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Article URL: https://80000hours.org/podcast/episodes/sam-winter-levy-nikita-lalwani-ai-nuclear-deterrence/

Comments URL: https://news.ycombinator.com/item?id=47355480

Points: 1

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Article URL: https://status.mongodb.com/incidents/7g5qmxgkc2y4

Comments URL: https://news.ycombinator.com/item?id=47355468

Points: 3

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Article URL: https://github.com/step-security/dev-machine-guard

Comments URL: https://news.ycombinator.com/item?id=47355463

Points: 4

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Mozzie started as a tool I built for my own workflow.

I like working on multiple things at once, but most development tools split the workflow across different places: tickets live in issue trackers, execution happens in terminals, and context gets lost between them. I wanted my work items and their context right next to the place where the work actually happens.

Mozzie is a local desktop workspace where each work item can spawn its own terminal or coding agent. The idea is that tasks become the primary interface — each one holds its context and can run commands or agents independently.

This makes it easier to work on many tasks in parallel without constantly switching between tools.

It’s still early, but I’ve been using it daily and it fits how I like to work.

Curious if anyone else prefers working this way or has tried something similar.

Comments URL: https://news.ycombinator.com/item?id=47355451

Points: 1

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Article URL: https://www.apideck.com/blog/api-design-principles-agentic-era

Comments URL: https://news.ycombinator.com/item?id=47355433

Points: 1

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Article URL: https://www.bbc.co.uk/news/articles/c4g23npxpwgo

Comments URL: https://news.ycombinator.com/item?id=47355431

Points: 1

# Comments: 0

Hey HN — I’m Veer and my cofounder is Suryaa. We're building Cumulus Labs (YC W26), and we're releasing our latest product IonRouter (https://ionrouter.io/), an inference API for open-source and fine tuned models. You swap in our base URL, keep your existing OpenAI client code, and get access to any model (open source or finetuned to you) running on our own inference engine.

The problem we kept running into: every inference provider is either fast-but-expensive (Together, Fireworks — you pay for always-on GPUs) or cheap-but-DIY (Modal, RunPod — you configure vLLM yourself and deal with slow cold starts). Neither felt right for teams that just want to ship.

Suryaa spent years building GPU orchestration infrastructure at TensorDock and production systems at Palantir. I led ML infrastructure and Linux kernel development for Space Force and NASA contracts where the stack had to actually work under pressure. When we started building AI products ourselves, we kept hitting the same wall: GPU infrastructure was either too expensive or too much work.

So we built IonAttention — a C++ inference runtime designed specifically around the GH200's memory architecture. Most inference stacks treat GH200 as a compatibility target (make sure vLLM runs, use CPU memory as overflow). We took a different approach and built around what makes the hardware actually interesting: a 900 GB/s coherent CPU-GPU link, 452GB of LPDDR5X sitting right next to the accelerator, and 72 ARM cores you can actually use.

Three things came out of that that we think are novel: (1) using hardware cache coherence to make CUDA graphs behave as if they have dynamic parameters at zero per-step cost — something that only works on GH200-class hardware; (2) eager KV block writeback driven by immutability rather than memory pressure, which drops eviction stalls from 10ms+ to under 0.25ms; (3) phantom-tile attention scheduling at small batch sizes that cuts attention time by over 60% in the worst-affected regimes. We wrote up the details at cumulus.blog/ionattention.

On multimodal pipelines we get better performance than big players (588 tok/s vs. Together AI's 298 on the same VLM workload). We're honest that p50 latency is currently worse (~1.46s vs. 0.74s) — that's the tradeoff we're actively working on.

Pricing is per token, no idle costs: GPT-OSS-120B is $0.02 in / $0.095 out, Qwen3.5-122B is $0.20 in / $1.60 out. Full model list and pricing at https://ionrouter.io.

You can try the playground at https://ionrouter.io/playground right now, no signup required, or drop your API key in and swap the base URL — it's one line. We built this so teams can see the power of our engine and eventually come to us for their finetuned model needs using the same solution.

We're curious what you think, especially if you're running finetuned or custom models — that's the use case we've invested the most in. What's broken, what would make this actually useful for you?

