed with no censorship.

They should fix the one-sidedness of the situation by bringing in the most, knowledgeable critics to put their best data on the table. We will also rerun experiments by potentially-corrupt parties to ensure we get the same results. We'll do science with people from many parts of the political and religious spectrum replicating it. They can contribute the people they'd trust to do the analyses. Then, we'll have ground-up, diverse, independent replication of results with high likelihood of catching paid liars.

That's my proposal for all contentious topics that have more political domination or financial marketing than actual science. Eventually, we might have a large number of experience scientists, too. People who do the whole process, not just "publish or perish" and "quantity over quality." And organizations paying them for their integrity.

Read sites like this to see how profit-based businesses (bootstrappers) do it.

https://www.indiehackers.com/

Also, strong, random-looking passwords for droplets or apps saved in a text file. Use the Digital Ocean guide on setting up a Linux box securely and the UFW firewall. Then, lighttpd, BunnyCDN (esp for SSL), and periodic updates.

Works so well that it's easy to forget they're running.

I'll add many have remote access that is like a serial port. Digital Ocean has a "recovery console" that appears to work in a similar way. It runs in your browser. It works when other stuff breaks.

Now, for Linux, they have a SSH client in the browser enabled by default. I use it to manage a pile of $5/mo droplets, like the author.

I was bothered by heavy polarization of Americans, individually and even in churches, that appears to ve driven mostly by media outlets who cherry pick and lie. The Left and Right report specific events so differently that their readers might as well live in different worlds. People need to ditch those sources where possible. If not, they need to have a mix of them while understanding their biases.

Originally for churches, my draft article below describes how this problem affects all individuals and institutions. I recommend solutions which include AllSides.com (amazing!) and search engines for retrieving news from multiple outlets. I have a prototype. Progress is slow on my tool because I work two jobs with my free time mostly going to ministry serving Christ and others.

https://heswithjesus.com/mediabias.html

I haven't finished reviewing and adding Drooid yet. I'll still link it because it's a good idea:

https://apps.apple.com/us/app/drooid-news-from-all-sides/id6...

https://play.google.com/store/apps/details?id=social.drooid&...

(Note: I'm not affiliated with or paid by any of these companies. I am a paying supported of AllSides because I believe they'll do a lot of good.)

We've always had good tools for program analysis and testing. They're usually exhorbitantly expensive.

I'm hoping the good results with AI models drive down the prices of traditional tools. Then, we can train open models to integrate with them.

The ARINC scheduler, RTOS, and redundancy have been used in safety-critical for decades. ARINC to the 90's. Most safety-critical microkernels, like INTEGRITY-178B and LynxOS-178B, came with a layer for that.

Their redundancy architecture is interesting. I'd be curious of what innovations went into rad-hard fabrication, too. Sandia Secure Processor (aka Score) was a neat example of rad-hard, secure processors.

Their simulation systems might be helpful for others, too. We've seen more interest in that from FoundationDB to TigerBeetle.

re llm I linked

It's designed for drafting legal documents for lawyers. It's pretrained on a ton of court documents.

re why generalists are better

Much knowledge we have builds on prior knowledge. The prior knowledge is often reused across domains. Analogous reasoning, important in creativity, also connects facts or heuristics across different domains. Also, just being better at English.

If training a coding LLM, it needs to understand English, any concepts you type in, intrinsic knowledge about your problems, general heuristics for problem solving, and code with has comments and issues. The comments and issues might contain or need any of the above.

That's why I believe generalist LLM's further trained on code work better than LLM's trained only on code.