Open Weights Isn't Open Training
15 by addiefoote8 | 3 comments on Hacker News.
Tuesday, 10 March 2026
Monday, 9 March 2026
Sunday, 8 March 2026
New top story on Hacker News: Show HN: Skir – like Protocol Buffer but better
Show HN: Skir – like Protocol Buffer but better
6 by gepheum | 1 comments on Hacker News.
Why I built Skir: https://ift.tt/tRBiqsO... Quick start: npx skir init All the config lives in one YML file. Website: https://skir.build GitHub: https://ift.tt/6XCwpPM Would love feedback especially from teams running mixed-language stacks.
6 by gepheum | 1 comments on Hacker News.
Why I built Skir: https://ift.tt/tRBiqsO... Quick start: npx skir init All the config lives in one YML file. Website: https://skir.build GitHub: https://ift.tt/6XCwpPM Would love feedback especially from teams running mixed-language stacks.
Saturday, 7 March 2026
New top story on Hacker News: Show HN: µJS, a 5KB alternative to Htmx and Turbo with zero dependencies
Show HN: µJS, a 5KB alternative to Htmx and Turbo with zero dependencies
43 by amaury_bouchard | 15 comments on Hacker News.
I built µJS because I wanted AJAX navigation without the verbosity of HTMX or the overhead of Turbo. It intercepts links and form submissions, fetches pages via AJAX, and swaps fragments of the DOM. Single
43 by amaury_bouchard | 15 comments on Hacker News.
I built µJS because I wanted AJAX navigation without the verbosity of HTMX or the overhead of Turbo. It intercepts links and form submissions, fetches pages via AJAX, and swaps fragments of the DOM. Single
Friday, 6 March 2026
New top story on Hacker News: Show HN: Claude-replay – A video-like player for Claude Code sessions
Show HN: Claude-replay – A video-like player for Claude Code sessions
16 by es617 | 7 comments on Hacker News.
I got tired of sharing AI demos with terminal screenshots or screen recordings. Claude Code already stores full session transcripts locally as JSONL files. Those logs contain everything: prompts, tool calls, thinking blocks, and timestamps. I built a small CLI tool that converts those logs into an interactive HTML replay. You can step through the session, jump through the timeline, expand tool calls, and inspect the full conversation. The output is a single self-contained HTML file — no dependencies. You can email it, host it anywhere, embed it in a blog post, and it works on mobile. Repo: https://ift.tt/4LzMP6m Example replay: https://es617.github.io/assets/demos/peripheral-uart-demo.ht...
16 by es617 | 7 comments on Hacker News.
I got tired of sharing AI demos with terminal screenshots or screen recordings. Claude Code already stores full session transcripts locally as JSONL files. Those logs contain everything: prompts, tool calls, thinking blocks, and timestamps. I built a small CLI tool that converts those logs into an interactive HTML replay. You can step through the session, jump through the timeline, expand tool calls, and inspect the full conversation. The output is a single self-contained HTML file — no dependencies. You can email it, host it anywhere, embed it in a blog post, and it works on mobile. Repo: https://ift.tt/4LzMP6m Example replay: https://es617.github.io/assets/demos/peripheral-uart-demo.ht...
Thursday, 5 March 2026
New top story on Hacker News: Launch HN: Vela (YC W26) – AI for complex scheduling
Launch HN: Vela (YC W26) – AI for complex scheduling
6 by Gobhanu | 8 comments on Hacker News.
