Top stories

Apple is in a fierce battle with competitors to secure chip production capacity from TSMC, the world's largest semiconductor manufacturer. The article explores Apple's efforts to maintain its technological edge by ensuring a steady supply of advanced chips for its products.

The article discusses security vulnerabilities affecting the Svelte ecosystem, including CVEs (Common Vulnerabilities and Exposures) that have been identified and addressed in various Svelte-related packages. It provides details on the nature of the vulnerabilities, their impact, and the steps taken by the Svelte community to mitigate these issues.

https://github.com/internetarchive/heritrix3

JuiceFS is an open-source distributed file system that provides a POSIX-compatible interface and is designed for high-performance cloud storage. It supports multiple cloud storage backends, including Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage, and is optimized for cloud-native applications.

Countless voiceless people sit alone every day and have no one to talk to, people of all ages, who don't feel that they can join any local groups. So they sit on social media all day when they're not at work or school. How can we solve this?

The article discusses the development of Claude, an AI assistant created by Anthropic. It explores the key features and capabilities of Claude, including its ability to engage in open-ended conversations, provide detailed and helpful responses, and maintain a consistent personality across interactions.

Aviator, a YC-backed company, is seeking a full-stack engineer to join their team. The role involves building user-facing products and backend systems to support the company's operations.

Shellbox is an online sandbox environment that allows users to run and test code snippets in various programming languages, including Bash, Python, and JavaScript, in a secure and isolated environment.

It turns out the inverse of the Hessian of a deep net is easy to apply to a vector. Doing this naively takes cubically many operations in the number of layers (so impractical), but it's possible to do this in time linear in the number of layers (so very practical)!

This is possible because the Hessian of a deep net has a matrix polynomial structure that factorizes nicely. The Hessian-inverse-product algorithm that takes advantage of this is similar to running backprop on a dual version of the deep net. It echoes an old idea of Pearlmutter's for computing Hessian-vector products.

Maybe this idea is useful as a preconditioner for stochastic gradient descent?

hi hn,

i built openwork, an open-source, local-first system inspired by claude cowork.

it’s a native desktop app that runs on top of opencode (opencode.ai). it’s basically an alternative gui for opencode, which (at least until now) has been more focused on technical folks.

the original seed for openwork was simple: i have a home server, and i wanted my wife and i to be able to run privileged workflows. things like controlling home assistant, or deploying custom web apps (e.g. our customs recipe app recipes.benjaminshafii.com), legal torrents, without living in a terminal.

our initial setup was running the opencode web server directly and sharing credentials to it. that worked, but i found the web ui unreliable and very unfriendly for non-technical users.

the goal with openwork is to bring the kind of workflows i’m used to running in the cli into a gui, while keeping a very deep extensibility mindset. ideally this grows into something closer to an obsidian-style ecosystem, but for agentic work.

some core principles i had in mind:

- open by design: no black boxes, no hosted lock-in. everything runs locally or on your own servers. (models don’t run locally yet, but both opencode and openwork are built with that future in mind.) - hyper extensible: skills are installable modules via a skill/package manager, using the native opencode plugin ecosystem. - non-technical by default: plans, progress, permissions, and artifacts are surfaced in the ui, not buried in logs.

you can already try it: - there’s an unsigned dmg - or you can clone the repo, install deps, and if you already have opencode running it should work right away

it’s very alpha, lots of rough edges. i’d love feedback on what feels the roughest or most confusing.

happy to answer questions.

Background: I manage an ecommerce website. Recent bot traffic is up. Most traffic can be traced to one or two IP addresses with hundreds of requests per day. These ip addresses don't have DNS records for reverse lookup, and when I map the requests in cloudflare, one address shows up as requesting from different data centers all over the US. What is going on here? Source IP example 173 . 245 . 58 . 0

Chicago, United States (ORD)

340 requests

San Jose, United States (SJC)

330 requests

Los Angeles, United States (LAX)

310 requests

Atlanta, United States (ATL)

310 requests

Dallas-Fort Worth, United States (DFW)

290 requests

Newark, United States (EWR)

280 requests

Washington, United States (IAD)

230 requests

Miami, United States (MIA)

210 requests

Boston, United States (BOS)

140 requests

Singapore, Singapore (SIN)

130 requests

Thanks for ideas.

This photo essay explores China's rapid expansion of renewable energy, showcasing its impressive progress in harnessing solar, wind, and hydropower to reduce its reliance on fossil fuels and curb greenhouse gas emissions.

Archaeologists have discovered a massive 15th-century cargo ship, the largest of its kind ever found, buried in a medieval harbor in the Netherlands. The well-preserved shipwreck provides a rare glimpse into the maritime trade and transportation of the medieval period.

The UK government held a record-breaking offshore wind auction, securing a total of 8 gigawatts (GW) of new offshore wind capacity at historically low prices. This auction demonstrates the growing competitiveness of offshore wind as a renewable energy source and the UK's commitment to transitioning to a clean energy future.

