You can watch a rock roll down a hill and derive the concept for the wheel.

Seems pretty self evident to me

The more money and security I made, the less beholden I felt to the workplace and things that weren't personal to me. Less anxiety, less need to appeal to others for survival/success (of course still try to be nice)

I've also always had tons of ideas and things I wanted to pursue outside of work. So never lacked for motivation or sense of purpose.

My dad recently retired and seems to not have much direction now, so it must be a somewhat individualistic response.

Always interesting to see others reactions!

The majority of the time you can infer the type from reading well written code (to the extent that the shape of the type matters in the context of that piece of code)

Humans are trained on human language. LLMs are trained on human language.

Thus something that is easier for a human to understand is likely easier for an LLM to understand.

That higher level language with well named variables reads more comprehensibly than code:VERB with:PREPOSITION types:NOUN, intermixed:ADJECTIVE, stems:VERB from:PREPOSITION first:ADJECTIVE principles:NOUN too:ADVERB

However, verbose typing is likely a negative for LLMs.

Algorithms written in "pseudo-code", aka a higher level language without type information, are far more readable to a human, and thus likely an LLM too.

In regards to control flow and general concept of what code is doing, types provide very little info over well named variables. In fact they often impair understanding by breaking up logic with implementation details.

I'd be curious to see some experiments around this, but I'd guess strongly typed languages where the type information is mostly hidden/inferred would have better generation accuracy from a semantics perspective (and likely worse from a type safety perspective, but can be corrected on compile/retry)

In this case, picking out "semantic" css classes on single dom nodes.

Was able to run it on my 4(?) year old M2 mbp with 16GB of ram and it runs in only 100ms or so per query. Probably it can run much faster, but haven't experimented with batching etc

With tight and targeted context control, you can use extremely small models for useful things. Ideally with problems where the harness can be mostly deterministic and you have known bounds on what you're trying to do

Due to widespread protests and an attempt to crack down on coordination. This chain of events was widely reported.

https://en.wikipedia.org/wiki/2026_Internet_blackout_in_Iran

I don't know in this case, but I'd guess

1) Cyber security risk (straightforward)

2) Software stocks have been declining materially on AI competition fears, which can potentially impair loans to those companies, which cascades if there are enough defaults.

The second is mostly just driven by misinformed investors IMO, but the valuation haircuts as a result are real and can impair existing loans.

(Not to say these software companies weren't overvalued to begin with, just the reasoning for the decline is misplaced imo)

While more general and perhaps the "ideal" end state once models run cheaply enough, you're always going to suffer from much higher latency and reduced cognition performance vs API/programmatically driven workflows. And strictly more expensive for the same result.

Why not update software to use API first workflows instead?

Humans learn many, and perhaps even the majority, of things through observed examples and inference of the "rules". Not from primers and top down explanation.

E.g. Observing language as a baby. Suddenly you can speak grammatically correctly even if you can't explain the grammar rules.

Or: Observing a game being played to form an understanding of the rules, rather than reading the rulebook

Further: the majority of "novel" insights are simply the combination of existing ideas.

Look at any new invention, music, art etc and you can almost always reasonably explain how the creator reached that endpoint. Even if it is a particularly novel combination of existing concepts.

E.g. observing a game being played to form an understanding of the rules, rather than reading the rulebook

Or: Observing language as a baby. Suddenly you can speak grammatically correctly even if you can't explain the grammar rules.

As stated in the article, 9% is the number of borrowers that defaulted, which was concentrated in smaller borrowers (thus smaller loans).

And then, again, you can say probably half of the dollar amount of those defaults are recoverable.

Bond defaults spiked to around 6% in aggregate in 2008, to use a worst case example.

A smart lender will not issue loans without real collateral. If you create a subsidiary, that subsidiary has to have sufficient collateral and cashflow to secure a loan.

It depends when you measure, but you can Google around and find figures in the 60-80% range. 80% may have been a bit on the optimistic end of the range. But it's important to note that a "default" doesn't imply a 0.

