Does make me feel like "developer experience" will mean something very different going forward. Models DGAF about pretty, they GAF about data/efficiency/etc.
I suspect it's because the IDEs expose different interfaces to interact with code & IDE tooling, which the models aren't as good with. Less training data, polluting context, etc. The model can just cook in the CLI. (This is just conjecture. Would love to know if that's wrong. )
Been all-in on Codex CLI with GPT 5 high for the past few days. It's really surprising to me how much better the same model is in CLI (vs. IDE/web UI).
the world-view presented by Karpathy in this podcast is long turbopuffer
he argues that models should have a simple "cognitive core" with a basic curriculum of knowledge, and search for anything else. models should be stripped of esoteric knowledge.
piped.video/watch?v=lXUZvyaj…
The best thing I’m happy with on macOS is Instruments. It’s really easy to use and super helpful. Rust developers can use cargo-instruments, developed by @cmyr, to make it even easier—just run:
cargo instruments -t time --bench random_access
👀 "This is the home of s2-lite — an open source, self-hostable implementation of the S2 API for durable streams. It will be backed by SlateDB, and it is currently in development."
Now working on giving turbopuffer similar performance on lexical search as Lucene. We're getting there but it hasn't been easy! Lucene is impressively fast on queries with many (10-15) terms.
👀 "This is the home of s2-lite — an open source, self-hostable implementation of the S2 API for durable streams. It will be backed by SlateDB, and it is currently in development."
9/ DevX: expose builder via FFI (removed SlateDBOptions), admin CLI to convert seq <-> timestamp, more precise ErrorKinds (incl. fencing), public DbSnapshot, and GCS docs.
7/ Durability + TTL semantics: apply durability max_seq to scans, fix TTL filtering, and prevent expired entries from reviving old values. Tighter retention and correctness across reads.
6/ Scan performance + correctness: configurable max_fetch_tasks for parallel fetch, bloom filter moved into the SST iterator, unified get/scan paths, and fixed merge iterator order. (Exposed in Go/Python bindings too.)
4/ Python API overhaul: snapshots, transactions, iterator scans, and built‑in metrics. Cleaner FFI and a unique Rust lib name to avoid collisions. Python users get first‑class ergonomics.
3/ Transactions API with Serializable Snapshot Isolation improvements: conflict checks, interior mutability for writes, and Db::begin() ergonomics. More predictable, safer multi‑op writes.
2/ Merge Operator. Native merge support across the write path, reads, and compaction. Great for counters, CRDT‑style updates, and time‑series upserts without full read‑modify‑write.
1/ SlateDB v0.9.0 is live! Highlights: Windows support, Merge Operator, new Transactions API (SI/SSI), a much‑improved Python API (snapshots, transactions, scans, metrics), and lots of perf + stability work. Notes below 👇