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Article URL: https://www.heyecho.app/

Comments URL: https://news.ycombinator.com/item?id=46955722

Points: 2

# Comments: 0

Hi HN — I’m the developer of NetViews, a macOS utility I built because I wanted better visibility into what was actually happening on my wired and wireless networks.

I live in the CLI, but for discovery and ongoing monitoring, I kept bouncing between tools, terminals, and mental context switches. I wanted something faster and more visual, without losing technical depth — so I built a GUI that brings my favorite diagnostics together in one place.

About three months ago, I shared an early version here and got a ton of great feedback. I listened: a new name (it was PingStalker), a longer trial, and a lot of new features. Today I’m excited to share NetViews 2.3.

NetViews started because I wanted to know if something on the network was scanning my machine. Once I had that, I wanted quick access to core details—external IP, Wi-Fi data, and local topology. Then I wanted more: fast, reliable scans using ARP tables and ICMP.

As a Wi-Fi engineer, I couldn’t stop there. I kept adding ways to surface what’s actually going on behind the scenes.

Discovery & Scanning: * ARP, ICMP, mDNS, and DNS discovery to enumerate every device on your subnet (IP, MAC, vendor, open ports). * Fast scans using ARP tables first, then ICMP, to avoid the usual “nmap wait”.

Wireless Visibility: * Detailed Wi-Fi connection performance and signal data. * Visual and audible tools to quickly locate the access point you’re associated with.

Monitoring & Timelines: * Connection and ping timelines over 1, 2, 4, or 8 hours. * Continuous “live ping” monitoring to visualize latency spikes, packet loss, and reconnects.

Low-level Traffic (but only what matters): * Live capture of DHCP, ARP, 802.1X, LLDP/CDP, ICMP, and off-subnet chatter. * mDNS decoded into human-readable output (this took months of deep dives).

Under the hood, it’s written in Swift. It uses low-level BSD sockets for ICMP and ARP, Apple’s Network framework for interface enumeration, and selectively wraps existing command-line tools where they’re still the best option. The focus has been on speed and low overhead.

I’d love feedback from anyone who builds or uses network diagnostic tools: - Does this fill a gap you’ve personally hit on macOS? - Are there better approaches to scan speed or event visualization that you’ve used? - What diagnostics do you still find yourself dropping to the CLI for?

Details and screenshots: https://netviews.app There’s a free trial and paid licenses; I’m funding development directly rather than ads or subscriptions. Licenses include free upgrades.

Happy to answer any technical questions about the implementation, Swift APIs, or macOS permission model.

Comments URL: https://news.ycombinator.com/item?id=46955712

Points: 3

# Comments: 0

Article URL: https://productfit.substack.com/p/why-speech-to-speech-apis-fail-when

Comments URL: https://news.ycombinator.com/item?id=46955710

Points: 2

# Comments: 0

I built a message broker that speaks the Kafka wire protocol, so any Kafka client (librdkafka, kafka-python, kcat, etc.) works without code changes.

The entire binary is 52KB. No JVM, no ZooKeeper, no third-party libraries — just C++20 with kqueue/epoll. Starts in <10ms, uses 0% CPU when idle. I built this because running Kafka locally for development is painful — gigabytes of RAM, slow startup, ZooKeeper/KRaft configuration. I just wanted something that accepts produce requests and gets out of the way. Technical details: - Single-threaded event loop (kqueue on macOS, epoll on Linux) - Memory-mapped log segments (1GB pre-allocated, sequential I/O) - Lock-free SPSC/MPSC ring buffers with cache-line alignment - Kafka protocol v0-v3 including flexible versions (ApiVersions, Metadata, Produce) - Auto-topic creation on first produce or metadata request The most interesting bug I hit: librdkafka sends ApiVersions v3, which uses Kafka's "flexible versions" encoding. But there's a special exception in the protocol — ApiVersions responses must NOT include header tagged_fields for backwards compatibility. One extra byte shifted every subsequent field, causing librdkafka to compute a ~34GB malloc that crashed immediately. Current limitations: no consumer groups, no replication, single-threaded, no auth. It's v0.1.0 — consume support is next. MIT licensed, runs on macOS (Apple Silicon + Intel) and Linux.

Comments URL: https://news.ycombinator.com/item?id=46955708

Points: 1

# Comments: 0

Article URL: https://expectedharm.github.io/

Comments URL: https://news.ycombinator.com/item?id=46955705

Points: 2

# Comments: 0

Article URL: https://constitution.org/2-Authors/ah/rpt_manufactures.pdf

Comments URL: https://news.ycombinator.com/item?id=46955546

Points: 1

# Comments: 0

Article URL: https://journals.aps.org/prl/accepted/10.1103/r793-p7ct

Comments URL: https://news.ycombinator.com/item?id=46955545

Points: 1

# Comments: 0

Article URL: https://en.wikipedia.org/wiki/Internet_background_noise

Comments URL: https://news.ycombinator.com/item?id=46955543

Points: 1

# Comments: 0

It’s managed hosting for OpenClaw aimed at people who want an always-on personal AI assistant but don’t want to self-host / debug infra.

