16:[[48082676,{"by":"hiAndrewQuinn","descendants":32,"id":48082676,"kids":[48083380,48083244,48084903,48084620,48098598,48084907,48083529,48085447,48092398],"score":192,"time":1778409186,"title":"Replacing a 3 GB SQLite db with a 10 MB FST (finite state transducer) binary","type":"story","url":"https://til.andrew-quinn.me/posts/replacing-a-3-gb-sqlite-database-with-a-7-mb-fst-finite-state-trandsucer-binary/"}],[48049538,{"by":"cammasmith","descendants":0,"id":48049538,"kids":[48092386,48049712],"score":2,"text":"$17","time":1778162232,"title":"Show HN: I built an SQL layer for a NoSQL database","type":"story"}],[48014124,{"by":"knazim","descendants":0,"id":48014124,"kids":[48022347,48019270,48014194,48015036],"score":5,"text":"$18","time":1777924697,"title":"SQL access to crypto market data, not just JSON","type":"story"}],[47963316,{"by":"ferriswil","descendants":59,"id":47963316,"kids":[47965587,47968104,47964945,47968488,47965046,47965477,47965607,47967579,47968912,47970222,47968938,47967038,47969016,47967075,47966655,47966998,47969340,47969469,48007077,47973168,47969496,47969365,47967064],"score":227,"time":1777560182,"title":"Honker – Durable queues, streams, pub/sub, and cron scheduler in a SQLite file","type":"story","url":"https://honker.dev/"}],[47966625,{"by":"KraftyOne","descendants":95,"id":47966625,"kids":[47967784,47967691,47967329,47970525,47968608,47967457,47971913,47968450,47970347,47977077,47971091,47970659,47970378,47969048,48007055,47968617,48001649,47982743,47967386],"score":192,"time":1777574722,"title":"Does Postgres Scale?","type":"story","url":"https://www.dbos.dev/blog/benchmarking-workflow-execution-scalability-on-postgres"}],[47935246,{"by":"hugocorreia90","descendants":48,"id":47935246,"kids":[47948027,47946087,47946008,47951662,47948212,47947613,47945572,47958581,47945506,47951886,47945941,47990475,47948625,47948762,47948836,47935304],"score":122,"text":"$19","time":1777386959,"title":"Show HN: Rocky – Rust SQL engine with branches, replay, column lineage","type":"story","url":"https://github.com/rocky-data/rocky"}],[47919314,{"by":"ramin2nt2","descendants":0,"id":47919314,"kids":[47931098,47919841,47927356],"score":6,"text":"I built a SQL analyst agent based on a simple idea: generating SQL is not the same as doing analysis.

Most text-to-SQL tools stop at producing a query. But real analysis is iterative. You explore the schema, run queries, adjust, and refine.

This project tries to model that loop instead of treating a query as the final output.

I wrote about the approach, challenges, and tradeoffs here:\nhttps://raminmousavi.dev/blog/building-a-sql-analyst-agent

Github repo:\nhttps://github.com/raminious/sql-analyst-ai-agent","time":1777280837,"title":"Show HN: Building a SQL analyst agent from scratch","type":"story","url":"https://raminmousavi.dev/blog/building-a-sql-analyst-agent"}],[47897140,{"by":"mooreds","descendants":106,"id":47897140,"kids":[47911512,47912303,47911999,47911661,47911580,47911766,47911677,47912486,47912422,47930884,47912451,47912632,47916034,47912821,47912195,47913972,47912113,47912108,47911536,47912507,47912232,47912549,47916666,47912758,47911952,47911880,47912177,47919514,47912700,47918954,47920227,47917320],"score":116,"time":1777074098,"title":"Agentic AI systems violate the implicit assumptions of database design","type":"story","url":"https://arpitbhayani.me/blogs/defensive-databases/"}],[47864454,{"by":"janandonly","descendants":52,"id":47864454,"kids":[47865571,47869643,47867277,47866751,47865487,47864629,47867940,47867610,47864730,47867980,47866376,47918949,47868122],"score":166,"time":1776869342,"title":"DuckDB 1.5.2 – SQL database that runs on laptop, server, in the browser","type":"story","url":"https://duckdb.org/2026/04/13/announcing-duckdb-152"}],[47883721,{"by":"ItaiZeilig","descendants":2,"id":47883721,"kids":[47890459,47887879,47883726],"score":2,"time":1776987862,"title":"Show HN: SQL Protocol – learn SQL by running real queries, with 1v1 PvP","type":"story","url":"https://sqlprotocol.com"}],[47886205,{"by":"matt_d","descendants":0,"id":47886205,"kids":[47887871],"score":2,"time":1777010826,"title":"How Do LLM Agents Think Through SQL Join Orders?","type":"story","url":"https://ucbskyadrs.github.io/blog/databricks/"}],[47833558,{"by":"thomasp85","descendants":85,"id":47833558,"kids":[47834138,47834545,47836056,47838395,47835902,47834471,47834346,47843565,47833559,47838102,47837996,47835461,47840565,47837936,47834510,47862974,47840392,47835545,47833906,47944519,47835570,47835397,47834158,47834042,47839390,47834790,47835416,47834499,47838109,47839925,47834946,47845595],"score":452,"time":1776689480,"title":"ggsql: A Grammar of Graphics for SQL","type":"story","url":"https://opensource.posit.co/blog/2026-04-20_ggsql_alpha_release/"}],[47767119,{"by":"pranabsarkar","descendants":33,"id":47767119,"kids":[47767126,47770220,47771339,47771593,47769655,47769273,47769623,47769433,47769941,47771769,47831776,47842722,47791856,47767756,47773803],"score":48,"text":"Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier.

YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does.

Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks.

Live on a 3-node Proxmox homelab cluster with multiple tenants. 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user:	mergisi
created:	July 15, 2020
karma:	11