I used DallE and Anki to teach my son to read. I'm not sure the HN crowd will be wowed by old tech, but this post leaves techs to the agents, and deals with the emotional aspects of learning I learned.
Yes, I wrote the clickbaity title with AI, but the rest of the post by hand.
I haven’t had major issues with sccs yet.
The linter enforces forward references so the cycle pain we do have is with dynamic/deffered imports, and it’s usually solved by splitting a module.
If you look at the pyrefly repo (metas new type checker), there are some deep thoughts about sccs, but I didn’t fully grok them.
I’m wrapping up a role where I spent a significant amount of time writing Triton kernels. It’s a fantastic tool, but the learning curve has some sharp edges. I wanted to share a few practical "notes from the field" for anyone moving beyond the very opaque docs.
You can do both ways but the latter is the more useful one. Duckdb is designed to read the data very fast and to operate on it fast. So you load a csv/json/parquet and then “create table” and Duckdb lays out the data in a way that makes it fast to read.
But you(I) wouldn’t use it like a
standard db where stuff gets constantly written in, rather like a tool to effectively analyze data that’s already somewhere
I use Duckdb as a data scientist / analyst. It’s amazing for working with large data locally, because it is very fast and there is almost 0 overhead for use.
For example, I helped an Israeli ngo analyze retailer pricing data (supermarkets must publish prices every day by law). Pandas chokes
on data that large, Postgres can handle it but aggregations are very slow. Duckdb is lightning fast.
The traditional alternative I’m familiar with is spark, but it’s
such a hassle to setup, expensive to run and not as fast on these kinds of use cases.
I will note that familiarity with Parquet and how columnar engines work is helpful. I have gotten tremendous performance increases when storing the data in a sorted
manner in a parquet file, which is ETL overhead.
Still, it’s a very powerful and convenient tool for working with large datasets locally
Postgres implements this[0] as well, and it's really wonderful.
It doesn't give a human the search experience they are used to, but for the superhuman who can write regex , this becomes a very cheap way to search data at scale.
I'm a native Hebrew speaker, where we have wild word morphology, and also worked on NLP at a large bank where we trained models to anlyze Bloomberg chats (that are almost english but behave like a DSL).
In both cases, "token-free" has been the way to go for extractive tasks like entity recognition.
It's unfortunately a technique that is off the beaten path, so a lot of extra engineering effort needs to be paid down. For example, when dealing with spans but embedding the text at the byte level, one needs to account for multi-byte codepoints that can throw off the alignment between "encoded" and raw bytes.
As the article alludes, the increase in sequence length can be very expensive. In extractive tasks, I've found it effective to use much lighter models with limited (but large) context length like ByteNet[0].
Thinking out loud, as always, there is no one-tool for everything. Often the summarization/few-shot capabilities of an off-the-shelf transformer are so far ahead that it's not worth building a model from ±scratch to solve the subtlties of tokenization. Other times, you don't need the unbounded context or have a simpler task and can forgo the power of off-the-shelf models for the specificity of a token free one.
I used this channel and it was so frustrating it might not as well
have existed .
While the stripe team is always nice and the tech support is great, when I had a much less sever case of “stripe wants to destroy my business” I could get no straight answer or help from the support team
I built LightTag and eventually
sold it. In my experience , at lower price points (<100 month) it’s a none issue. If you can build a self serve funnel, get people in it and convert them no one will care if your 1 person or 100.
Once you go up in price point it becomes a bigger issue, I had many deals die when they realized I was a 1 man show.
That shouldn’t stop you. If you close a 50k
recurring deal once a year, that still adds up to a great income.
It’s been a long time since I touched c++, so pardon my naïveté. I’d have assumed that optimized thread pools were a done thing. What’s new here and why was there a gap ?
I think you should clarify your goal.
If you want to learn coding and specifically Django then this is a good project.
If you just want a blog there are other tools that are easier like
Jekyl and Hugo
I used to use redux-orm which offered a similar promise. This library looks seems more mature and thought out, kudos!
My app is still alive and kicking and I ended up implementing many of the ideas here myself, in a less reusable fashion.
A primary motivator was that as the use cases evolve, a storage model optimized for application specific access patterns becomes compelling.
The convenience of having properly implemented relational semantics and performance enhancements of Indices is huge, but the risk of hitting assumptions that don't fit your use case is also non-trivial
Keeping with the theme of insider experience, you might want to reach out for companies doing auditing, fraud detection etc. A domain expert who knows ML is a hot find
I remember windows 95 made a sound when it started. Maybe now that boots are fast there is no need to notify a user who may have focused on something else while waiting ?
I worked at Citi for a while and this pain sounds familiar. It once took me 6 months of hounding an MD to get approval for my app to connect to a particular database.
It sounds like you're very motivated and presumably talented, but the organizational friction doesn't really allow for your blessings to shine.
On the other hand, this is the reality for many many people and many many dollars. It's a great opportunity to both develop empathy for a kind of user you never imagined (all those people trapped in these processes for life) and to learn new ways to excel, within the context you're currently in.
When I was at Citi I learned a lot about communicating with stakeholders and fostering internal relationships which has been very useful in my career. Maybe you can find the same there ?