What it does

- 1 minute setup: sign in with Google → connect Telegram → connect your LLM provider - Isolated runtime: your OpenClaw instance runs isolated (smaller blast radius vs “run on your laptop/VPS”) - Access control: allowlist who can message your bot (DMs + groups) - Keys handled safely: credentials stored encrypted and kept separate from the OpenClaw instance - Usage + cost visibility: dashboard for tokens/cost so you can spot “token burn” - Tutorials: step-by-step guides for OpenAI / Anthropic / Gemini / OpenRouter / Kimi keys + setup credentials - Multiple instances: you can spin up several OpenClaw instances (a "personal AI team" with roles)

Current status / limitations

- Telegram is supported today; more channels later. - You bring your own model/provider keys (BYOK). - Early-stage project — I’d love feedback on onboarding, security assumptions, and what features matter most.

Comments URL: https://news.ycombinator.com/item?id=46955517

Points: 1

# Comments: 0

I’ve seen many teams rely heavily on P50/P95/P99 latency numbers, but still miss real user pain or misdiagnose incidents.

Recently I tried to write down a more systematic way to reason about latency distributions in production: how different distribution shapes behave, why aggregation and sampling often lie to us, and why segmentation (by endpoint, tenant, region, workload) usually matters more than adding more percentiles.

I’m curious how others here approach this in practice:

Do you have a mental model for interpreting P99 during incidents?

What charts or breakdowns have actually helped you debug latency issues?

Have you been burned by “good-looking” percentiles that hid real problems?

I wrote up my notes here for reference: https://optyxstack.com/performance/latency-distributions-in-practice-reading-p50-p95-p99-without-fooling-yourself

Would love to hear how people handle this in real systems.

Comments URL: https://news.ycombinator.com/item?id=46955498

Points: 1

# Comments: 0

Article URL: https://gilpignol.substack.com/p/trapped-between-pitch-disclaimer

Comments URL: https://news.ycombinator.com/item?id=46955497

Points: 1

# Comments: 0

Article URL: https://github.com/Palmerschallon/Dharma_Code/blob/main/paper/vocab_priming_confound.pdf

Comments URL: https://news.ycombinator.com/item?id=46955474

Points: 1

# Comments: 0

Article URL: https://blog.skipup.ai/buying-committee-demo-scheduling-problem/

Comments URL: https://news.ycombinator.com/item?id=46955464

Points: 1

# Comments: 0

I've been playing around with a bunch of AI image and video models and got tired of jumping between different sites, so I put them all in one place.

Right now there are 45+ models you can try — text-to-image, text-to-video, image-to-video, effects, etc. You can pick a model yourself or just let it auto-select one for you. No signup needed to try it out.

Honestly I'm really looking forward to Seedance 2.0 — it can do multi-shot video in one go and you can feed it images, video, and audio as references instead of just text. That's huge. The moment the API is available I'm plugging it in.

Would love to hear what you think.

https://seedance2.live

Comments URL: https://news.ycombinator.com/item?id=46955441

Points: 1

# Comments: 1

I’m the creator of https://grok-video.org, a web platform built for high-quality AI video generation.

What’s currently available:

Grok Video Integration: Full support for Text-to-Video and Image-to-Video using Grok’s video architecture.

Fast Rendering: Optimized for speed to help creators iterate quickly.

The Roadmap: The current version is powered by Grok, but I am actively working on supporting additional open-weights models like Wan 2.1 and Tencent Hunyuan in the coming updates to provide more creative variety.

Join our Contest: We just launched the Grok Video Contest to encourage creators to share their best AI-generated clips. Check it out here: https://grok-video.org/grok-contest

I’d love to hear your thoughts on the user experience and any features you'd like to see added.

website: https://grok-video.org/

Comments URL: https://news.ycombinator.com/item?id=46955437

Points: 2

# Comments: 0

Article URL: https://jmmv.dev/2026/02/one-week-with-claude-code.html

Comments URL: https://news.ycombinator.com/item?id=46955436

Points: 1

# Comments: 0

Article URL: https://ivanbercovich.com/2026/the-software-business

Comments URL: https://news.ycombinator.com/item?id=46955419

Points: 1

# Comments: 0

Article URL: https://www.thebignewsletter.com/p/monopoly-round-up-the-2-trillion

Comments URL: https://news.ycombinator.com/item?id=46955415

Points: 1

# Comments: 0

Article URL: https://helixnotes.com

Comments URL: https://news.ycombinator.com/item?id=46955400

Points: 4

# Comments: 0

Article URL: https://arxiv.org/abs/2505.13076

Comments URL: https://news.ycombinator.com/item?id=46955396

Points: 2

# Comments: 1