The article discusses the design and implementation of the Fly.io platform, a global application delivery network that aims to make it easier to deploy and scale web applications. It covers the technical challenges Fly.io faced and the solutions they developed to provide a seamless user experience.

Hi HN! In the past few months my team and I have been working on Tusk Drift, a system that records real API traffic from your service, then replays those requests as deterministic tests. Outbound I/O (databases, HTTP calls, etc.) gets automatically mocked using the recorded data.

Problem we're trying to solve: Writing API tests is tedious, and hand-written mocks drift from reality. We wanted tests that stay realistic because they come from real traffic.

versus mocking libraries: Tools like VCR/Nock intercept HTTP within your tests. Tusk Drift records full request/response traces externally (HTTP, DB, Redis, etc.) and replays them against your running service, no test code or fixtures to write/maintain.

How it works:

1. Add a lightweight SDK (we currently support Python and Node.js)

2. Record traffic in any environment.

3. Run `tusk run`, the CLI sandboxes your service and serves mocks via Unix socket

We run this in CI on every PR. Also been using it as a test harness for AI coding agents, they can make changes, run `tusk run`, and get immediate feedback without needing live dependencies.

Source: https://github.com/Use-Tusk/tusk-drift-cli

Demo: https://github.com/Use-Tusk/drift-node-demo

Happy to answer questions!

The article discusses the CodeBreach vulnerability found in AWS CodeBuild, which could allow attackers to execute arbitrary code and gain unauthorized access to sensitive data. The researchers at Wiz discovered and reported the vulnerability, which has since been patched by AWS.

The article discusses the use of AI in journalism, highlighting how it can help journalists more efficiently process and analyze large amounts of data, and generate content to complement human-written articles. It explores the potential benefits and challenges of incorporating AI into journalistic workflows.

The article describes the author's journey of self-directed learning, highlighting the importance of curiosity, persistence, and a willingness to experiment in order to gain mastery over various subjects and skills.

The article provides a first-hand account of the author's experience using Claude, an AI assistant created by Anthropic. It discusses the capabilities and limitations of Claude, focusing on its natural language processing and ability to assist with a variety of tasks.

Hi HN,

My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag...

Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site.

Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we handle the rendering and return clean, structured data for the LLM.

How it works under the hood:

- Escalation Logic: We don't spin up a full browser instance for every request (which is slow and expensive). We attempt lightweight fetches first, escalating to full browser automation only when the site requires JS execution/hydration.

- Token Optimization: Raw HTML is noisy and burns context window tokens. We process the DOM to strip non-content elements and return a markdown-friendly structure that is optimized for LLM consumption.

- Infrastructure Stability: Scaling headless browsers is notoriously hard (zombie processes, memory leaks, crashing instances). We manage the fleet lifecycle and orchestration so you can run thousands of concurrent requests without maintaining the underlying grid.

On Ethics: Since we are backed by Mozilla, we are strict about how this interacts with the open web.

- We respect robots.txt rules.

- We identify our User Agent.

- We do not use requests/content to train models.

- Data is ephemeral and discarded after the task.

The linked post goes into more detail on the infrastructure and why we think browsing needs to be a distinct layer in the AI stack.

This is obviously a very new space and we're all learning together. There are plenty of known unknowns (and likely even more unknown unknowns) when it comes to agentic browsing, so we’d genuinely appreciate your feedback, questions, and tips.

Happy to answer questions about the stack, our architecture, or the challenges of building browser infrastructure.

During a recent subway outage in San Francisco I decided to build a webapp in the spirit of "Do I Need an Umbrella," basically to answer the question "Should I take the subway or a bus?"

In the interest of learning new tools I decided to vibe code it as much as possible.

First, I had Claude Code write a background script to download images of the real-time circuit diagram of the subway, which are publicly available here: http://sfmunicentral.com/

Next I had it build an image labeler tool in tkinter, which turned out to need a lot of manual tweaking before I could even get to the labeling. Seemed like the right tool for the job, but it would have saved time if I'd built it from scratch myself.

The most interesting part was turning the labeled image data into predictions with pytorch. Claude wrote the initial script fairly quickly, but as these things go it required manual tweaking and second guessing myself on the images flagged as outliers. I'll admit I got embarrassingly far along before realizing that Claude hadn't enabled pytorch's GPU support; a real facepalm moment on my part.

For those curious, brave, or crazy enough to dive in the source code is available here under an MIT license: https://github.com/MrEricSir/munimet.ro

The article discusses the importance of data as a strategic asset and key competitive advantage in the modern business landscape. It emphasizes that companies should focus on collecting, analyzing, and leveraging data to drive innovation and stay ahead of the competition.

The article discusses Palantir, a data analytics company whose software is used by U.S. Immigration and Customs Enforcement (ICE) to identify and target immigrant communities for deportation raids. It examines the ethical concerns around Palantir's technology being utilized by law enforcement agencies for surveillance and enforcement purposes.

GoScript is an open-source scripting language that enables developers to write powerful, dynamic scripts for automating tasks and building applications. It provides a simple and intuitive syntax, allowing for rapid development and integration with various other technologies.