As a recurse alum (s14 batch 2) I loved reading this. I loved my time at recurse and learned lots.
This highlight from the post really resonates:
“ Real growth happens at the boundary of what you can do and what you can almost do. Used well, LLMs can help you more quickly find or even expand your edge, but they risk creating a gap between the edge of what you can produce and what you can understand.
RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.”
Sure. Our numbers are smaller, as our org is much smaller but I think they hold up.
We have under 100 engineers at BuzzFeed, so for us we reduce time spent supporting applications by a single percentage is almost equivalent to having an extra engineer!
On a typical day we'll do 180+ deploys. That'll be roughly 60% stage, 40% production. To deploy outside of continuous deployments (e.g. to push a branch build to a stage environment), is as simple as browsing to our deploy UI, selecting the service you want to deploy, the version, and the cluster to deploy too. It takes an engineer about 40 seconds to do that (time measured from typing URL, to hitting the deploy button)
Continuous deployments means for production deploys for master, that step is automated. So that's a saving (in engineer time) of around 40 seconds per deploy. Over the last month over 1400 deploys were made via continuous deployment.
This means from a developer productivity perspective roughly 2 engineer days per month (1400 deploys * 40 secs per deploy / 3600 to get into hours / 8 hours per day) ) are being saved as a result of this change.
However this is really a side benefit. Our real motivation for making this change was to ensure that all master builds are deployed, and that each of our ~500 micro services is always on latest master.
This is to reduce developer anxiety of deploying over a branch build and thus minimize stress around making change.
The productivity side benefit is pretty nice though!
This is somewhat intentional I think to guide you toward buying Enterprise edition, which since 11G includes plan baselines ( see https://docs.oracle.com/cd/B28359_01/server.111/b28274/optpl... ) - which allows you to lock a SQL queryID to a specific plan.
We’re looking for engineers who understand how good tools can shape company culture, and who are passionate about creating tools and automation to develop and improve the platform that underpins BuzzFeed.
Email me - [email protected] - with any questions, or apply directly to the job specs above.
Indeed. And considering the hundreds of millions[1] of people who pass through london’s stations each year, that would means hundreds of thousands of false positives.
While I agree with the aspiration, I don’t see how it’s possible to use facial recognition to flag “america’s Most wanted” or to look for missing children without mass surveillance though?
It only works if it scans _everyone_ .
Additionally what happens when the technology isn’t perfect and innocent people get mistakenly flagged as persons of interest?
The other thing is once it’s installed and in operation, what’s to stop it being used for other purposes? - being used to target people peacefully protesting against the government or whatever.
“When the animals are ill, chemically synthesised allopathic veterinary medicinal products including antibiotics may be used where necessary and under strict conditions. This is only allowed when the use of phytotherapeutic, homeopathic and other products is inappropriate.”
My own experience ( North east US) has been somewhat different recently.
Yes routes from Apple maps, may appear longer or more convoluted at first glance. However after using it ( due to CarPlay) for a while on routes I had previously regularly done using google maps, I inferred a reasoning for that.
On the ~90 minute journey to my in-laws, the predicted journey time, is generally advertised as being quicker on google, but in practice the time difference is marginal.
What was different in my experience anyway, is that Apple maps seems to try to minimize left turns where appropriate.
The benefit being a noticeably less stressful journey.
Their traffic flow monitoring with per second granularity was utterly amazing. It had almost no overhead, it gave the equivalent of netflow data for instances in EC2, way before AWS gave VPC flow logs. It was incredible.
Alas their sales team were incredibly difficult to deal with, managing to both seem to not know how or what they were selling, whilst at the same time being incredibly heavy handed, escalating to my VP over a minor delay in contract signing and causing trouble to the point I had to almost beg my VP not to tear up the contract because of their actions.
As a result of what I perceive to be the sales org's failure, the company was forced to pivot into a generic monitoring product, which lost its identity and got borged by BMC.
There's nothing like boundary on the market today, if there was I'd be lining up to buy it.
It’s one of our two standard languages - the other being Python - and whilst the vast majority of our services are Python, Golang is being used for growing and significant number too.
Touching on my first point, we have observed people enjoy writing Go apps, and it is a great fit particularly where performance and scalability are needed.
Therefore when engineers have moved to another team internally, they often will evangelize Golang to their new team members.
So we expect it to continue to grow and thrive here!
Right now we have a dependency on Google as an OAuth2 provider, as that's what we use internally at BuzzFeed. However we've designed sso to allow us to easily add other providers.
As a recurse alum (s14 batch 2) I loved reading this. I loved my time at recurse and learned lots. This highlight from the post really resonates:
“ Real growth happens at the boundary of what you can do and what you can almost do. Used well, LLMs can help you more quickly find or even expand your edge, but they risk creating a gap between the edge of what you can produce and what you can understand.
RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.”