#bigquery #streaminganalytics #logsaremetrics #structuredlogging @elixirlang @elixirphoenix #myelixirstatus

One of the first Elixir libraries I wrote was a NIF for the Arcade Learning Environment. It was really bad Elixir. Anyway, I revamped it and here's a video of Axon playing Breakout from a Livebook

QuackDB 0.5 is out 🦆 I’m increasingly convinced that @duckdb is becoming one of the best storage choices for hobby to medium-sized projects. Postgres is still the obvious default for highly concurrent OLTP systems, but many small and medium products are read-heavy, append-heavy, analytical, or operated by a small team. For those workloads, DuckDB’s columnar execution model, vectorized engine, rich SQL surface, and embedded/local-first deployment model can be a much better fit. I intend to use DuckDB as the primary storage layer for the platform I’m building, so QuackDB focuses on the parts I need in a real Elixir app: supervision, connection pooling, Ecto, fast append paths, dataframes, telemetry, and helpers for DuckDB’s analytical SQL. 0.5 focuses on making the Ecto and analytical side much more complete: - append through Ecto with defaults and returning - using an Ecto repo directly with QuackDB native query/append APIs - advanced join patterns, including semi/anti and ASOF-style queries - DuckDB star/COLUMNS expressions in SQL and Ecto - broader LIST/MAP/STRUCT helpers - PIVOT, UNPIVOT, GROUPING SETS, ROLLUP, and CUBE builders - LIST lambdas in Ecto with `fn` syntax and `case_when` - better nullable/schema type handling for append-heavy workloads QuackDB also continues to cover the broader integration surface: supervised DuckDB, DBConnection/Ecto, native append, Explorer dataframes, Table.Reader results, Geo/WKB, telemetry, managed DuckDB binaries, and DuckDB-specific SQL helpers. github.com/elixir-vibe/quack…

Announcing deflua.com 🎉 The new home for Lua, the pure-Elixir Lua 5.3 VM for @elixirlang. Scriptable, sandboxed, stupid easy. Embed untrusted code (AI agent tools, user formulas, plugins) all on the BEAM, zero NIFs. Plus a live playground 👇

Slack needs to auto tag messages that were AI generated.

Have it build tools for itself. What are we calling this now? Code mode?

I'm pretty sure the only reason to use Kafka for telemetry data in 2026 is muscle memory. A simple, open source, S3-backed pipeline can shuttle 1Gb/s of logs to @ClickHouseDB for $200/mo with 2.8s p50 latency. Fast enough for us. opendata.dev/blog/ingesting-…

you mean there is nuance in the compute storage separation discourse? strange

wait wut

Email > Slack I’ll fight for this one

Been thinking about an email interface to Slack for boomers

Until now, LiveVue supported only two SSR modes: ➡️ Vitejs-based (for development) ➡️ Nodejs (for production) Now I'm introducting a third one: ✅ QuickBeam It's running QuickJS as NIF, which is more performant. Now a default mode! Thanks @dan_note for contribution!

erlang_quic 1.3.0 is out. First production-ready release. Pure-Erlang QUIC HTTP/3, zero C deps. - RFC 9000 / 9001 9114 / 9204 - Priorities, datagrams, CONNECT - -proto_dist quic for clusters - OTP 27 github.com/benoitc/erlang_qu…

PI is now over 50% of agent usage at Shopify internally.

wat

This is my first big Elixir project. I reached for it because I needed to manage the state of a lot of docker compose clusters to run the dev servers and docker containers to run agents. The supervisor tree was crucial to getting it working.

pure implementation of QUIC in erlang performs well now . github.com/benoitc/erlang_qu…

