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GrapheneOS is a privacy and security-focused Android operating system that aims to provide a more secure and private alternative to mainstream Android versions. It emphasizes strong security measures, app sandboxing, and user privacy, making it a compelling choice for those concerned about digital privacy and security.

The article discusses Stephen Colbert's complaint to the FCC about a Texas state representative's actions during a segment on Colbert's show. Colbert claims the representative's behavior violated FCC regulations, and the article examines the potential implications of Colbert's complaint.

The article discusses Gabriel Cabrera's journey as a software engineer, highlighting his passion for building and designing products that solve real-world problems. It covers his professional experiences, projects, and insights into the tech industry.

The article discusses the challenges facing American pension funds, which have struggled to meet their investment targets for years. It examines how these funds have increasingly turned to alternative investments like private equity, but argues that simpler index funds like those offered by Vanguard have outperformed many pension funds' more complex investment strategies.

WD and Seagate, two leading hard drive manufacturers, have confirmed that their hard drive production for 2026 is already sold out, highlighting the ongoing global demand for storage solutions.

The article discusses the process of building a tunnel, including the various challenges and considerations involved, such as geological surveys, regulatory approvals, construction methods, and project management. It provides a comprehensive overview of the key steps and factors that must be taken into account when undertaking a tunnel-building project.

The article discusses the phenomenon of individual optimism, where people maintain a positive outlook despite societal and global challenges. It explores how this individual optimism can coexist with broader pessimism about the state of the world and the future.

A high-performance internet radio streaming server written in Python with Cython optimizations.

Donation.Watch is a platform that provides transparency and insights into charitable donations, allowing users to explore how organizations are using their funds and make informed decisions about their contributions.

The article explores how Japan's economic and social landscape reflects the characteristics of late-stage capitalism, including declining birth rates, wealth inequality, and corporate dominance. It suggests that Japan's situation serves as a cautionary tale for other nations facing similar challenges under late-stage capitalism.

The article discusses concerns about job loss due to automation and technological advancements, considering the potential impact on different industries and the need for societal adaptation to mitigate the challenges posed by these changes.

This article discusses the importance of monitoring credential and data breach information to protect against cybersecurity threats. It highlights the need for organizations to stay informed about data breaches and compromised credentials to mitigate risks and prevent unauthorized access to sensitive information.

Hey HN, I am Dimittri and we’re building Sonarly (https://sonarly.com), an AI engineer for production. It connects to your observability tools like Sentry, Datadog, or user feedback channels, triages issues, and fixes them to cut your resolution time. Here's a demo: https://www.youtube.com/watch?v=rr3VHv0eRdw.

Sonarly is really about removing the noise from production alerts by grouping duplicates and returning a root cause analysis to save time to on-call engineers and literally cut your MTTR.

Before starting this company, my co-founder and I had a B2C app in edtech and had, some days, thousands of users using the app. We pushed several times a day, relying on user feedback. Then we set up Sentry, it was catching a lot of bugs, but we had up to 50 alerts a day. With 2 people it's a lot. We took a lot of time filtering the noise to find the real signal so we knew which bug to focus on.

At the same time, we saw how important it is to fix a bug fast when it hits users. A bug means in the worst case a churn and at best a frustrated user. And there are always bugs in production, due to code errors, database mismatches, infrastructure overload, and many issues are linked to a specific user behavior. You can't catch all these beforehand, even with E2E tests or AI code reviews (which catch a lot of bugs but obviously not all, plus it takes time to run at each deployment). This is even more true with vibe-coding (or agentic engineering).

We started Sonarly with this idea. More software than ever is being built and users should have the best experience possible on every product. The main idea of Sonarly is to reduce the MTTR (Mean Time To Repair).

We started by recreating a Sentry-like tool but without the noise, using only text and session replays as the interface. We built our own frontend tracker (based on open-source rrweb) and used the backend Sentry SDK (open source as well). Companies could just add another tracker in the frontend and add a DSN in their Sentry config to send data to us in addition to Sentry.

We wanted to build an interface where you don't need to check logs, dashboards, traces, metrics, and code, as the agent would do it for you with plain English to explain the "what," "why," and "how do I fix it."

