I had a serious sense of FOMO after working at startups throughout my 20s. In my early 30s I joined a FAANG company and absolutely hated it, left FAANG after a year and came back to startups. There are some really interesting processes that these companies use, but the problems that they have aren't broadly applicable to startups so it's not so much about "the right way" as it is about "their way". However, if you really want to try it there's very little downside. I found it pretty easy to jump from a FAANG back into startups and now I can sell "that I've seen both sides" when interviewing / managing.
I've been using reclaim.ai for time blocking and really liking it. One additional feature reclaim.ai has is "smart" 1:1s which help me to schedule recurring meetings with my team (which is super helpful with our WFH global team).
There seem to be several calendar management apps competing in this space right now, excited to see more productivity features being built from the competition.
So is the primary problem JIRA or any ticketing system? My read on this is that measuring metrics from JIRA is a very slipery slope that usually leads to disaster, but I've also worked on teams where feature/stories were poorly researched and conversations about refining those features was a total cluster
I haven't tried it, it does look really interesting, although most of the time the problem is that the finance/ops/etc. team already had something really complicated in Excel and the question is "what should stay in excel, and what should be reimplemented in some other system".
I've been tasked with migrating an excel model to a "real language" (usually by breaking it apart and re-implementing it via a combination of ETL and data warehouse jobs). I've never found a great way to run excel in a headless way, so in addition to not having version control for it, it's hard to "deploy" it when it grows beyond a single person's machine. I wish there was more of a gradient between Excel and "real systems".
I know there are limits to battery technology but I'd much rather see the watch track towards 7 days than see additional sensors / a larger brighter screen.
This may be compounded by the fact that the watch needs its own special charging cable, my main pain point is that I always forget to pack the watch charger when I travel so my watch always dies if I'm going for a night.
Talked with an SEC guy at a conference one time, they have access to every order that touches any American exchange (filled or not) in a huge data warehouse, apparently most of this detection is just a series of SQL queries.
Location: Chattanooga, TN
Remote: Yes
Willing to relocate: Yes (planning on relocating to NYC in July for partner's job).
Technologies: Python | Django | React | React Native | Terraform | Kubernetes | Airflow | AWS |
LinkedIn: https://www.linkedin.com/in/adam-haney-b0b31813/
Email: adam.jacob.haney [at] gmail
About: Serial CTO / Engineering Manager with experience in startups ranging from seed stage to Series C. I'm capable as an individual contributor but would be more interested in a company that's reaching the growth stage and is looking for an experienced manager to help them scale the team/processes. I'm comfortable (and enjoy) wearing multiple different hats and combining my business and tech experience to solve problems.
This does make the assumption that the parents are doing well enough to be able to support their adult children. There are some situations where the parents need the support of adult children, instead of the other way around.
Might be off topic, but does anyone know what data providers / sources sites like Hipmunk and Kayak are pulling this data from? Is there a centralized source for flight data that’s freely available or is this pay to play? I’m trying to understand the business model and mechanics of how they sourced the information they indexed.
They all have their strengths and weaknesses. Having used all three, I’d start with Metabase. It’s super easy to setup and I’d argue it has the most intuitive interface for non-technical end users.
I'm not sure that it's a completely different paradigm but Datomic (https://www.datomic.com/) is a pretty unique database if you're interested in thought provoking design. Here's a cool video of Rich Hickey (the creator of Datomic and Clojure) talking about the underlying data structures and design decisions: https://www.datomic.com/
I use a versioned S3 bucket to store state in my project, terraform also supports "locks" to avoid race conditions/parallel execution. I've set up buildkite to run `terraform plan` and then if the output looks I can approve the next step to run `terraform apply`.
I know it's hard to make a buck with an open source business model but deciding to charge more for security related features is always so frustrating to me. It leads to a culture of insecure deployments in environments when the business is trying to save money. Differentiate on storage or number of cores or something, anything but auth/security. I'm glad they've finally reversed this.
I’d make the assumption that any client/server connection has some sort of logging mechanism? I don’t think browser fingerprinting and large scale cross site tracking is good but it seems a hard to take issue with being counted when visiting someone else’s website.
I believe their point was that a central authority would have finer grained censorship control with the v2 bucket scheme instead of the old path based scheme.
This is almost exactly how Pycharm works with testing/auto complete/coverage.
It has support to use a a particular interpreter, specific virtual env, or it lets you say “my interpreter is inside this docker container”, which is even allowed to be on a remote host (although I’ve always used docker running locally).
I'm really excited for this, there are so many times that I override backend code to pre-compute a column and then save it, the ability to just define a generated value that is indexable that Postgres can keep up to date without triggers is really exciting.
Wagtail, a CMS system for Django uses a very similar abstraction to this for their StreamField[1] type and I've found it incredibly useful. It allows frontend services and backend services to easily implement custom types, and treats streams of content as a list of 'blocks'.
I’ve been really happy splitting the difference between rails style server side rendering and full client side JS apps using Django. I can easily build REST or GraphQL endpoints for client side web apps to consume, but I have the ability to easily render out server side templates for more lightweight pages. Add to that that the python ecosystem is great for data science / data engineering tasks so it’s easy for developers with different backgrounds to cross over / reuse data model code between projects/tasks.