Well that didn’t take long. Linear’s launching a code aware agent.

Mine decided to jump onto my frozen in-ground pool yesterday and run away from us (yet remain on the surface of the dangerous pool) when we tried to help it out.

I know I could argue that this workflow’s superfluous if copilot can actually do the code changes and I could review that but I don’t want to be doing many iterations on PRs, I just want to offer technical guidance to avoid going down the wrong path.

I know I can checkout the code and run it in planning mode, or just use Claude Code or other, but am looking for a solution that runs fully in the browser and is codebase aware.

I like the workflow when I just assign a ticket to github copilot and it gets done, but sometimes I just want it to expand the ticket, let me review/adjust and assign to a human.

Any fun AI tools to recommend that take a ticket from Linear, checks out the code and rewrites the ticket with architectural/technical guidance and planning (change these files in this way) without actually implementing it ?

What’s the secret?

My list of cold calling secrets:
- Not mentioning their car’s extended warranty in my script
- That is all

I think it conveys what it does well.

It does make me wonder what actually does a report look like.

I’d be hesitant if all it does is tell me random facts like “people love my puppy” as opposed to actionable growth tactics for future content (which may merge my comments with external trends)

Didn’t need incremental loads for that project but would likely attack that with some dagster wizardry should the need arise

Hoping you don’t stumble into a new gif-like debate

Problem is we’re in the small window before all of AI’s answers are guided by advertisers and the cycle repeats itself

Is using 67 AI agents better than 12 or 4?

My LinkedIn feed is full of these humble brags.

What are your thoughts about the inverse direction?

Can a developer use AI to become a CFO? You’ve done it the hard way, but are these new tools letting anyone with a thirst for knowledge do it a lot faster?

It’s gonna be a fun ride for everyone who isn’t afraid to learn new things

Not 4pm deploy to production and leave for the weekend?

What we’re working with
• LLMs
• AI agents
• Machine learning models
• Unstructured data (video, voice, text)
• Modern data engineering and cloud tooling

Location
• Gatineau / Ottawa, NYC or remote

DM me.

Open roles
• Head of Data Engineering: pipelines, modeling, and foundations
• Head of Applied ML Engineering: predictive models and interpretable signals
• Head of AI Agent Engineering: agent workflows
• Founding Product Engineer (Full-Stack): product surfaces and system integration

I’m hiring four technical leaders for a new AI company in the multi-unit space.

The team
• Two cofounders, both exited CEOs
• Backgrounds in data and analytics and multi-unit operations
• Backed by top-tier NYC VCs

Open to trying it out!

Vanna is good for the loading up the schema plus docs into a vector db for RAG part, just the charts part are weaker.

I want the graph part too, that’s where Vanna w/ plotly is failing. It’s not rendering when I use booleans or timestamps and it does scatter plots at inappropriate times

Vanna.AI - Personalized AI SQL Agent

Anyone got open source alternatives to vanna.ai for text to SQL?

The SQL part is pretty good but the plotly charts it recommends are wonky.

I’ve also been playing with dbt - and considered sqlmesh instead.

Chose to go deeper with dbt as I feel like sqlmesh’s real value shines when you want to avoid transforming the data both in dev and prod.

… and I’m just prototyping stuff locally to play with different open source BI frontends.

Last week I did some experimentation with unstructured.io to extract content out of some pdfs.

Also played with github.com/getomni-ai/z... as a more lightweight option.

Overall, vision models do a much better job than classic OCR (ex: tesseract) on tables in docs.

Review of Data Orchestration Landscape ... and honest one ...

Here’s a decent overview of the data pipeline orchestration tools on the market.

dataengineeringcentral.substack.com/p/review-of-...

Does this API exist in both the hosted and self-hosted versions?

When reading the docs last week I sometimes got mixed up in what features needed a subscription.

Nice! Hello!

Is the api giving you just the metadata of the metric or also translating to the SQL you’d run to build charts like you do in lightdash itself?

Lightdash may be a good UI option for me, but then I’m defining metrics at the presentation layer and tightly coupled with it.

I’m looking for something to be able to express KPIs centrally and cleanly, and have the BI layer autogenerated from it.

Dbt semantic layer could be that, but doesn’t seem like many open source BI layers support it.

I spent some time this week playing with some tools to setup a data pipeline for simple BI and data science.

Played with airbyte, dbt, duckdb and metabase.

Planning on trying lightdash next week.

Trying to avoid hosted data warehouses and use open source for the full chain.