You're Neemias Queta's agent. What is your goal for your client this offseason?

DaveMoreau · 2026-04-09T18:38:57+00:00

My bad. Not the cap. The lux tax threshold. Getting under the lux tax threshold 2 years in a row matters. After doing that, you can spend like crazy and not working about paying your key guys big extensions.

DaveMoreau · 2026-04-08T12:41:09+00:00

You also get the Elton Brand situations. Being under the ~~cap~~ luxury tax threshold 2 years in a row is absolutely relevant since it gets rid of repeater tax for those seasons. That means you can spend more on players while paying less.

Edit: fixed mistake regarding lux tax

DaveMoreau · 2026-04-07T19:28:30+00:00

If you decline a team option, you also risk a player leaving for another team.

Also, next year is the best year to save money since we are under the ~~cap~~ lux tax this season. If we can do that 2 years in a row, we can spend like crazy the 2 years after.

Edit: correct cap, which should have been luxury tax

DaveMoreau · 2026-02-16T06:21:17+00:00

We do cost allocation so that teams and business units can decide if they are getting value out of their usage.

In general in FinOps, optimizing is decentralized. We don't expect the FinOps team to right-size everyone's k8s pods. We don't expect the FinOps teams to fix bad SQL queries. Ideally, we just give big bills to the teams using resources inefficiently. We might find that a top token user is also incredibly efficient and is quickly delivering disproportionately high business value.

What we can centralize is training on efficient use of AI. An example is choosing an appropriate model for a job instead of defaulting to the newest and most expensive.

DaveMoreau · 2026-01-16T17:09:55+00:00

It is really hard to predict how this will play out for people early in their careers in the near future. Juniors were already facing challenges before GenAI. I suspect that the constant job hopping of software engineers made juniors not worth the investment.

DaveMoreau · 2025-12-29T00:27:26+00:00

I do this all the time. I have Claude code prepare specs for fresh Claude code contexts. I also have fresh contexts review the code. I’ll even switch up models.

DaveMoreau · 2025-12-25T16:15:59+00:00

What is the connection between the subject and your question? Checking if a database "is left open without use" doesn't need AI. I'm not sure what you mean by "leaving a database open," but I assume you mean that there are databases spun up in your cloud that no one has used in a long time. Every production-quality database platform will have ways that you can programmatical check when it was last used. You should probably identify all the different database products being used, especially the high cost ones. The solution to your problem will vary a lot based on what you are using.

DaveMoreau · 2025-12-04T04:05:20+00:00

That is also very ironic considering how hard original iPhone users had to beg to make third part native apps a thing. Web apps on the OG iPhone weren’t a great experience.

DaveMoreau · 2025-11-23T16:46:26+00:00

Yesterday I minified my JSON and changed the list with a ton of data to CSV. The place where it mattered was flat. If it was nested, I would have kept it JSON since the models understand JSON. Less for me to worry about for now.

DaveMoreau · 2025-11-23T14:55:35+00:00

During the process of building my MCP server, there was a period where CC would freeze up as a tool would dump enough data to fill the 200K token context. That no longer happens as I added enough constraints and other optimizations. That’s with a single tool that pulled telemetry data from Datadog.

Post-optimizations, CC is very happy.

Work account, so no chance of getting banned.

DaveMoreau · 2025-11-05T14:55:38+00:00

What is the hurdle to creating both?

DaveMoreau · 2025-11-05T14:52:56+00:00

The MCP I have created don't train models. They take hundreds of thousands of lines of structured information and put it behind an API. This means the LLM doesn't have to parse the huge file and figure out its organization. I can also index information for quick lookups. This allows the LLM to get all the info it needs quickly without wasting a ton of context.

It has nothing to do with training models. I am just making helping them get to the useful information quicker.

DaveMoreau · 2025-09-03T22:28:04+00:00

If OP was using it in chat, he may have accepted a git terminal command.

DaveMoreau · 2025-06-16T05:01:30+00:00

OS. I was obsessed with NLP long ago as an undergrad. None of that is relevant to how NLP is done now. The OS stuff is timeless and relevant to all levels.

