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peytoncasper

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www.browserbase.com 8mo ago

We Forked Chromium

peytoncasper
4pts1
news.ycombinator.com 1y ago

Gemini Flash 2.0 Experimental: A bit more accurate, but slower

peytoncasper
3pts1
coffeeblack.ai 1y ago

AI Data Extraction Leaderboard

peytoncasper
2pts0
peytoncasper.com 1y ago

OpenAI Swarm – Recipe Scraping Agent

peytoncasper
1pts0
peytoncasper.com 1y ago

Evaluating the Effects of Fine Tuning on Creative Writing

peytoncasper
1pts0
coffeeblack.ai 1y ago

Evaluating Webpage Fact Extraction with Braintrust

peytoncasper
2pts0
coffeeblack.ai 1y ago

The McFlurry Index

peytoncasper
4pts2
coffeeblack.ai 1y ago

Mapping San Francisco's Air Conditioning Amidst the Heatwave

peytoncasper
2pts0
medium.com 4y ago

Terraform Cloud Onboarding

peytoncasper
3pts0
medium.com 4y ago

Exploring Excelize with Go

peytoncasper
1pts0
medium.com 4y ago

7 Days of Bootstraps: Days 5 and 6

peytoncasper
1pts0
medium.com 4y ago

7 Days of Bootstraps: Day 4

peytoncasper
1pts0
medium.com 4y ago

7 Days of Bootstraps: Day 3

peytoncasper
1pts0
medium.com 4y ago

7 Days of Bootstraps: Day 2

peytoncasper
1pts0
medium.com 4y ago

7 Days of Bootstraps: Day 1

peytoncasper
2pts0
medium.com 4y ago

7 Days of Bootstraps

peytoncasper
2pts0
medium.com 5y ago

Use Cases for the Terraform CDK

peytoncasper
2pts0
www.nasdaq.com 5y ago

An Updated Intern's Guide to the Market Structure Galaxy

peytoncasper
1pts0
www.tensorflow.org 5y ago

TensorFlow Quantum

peytoncasper
2pts0
medium.com 5y ago

Tic Tac Consul – Multi-Cloud, Serverless Networking

peytoncasper
1pts0
www.w3.org 5y ago

Proposed Web Machine Learning Working Group

peytoncasper
1pts0
www.r2d3.us 6y ago

A visual introduction to machine learning

peytoncasper
3pts0
medium.com 6y ago

Monitoring and Logging for Terraform Enterprise

peytoncasper
1pts0
medium.com 6y ago

Tint: Multi-Cloud Cost Visualization for Terraform

peytoncasper
2pts0
medium.com 6y ago

Automating Terraform Policy Enforcement with Sentinel and ServiceNow

peytoncasper
2pts0
medium.com 6y ago

Google Config Connector: Deploying Spanner from Kubernetes

peytoncasper
2pts0
github.com 6y ago

Show HN: HashiMash – Fleet Management Demo with Consul, Nomad, Vault, Terraform

peytoncasper
3pts0

Hi! I work in identity products at Browserbase. I’ve spent a fair amount of time lately thinking about how to layer RBAC across the web.

Do you think callbacks are how this gets done?

I’m not taking a side of whether Elon is right or wrong.

However, you mentioned that today is an admission that the bot problem is worse. However that not true, the restrictions are specifically on viewing data (scrapers) rather than creating data (bots).

I'm truly amazed at this opinion which seems to be prevalent on this thread. The models are tools that already have the ability to accelerate your work now, and will massively improve over the next decade. Comparing it to things we already know how to do provides the ability to benchmark and see how much value it brings to the table. Being on the forefront, exploring, and hacking on side projects has always been the best way to understand new stuff.

"Using cars to drive down roads we already know how to traverse with horses and just add more complexity is bound to not go anywhere useful in my opinion."

Find them on LinkedIn and ask to grab a cup of coffee or jump on a Zoom. Be open about the fact that you're interested in what makes a good member of the team. Ask about what you can be upskilling on. Be friendly.

Also doesn't hurt to reach out to potential managers as they'll be able to provide more direct feedback.

I won't pretend to know how top tier ML companies hire for research positions, but for literally any other job, the cost of applying is trivial. Throw in some outbound to members on the team for a potential connection and it seems like it's worth the cost for a dream job?

Add to the fact that OpenAI is almost certainly going to be expanding wildly given that they have taken on a 10B investment, it seems like as good as a time as any.

The phrase usually used is a "leaky bucket". Sure a company can operate in a neutral space, but it's unlikely that overtime their revenue growth will match their customer churn.

At the same time, a lack of growth limits how much can be invested on staying relevant in terms of R&D which further separates them from competitors with a positive growth trajectory.

Its also incredibly hard to kickstart a growth engine once it has slowed down. The net result is a flywheel but in the opposite direction.

PE buys companies like this to slow that negative growth flywheel down attempting to stay neutral until the investment is paid off at which point profit can be made.

The long tail is massive attrition as well, because few people want to work at a sinking ship or rather a ship unwilling to invest internally.

Neutral isn't bad, but it's very hard to exist.

I feel as if there needs to be a modification to the phrase "Data Driven". Data, generally, will not help you predict the future. It is however, fantastic at finding bias, inefficient existing processes and tracking the results of your changes.

When applied blindly you end up with scenario you described. Product direction that says something isn't necessary because the question was never asked to begin with.

Science without the hypothesis.

"People seem to accept that it's all downhill from here"

Strange, I know plenty of people that do see an upside, including myself. It's a choice to believe things can and will get better.

Unless you have a crystal ball, we're both choosing to believe in an unknown future. So choosing to believe it will get worse is no different from believing it will get better. Although one is arguably better for mental health.

This is a really awesome application of GPT-3. You should definitely consider turning this into a service and adding the ability to generate Anki decks or flash cards from summary notes.

That coupled with the quizzing capability could be huge for students in medical school for example.