Of course this will depend on the covenants, underwriting standards, type of collateral.

I would guess software equity collateral recovery rates are lower than hard assets like a building. (Which is why I personally don't like Software loans, nothing to do with AI)

> Correct. The question is if 20% is enough, and if a 20% markdown creates a vicious cycle as funding for e.g. re- or follow-on financing dries up.

I think it's almost certain that new fundraising for private credit will be materially hindered going forward. But this just limits the growth rate of these firms, does not introduce any "collapse" risk.

They may move from net inflows to net outflows and bleed AUM over a period of some years.

If NAVs were inflated previously, they may be forced to mark down the NAV to meet redemptions rather than using inflows to payoff older investors.

In the world of credit, 20% is an enormous haircut. Again, senior secured loans fell by around 30% peak to trough in 2008.

We have the public BDC market as a comparison point where the average price/book is around 0.80x. So the public market is willing to buy credit strategies at a 20% discount to stated NAV.

The real systemic risk here, if we were to reach for one, is really that these fears become self fulfilling.

If investors pull funds out of credit strategies en-masse, there is no first order systemic issue, but it means borrowers of many outstanding loans may not be able to secure refinancing as money is drying up.

This could lead to a self-fulfilling default cycle. But this would be a fear driven default cycle, there is no fundamental issue with cash flows of borrowers or otherwise (in aggregate, currently).

Finally, in regards to the asset managers themselves, many are quite diversified.

Yes, they have private credit funds, but many have real estate funds, buyout funds etc. OWL is one of the biggest managers of data center funds, for example (which they also got hammered for on AI bubble fears)

Given how depressed pricing is in public REITs, for example, I expect a lot of asset managers to pivot towards more real asset funds.

Important Facts:

1) The majority of private credit funds are classed as "permanent capital". When you put money into these vehicles, you give the Asset Manager discretion over when to give the money back. Redemptions are often gated at ~5% per quarter.

(So there cannot, by definition, be a run on the bank)

2) Credit is senior to equity, so if you expect mass defaults in private credit, it means the majority of private equity is effectively wiped out. Private equity has to be effectively a 0 before private credit takes any losses.

3) The average "recovery rate" for senior secured loans is 80%. Even if private equity gets wiped to 0, the loss that private credit incurs is cushioned significantly by the collateral backing the loan. These are not unsecured loans the borrower can just walk away from.

(The price of senior secured loans dropped by ~30% in 2008, as a worst case datapoint)

4) Default rates on many of the major private credit managers is ~<1% in recent years. There are other estimates stating higher default rates, but that often classifies PIK income as a default. A loan modified and extended with added PIK that ultimately gets repaid is not a "true" default.

5) Finally, it's true that NAVs are likely overstated, but generally it's by a modest amount. Every Asset Manager today could go out tomorrow, mark NAVs down by 20% and suddenly there is no crisis.

(The stocks of Asset Managers have already traded down such that this seems expected and priced in anyway)

Cliffs in policies will always lead to players working around the cliffs.

E.g. in NYC there is an additional 1% sales tax on home sales above 1 million dollars.

So nobody in the market would ever sell a home between 1m and 1.01m as the tax increase is greater than the sales price.

These are failed policy implementations (in the above example the tax should be marginal, not thresholded)

Any policy which does not account for individual actors optimizing financially is a badly designed policy.

There are numerous similar examples re: CRE when requiring subsidized housing units for certain sizes of development. Often it's more lucrative to build smaller and get around subsidized unit requirements.

You can call them "asshats", but I'd rather live and discuss policy in reality.

Many of these new, clearly strictly punitively intended, taxes aimed at the wealthy will have the same logical outcome.

Show me the incentive and I'll show you the result

Expecting companies, people etc to do the "right thing" when it's financially disincentivized usually doesn't work out.

Same will happen in regards to all these new taxes reinforcing existing population migration trends.

Remains to be seen, as the next legislative session isn't until 2027.

(Remains to be seen whether that's true)

Just spawn the agent in one of the subprojects