We quickly realized companies don't want to add a new tracker or change their monitoring stack, as these platforms do the job they're supposed to do. So we decided to build above them. Now we connect to tools like Sentry, Datadog, Slack user feedback channels, and other integrations.

Claude Code is so good at writing code, but handling runtime issues requires more than just raw coding ability. It demands deep runtime context, immediate reactivity, and intelligent triage, you can’t simply pipe every alert directly into an agent. That’s why our first step is converting noise into signal. We group duplicates and filter false positives to isolate clear issues. Once we have a confirmed signal, we trigger Claude Code with the exact context it needs, like the specific Sentry issue and relevant logs fetched via MCP (mostly using grep on Datadog/Grafana). However, things get exponentially harder with multi-repo and multi-service architectures.

So we built an internal map of the production system that is basically a .md file updated dynamically. It shows every link between different services, logs, and metrics so that Claude Code can understand the issue faster.

One of our users using Sentry was receiving ~180 alerts/day. Here is what their workflow looked like:

- Receive the alert

- 1) Defocus from their current task or wake up, or 2) don't look at the alert at all (most of the time)

- Go check dashboards to find the root cause (if infra type) or read the stack trace, events, etc.

- Try to figure out if it was a false positive or a real problem (or a known problem already in the fixes pipeline)

- Then fix by giving Claude Code the correct context

We started by cutting the noise and went from 180/day to 50/day (by grouping issues) and giving a severity based on the impact on the user/infra. This brings it down to 5 issues to focus on in the current day. Triage happens in 3 steps: deduplicating before triggering a coding agent, gathering the root cause for each alert, and re-grouping by RCA.

We launched self-serve (https://sonarly.com) and we would love to have feedback from engineers. Especially curious about your current workflows when you receive an alert from any of these channels like Sentry (error tracking), Datadog (APM), or user feedback. How do you assign who should fix it? Where do you take your context from to fix the issue? Do you have any automated workflow to fix every bug, and do you have anything you use currently to filter the noise from alerts?

We have a large free tier as we mainly want feedback. You can self-serve under 2 min. I'll be in the thread with my co-founder to answer your questions, give more technical details, and take your feedback: positive, negative, brutal, everything's constructive!

The article explores the complex and controversial connections between prominent tech figures like Peter Thiel, Elon Musk, and the late Jeffrey Epstein, examining the ways in which powerful individuals used their influence and wealth to navigate social and political landscapes.

The article discusses emails between Jeffrey Epstein and prominent figures such as Kathy Ruemmler and Elizabeth Warren, revealing their interactions and the potential implications for class-based power dynamics.

Hey HN, I am Alex. I am open sourcing Data Studio, a lightweight data exploration IDE in your browser that runs locally.

Try it: https://local.dataspren.com (no account needed, runs locally)

More information: https://github.com/dataspren-analytics/data-studio

I love working with data (Postgres, SQL, DuckDB, DBT, Iceberg, ...). I always wanted a data exploration tool that runs in my browser and just works. Without any infra or privacy concerns (DuckDB UI came quite close).

Features:

  - Data Notebooks
    - SQL cells work like DBT models (they materialize to views)
    - Use Python functions inside of SQL queries
    - Use DB views directly in Python as dataframes
  - Transform Excel files with SQL
  - You can open .parquet, .csv, .xlsx, .json files nicely formatted

Happy to hear about your feedback <3

The article discusses GitShow, an open-source web application that provides a visual representation of a GitHub user's activity and contributions. It highlights the key features of GitShow, including its ability to display a user's commit history, repositories, and activity trends over time.

The article discusses the urunC agent, a lightweight and efficient process manager that can be used to run and manage services on Linux systems. It highlights the key features of urunC, such as its simplicity, resource-efficiency, and support for advanced capabilities like service dependencies and automatic restarts.

This article discusses the current state of the U.S. economy, including concerns about inflation, the Federal Reserve's interest rate hikes, and the potential for a recession. It explores the impact of these economic factors on consumers and businesses, and examines the challenges facing policymakers as they navigate the uncertain economic landscape.

The article discusses a significant decline in Tesla sales across several European countries, including the UK, Norway, the Netherlands, Germany, Spain, Sweden, Denmark, and Portugal. It suggests that the drop in sales may be due to increased competition from other electric vehicle manufacturers in the region.