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https://www.anthropic.com/claude-sonnet-4-6-system-card [pdf]

https://x.com/claudeai/status/2023817132581208353 [video]

The article discusses the gofix tool, which is a command-line tool that can automatically update Go code to use the latest language features and standard library changes. It provides details on how to use gofix, its capabilities, and the types of changes it can make to improve the maintainability and modernization of Go codebases.

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 the implementation of async/await functionality on the GPU, exploring the challenges and potential benefits of this approach. It highlights how this technology can improve performance and efficiency in GPU-accelerated applications.

Gentoo has announced that they are transitioning their source code hosting from GitHub to Codeberg, a community-driven, non-profit platform. This move aims to provide a more privacy-focused and ethically aligned hosting solution for the Gentoo project.

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 explores the challenges of teaching computer science students to design hardware, highlighting the need for integrated coursework, hands-on experience, and bridging the gap between software and hardware development.

We're killing Bloomberg chat (their crown jewel). We're building the fastest growing network of buyside investors. We facilitate voice conversations between investors on demand and are creating a library of these transcripts for our users to read. We work with a lot of interesting folks and some of the most well known funds in the world.

We are onboarding 1-2 funds a day and recently took in more money at a mark up from existing and new investors.

Join us- https://www.ycombinator.com/companies/trata/jobs/FTbjpSQ-fou...

The article provides a comprehensive weather forecast for the city of SFO, including detailed information on temperature, precipitation, wind, and other meteorological conditions for the next several days.

The book page links to a blog post that explains how I got about it (and has a link to sample content), but the TL&DR is that I could not find a lot of books that were on "our" history _and_ were larded with technical details. So I set about writing one, and some five years later I'm happy to share the result. I think it's one of the few "computer history" books that has tons of code, but correct me if I'm wrong (I wrote this both to tell a story and to learn :-)).

My favorite languages are Smalltalk and Lisp, but as an Emacs user, I've been using the latter for much longer and for my current projects, Common Lisp is a better fit, so I call myself "a Lisp-er" these days. If people like what I did, I do have plans to write some more (but probably only after I retire, writing next to a full-time job is heard). Maybe on Smalltalk, maybe on computer networks - two topics close to my heart.

And a shout-out to Dick Gabriel, he contributed some great personal memories about the man who started it all, John McCarthy.

I've been teaching LLMs to play Magic: The Gathering recently, via MCP tools hooked up to the open-source XMage codebase. It's still pretty buggy and I think there's significant room for existing models to get better at it via tooling improvements, but it pretty much works today. The ratings for expensive frontier models are artificially low right now because I've been focusing on cheaper models until I work out the bugs, so they don't have a lot of games in the system.

Related: https://cosmo.tardis.ac/files/2026-02-12-az-rl-and-spsa.html

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!

Hi HN,

I’ve been experimenting with archive format design and built 6cy as a research project.

The goal is not to replace zip/7z, but to explore: • block-level codec polymorphism (different compression per block) • streaming-first layout (no global seek required) • better crash recovery characteristics • plugin-based architecture so proprietary codecs can exist without changing the format

Right now this is an experimental v0.x format. The specification may still change and compatibility is not guaranteed yet.

I’m mainly looking for feedback on the format design rather than performance comparisons.

Thanks for taking a look.

The article discusses the demise of Show HN, a popular feature on Hacker News that allowed users to showcase their projects. It explores the reasons behind its decline, including changes in the platform's priorities and the challenges faced by smaller creators in gaining visibility.

We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product.

Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds something, the check fails with one click to accept a diff. Otherwise, it passes silently.

Here’s one of ours:

  .continue/checks/metrics-integrity.md

  ---
  name: Metrics Integrity
  description: Detects changes that could inflate, deflate, or corrupt metrics (session counts, event accuracy, etc.)
  ---

  Review this PR for changes that could unintentionally distort metrics.
  These bugs are insidious because they corrupt dashboards without triggering errors or test failures.

  Check for:
  - "Find or create" patterns where the "find" is too narrow, causing entity duplication (e.g. querying only active sessions, missing completed ones, so every new commit creates a duplicate)
  - Event tracking calls inside loops or retry paths that fire multiple times per logical action
  - Refactors that accidentally remove or move tracking calls to a path that executes with different frequency

  Key files: anything containing `posthog.capture` or `trackEvent`

---

To get started, paste this into Claude Code or your coding agent of choice:

  Help me write checks for this codebase: https://continue.dev/walkthrough

- Explore the codebase and use the `gh` CLI to read past review comments

- Write checks to `.continue/checks/`

- Optionally, show you how to run them locally or in CI

Would love your feedback!

The article explores the concept of 'semantic ablation', where AI systems are trained to selectively remove or alter specific semantic elements from text, potentially enabling more nuanced and controlled text generation. The technique could have applications in areas like content moderation, creative writing, and generating text tailored to specific audiences.

The article discusses the use of four-column ASCII art as a form of creative expression, highlighting its technical constraints and the artistic challenges involved in crafting intricate designs within these limitations.

The article discusses small block ciphers, a type of cryptographic algorithm used to secure data. It provides an overview of the history, design principles, and applications of small block ciphers, highlighting their importance in modern cryptography.

This article discusses the implementation of Hamming distance in SQLite for hybrid search, allowing users to perform approximate string matching and find similar results based on the distance between search terms and data.

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

The article explores a private school that is utilizing AI-powered technologies to personalize student learning, raising concerns about the ethical implications and potential unintended consequences of this approach.

The article discusses the rise of TeamSpeak, a voice chat app that is gaining popularity as a Discord alternative, particularly due to its age verification feature which allows for stricter moderation of online communities.

The article discusses the rise of the Triforce, a popular game emulator for the Nintendo GameCube and Wii consoles. It highlights the emulator's key features, its development history, and its growing popularity among the gaming community.

Wax is an open-source, decentralized blockchain platform designed for building secure and scalable decentralized applications (dApps). It provides a flexible and efficient infrastructure for developers to create and deploy their own dApps, leveraging the benefits of blockchain technology.

Fun little side project I built after learning about circuit bending in cameras for intentional glitch effect. It is browser based camera toy where you "rewire" CCD pin pairs, turn knobs to get different glitch artefacts in real time to capture as photos. I had fun learning to simulate different pin modes - channel split, hue/phase shifts, horizontal clock delays, colour kill etc.

Here are some photos taken: https://glitchycam.com/gallery

I intentionally leaned towards skeuomorphic design for nostalgia. I miss the days where I'd spend hours making a button to look like a physical button. Here I chose to make it look like a "good enough" Teenage Engineering device UI.

I tested/used GPT-5.3-Codex to build this from scratch, since there was a lot of hype around it on X. Maybe I wasn’t using it right, but I found it needed a lot of code cleanup at every step and a lot of hand holding along the way. It missed details/nuances and didn't land the skeuomorphic buttons or the interaction polish. It mostly helped with boilerplate where there wasn't much thinking/detailing. It did give a basic starting point for the effect calculations, but didn't really move the needle on the details.

Please give it a go and let me know what you think - your photos and video never leave your browser (you can download them if you choose to). Everything is processed locally in your browser (works offline), nothing is uploaded or seen by anyone.