aloknnikhil

Isn't that what Claude Desktop / Codex is for?

Something in their eyes. Looks very robotic / lifeless for me. And the sound-mixing is very off. Clearly feels like the voice was layered on top of whatever sound is in the background and not blended.

Isn't this specifically for immigrant Visas? The work and student Visas (H1B / F1) are non-immigrant Visas.

It's OK because I'm not prompting the person who tweeted for my usecases.

As long as agents are fuzzy (which they will continue to be with the Transformers architecture), the need to validate will continue to exist. I cannot imagine merging code without at least 1 human review.

The typical issues I have seen with LLMs / Agents tend to be reactive in their fixes. So they tend to "patch" the symptom more than "fix" the root cause. Interested to see how you solve this problem.

You could have stopped at

> The folks at Wall Street do not understand

https://status.pki.goog/ is not reporting any issues

Since when did having isolated environments for dev / test and prod become a bad pattern?

It's not that entry-level jobs / interns are irrelevant. It's more that entry-level has been redefined and it requires significant uplevelling in terms of skills necessary to do a job at that level. That's not necessarily a bad thing. As others have said here, I would be more willing to hand-off more complex tasks to interns / junior engineers because my expectation is they leverage AI to tackle it faster and learn in the process.

In the list of things that DiceDB is at the top, you should add "an in-memory database". Pretty critical thing to leave out right at the top.

At first glance, that just seems like a bunch of libraries linked together to form a binary. That is not open-source. I completely agree with you that there is just not enough clarity out there. For my education, following up with my earlier example, can I remove the layers that have references to all chapters / laws in the constitution except for the ones meant for real-estate? How would I do that with the approaches you mentioned here?

Fundamentally, if I have to "reverse-engineer" something, then it's not open-source.

I completely disagree with you. The fundamental problem with your concept of open source is it goes against what open source really is. The ability for you to completely change what a piece of software can do. IMO, even with LLMs, models are "executables" and weights are "configuration". Yes, of course you can tune the weights by changing the values, but that's the most I can do. Can I actually add "features" to the model? Perhaps you "open-sourced" an LLM model trained on the United States Constitution. Can I change the model to then be a specialist in real estate law? Not with weights. I need it to learn case histories to extend its "feature-set". Without data and the mechanism to reproduce the model, how is this "open-source"?

I couldn't bother myself to read the whole article. Got GPT-4 to summarize the main points. Not as much insight as I thought I would get going in.

1. *Testing in Staging vs. Production*: - Most engineers prefer testing in staging due to a sense of control. - There's a misconception that it's an either/or situation between staging and production testing. In reality, both are necessary.

2. *Importance of Production Testing*: - Staging environments can’t replicate all possible real-world scenarios. - Production testing is essential to identify complex, real-world issues missed in staging.

3. *Uber's Approach to Testing*: - Uber tests its payment systems in production. - They have developed tools (Cerberus and Deputy) to facilitate transparent interaction with real systems and gather responses effectively.

4. *Every Deployment as an Experiment*: - Every deployment is treated as a hypothesis to be validated against business metrics. - Metrics and monitoring are crucial to determine the success of deployment.

5. *First Rollout Region*: - Uber chooses a specific first rollout region to minimize risk and impact. - Initial rollouts are conducted in regions that are small but significant for practical monitoring.

6. *Canary Deployments*: - Uber conducts canary deployments to a subset of users to detect and mitigate potential issues early. - This approach helps in identifying and fixing issues with minimal impact.

7. *Examples of Issues Discovered Early*: - Uber detected significant issues with GooglePay during its cautious rollout in Portugal, which would have been difficult to identify in a staging environment alone.

8. *Philosophy on Software Quality*: - True robustness and resiliency come from real-world usage and the continuous fixing of encountered issues. - Only production can provide the real stakes and conditions needed for thorough validation.

9. *Author and Newsletter*: - Alvaro Duran, author of “The Payments Engineer Playbook”, emphasizes the importance of sharing and learning from real-world experiences in payments systems. - Encourages readers to engage with the content and share it with colleagues for broader impact.

We can help. Please reach out at: founders@omnistrate.com

https://omnistrate.com is our product

It's down again today

I thought the hottest place on the planet was the Lut Desert. https://whc.unesco.org/en/list/1505/

Very cool! Thanks for sharing mydumper

Fair question. My motivation was mainly to understand if there was something specific that drove that choice. Better question for you maybe: For any new project, which one would you choose and why?

But with all the background OS activity, will this chip ever sustain turbo to 6.2 for noticeable periods? Pure benchmarking win imo.

The USGS also has a live list of these: https://earthquake.usgs.gov/earthquakes/map/?extent=-29.5352...

This is the original author / article - https://medium.com/@mbianchidev/2023-devops-is-terrible-ec88...

I personally wouldn’t use this. I don’t know if your point about information being “locked” in written form is even being addressed here. There are so many audio books out there but I personally only really enjoy audio books delivered by the author themselves or someone who can actually capture the nuances in the text. So I think you’ll just end up moving this information from being locked in prose to being locked in sound, unless you can accurately capture the tone, nuances and the context around the whole text.

It seems to me, this is trying really hard to shoehorn Terraform into managing at scale. For multi-account, multi-org, multi-region, multi-cloud deployments is Terraform really supposed to be the state of the art? How do you even get visibility into the various deployment workflows?

100% agree on Macs. Personally I have never had to play IT support when my parents had a Mac. Now with Windows, it’s basically whack-a-mole. Some days the camera stops working and some days WhatsApp doesn’t startup. And they are not power users by any means. So they don’t muck around with their setup either.

I agree with OP to some extent here. A phone is critical “infrastructure”. A high bar is basically a given at this point if you’re trying to pitch someone to use your product. Or it’ll remain an experiment and never go mainstream. Apple prioritized experience over features early. Might be something these projects should consider if they are serious about adoption.

The range of SKUs is a problem AMD and Intel (particularly) brought upon themselves. It’s not easy for anyone trying to buy a laptop to truly estimate the power of the processor in there without first going through a review video. Id take the narrow range over that mess.

Try setting up a multi-people intra-day rotation (like you would for follow the sun models across time zones). It’ll make you cry.

Not exactly. I’m talking about this workflow. https://www.datadoghq.com/blog/mute-datadog-alerts-planned-d...

You use specific tags to mute a monitor by adding a scope. This does not scale well with the UI.

Second this. It’s pretty terrible when you use it at scale particularly. Eg: If you have a monitor with some scopes that you use for exceptions, it gets gnarly in the UI when you have like 50 of them. And the inconsistent UI. Notebooks are cool but so half-assed in their features that it kind becomes useless for anything other than basic charts. Oh and feature discovery is a pain too. It took me so long to figure out a way to add a second Y-axis to my charts.