I got into quite a lot of rabbit holes with AI. Most of them were "productive", some of them were not.

80% it will talk you out of delusions or obviously dumb ideas. 20% of the time it will reinforce them

I don't count calories. I went off Ozempic (now Mounjaro) and I gain weight at about 0.5-1kg a month.

As I am resistance (gym) training, significant % of that ends up being muscle mass rather than fat.

So I end up taking Mounjaro for about 1-2 months every 3-4 months, approximately 33% of the time being "on".

Funnily, I end up with bulk/cut periods without doing them explicitly. This ends up working well for growing muscles.

Notice all people in the story are women. I guess pairing GLP-likes with bodybuilding works quite well for men. As times goes on, I end up needing mounjaro less due to my increased muscle mass.

In addition to L1, O1 is also often gamed. $100K for H1B is mostly "posturing" at this point, as voters don't know about other options.

My guess is that Google/OpenAI are eyeing each other - whoever does this first.

Why would that work? It's a proven business model. Example: I use LLMs for product research (e.g. which washing machine to buy). Retailer pays if link to their website is included in the results. Don't want to pay? Then redirect the user to buy it on Walmart instead of Amazon.

There are some nice effects - simulating sword fighting, shooting, etc.

It's just benefits still outweigh the cost. Getting to "good enough" for most people is just not possible in short and midterm.

Thankfully this won't happen with LLMs, as compute is too expensive so execs can't just take an easy way out of optimizing for number of questions asked

In reality I prefer different model, for different things, and quite often it's because model X is tuned to return more of my preference - e.g. Gemini tends to be usually the best in non-english, chatgpt works better for me personally for health questions, ...

In my previous company I had a good setup for costs monitoring - including release to release comparisons, drill downs, statistics, etc.

After each release I looked at this data. It saved a lot of $, by simple fixes like "why we are calling this API twice?".

It also quite some issues that weren't strictly customer related, but weren't apparent from other type of data (you will always have some "unknown unknowns" in your monitoring, and costs data seem to be pretty wide net to catch some of those)

So even if you put some "watermarks" in your AI generation, it's plausible defense to find publicly posted content with those watermarks.

Maybe it's explained in the article, but I can't access it, as it's paywalled.

I tend to ask multiple models and if they all give me roughly the same answer, then it's probably right.

Knowledge is a necessary, but not sufficient component of power

Or in other words observability is a necessary, but not sufficient component of optimization.

On very low level it's up to your manager. As time goes, even as IC you have a lot of agency. It's not just company selection, team selection, but also which part of the project you are working on and how you are approaching solving it.

Of course "if everyone does this, who will fix the bugs". However, the quickest promoted people I've seen are the people who were excellent at politics-izing (and sometimes foresight) the best work assigned to them.

I gained muscle, as I started weightlifting (modified 5x5 program 3-4 times a week) and was supplementing with protein isolate (about 50g a day).

My subjective feeling is that even if "Ozempic makes you lose muscle faster than the same caloric deficit without it" is true, this effect is very small.

Vast majority of muscle loss comes from no resistance exercise, low protein, much faster weight loss than possible "naturally".

The problem is that 99% of theories are hard to scale.

I am not an expert, as I work adjacent to this field, but I see the inverse - dumbing down theory to increase parallelism/scalability.

P is what most people would consider 10x engineer (but not this article). Can get anything done few times quicker than anyone else. It's like 10 junior engineers stacked in one person. But it often would be unmaintainable mess.

M is a lot like article describes. Understands what needs to be done and creates good technical designs. Often unf*s mess created by P. Delivers business value. Do not write a lot of code.

It's funny that depending on whom you ask, M or P would be the 10x engineer and other would be the bad engineer. Real 10x engineer can wear the M or P hat depending on circumstances.

I'll give an "another side" perspective. My company was much smaller. Out of 10+ "I found a vulnerability" emails I got last year, all were something like mass-produced emails generated based on an automated vulnerability scanning tool.

Investigating all of those for "is it really an issue" is more work than it seems. For many companies looking to improve security, there are higher ROI things to do than investigating all of those emails.

Although the name is "air gapped" - it does not have to be if client doesn't want air gapping. It's "buy commodity hardware from vendors HP, we will give you software and training to manage it".

Much leaner stack than whole GCP/AWS/Azure, but deployed "on-prem" with "cloud-like" experience.

It's just uneconomical to do actual flying cars due to costs and fuel inefficiency. Flying cars will happen when if we have breakthroughs in battery technology and potentially energy generation.