Hi HN! We're Gobhanu and Saatvik (brothers), building Vela ( https://tryvela.ai ) - AI agents that handle multi-party, multi-channel scheduling. Scheduling is a constraint satisfaction problem disguised as email! It’s easy when it’s two people, one timezone, one channel. But it becomes a constraint satisfaction problem when inputs are unstructured natural language across multiple communication channels, constraints change mid-solve, and the objective function includes social dynamics that don't exist formally anywhere. What if scheduling just happened? For example: a recruiter sends one message, and every interview across five candidates, three hiring managers, and two time zones gets booked, confirmed, and updated automatically. No links, no back-and-forth, no one spending hours with 20 emails. Everyone just gets the right invite at the right time, on whatever channel they actually use. That's what we built Vela to do. You loop in Vela into your emails, SMS, WhatsApp, Slack, phone or integrate into an ATS etc and it takes over: reads context, checks calendars, proposes times, follows up when people ghost, and rebooks when things shift. One of our first customers is a staffing firm that searched for a scheduling solution for almost eight years. Their coordinators manage hundreds of candidate-client interviews where each side needs separate email threads, separate Zoom accounts to avoid double-booking links, and calendar invites connecting parties who never directly communicate. A client reschedules one interview and it cascades into four others. A candidate responds on SMS to a thread that started on email. Vela solved this in just 10 minutes of onboarding. The hardest part has been the data problem. Scheduling behavior varies enormously across populations. C-suite folks respond to email within hours and expect formal 3-option proposals. Truck drivers applying for logistics roles respond to SMS at odd hours from shared devices with "y tm wrks." The failure mode isn't parsing -- it's applying the wrong interaction pattern for the wrong segment and watching the conversation die. We've been building behavioral datasets from thousands of real interactions: response latency by role, channel preference by demographic, follow-up timing curves, how many options to propose before you hit decision paralysis. This data doesn't exist anywhere. The core agent challenge is state across channels. When someone responds on SMS to a thread that started in email, Vela needs to unify identity, merge context, and continue without losing information. Phone numbers don't map cleanly to emails, people use nicknames on text, shared devices mean the responder might not be who you reached out to. Temporal NLU is its own problem -- "next Friday" means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can't be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong. We're live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site ( https://ift.tt/l679RKr ). You can check out a demo here: https://www.youtube.com/watch?v=MzUOjSG5Uvw . We'd love feedback from anyone who's worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!
6 by Gobhanu | 8 comments on Hacker News.
Hi HN! We're Gobhanu and Saatvik (brothers), building Vela ( https://tryvela.ai ) - AI agents that handle multi-party, multi-channel scheduling. Scheduling is a constraint satisfaction problem disguised as email! It’s easy when it’s two people, one timezone, one channel. But it becomes a constraint satisfaction problem when inputs are unstructured natural language across multiple communication channels, constraints change mid-solve, and the objective function includes social dynamics that don't exist formally anywhere. What if scheduling just happened? For example: a recruiter sends one message, and every interview across five candidates, three hiring managers, and two time zones gets booked, confirmed, and updated automatically. No links, no back-and-forth, no one spending hours with 20 emails. Everyone just gets the right invite at the right time, on whatever channel they actually use. That's what we built Vela to do. You loop in Vela into your emails, SMS, WhatsApp, Slack, phone or integrate into an ATS etc and it takes over: reads context, checks calendars, proposes times, follows up when people ghost, and rebooks when things shift. One of our first customers is a staffing firm that searched for a scheduling solution for almost eight years. Their coordinators manage hundreds of candidate-client interviews where each side needs separate email threads, separate Zoom accounts to avoid double-booking links, and calendar invites connecting parties who never directly communicate. A client reschedules one interview and it cascades into four others. A candidate responds on SMS to a thread that started on email. Vela solved this in just 10 minutes of onboarding. The hardest part has been the data problem. Scheduling behavior varies enormously across populations. C-suite folks respond to email within hours and expect formal 3-option proposals. Truck drivers applying for logistics roles respond to SMS at odd hours from shared devices with "y tm wrks." The failure mode isn't parsing -- it's applying the wrong interaction pattern for the wrong segment and watching the conversation die. We've been building behavioral datasets from thousands of real interactions: response latency by role, channel preference by demographic, follow-up timing curves, how many options to propose before you hit decision paralysis. This data doesn't exist anywhere. The core agent challenge is state across channels. When someone responds on SMS to a thread that started in email, Vela needs to unify identity, merge context, and continue without losing information. Phone numbers don't map cleanly to emails, people use nicknames on text, shared devices mean the responder might not be who you reached out to. Temporal NLU is its own problem -- "next Friday" means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can't be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong. We're live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site ( https://ift.tt/l679RKr ). You can check out a demo here: https://www.youtube.com/watch?v=MzUOjSG5Uvw . We'd love feedback from anyone who's worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!
Subscribe to:
Comments (Atom)