HN user

mritchie712

2,964 karma

Founder at Definite (https://www.definite.app/).

Definite is ETL, a data warehouse and BI in one app.

Previously founded SeekWell (https://seekwell.io/), acquired in 2021.

twitter: https://twitter.com/thisritchie

email: mike@definite.app

meet.hn/city/us-Philadelphia

Posts57
Comments1,195
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twitter.com 1mo ago

Fable 5 is Anthropic's most "honest" model

mritchie712
3pts2
www.definite.app 1mo ago

"Hi" Is Home Improvement, Not Hawaii

mritchie712
2pts1
www.definite.app 6mo ago

Cc-search: a skill to search Claude Code sessions

mritchie712
3pts0
www.definite.app 1y ago

Target-ducklake: connect 500 sources to Ducklake

mritchie712
1pts1
www.definite.app 1y ago

DuckLake: Data Lake on Acid

mritchie712
1pts1
pydantic.dev 1y ago

We Built an AI Data Team with Pydantic AI

mritchie712
1pts0
pydantic.dev 1y ago

Building a Data Team That Never Sleeps with Pydantic AI

mritchie712
1pts0
www.definite.app 1y ago

Calculating MRR in SQL

mritchie712
11pts2
www.definite.app 1y ago

Understanding Smallpond and 3FS

mritchie712
262pts47
www.definite.app 1y ago

Running Apache Iceberg and DuckDB on AWS

mritchie712
1pts0
www.definite.app 1y ago

Adding concurrent read/write to DuckDB with Arrow Flight

mritchie712
94pts32
www.definite.app 1y ago

Running a cheap (data) lakehouse on AWS

mritchie712
2pts0
github.com 1y ago

Pg_DuckDB: Run DuckDB in Postgres

mritchie712
5pts0
www.definite.app 1y ago

Running Iceberg and DuckDB on Google Cloud

mritchie712
1pts0
www.definite.app 2y ago

How to Query Apache Iceberg with DuckDB

mritchie712
2pts0
www.definite.app 2y ago

Using DuckDB with Iceberg (with notebook example)

mritchie712
2pts0
www.definite.app 2y ago

Snowflake to DuckDB: a 70% cheaper data warehouse

mritchie712
4pts0
ollama.com 2y ago

Run llama3 locally with 1M token context

mritchie712
204pts72
www.definite.app 2y ago

Show HN: Query S3 with SQL and build dashboards

mritchie712
2pts0
www.definite.app 2y ago

Show HN: Talk to Your S3 Data

mritchie712
4pts0
www.definite.app 2y ago

OverkiLLM: Thousands of LLM Calls to Write an H1

mritchie712
1pts0
www.definite.app 2y ago

Query any ducking thing: Postgres, parquet, JSON, CSV, and Sheets

mritchie712
1pts0
www.definite.app 2y ago

Show HN: Definite – Get a data stack (database, ETL, and BI) in one app

mritchie712
7pts3
www.definite.app 2y ago

Attribution tracking in 200 lines of JavaScript

mritchie712
3pts0
twitter.com 3y ago

ChatGPT Code Interpreter is down

mritchie712
6pts6
twitter.com 3y ago

LLMs: “Always Respond with JSON”

mritchie712
5pts1
issuetracker.google.com 3y ago

HN dataset in BigQuery stopped updating on 2022-11-16

mritchie712
8pts1
linear.app 3y ago

Linear Method for Software Development

mritchie712
1pts1
luabase.com 3y ago

Show HN: A Data Stack for Web3

mritchie712
4pts3
blog.luabase.com 3y ago

How I came up with the idea for my 2nd startup

mritchie712
1pts0

I'm not going to care about open models until some blend of the below becomes true:

1. the labs stop offering max plans

2. really smart open models can easily be run on my mac

3. TPS (token per second) AND intelligence are gpt5.6 level

on #1, it's nearly impossible for me to run out of codex tokens right now (I have 4 resets banked) and Fable 5 seems to be sticking around for the foreseeable future. I have virtually unlimited token usage for $400 a month, so open models being cheaper doesn't appeal to me.

on 2 and 3, benchmarks are showing some of the open models at around opus4.8 levels, which is incredible! But running them locally at anywhere near the TPS of cloud inference is far off. I can run a smaller (dumber) open model locally and get good TPS, but see #1, whats the point?

This is far cooler than how I've been using my Pi.

I've started building a box for managing agents. I hate sitting in front of my Mac all day flipping between terminals. I wanted something that was audio and whiteboard / paper first.

