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Some things are worth waiting for -- like Apple's latest smartphone, which is expected to debut in the coming months.

With millions of businesses now using AWS, it’s vital for IT security teams to have oversight, but the company offers a plethora of tools to make managing it easier. Find out how cyber pros are using them in the wild

Article URL: https://www.yalelawjournal.org/note/amazons-antitrust-paradox

Comments URL: https://news.ycombinator.com/item?id=44135234

Points: 3

# Comments: 0

Article URL: https://www.nytimes.com/2025/05/30/opinion/elon-musk-doge-usaid.html

Comments URL: https://news.ycombinator.com/item?id=44135229

Points: 2

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Article URL: https://emacs-config-generator.fly.dev/

Comments URL: https://news.ycombinator.com/item?id=44135227

Points: 2

# Comments: 0

Inspired by a similar thread on Lobste.rs https://lobste.rs/s/ytefme/when_was_last_time_you_broke_production

Comments URL: https://news.ycombinator.com/item?id=44135217

Points: 1

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Article URL: https://arxiv.org/abs/2505.23725

Comments URL: https://news.ycombinator.com/item?id=44135215

Points: 1

# Comments: 0

I’ve been working on a practical guide for time series forecasting in Python, called Mastering Modern Time Series Forecasting. The goal is to bridge the gap between theory and implementation. It covers both classical statistical models (ARIMA, SARIMA, Prophet) and modern machine/deep learning approaches (N-BEATS, Transformers, Temporal Fusion Transformer). The code examples use Python libraries like statsmodels, scikit-learn, PyTorch, and Darts, and the book focuses on real-world workflows: messy data, feature engineering, model selection, and evaluation. I wrote this after struggling to find forecasting resources that were both practical and up-to-date — especially for applied ML practitioners. You can find the book here: https://valeman.gumroad.com/l/MasteringModernTimeSeriesForec... https://leanpub.com/mastering_modern_time_series_forecasting Happy to answer questions or hear feedback from anyone working with time series forecasting or Python ML tools.

Comments URL: https://news.ycombinator.com/item?id=44135196

Points: 1

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Article URL: https://meta.stackoverflow.com/questions/433999/an-experiment-on-pairing-askers-with-experts-in-the-community

Comments URL: https://news.ycombinator.com/item?id=44135182

Points: 1

# Comments: 0

The Switch 2 needs microSD Express cards if you want to expand its memory, so you'll want to get your hands on these to get things sorted.

Article URL: https://chat.stackoverflow.com

Comments URL: https://news.ycombinator.com/item?id=44135137

Points: 1

# Comments: 1

Article URL: https://restofworld.org/2025/brazil-dwallet-user-data-pilot/

Comments URL: https://news.ycombinator.com/item?id=44135136

Points: 1

# Comments: 0

From their status page:

Github is experiencing some service issues:

Disruption with some GitHub servicesSubscribe Investigating - We are currently investigating this issue. May 30, 2025 - 11:20 UTC

I found out when I tried to push my changes and it just got back an error:

$ git push Enumerating objects: 59, done. Counting objects: 100% (59/59), done. Delta compression using up to 12 threads Compressing objects: 100% (43/43), done. send-pack: unexpected disconnect while reading sideband packet Writing objects: 100% (49/49), 6.84 MiB | 7.34 MiB/s, done. Total 49 (delta 16), reused 0 (delta 0), pack-reused 0 fatal: the remote end hung up unexpectedly Everything up-to-date

I've been debugging it for the last 15 minutes and now I see they updated their status, so might be related?

Comments URL: https://news.ycombinator.com/item?id=44135124

Points: 1

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Article URL: https://docs.google.com/document/d/1Dm9UrhgaRMu_DjOz5KluO7ibOsPaUVQgwnC5-ccw9_g/edit?tab=t.0

Comments URL: https://news.ycombinator.com/item?id=44135109

Points: 2

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Article URL: https://www.newgrounds.com/portal/view/982049

Comments URL: https://news.ycombinator.com/item?id=44135076

Points: 1

# Comments: 1

Article URL: https://unsloth.ai/blog/deepseek-r1-0528

Comments URL: https://news.ycombinator.com/item?id=44135071

Points: 1

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Article URL: https://www.stitz-zeager.com/

Comments URL: https://news.ycombinator.com/item?id=44135054

Points: 1

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Article URL: https://github.com/agrahn/Android-Password-Store

Comments URL: https://news.ycombinator.com/item?id=44135028

Points: 1

# Comments: 1

A Theory of Everything for Software

After six years in software development, I began noticing a pattern: most problems were just variations of ones I’d already solved. That raised a deeper question: should these problems even exist?

At its core, software is a flowchart—compute nodes for logic, decision nodes for branching. It boils down to bitwise operations and system calls. But over time, we’ve layered on protocols, libraries, tools, and frameworks—all in the name of abstraction. To what end?

That question sent me down a path to uncover the core principles of software—what I call a “theory of everything” for building robust, scalable, maintainable systems. Along the way, I identified six persistent pain points:

Readability – Enables faster learning and iteration

Correctness – Software should do what it claims

Scalability – Covers monitoring, alerting, and resource limits

Distributivity / Dark Matter – Includes client libraries and emulation

Reproducibility – Debugging should be deterministic

Security – Internal soundness and external threat modeling

To ground this, let’s simulate the journey of building a SaaS product.

The Journey Begins

Early on, speed was my top priority. JavaScript and Python allowed quick iteration but sacrificed correctness. Go and Rust provided safety but slowed feedback. Choosing a language meant picking between speed and reliability.

I launched an MVP and brought on two engineers. Initially, things went well. But soon, bug reports piled up. While debugging a small codebase was easy, the growing surface area made issues harder to trace. We realized correctness had to be built-in, so we enforced test coverage.

Scaling the Product

As usage grew globally, we had to go distributed. This meant thinking beyond individual services—we needed system-wide stability.

We shifted from a reactive approach to a proactive one: we added monitoring, alerting, and centralized logging to catch issues early. Backward compatibility also became critical—new deployments couldn’t break old clients.

Debugging and Security

To reduce debugging time, we captured client-side data and added tracing. At the same time, we hardened the system against DDoS attacks and vulnerabilities.

But with every new tool, library, or third-party service, we lost control and added complexity. Security remained fragile—often treated as an afterthought.

A Different Vision All of this led to a new idea: what if a compiler could handle this out-of-the-box?

Languages like C, Java, Go, and Rust are great for writing programs. But software is more than code. It needs built-in correctness, observability, scalability, debuggability, and security. Today, these are bolted on manually—through CI, test suites, dashboards, infra tools, and cloud APIs.

What if they were built in from day one?

I’m working on a new kind of compiler—not just to generate binaries, but to help you build complete software systems. The goal: eliminate the need for manual integrations and third-party tools.

No config. No boilerplate.

You won’t need a database, a stream processor, or a deployment manager. The compiler is the platform. Run the binary, expose a port, and you’re live.

Software, as it should be—correct by construction, observable by design, secure by default.

Comments URL: https://news.ycombinator.com/item?id=44135004

Points: 1

# Comments: 0

Article URL: https://mssv.net/2025/05/30/beyond-agency/

Comments URL: https://news.ycombinator.com/item?id=44134980

Points: 2

# Comments: 0