Reach 1.5.0 — effect classification accuracy went from 11% to 89%. The analyzer couldn't tell 𝚁𝚎𝚙𝚘.𝚒𝚗𝚜𝚎𝚛𝚝!() from 𝙴𝚗𝚞𝚖.𝚖𝚊𝚙(). Now it can. What changed: — Alias/import resolution. 𝚊𝚕𝚒𝚊𝚜 𝙵𝚘𝚘.𝙱𝚊𝚛 then 𝙱𝚊𝚛.𝚌𝚊𝚕𝚕() was invisible to coupling analysis. Multi-alias 𝚊𝚕𝚒𝚊𝚜 𝙵𝚘𝚘.{𝙰, 𝙱} and 𝚒𝚖𝚙𝚘𝚛𝚝 too — Cross-module effect inference. Pure functions recognized transitively across modules — Plugin 𝚌𝚕𝚊𝚜𝚜𝚒𝚏𝚢_𝚎𝚏𝚏𝚎𝚌𝚝/𝟷 callback for all 8 built-in plugins. 𝚁𝚎𝚙𝚘.𝚊𝚕𝚕 → read, 𝚁𝚎𝚙𝚘.𝚒𝚗𝚜𝚎𝚛𝚝! → write, 𝚊𝚜𝚜𝚒𝚐𝚗 → pure — On Elixir 1.19 reads compiler-inferred types from ExCk BEAM chunk 7 new commands: 𝚌𝚘𝚞𝚙𝚕𝚒𝚗𝚐, 𝚑𝚘𝚝𝚜𝚙𝚘𝚝𝚜, 𝚍𝚎𝚙𝚝𝚑, 𝚎𝚏𝚏𝚎𝚌𝚝𝚜, 𝚋𝚘𝚞𝚗𝚍𝚊𝚛𝚒𝚎𝚜, 𝚡𝚛𝚎𝚏, 𝚌𝚘𝚗𝚌𝚞𝚛𝚛𝚎𝚗𝚌𝚢. All take a path filter: 𝚖𝚒𝚡 𝚛𝚎𝚊𝚌𝚑.𝚑𝚘𝚝𝚜𝚙𝚘𝚝𝚜 𝚕𝚒𝚋/𝚖𝚢_𝚊𝚙𝚙/ Tested on 7 real codebases (up to 817 files). Zero crashes, 30% faster. github.com/dannote/reach/rel…

PgQue v0.1.0 is out. PgQ -- the Postgres queue system built at Skype 20 years ago for 1B-user-scale workloads -- repackaged for the managed-Postgres era. One SQL file. No C extension. No external daemon. pg_cron to tick. Why bother reviving a 2007 architecture? Every major Postgres queue in production today uses some flavor of SKIP LOCKED UPDATE/DELETE. It works under light load. When you have more data and higher load, it degrades predictably. Then you get posts like these: - Brandur at Heroku, 2015: 60k job backlog in one hour from a single open transaction - PlanetScale, 2026: death spiral at 800 jobs/sec - River issue #59, awa issue #169 and so on, Oban's partitioning work, PGMQ's autovacuum tuning guide and duct-taping with pg_partman The core issue is how Postgres MVCC is implemented and how we deal with it. Dead tuples in the hot path, xmin horizon pinned, vacuum falling behind, query performance quickly degrades. This happens every time you run pg_dump, execute an analytical query, or have a lagging/unused logical replication slot. PgQ solved this in 2007 with snapshot-based batching and TRUNCATE rotation -- zero dead tuples in the event path, by design. But PgQ needed a C extension and an external daemon. Which means it doesn't run on RDS, Aurora, Cloud SQL, AlloyDB, Supabase, or Neon -- i.e., where most Postgres lives now. PgQue closes that gap. 💎 Pure SQL PL/pgSQL (PgQ engine) 👩‍💻 \i sql/pgque.sql -- you're done 🕑 pg_cron replaces pgqd (optional, recommended) 💻 Python, Go, TypeScript client examples shipped 💙 Apache 2.0 Trade-off: end-to-end event delivery latency is up to a second, it depends on ticking frequency. If you need sub-3ms job dispatch, use River, Oban, or graphile-worker (and avoid anything that blocks xmin horizon). If you need high-throughput event streaming with fan-out inside Postgres -- Kafka-shaped, without Kafka and dealing with transactional outbox implementation -- this is the right shape of tool. Kudos to Marko Kreen and Skype engineers who implemented this decades ago, for the original PgQ, and to Alexander Kukushkin whose recent "Rediscovering PgQ" talk brought this quiet corner of the Postgres ecosystem back into view. Stars, issues, PRs, and honest criticism all welcome. Link 👇

for the erlang, elixir, and gleam fans - you can now use DOs to create and manage supervision hierarchies of child DOs 😎

The untold engineering story here is that this is powered by JS scripts that customers can author to validate and transform an event as it comes in. We built that by compiling JS to wasm via javy and executing wasm in elixir via wasmex (with some tight execution guarantees as it's on the critical path). I know sandboxes are all the rage right now, but elixir really is incredible for this sort of thing.