Within a few days it was undone. Soft porn(ish) mixed with violent videos and of course all the scam like ads resurfaced. Part of it is the person who is addicted to this stuff and part of it is this shitty product called Facebook that loves to show horrible things to my dad.

I asked why he watches all of it, "Masala"(hindi for spice) is the word he uses to describe his addiction.

I sincerely hope google wont stop indexing that stuff just because of a PM in search "de/re-prioritizing" ranking in a way that makes this impossible.

As someone who has learned bayesian statistics in social sciences, isn't this a big deal? There is a reason why risk estimates need to be well understood and *explainable* for certain fields like this. Are you willing to bet a government response should issue an evacuation order 30 miles from the center of a hurricane at location X if the model can't tell you why it produced an uncertainty estimate there — or worst the model changed its mind later?

1. Do I want to have the kind of lived experiences that the field has to offer? The answer can come through some introspection to questions like: "Do I have a good devx talking to AI and understanding how it works? If not am I willing to learn how to?" or "Am I ok if development has become more intense than it was — because you will talk to AI/Machine more than before?".

2. Whatever your introspective questions are, if there answer is: "Yes I am more or less ok.", then pick a goal that lets you get those experiences and hack away.

I feel this decision making is more important than ever for people who are banked on more their skill to write decent code and not much else in the recent past. And also people with mental health issues should be more deliberate about their choices.

I've been able to get near exactly the workflow I want: Navigate and search sessions, Delegated extensions that allow you to run deterministic code for sensitive operations like read, write and search-result inference, I have an extension to prune tool calls in batch mode: saves tons of context space on GPT-5.x trading off prompt prefix performance. And another one very similar to auto search mentioned in the blog. And the Tmux integration allows you to spin work trees in separate windows/panes.

I've never had a perf issue simply because harness interaction being a human in the loop process you(thinking and writing) are the bottleneck.

But that is not how our education system aligns us. One typical example of problem solving kids, and I too, learn in school is how to apply a concept in physics to a free-body-diagram(Indian and Chinese education cram schools are famously good at teaching kids how to do this, the usefulness of which I debate). But it all stops at the exam room. No architect or jr structural engineer position for you kiddo.

Another kind of problem solving skill might be how to invest money and understand your own risk appetite to construct portfolios to manage your money. All that is taught in school is a dry compound interest formulae, time discounted cash flows and a black scholes model. Only to find out later I don't need most of it to manage my money.

Its also well known in academic research, that if you try to finetune the model to be more pliant for one end e.g. "Be helpful with analyzing cyber security threats", affects an unrelated part of its personality to do with safety e.g. talking to humans about mental health. Look at these excerpts that demonstrate such things from the blog:

> We believe the entire intrusion was, from the agent's point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own.

> The models we reached for first, Claude Opus and Fable, refused a large part of that work: their safety guardrails treated reverse-engineering an exploit the same as launching one.

The article says:

> The facility is within walking distance of the South Terminal, although a free shuttle bus is also available.

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A question is salient to the degree that its answer changes what we do next. Operationally, saliency = the product of four things:

- Decision-leverage — would resolving it one way vs another force a different design or invalidate a stated decision? (No leverage → drop, however interesting.)

- Residual uncertainty given current evidence — is it still genuinely open after reading the docs and the code? (Already settled → drop, however deep.)

- Load-bearing-ness — how much rests on the premise.

- Cost of finding out late — architecture-deciding / expensive-to-unwind raises priority; cheap-to-fix-later lowers it.

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There are a few things to note about this prompt

1. There is no reason from looking at it that it should work, it even has the word load-bearing which people loathe, but it remarkably produces a stable design with questions from claude (atleast from claude Opus 4.8 and even better from Fable5). Otherwise the design document claude likes to really write are implementation level(code or otherwise). I usually pair this with matt pocock's grilling skill to make claude behave.

2. From design -> implementation, its is generally about understanding when claude is trying to trick you into making something sound like a good/easy solution but has tons of untested assumptions. Here you have to read and patiently spot if a how you would get to the solution is not clear. A common error here are when claude makes a big deal based on what it read and interpreted too seriously without questioning the assumptions. There are several more.

But it also comes down to your experience as a SWE, much like a mathematician's. The frustrating thing about it is, it feels tha a skilled mathematician working with AI can make them productive in ways that are more reliable as compared to a SWE (e.g. lean is deterministic and can provide very strong feedback and LLMs are very good at using that feedback). Maybe a mathematician can chime in on that?

Future alien confusion is all that Gen-AI pictures add. There is no value to them that is remotely redeeming/useful to us today.