2,239 karma · joined April 9, 2008
I haven't looked that hard, but I can't find articles about this type of eval testing, curious to hear if others have approached writing APIs in this way.
But California's clear tiers of ebike regulations are meaningless without enforcement. Over the past half decade blue states have become unwilling to enforce almost any laws. when they do enforce the laws it is sporadically. This matters for ebikes, it matters more for cars. Running a stop sign is absolutely not enforced any more.
Python/Jupyter developer in Boston. I build data tools and I start by talking to the people who'll use them, because code that doesn't get adopted is wasted work. My main thing is Buckaroo (github.com/paddymul/buckaroo, ~680 stars), an open-source data table for Jupyter over Pandas/Polars. I built both the data layer and the React frontend. Looking for a team building data tooling (as a product or in house).
I used to live in Newport, RI. I love sailing and introducing people to the world of sailing. When I had guests I asked them to watch this NBC video about Ted's 77 campaign [1]. It really captures the history of Newport, sailing, and Ted
[1] https://www.youtube.com/watch?v=tr7-BwzceYI&list=PLXEMPXZ3PY...
I used to live in Newport, RI. I love sailing and introducing people to the world of sailing. When I had guests I asked them to watch this NBC video about Ted's 77 campaign [1]. It really captures the history of Newport, sailing, and Ted
[1] https://www.youtube.com/watch?v=tr7-BwzceYI&list=PLXEMPXZ3PY...
I think Sun and HP had some 3d capabilities, but it was mostly aimed at engineering/CAD
https://www.vannattabros.com/dozer.html -- A detail page from the site, not well organized but so much great info about heavy equipment and logging.
I want a system that enforces planning, tests, and adversarial review (preferably by a different company's model). This is more for features, less for overall planning, but a similar workflow could be built for planning.
1. Prompt 2. Research 3. Plan (including the tests that will be written to verify the feature) 4. adversarial review of plan 5. implementation of tests, CI must fail on the tests 6. adversarial review verifying that the tests match with the plan 7. implementation to make the tests pass. 8. adversarial PR review of implementation
I want to be able to check on the status of PRs based on how far along they are, read the plans, suggest changes, read the tests, suggest changes. I want a web UI for that, I don't want to be doing all of this in multiple terminal windows.
A key feature that I want is that if a step fails, especially because of adversarial review, the whole PR branch is force pushed back to the previous state. so say #6 fails, #5 is re-invoked with the review information. Or if I come to the system and a PR is at #8, and I don't like the plan, then I make some edits to the plan (#3), the PR is reset to the git commit after the original plan, and the LLM is reinvoked with either my new plan or more likely my edits to the plan, then everything flows through again.
I want to be able to sit down, tend to a bunch of issues, then come back in a couple of hours and see progress.
I have a design for this of course. I haven't implemented it yet.
PRs are a defacto communication and coordination bus between different code review tools, its all a mess.
LLMs make it worse because I'm pushing more code to github than ever before, and it just isn't setup to deal with this type of workload when it is working well.
Basically what I would want is write a commit (because I want to commit early and often) then run the lint (and tests) in a sandboxed environment. if they pass, great. if they fail and HERAD has moved ahead of the failing commit, create a "FIXME" branch off the failure. back on main or whatever branch head was pointed at, if tests start passing, you probably never need to revisit the failure.
I want to know about local test failures before I push to remote with full CI.
automatic branching and workflow stuff is optional. the core idea is great.
I had a RWD pickup with snow tires and went anywhere I wanted to through two utah winters and many vermont ones too.
I recently integrated Lazy Polars and running analytics in background processes so I can reliably provide a fast table viewing experience on dataframes that would normally exhaust memory of the jupyter kernel. Analytics are run column by column and results are written to cache, if a column fits into memory individually, summary stats for the entire dataframe can be computed.
Here's a demo video of scrolling through 19M rows, and running background summary stats.
Would putting an aftermarket oil pump in these modern engines protect them or is it a deeper design issue?
Also, what key decisions do other data catalogs make via your choices? What led to those decisions and what is the benefit to users?
The economically efficient way to get the fuel economy result would have been to increase gasoline taxes, but that's a non starter politically. Higher gas prices would allow people to choose to keep a cheap gas guzzling truck/car, buy a new more efficient and expensive car, or buy a new slightly more efficient slightly more expensive car. It would have been simpler though and given consumers more choice.