Comments URL: https://news.ycombinator.com/item?id=47355410

Points: 5

# Comments: 0

If you're in the middle of applying for a planning or zoning permit, there is some unwelcome news: cyber-criminals have found a way to exploit the bureaucratic tedium of the process against you. Read more in my article on the Fortra blog.

The unit formerly known as Google Fiber is spun out and plans to offer wider fiber internet service.

A bipartisan bill would force the FBI to get a warrant to read Americans’ messages and ban the federal purchase of commercial data on US residents ahead of a critical April deadline.

On March 3, 2026, Google warned about a powerful exploit kit targeting Apple iPhone models running iOS version 13.0 (released in September 2019) up to version 17.2.1 (released in December 2023).

In the latest security updates, Apple patched the vulnerabilities used in the Coruna exploit kit for older mobile devices that can no longer be updated to the latest iOS version. For newer iOS versions, patches associated with the Coruna exploit were already shipped in iOS 16.6 through 17.2 in updates released in 2023 and 2024.

The Coruna exploit kit was first observed in highly targeted attacks, but was later seen in watering hole attacks targeting Ukrainian users by a suspected Russian espionage group. Later still, it appeared on a very large set of fake Chinese financial websites, suggesting the exploit was being used by more mainstream cybercriminals.

The exploit relies on WebKit vulnerabilities (CVE-2023-43000 and CVE-2024-23222) that can be triggered by processing  maliciously crafted web content, and then gains kernel privileges by abusing a separate kernel vulnerability tracked as CVE-2023-41974.

The table below shows which updates are available and points you to the relevant security content for that operating system (OS).

iOS 16.7.15 and iPadOS 16.7.15iPhone 8, iPhone 8 Plus, iPhone X, iPad (5th generation), iPad Pro 9.7-inch, and iPad Pro 12.9-inch (1st generation)iOS 15.8.7 and iPadOS 15.8.7iPhone XS, iPhone XS Max, iPhone XR, iPad (7th generation) How to update your iPhone or iPad

For iOS and iPadOS users, here’s how to check if you’re using the latest software version:

• Go to Settings > General > Software Update. You will see if there are updates available and be guided through installing them.
• Turn on Automatic Updates if you haven’t already. You’ll find it on the same screen.

We don’t just report on phone security—we provide it

Cybersecurity risks should never spread beyond a headline. Keep threats off your mobile devices by downloading Malwarebytes for iOS, and Malwarebytes for Android today.

Native Desktop is a toolkit for building native desktop applications using modern web technologies without dealing with the usual complexity of desktop tooling. It focuses on providing a simple developer experience where you can scaffold, build, and distribute desktop apps using familiar workflows and a modular package ecosystem. Instead of forcing developers to manage complicated native environments, Native Desktop provides a CLI and a set of packages that handle the heavy lifting while keeping projects flexible and maintainable. The goal is to let developers move from an idea to a working desktop application quickly while still having full control over architecture and distribution. The project is designed for developers who already build with modern web stacks and want a straightforward way to turn those applications into desktop software without reinventing the entire toolchain.

Comments URL: https://news.ycombinator.com/item?id=47354047

Points: 1

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Article URL: https://www.apolloacademy.com/software-maturity-wall/

Comments URL: https://news.ycombinator.com/item?id=47354042

Points: 1

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Article URL: https://github.com/cheikh2shift/godex

Comments URL: https://news.ycombinator.com/item?id=47354029

Points: 1

# Comments: 0

Hey HN — I kept seeing developers describe the same frustration: the commit-push-wait-read-logs cycle when debugging CI pipelines. So I built PipeStep.

PipeStep parses your GitHub Actions YAML, spins up the right Docker container, and gives you a step-through debugger for your run: shell commands. You can:

- Pause before each step and inspect the container state - Shell into the running container mid-pipeline (press I) - Set breakpoints on specific steps (press B) - Retry failed steps or skip past others

It deliberately does not try to replicate the full GitHub Actions runtime — no secrets, no matrix builds, no uses: action execution. For full local workflow runs, use act. PipeStep is for when things break and you need to figure out why without pushing 10 more commits. Think of it as gdb for your CI pipeline rather than a local GitHub runner.

pip install pipestep (v0.1.2) · Python 3.11+ · MIT · Requires Docker

Would love feedback, especially from people who've hit the same pain point. Known limitations are documented in the README + have some issues in there that I'd love eyeballs on!

