eigen-vector
Show HN: Chaser (beta) – Share Mac windows as screenshots and UI context
Chaser is a macos utility you can use to capture more than a screenshot. Screenshots are good tools to show your agents what you're seeing, but there's far more that goes on behind the scenes - the layout, button states, mapping of objects on your application, things that are out of your line of sight - all help make the context better. Chaser uses macos accessibility features to help you...
Somehow yegge also has problems with the adoption curve being consistent with other companies. Whatever that means.
Show HN: Sift – local, de-noised GitHub notifications
https://github.com/vignesh07/sift
Sift is an open source local GitHub inbox organizer.GitHub notifications are good at collecting activity and weak at prioritizing it. Sift pulls your inbox and adjacent repo activity into a local SQLite database, classifies it with explicit rules, and gives you a browser UI to work through it by signal instead of by noise.
This time around I prompted the models not necessarily to be adversarial - i didn't ask them to try and fool the reader. But i gave them contextual info - something to the effect of "you're a user posting on hacker news"
Yeah there are some very obvious tells, but the models that are most capable are very good at writing like human.
Especially when the human responses for reddit or HN prompts were presumably made after reading the content of the article or the post; whilw the model is simply going off of the title.
Show HN: Slop or not – can you tell AI writing from human in everyday contexts?
I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out.The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and...
Show HN: Talkatui – WWE style live commentary for your AI coding sessions
• Back navigation: good call. I'll make "Esc back" more explicit everywhere and add Backspace as an alias for back (and mention it in h help).
• Enter on Domains not working: it should drill down to flows. If you can share your OS/terminal + whether you installed from release vs cargo install, I'll try to reproduce and fix in the next release.
• "Clicking" wording: agreed — mouse isn't supported right now. I'll change Ul copy to say Select (and keep "Enter = drill down").
• Packets screen truncation: yep, needs scrolling/paging. On the list views I already do r/; l'll add page scroll and horizontal handling where it makes sense.
• Numbered weird options: great idea - I'll map 1..9 to jump-select and Enter (or open directly).
• Domain names in packets: yes, I want that. I already collect DNS/SNI/HTTP host hints; next step is showing hostname labels alongside IPs when I have more confident mapping.
• "f stream" definition: I'll clarify it as "Follow stream (reassembled payload)" and add a glossary/help entry.
• Inspecting encrypted/compressed data: totally fair. The tool can't decrypt TLS without keys, but it can make it clearer what's happening (SNI/ ALPN, sizes, timing, resets/retransmits). I'Il improve "Explain" to say what's possible vs not.
Super useful feedback thanks!
The direction I want to push it in is exactly what you're describing; make it a learning tool, where each detector/view answers: 1) What am I seeing? (plain language) 2) Why might it matter? 3) What's the next click? 4) What term should I learn? (glossary link)
If you're about to teach a Wireshark module next week, two super useful things would be: • 3-5 common lab prompts you give students (e.g. "identify the DNS failure," "find the top talker," "spot a TCP reset," "why is this slow?") • one small pcap you already use (or even just describe its scenario)
I can tune Overview/Weird/Explain around those and make the guided layer feel like the main product rather than a thin overlay. Also: if your students are GUl-only early on, that's a good callout - I should improve the README to frame Babyshark as "guided analysis," not "terminal is easier than GUI."
I'm also happy for your students to get hands on by sending PRs for things they wish are intuitive from the get go.
tshark is the engine; Babyshark is the guided Ul on top of it. • tshark: raw packet/field dump + powerful filters, but you have to know what fields to ask for and how to stitch the story together. • Babyshark: gives you an opinionated workflow (Overview → Domains/Weird → Flows → Packets/ Stream) with "explain/why it matters" text, curated detectors, and one-key drilldowns.
For live capture, Babyshark uses tshark -T fields to extract things like DNS qname / TLS SNI / HTTP host; for offline PCAP it parses enough to build flows + summaries.
So: if you already live in tshark one-liners, tshark is faster. If you're trying to understand what's happening or teach/debug quickly, Babyshark is a nicer front-end.
- *Overview dashboard*: immediately surfaces top talkers/flows + “what should I click next” instead of dropping you into the full packet list. - *Domains-first pivot*: `D` shows hostnames and lets you jump from a domain → the relevant flows. It also works when DNS answers aren’t visible (DoH/DoT/cached) by using observed IPs from SNI/Host flows. - *Weird stuff*: `W` is a curated set of “likely problems” (retransmits/out-of-order hints, resets, handshake issues, DNS failures when visible) with a short “why it matters” and a drill-down. - *Explain*: `?` gives plain-English hints for a selected flow + suggested next steps (follow stream, filter, pivot to domains/weird).
So it’s basically a guided triage layer on top of tshark/pcap data, with the “where do I start?” path baked in.
If you’ve got a specific teaching use-case (e.g. “why is this slow?” or “which host is generating traffic?”), I’d love to tune the Overview/Weird detectors around that. Open to PRs as well.