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Learn how Encore works and how it helps backend developers build cloud based backend applications with a flow state developer experience.

If you are just getting started with Blueprints, this provides a high-level overview of what they are and what they can do.

An archive of the best articles from Marc Andreessen’s now defunct blog

The inaugural Wasm I/O conference took place last month, bringing together industry leaders and experts in WebAssembly to share their insights on the future of the technology. As Flaki, one of our developer advocates, wrote to the rest of the team: ...

Announcing Buildless, a new remote build caching system. Cacheflow is a blog from the team behind Elide and Buildless.

As a follow-up to my post on SaaS isolation patterns, I'm looking at different application-level isolation patterns – containers. There's a whole spectrum of choices, each with different strengths and weaknesses. Virtualize the Hardware – Virtual Machines. The first and oldest class of containers is the virtual machine. An emulator called a hypervisor emulates physical hardware – everything from CPUs to Floppy drives. There are two main classes of hypervisors – ones that work directly on the h

"We Sign Tomorrow?" is the story of Paddle acquiring Profitwell. Two conference friends. The CEOs of Paddle and ProfitWell find themselves in a position where one can acquire the other’s company to work together to change the shape of the market. But they must go to hell and back to sign the deal before a competitor jumps on the opportunity to Acquire ProfitWell. COMING SOON Save your seat for the online premiere wesigntomorrow.com

The recent and rapid advance of AI has rightfully giving many in software real doubts about the future of their profession. I'd probably still wager that the fears are overstated – that we also got prematurely euphoric about the imminent prospects of self-driving cars – and that AI generating code is different from it evolving existing...

Recent images of locals looking on as their former town is submerged

Let’s say you wanted to create a fine-tuned LLM that (1) fixes code and (2) optimizes code. But you aren’t GitHub and don’t have access to a large amount of training data. You could perform some model arbitrage from a larger model but also find data in more interesting places. Fortunately, we have tools specially made for (1) finding runtime errors and (2) optimizing code. Compilers.

The Jevons Paradox is when an increase in the efficiency of a resource leads to an increase in overall consumption. This happens when the elasticity of demand is sufficiently high. The classic example is that as we learned how to convert coal into energy more efficiently, we consumed more coal overall. Consumers with fuel-efficient cars tend to travel more and therefore consume more fuel.

I've been writing custom software for a long time and one of the things that annoys me most is when a client adopts the position that there is a silver bullet which will reduce or remove the inherent complexity of this task. This happens more often than you'd think and guess what? They are almost always wrong.

Learn how to think about finance, economics, and corporate strategy.

Serverless Cloud spins off as Ampt while Serverless Inc. stays committed to the observability mission.

AI is so hot right now. We’ve had 100+ investors reach out. We don't have time to meet with everyone so instead we're sharing our investor presentation with the world: https://t.co/QUDHpuDK2mMore than anything we hope this transparency builds customer trust. pic.twitter.com/DtkipzBz7E— Dan Siroker (@dsiroker) April 14, 2023

F. Scott Fitzgerald said intelligence is “the ability to hold two opposing ideas in your head at the same time…

Ernest Shackleton’s ship, the Endurance, became stuck in Antarctic ice.

We sometimes face issues with the Twitter API. Using your own Twitter API key with Tweet Hunter is the best way to make sure you increase the limits of your account and bypass some of the problems Twitter can cause.

Supabase design and architecture

Stackr Labs’ mission is ambitious yet simple: Empower every developer to build scalable web3 apps.

For those of you who use Apple computers with Android phones

LLMs are easier than ever to get started with. You don’t need cleaned data. You don’t need a data pipeline. The payload is often just plain text. Application developers are empowered to get initial results without help. But foundational models won’t be enough. (see:

Python has long had a monopoly on data workflows — everything from data analysis to data science to machine learning. Anything that can't be done in SQL is done in Python. But Python won't be the language for LLMs. Why did Python become the language for data workflows? * Cross-platform. Data analysts are much more likely to work on Windows. Python was one of the first languages to have a simple cross-platform toolchain. * Dynamic Typing. Data science is often exploratory. As a result, code c

Deno Deploy is one of the fastest ways to deploy an app. How does it work?