Comments URL: https://news.ycombinator.com/item?id=47354008

Points: 3

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Article URL: https://arstechnica.com/gadgets/2026/03/more-modular-design-makes-macbook-neo-easier-to-fix-than-other-apple-laptops/

Comments URL: https://news.ycombinator.com/item?id=47353993

Points: 3

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Article URL: https://twolongos.com/3/12/an-agentic-workflow-march-2026-edition/

Comments URL: https://news.ycombinator.com/item?id=47353989

Points: 2

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Article URL: https://privateequityvet.org/vet-list/

Comments URL: https://news.ycombinator.com/item?id=47353983

Points: 2

# Comments: 0

Hi HN, I'm Robel. I built LogClaw because I was tired of paying for Datadog and still waking up to pages that said "something is wrong" with no context.

LogClaw is an open-source log intelligence platform that runs on Kubernetes. It ingests logs via OpenTelemetry and detects anomalies using signal-based composite scoring — not simple threshold alerting. The system extracts 8 failure-type signals (OOM, crashes, resource exhaustion, dependency failures, DB deadlocks, timeouts, connection errors, auth failures), combines them with statistical z-score analysis, blast radius, error velocity, and recurrence signals into a composite score. Critical failures (OOM, panics) trigger the immediate detection path in <100ms — before a time window even completes. The detection achieves 99.8% for critical failures while filtering noise (validation errors and 404s don't fire incidents).

Once an anomaly is confirmed, a 5-layer trace correlation engine groups logs by traceId, maps service dependencies, tracks error propagation cascades, and computes blast radius across affected services. Then the Ticketing Agent pulls the correlated timeline, sends it to an LLM for root cause analysis, and creates a deduplicated ticket on Jira, ServiceNow, PagerDuty, OpsGenie, Slack, or Zammad. The loop from log noise to a filed ticket is about 90 seconds.

Architecture: OTel Collector → Kafka (Strimzi, KRaft mode) → Bridge (Python, 4 concurrent threads: ETL, anomaly detection, OpenSearch indexing, trace correlation) → OpenSearch + Ticketing Agent. The AI layer supports OpenAI, Claude, or Ollama for fully air-gapped deployments. Everything deploys with a single Helm chart per tenant, namespace-isolated, no shared data plane.

To try it locally: https://docs.logclaw.ai/local-development

What it does NOT do yet: - Metrics and traces — this is logs-only right now. Metrics support is on the roadmap. - The anomaly detection is signal-based + statistical (composite scoring with z-score), not deep learning. It catches 99.8% of critical failures but won't detect subtle performance drift patterns yet. - The dashboard is functional but basic. We use OpenSearch Dashboards for the heavy lifting.

Licensed Apache 2.0. The managed cloud version is $0.30/GB ingested if you don't want to self-host.

Hi HN — I’m Robel. I built LogClaw after getting tired of waking up to alerts that only said “something is wrong” with no context. LogClaw is an open-source log intelligence platform for Kubernetes. It ingests logs via OpenTelemetry and detects operational failures using signal-based anomaly detection rather than simple thresholds. Instead of looking at a single metric, LogClaw extracts failure signals from logs (OOMs, crashes, dependency failures, DB deadlocks, timeouts, etc.) and combines them with statistical signals like error velocity, recurrence, z-score anomalies, and blast radius to compute a composite anomaly score. Critical failures bypass time windows and trigger detection in <100ms. Once an anomaly is confirmed, a correlation engine reconstructs the trace timeline across services, detects error propagation, and computes the blast radius. A ticketing agent then generates a root-cause summary and creates deduplicated incidents in Jira, ServiceNow, PagerDuty, OpsGenie, Slack, or Zammad. Architecture: OTel Collector → Kafka → Detection Engine → OpenSearch → Ticketing Agent Repo: https://github.com/logclaw/logclaw Would love feedback from people running large production systems.

Comments URL: https://news.ycombinator.com/item?id=47353981

Points: 2

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