Do you know about tail recursion?
Today I was solving a TypeScript challenge and ran into:
`Type instantiation is excessively deep and possibly infinite`
That sent me down a rabbit hole of recursive types, compiler limits, and why tail recursion with accumulators can make a huge difference.
#typescript #ts
Was reading about the proposed HTTP QUERY method and honestly it makes a lot of sense.
There’s always been this awkward gap where some operations are clearly reads, but GET becomes messy because the payload is too complex, so people fall back to POST /search or similar.
QUERY feels like a cleaner semantic fit for that middle ground.
The more I read about Lakebase, the more it feels like the real problem they’re targeting isn’t “Postgres is bad” but:
why does a transactional DB node still have to own everything?
compute + WAL + data files + failover burden + replica story
If you decouple those:
Postgres compute can be stateless
WAL durability can live in a replicated log layer
data/page storage can live outside the DB node
analytics doesn’t have to mean CDC + another copy
@Brennan_Lup Developed and launched Screenr(https://t.co/rH4ldrcf4o), securing multiple paying clients and validating market demand.
my portfolio - https://t.co/kcBqeaKtnI
Today I learned that extends isn't just for inheritance or generic constraints.
Inside conditional types, it can behave like an implicit loop over union members.
Today I learned S3 can directly publish events to queues 😭
I somehow missed this for years and literally had a worker sitting there just to watch uploads and push messages downstream.
Sometimes "system design improvements" are just discovering AWS features that existed the whole time
First thing I'd run:
SELECT * FROM timescaledb_information.chunks
ORDER BY range_start DESC;
Count how many chunks your query touches.
That number explains more than you think.
5/ Bonus: we also denormalized a few hot-path joins.
Two small changes. Significant performance gains.
If your TimescaleDB setup feels slow despite indexes, audit chunk size before anything else.
I want to share how effecient chunks are in timescale DB
So We had a TimescaleDB table with millions of rows. Nothing looked obviously broken. Queries were around 50–60ms. Not terrible. But every time we opened EXPLAIN, we kept seeing Parallel Seq Scans.
At first we thought: missing indexes? bad query? compression issue?
Nope.
After digging for a while, we found the problem in a place we almost never think to check.
it wasn't indexing.
It was chunk size. 🧵
@LangChainAI is cooked
someone built an open-source alternative to LangSmith Deployment, called it Langship, published a "Dear LangChain" open letter, and rented a truck to circle Interrupt with a QR to the manifesto
most aggressive dev-tools launch since vercel vs netlify
#langship #langchain