Last week-end I was exploring the current possibilities of automated Ghidra analysis with Codex. My first attempt derailed quickly, but after giving it the pyghidra documentation, it reliably wrote Python scripts that would alter data types etc. exactly how I wanted, but based on fixed rules.
My next goal would be to incorporate LLM decisions into the process, e.g. let the LLM come up with a guess at a meaningful function name to make it easier to read, stuff like that. I made a skill for this functionality and let Codex plough through in agentic mode. I stopped it after a while as I was not sure what it was doing, and I didn't have more time to work on it since. I would need to do some sanity checks on the ones it has already renamed.
Would be curious what workflows others have already devised? Is MCP the way to go?
Is there a place where people discuss these things?
I've been wondering the same. Apparently, where I live (HCOL) people don't care about ever paying off the full amount, as long as the monthly installment is low. Regulations only require that you paid off 33% of the house value after 15 years.
You do need to start at 20% capitalization though, which at current prices means they need to own assets already (market risk), or have bonkers amounts of currency sitting around (inflation risk). I understand the FOMO some people must have.
I wonder if ultimately there is a cascade happening, where increased valuations lead previous owners to either take new mortgages, or sell their old house, and pay more for a new one. Which creates a cycle of ever increasing prices? I've never read anything though whether such an effect exists.
When I read stories like this about cool technology from the past that people can still enjoy today, I think it's a pity that the source code is locked away and lost as people and companies move on to new things.
IMO, there should be some kind of archive that conserves and publishes them after some time has passed, so that they could be ported to new hardware and kept accessible. and somehow documented for future historians.
> What neuromorphic hardware can I buy to run your code/ the SNN?
Current neuromorphic hardware is not easily accesible, but you can simulate spiking neural networks. Check out, e.g. https://brian2.readthedocs.io/en/stable/ or Nengo.ai
Empirically, you could have bought a share of the SPX at any point in time, and sold it with profit later. The real problem is what happened in the time between, and whether you were able to hold on.
Nice reminder of some general principles. I wonder how well it applies to young scientists given today's academic career prospects, though.
> If you want to make progress in any area, you need to be willing to give up your best ideas from time to time. [...] Medawar notes that he twice spent two whole years trying to corroborate groundless hypotheses.
Unfortunately, being willing to scrap your idea is only one part of the equation. Securing funding after 2 "failed" post-docs is an entirely different matter.
Last week-end I was exploring the current possibilities of automated Ghidra analysis with Codex. My first attempt derailed quickly, but after giving it the pyghidra documentation, it reliably wrote Python scripts that would alter data types etc. exactly how I wanted, but based on fixed rules.
My next goal would be to incorporate LLM decisions into the process, e.g. let the LLM come up with a guess at a meaningful function name to make it easier to read, stuff like that. I made a skill for this functionality and let Codex plough through in agentic mode. I stopped it after a while as I was not sure what it was doing, and I didn't have more time to work on it since. I would need to do some sanity checks on the ones it has already renamed.
Would be curious what workflows others have already devised? Is MCP the way to go?
Is there a place where people discuss these things?