15:[[50002798,{"by":"ViolaWang","descendants":0,"id":50002798,"kids":[50023623],"score":2,"text":"Fellow founders, I know AI writing is strictly forbidden here. But I was wondering if yall hand write every single post on X or on Linkedin for ship updates or user engagement posts. \nFor small team like mine, I'm in charge of my own account. Not gonna lie, content batching and writing everything from scratch are extra work for me. \nOn the other side, when I outsource my content generation to AI, my personality gets washed away. \nI'm curious how other founders approach this. \nDo you use AI to some extent for content batching, drafting or editing? How do you draw the line?","time":1791444258,"title":"Ask HN: How do you use AI for building in public?","type":"story"}],[49917811,{"by":"kunggaochicken","descendants":2,"id":49917811,"kids":[49917812,49919380],"score":2,"time":1790829715,"title":"Continual Outreach – A self refining customer discovery process","type":"story","url":"https://github.com/giga-james/continual-outreach"}],[49872106,{"by":"arpanghoshal","descendants":4,"id":49872106,"kids":[49872301,49872247,49901717],"score":2,"text":"I am scared of Show HN: what if it is a flop show :(","time":1790554080,"title":"How do I promote an anti AI slop tool I built? It's free and genuine","type":"story"}],[49376270,{"by":"whyleyc","descendants":8,"id":49376270,"kids":[49437894,49443532,49427346,49376854,49380708,49378969,49376798,49386810,49442843,49381309],"score":8,"time":1787240784,"title":"Outbid – A Million Dollar Homepage for 2026?","type":"story","url":"https://outbid.lol/"}],[48082676,{"by":"hiAndrewQuinn","descendants":32,"id":48082676,"kids":[48083380,48083244,48084903,48084620,48098598,48084907,48083529,48085447,48092398],"score":193,"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":"$16","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":"$17","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":"$18","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. Alpha — primary user is me, looking for the second one.","time":1776181261,"title":"Show HN: A memory database that forgets, consolidates, and detects contradiction","type":"story","url":"https://github.com/yantrikos/yantrikdb-server"}],[47755209,{"by":"tosh","descendants":50,"id":47755209,"kids":[47756976,47756858,47760346,47757966,47757057,47760009,47757445,47757198,47760992,47757032,47759783,47831718,47760966],"score":124,"time":1776100951,"title":"B-trees and database indexes (2024)","type":"story","url":"https://planetscale.com/blog/btrees-and-database-indexes"}],[47768158,{"by":"eigenBasis","descendants":1,"id":47768158,"kids":[47768379,47803736,47768544],"score":2,"time":1776185790,"title":"The Missing Context Layer: Why Your LLM Agent Can't Do More Than Text-to-SQL","type":"story","url":"https://airbyte.com/blog/the-missing-context-layer"}],[47744683,{"by":"jeeybee","descendants":10,"id":47744683,"kids":[47757982,47756377,47762336,47756660,47745626,47803725,47767578,47791358,47744756,47763932],"score":9,"text":"I got tired of repeating myself to my LLM every session. rekal is an MCP server that stores memories in SQLite and retrieves them with hybrid search (BM25 + vectors + recency decay). One file, local embeddings, no API keys.","time":1776029153,"title":"Show HN: Rekal – Long-term memory for LLMs in a single SQLite file","type":"story","url":"https://github.com/janbjorge/rekal"}],[47767676,{"by":"petalmind","descendants":83,"id":47767676,"kids":[47772746,47768971,47771076,47770379,47768442,47773704,47770429,47769777,47768004,47775471,47777752,47770836,47776745,47778114,47773694,47768323,47769195,47778235,47777915,47789777],"score":203,"time":1776183769,"title":"5NF and Database Design","type":"story","url":"https://kb.databasedesignbook.com/posts/5nf/"}],[47761723,{"by":"adunk","descendants":9,"id":47761723,"kids":[47763288,47765212,47777664,47766449,47763964,47766509,47763146,47765195,47789768,47766487],"score":72,"time":1776145609,"title":"MOS tech 6502 8-bit microprocessor in pure SQL powered by Postgres","type":"story","url":"https://github.com/lasect/pg_6502"}],[47637353,{"by":"thunderbong","descendants":129,"id":47637353,"kids":[47638212,47641494,47676278,47638428,47637416,47639108,47678795,47680806,47638918,47638567,47679715,47679680,47639202,47687327,47677151,47679801,47638344,47679785,47679306,47679562,47680941,47677064,47679422,47638290,47700954,47681377,47679319,47679980,47677857,47677737,47678716,47682469,47679181,47688060,47677413,47678984,47678982],"score":209,"time":1775294147,"title":"SQLite in Production: Lessons from Running a Store on a Single File","type":"story","url":"https://ultrathink.art/blog/sqlite-in-production-lessons"}],[47616704,{"by":"thunderbong","descendants":63,"id":47616704,"kids":[47619596,47618197,47619545,47618967,47618402,47618798,47621263,47618572,47617111,47618071,47619174,47620028,47618718,47619646,47619569,47622524,47617045,47624869,47688033,47619231,47617382,47622335],"score":244,"time":1775147679,"title":"Modern SQLite: Features You Didn't Know It Had","type":"story","url":"https://slicker.me/sqlite/features.htm"}]]

1.	1 points by mergisi 19 days ago \| discuss
2.	1 points by mergisi 51 days ago \| discuss
3.	1 points by mergisi 56 days ago \| discuss
4.	1 points by mergisi 57 days ago \| discuss

user:	mergisi
created:	July 15, 2020
karma:	11