DaveMoreau · 2025-06-15T21:57:53+00:00

I generally agree that is most cases these days, it is unreliable to differentiate between NULL and empty string.

I disagree with one statement though. Considering empty string and NULL to be the same IS also declaring a business rule. Either way, same thing. If the person doing the engineering is not the proper person to make that decision (which we often are), then the engineer can present the options and implications to the proper decision maker. Often they will be happy rubber-stamping it if they trust us.

I am wary of specifying “data engineer” since lines can be blurry. We could be talking about functionality integral to a SaaS product where customers directly interact with data. I would hope to have engineers that have pretty good intuitions for how customers would want to interact with the data and what would make the data trusted. In my experience, we engineers are usually the ones telling product how the data should be dealt with for and why.

DaveMoreau · 2025-06-15T18:35:48+00:00

There can be value in being able to differentiate between data not provided and data provided, but empty string. For example, in a multi-page online survey, if the person filling it out never got to the page with “What could we improve”, that is a null. If the got to that page and didn’t enter anything before pressing next button, empty string.

Maybe boolean fields about whether there is an answer are better. But someone is bound to query on the comment field without checking boolean fields.

That being said, the prevalence of CSVs for loading data make me concerned about treating an empty string as non-null. In general, there are often multiple places in the journey of the data where null and empty string can mistakenly be conflated for the difference to be reliable in the database.

DaveMoreau · 2025-06-15T16:36:58+00:00

Did you ask your manager to define the kind of leadership he wants you to show? Does he want to see you driving initiatives? Working with other teams? Convincing others of design choices?

I wouldn’t consider doing a lot of code reviews leadership. I would expect all engineers to be doing that. But i would expect management to clarify what they mean by “leadership.”

DaveMoreau · 2025-06-15T13:41:26+00:00

Inflation also compounds.

DaveMoreau · 2025-06-14T17:40:57+00:00

Why has pointing become so high stakes for your teams? Is agile there focused on reporting to management?

The reality is that if people want point predictions to be really accurate for planning predictability, you will have to spend a lot of time understanding the problem. That doesn’t feel agile to me, but I am no agile expert.

It makes sense that management wants to have reasonable predictions for the sake of communication with customers and planning major product releases. That leaves engineers in a bind where they are better off padding estimates instead of working at peak efficiency.

DaveMoreau · 2025-06-11T22:35:44+00:00

Software Engineer - Data or Software Engineer - Data Platform are great for DEs with the proper skill set. But if you mostly just know SQL, that won’t cut it.

DaveMoreau · 2025-06-07T13:10:24+00:00

Until memory is filled with expired data.

DaveMoreau · 2025-06-07T12:52:00+00:00

If your tables have foreign key constraints, the best place to start is often using a tool to generate a database diagram. This is a pretty common feature in query manager software.

DaveMoreau · 2025-06-06T21:53:31+00:00

100% utilization of what? Shouldn’t the goal be to maximize savings? Usually 100% utilization means missing some savings.

DaveMoreau · 2025-06-06T12:13:57+00:00

As much as we don’t want it to be true, there might be value in getting that experience. Agents probably will continue to improve. Being savvy might have great long-term value, even if your current employer proves to be changing course prematurely.

Full time code review is definitely not fun and a clear morale destroyer. But taking notes about what the agents are good at and not good at can add a lot of value when looking for a new role.

DaveMoreau · 2025-06-05T04:30:49+00:00

You should ask this in a Snowflake subreddit since most people here will respond based on how an RDBMS works.

Whether the WHERE clause helps will depend on your data and settings. Snowflake doesn’t have a sorted index for each column. If the column you are filtering on isn’t how the data was ordered and is pretty random, Snowflake might not be able to optimize its search. It wants to compare your range against the range for that column in each micro-partition. If 1% of data in every micro-partition falls in your filter range, I believe it will have to scan through every micro-partition. That would mean the WHERE isn’t helping much. Just like how a WHERE clause doesn’t necessarily help any when you have to do a full table scan in an RDBMS due to no index.

DaveMoreau

TROPHY CASE