Interesting! I did an internship where I tried to use transducers for fast information extraction.
In theory, you can use FST's for fast approximate parsing.
I didn't really work out, but I had lots of fun implementing a libary to compose FST's and explore cool algorithms to compose them. Not much business value was delivered, but I learned a lot.
This year I switched to a new job, using programming languages that I was less familiar with.
Asking a LLM to translate between languages works really well most of the time.
It's also a great way to learn which libraries are the standard solution for a language. It really accelerated my learning process.
Sure, there is the occasional too literal translation or hallucination, but I found this useful enough.
Have you noticed any difference in picking up the language(s) yourself? As in, do you think you'd be more fluent in it by now without all the help? Or perhaps less? Genuine question.
I do tons of TypeScript in my side projects and in real life, and I usually feel heavy frustrations when I stray away.
When I stray out of this (e.g. I started doing a lot of IoT, ML and Robotics projects, where I can't always use TypeScript). I think one key thing that LLMs have helped me is that I can ask why something is X without having to worry about sounding stupid or annoying.
So I think it has enabled me at least a way to get out of the TypeScript zone more worry free without losing productivity. And I do think I learn a lot, although I'm relating a lot of it on my JS/TS heavy experience.
To me the ability to ask stupid questions without fear of judgment or accidentally offending someone - it's just amazing.
I used to overthink a lot before LLMs, but they have helped me with that aspect, I think a lot.
I sometimes think that no one except LLMs would have the patience for me if I didn't filter my thoughts always.
Well said. CharGPT is almost the opposite of stackoverflow -- you can ask a stupid question, and ask why a language is designed in such a way, and get nice, patient, nuanced answer without judgment or starting a war.
'Serverless' has it's uses, but not for everything
- Serverless can get very expensive
- DevEx is less than stellar, can't run a debugger
- Vendor lock-in
- You might be forced to update when they stop supporting older runtime versions
I can see this being true to a lot of old jobs, like my brother's first job that basically was to transcribe audio tapes. whisper can do it in no time, that's crazy.
I’ve had a similar experience extracting transactions from my PDF bank statements [1]. GPT-4o and GPT-4o-mini perform as well the janky regex parser I wrote a few years ago. The fact that they can zero shot the problem makes me think there’s a lot of bank statements in the training data.
Well, your first job today would be writing that 100 line Python script then doing something 100x more interesting with the events than writing truck loads of regexs?
No, his first job would be a more senior developer writing 100 line Python script instead of hiring an intern to write a truck load of RegExs. After that dev saved time just writing the script over mentoring/explaining/hiring the intern, that dev would then do the more interesting things with the events.
The difference is in how easy it is to detect the cause of problems.
Mistakes like wrong function names are mostly easy to find and fix.
Mistakes when using core.async can be very hard to track down.
I think you mean <! and <!! - first one is parking take for use inside go blocks (lightweight threads) and second is blocking take for use outside them. I have sometimes wondered if they should have just called them, like, "take!" and "take!!" or even better "take-parking" and "take-blocking". Even if personally it was not hard for me to learn them, versus the whole model of async and the rules around go blocks.
I haven't heard complaints about the thread pool before, I thought it just matched your number of cores by default but could be configured. I do know if you do blocking takes (<!!) where you're supposed to do parking takes (<!) the lightweight threads block the entire parent "real" thread and you can get thread exhaustion, maybe it was that?
GCP has the superior UI, api and design.
I prefer it over the other clouds: Azure plain sucks and AWS has too much bloat and does IAM/accounts worse.
In my experience GCP's core services are very stable: I had a site running on free tier App Engine for over 10 years without any supervision.
However it is clear that many GCP products are run by skeleton crews and will not improve. Documentation is also lacking sometimes.
Dataform for example is conceptually a great tool, but hampered by really basic UI bugs.
I found Datastream (change data capture tool) impossible to use. You would think that shoveling data between 2 GCP products (Postgres and BigQuery) would be easy, but I spend a week fiddling with obscure network settings before giving up.
> GCP has the superior UI, api and design. I prefer it over the other clouds: Azure plain sucks and AWS has too much bloat and does IAM/accounts worse.
I agree.
> In my experience GCP's core services are very stable: I had a site running on free tier App Engine for over 10 years without any supervision.
I won't be surprised that Google App Engine is already in maintenance mode.
> In my experience GCP's core services are very stable
Would you call Google Domains a core service or not?
Would you call Container Registry a core service or not?
