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mwarkentin

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cra.mr 2y ago

Secure Yo Self

mwarkentin
1pts0
sentry.engineering 2y ago

A $3M Dropdown

mwarkentin
1pts0
fsl.software 2y ago

Functional Source License

mwarkentin
2pts1
sentry.engineering 2y ago

Lost in the Haystack: Optimizing an Expensive ClickHouse Query

mwarkentin
1pts0
planetscale.com 3y ago

PlanetScale Boost

mwarkentin
210pts1
blog.sentry.io 3y ago

We Just Gave $260k to Open Source Maintainers

mwarkentin
150pts38
isovalent.com 4y ago

Tetragon – eBPF-Based Security Observability and Runtime Enforcement

mwarkentin
6pts0
blog.cloudflare.com 4y ago

Cloudflare: Pages Plugins

mwarkentin
7pts0
blog.cloudflare.com 4y ago

Cloudflare Magic Nat

mwarkentin
12pts1
aws.amazon.com 4y ago

AWS Step Functions Supports 200 Services to Enable Easier Workflow Automation

mwarkentin
3pts0
aws.amazon.com 5y ago

AWS Application Load Balancer Support for End-to-End HTTP/2 and gRPC

mwarkentin
2pts0
aws.amazon.com 7y ago

AWS Fargate Price Reduction – Up to 50%

mwarkentin
14pts1
www.hashicorp.com 7y ago

Terraform Collaboration for Everyone

mwarkentin
103pts52
aws.amazon.com 9y ago

AWS ELB adds support for Host-based routing on Application Load Balancer

mwarkentin
103pts21
aws.amazon.com 9y ago

Introducing Amazon ECS task placement policies

mwarkentin
12pts1
aws.amazon.com 9y ago

AWS Organizations: Centrally Manage Multiple AWS Accounts

mwarkentin
245pts53
aws.amazon.com 9y ago

Revolutionizing S3 Storage Management with 4 new features

mwarkentin
7pts0
medium.com 10y ago

AWS EFS – The Container Friendly File System

mwarkentin
3pts0
convox.com 10y ago

Challenges of Deployment to ECS

mwarkentin
80pts37
aws.amazon.com 10y ago

AWS CloudFormation Adds Support for Amazon VPC NAT Gateway and More

mwarkentin
3pts2
blog.acolyer.org 10y ago

Distributed Transactions with Consistency, Availability, and Performance

mwarkentin
2pts0
coreos.com 10y ago

Kubernetes on AWS

mwarkentin
3pts0
hashicorp.com 11y ago

Consul 0.5

mwarkentin
5pts1
shrinkray.io 11y ago

Show HN: One click image optimization for GitHub

mwarkentin
34pts52

Is it? If the spec is as detailed as the code would be? If you make a change to one part of the spec do you now have inconsistencies that the LLM is going to have to resolve in some way? Are we going to have a compiler, or type checker type tools for the spec to catch these errors sooner?

Uv solves this (with some new standards). ./script.py will now install the python version, create a venv, and install dependencies (very quickly) if they don’t exist already.

#!/usr/bin/env -S uv run --script # /// script # requires-python = ">=3.12" # dependencies = [ # "ffmpeg-normalize", # ] # ///

Specifically, the paper estimates that Llama 3.1 70B has memorized 42 percent of the first Harry Potter book well enough to reproduce 50-token excerpts at least half the time. (I’ll unpack how this was measured in the next section.)

Interestingly, Llama 1 65B, a similar-sized model released in February 2023, had memorized only 4.4 percent of Harry Potter and the Sorcerer's Stone. This suggests that despite the potential legal liability, Meta did not do much to prevent memorization as it trained Llama 3. At least for this book, the problem got much worse between Llama 1 and Llama 3.

Harry Potter and the Sorcerer's Stone was one of dozens of books tested by the researchers. They found that Llama 3.1 70B was far more likely to reproduce popular books—such as The Hobbit and George Orwell’s 1984—than obscure ones. And for most books, Llama 3.1 70B memorized more than any of the other models.

Back when we first tried out DMS there was a fun bug where booleans were interpreted as strings, so all false records were interpreted as “false” and translated to true on the target DB. It was fixed shortly after but was a good reminder to validate your data during a migration.

I leave my laptop plugged in 95% of the time so in practice this caps it at 80% (helpful).

I suspect quite a few people do that so it probably is net-good overall, but agree it doesn’t work well if you’re moving around a lot.

Pretty sure we had people spin up a Slack instance at our company while we were still on Hipchat officially. Slack was a big improvement over what we had before that IMO.

The Functional Source License (FSL) is a source-available non-compete license that converts to Apache 2.0 or MIT after one year.

We're using their "stable" release channel which means most of our clusters are currently on a version of 1.25. We only have a few deployments using PVCs so impact is pretty minimal anyways for us as far as I can tell.