GPT-5.4 Thinking System Card
41 by mudkipdev | 10 comments on Hacker News.
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!
New top story on Hacker News: Show HN: PageAgent, A GUI agent that lives inside your web app
Show HN: PageAgent, A GUI agent that lives inside your web app
22 by simon_luv_pho | 5 comments on Hacker News.
Title: Show HN: PageAgent, A GUI agent that lives inside your web app Hi HN, I'm building PageAgent, an open-source (MIT) library that embeds an AI agent directly into your frontend. I built this because I believe there's a massive design space for deploying general agents natively inside the web apps we already use, rather than treating the web merely as a dumb target for isolated bots. Currently, most AI agents operate from external clients or server-side programs, effectively leaving web development out of the AI ecosystem. I'm experimenting with an "inside-out" paradigm instead. By dropping the library into a page, you get a client-side agent that interacts natively with the live DOM tree and inherits the user's active session out of the box, which works perfectly for SPAs. To handle cross-page tasks, I built an optional browser extension that acts as a "bridge". This allows the web-page agent to control the entire browser with explicit user authorization. Instead of a desktop app controlling your browser, your web app is empowered to act as a general agent that can navigate the broader web. I'd love to start a conversation about the viability of this architecture, and what you all think about the future of in-app general agents. Happy to answer any questions!
22 by simon_luv_pho | 5 comments on Hacker News.
Title: Show HN: PageAgent, A GUI agent that lives inside your web app Hi HN, I'm building PageAgent, an open-source (MIT) library that embeds an AI agent directly into your frontend. I built this because I believe there's a massive design space for deploying general agents natively inside the web apps we already use, rather than treating the web merely as a dumb target for isolated bots. Currently, most AI agents operate from external clients or server-side programs, effectively leaving web development out of the AI ecosystem. I'm experimenting with an "inside-out" paradigm instead. By dropping the library into a page, you get a client-side agent that interacts natively with the live DOM tree and inherits the user's active session out of the box, which works perfectly for SPAs. To handle cross-page tasks, I built an optional browser extension that acts as a "bridge". This allows the web-page agent to control the entire browser with explicit user authorization. Instead of a desktop app controlling your browser, your web app is empowered to act as a general agent that can navigate the broader web. I'd love to start a conversation about the viability of this architecture, and what you all think about the future of in-app general agents. Happy to answer any questions!
Wednesday, 4 March 2026
Tuesday, 3 March 2026
New top story on Hacker News: Show HN: Open-Source Article 12 Logging Infrastructure for the EU AI Act
Show HN: Open-Source Article 12 Logging Infrastructure for the EU AI Act
12 by systima | 0 comments on Hacker News.
EU legislation (which affects UK and US companies in many cases) requires being able to truly reconstruct agentic events. I've worked in a number of regulated industries off & on for years, and recently hit this gap. We already had strong observability, but if someone asked me to prove exactly what happened for a specific AI decision X months ago (and demonstrate that the log trail had not been altered), I could not. The EU AI Act has already entered force, and its Article 12 kicks-in in August this year, requiring automatic event recording and six-month retention for high-risk systems, which many legal commentators have suggested reads more like an append-only ledger requirement than standard application logging. With this in mind, we built a small free, open-source TypeScript library for Node apps using the Vercel AI SDK that captures inference as an append-only log. It wraps the model in middleware, automatically logs every inference call to structured JSONL in your own S3 bucket, chains entries with SHA-256 hashes for tamper detection, enforces a 180-day retention floor, and provides a CLI to reconstruct a decision and verify integrity. There is also a coverage command that flags likely gaps (in practice omissions are a bigger risk than edits). The library is deliberately simple: TS, targeting Vercel AI SDK middleware, S3 or local fs, linear hash chaining. It also works with Mastra (agentic framework), and I am happy to expand its integrations via PRs. Blog post with link to repo: https://ift.tt/TrFiPwB I'd value feedback, thoughts, and any critique.
12 by systima | 0 comments on Hacker News.
EU legislation (which affects UK and US companies in many cases) requires being able to truly reconstruct agentic events. I've worked in a number of regulated industries off & on for years, and recently hit this gap. We already had strong observability, but if someone asked me to prove exactly what happened for a specific AI decision X months ago (and demonstrate that the log trail had not been altered), I could not. The EU AI Act has already entered force, and its Article 12 kicks-in in August this year, requiring automatic event recording and six-month retention for high-risk systems, which many legal commentators have suggested reads more like an append-only ledger requirement than standard application logging. With this in mind, we built a small free, open-source TypeScript library for Node apps using the Vercel AI SDK that captures inference as an append-only log. It wraps the model in middleware, automatically logs every inference call to structured JSONL in your own S3 bucket, chains entries with SHA-256 hashes for tamper detection, enforces a 180-day retention floor, and provides a CLI to reconstruct a decision and verify integrity. There is also a coverage command that flags likely gaps (in practice omissions are a bigger risk than edits). The library is deliberately simple: TS, targeting Vercel AI SDK middleware, S3 or local fs, linear hash chaining. It also works with Mastra (agentic framework), and I am happy to expand its integrations via PRs. Blog post with link to repo: https://ift.tt/TrFiPwB I'd value feedback, thoughts, and any critique.
Monday, 2 March 2026
Sunday, 1 March 2026
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