The Pi has a mic, camera, and projector hooked up. The Pi is always listening, so I just say most commands (i.e. "check logs on backend service, customer xyz said abc is wrong"). I can tell it "look at the board" and it can see things I've written or drawn and can project on top of or alongside anything on the board.

Not sure yet if it's more efficient, but it's definitely more fun.

here's how this likely went down:

1. they found the dataset and thought "i bet there are weird order combos i could write a blog post about"

2. they did all the analysis and found nothing all that interesting

3. posted it anyway

[dead] 7 days ago

this works well when you don't have customers

Looks cool! It's a lot of work to get a full data stack set up and people are losing interest in stitching the pieces (ETL, warehouse, BI) together.

Agents made bad inferences because they had no context on the business

We've been working on this since before the chatgpt launch.

We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.

But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.

https://www.definite.app/blog/ontology-ai-analytics

For anyone that doesn't use twitter:

I index all my local Claude Code sessions in DuckDB. I have 202,381 messages in the last 30 days.

There's been a steady increase since Opus 4.6 in the model saying "honest".

It probably shouldn't, but this bugs me.

Should I assume most of the time you're lying and you're being honest in this one message?

I was pumped in the first few hours of Fable where this had seemingly been "fixed". 100+ messages and no "honest" to be seen. But it didn't last.

Within a few hours, Fable proved itself to be the most honest model to date.

Here is the rate at which visible assistant text contained the string "honest" (case-insensitive), split by model:

  claude-fable-5:             25 / 1,397   = 1.7895%
  claude-opus-4-8:            83 / 5,818   = 1.4266%
  claude-opus-4-7:           163 / 16,432  = 0.9920%
  claude-opus-4-6:            18 / 5,877   = 0.3063%
  claude-haiku-4-5-20251001:   0 / 71      = 0.0000%
  claude-sonnet-4-6:           0 / 4       = 0.0000%

The day was to begin like any other, with Antigravity open

This unexpected shift completely broke my preferred workflow

it might not have been so unexpected if you knew you were one of ~15 people that start their day with Antigravity

Can I use DuckDB with Quack as the catalog database for DuckLake?

Not yet, but we are working on it!

Seems like a niche use case, but it's the one I'm most interested in.

Our lakehouse uses ducklake with postgres as the catalog. Seems like a DuckDB / Quack catalog would be an excellent alternative.

We previously had our (https://www.definite.app/) agent write reports / dashboards in a YAML spec that would get rendered by our frontend framework (i.e. charts and tables).

For example, user says "build a report with revenue and orders by month and show 100 most recent orders". The agent would write a spec that would get rendered by our frontend.

This runs fast, but we were drowning in feature requests for what the framework could render (e.g. "I don't want labels here", "I DO want labels there", "can this chart be a heatmap", etc.)

A few months ago, we let the agent just write HTML instead. It takes longer to generate, but you get unlimited customization.

There are a host of issues with the new approach (non-technical users debugging a monstrous app they created), but net-net our customers like it much better.

looks like you work at github.

I completely understand a "people who give a shit stick around" mentality if you work there, but you can't expect users who run a business on it to stick around if it's broken.

Claude Design 3 months ago

I don't like the "I canceled my x subscription" hype posts, but I did cancel Figma today. We've barely used it in months and this was the nail in the coffin.

ChatGPT for Excel 3 months ago

I remembered this post from (only) 3 years ago:

Show HN: I've built a C# IDE, Runtime, and AppStore inside Excel

670 points | 179 comments

One of the main use cases was to analyze Excel data with SQL. I'm the kind of nerd that loves stuff like that, but stuff like that seems completely obsolete now.

[0] https://news.ycombinator.com/item?id=34516366

this was true a year ago, but if you give an agent a new spec to follow (e.g. a .md file), it will follow it.

we have a custom .yaml spec for data pipelines in our product and the agent follows it as well as anything in the training data.

while I agree you don't need to build a new thing "for agents", you can get them to understand new things, that are not in the training data, very easily.

S3 Files 4 months ago

tldr: this caches your S3 data in EFS.

we run datalakes using DuckLake and this sounds really useful. GCP should follow suit quickly.

what's the use case for cortex? is anyone here using it?

We run a lakehouse product (https://www.definite.app/) and I still don't get who the user is for cortex. Our users are either:

non-technical: wants to use the agent we have built into our web app

technical: wants to use their own agent (e.g. claude, cursor) and connect via MCP / API.

why does snowflake need it's own agentic CLI?