AI model releases, new papers, and open-source projects from the past 24 hours

No. There is no official, authoritative 'everything released in the last 24 hours' feed for AI. The closest live sources are Hugging Face Models sorted by created (huggingface.co/models?sort=created), Hugging Face Daily Papers (huggingface.co/papers), the arXiv cs.AI recent listing (arxiv.org/list/cs.AI/recent), and GitHub Trending with the range set to daily (github.com/trending?since=daily). Each one is a different slice: model weights, papers, papers again at the raw level, and open-source code. To get a real picture of a single day you have to read all four, because no one of them sees the whole field.

Because they are hand-written snapshots with a date stamp, and the field moves faster than the publishing cadence. A page titled with a specific day was usually drafted a day or two earlier, covers whatever the author happened to see, and is never updated. By the time it ranks, the 24 hours it describes are long gone and several of the releases it lists have been superseded. They are fine as historical context. They are useless as a current view. The only way to get a genuinely fresh past-24h picture is to read the live sources yourself, or have something read them for you on a schedule.

Open four tabs: Hugging Face Models sorted by created, Hugging Face Daily Papers on the Daily tab, arXiv cs.AI recent, and GitHub Trending with since=daily. Scroll each until the timestamps cross the 24-hour line, note anything with traction, and stop. It takes about ten minutes if you do it every day and know what you are looking for. The problem is not that it is hard. The problem is that it is the same ten minutes every single morning, which is exactly the kind of repetitive, low-judgment task worth handing to an agent.

Fazm has a built-in routine scheduler. You save a prompt like 'open these four pages, pull anything posted in the last 24 hours, and write me a digest' and attach a schedule. The scheduler (RoutineScheduler.swift) polls for due jobs every 60 seconds. When your routine is due, it spawns a headless agent (cron-runner.mjs) that drives your actual browser through the bundled Playwright extension, reads each source page, and writes the result back into your chat history. You read the digest, not the raw feeds.

Two reasons. First, several of these sources render content in JavaScript and gate parts behind a logged-in session. An agent driving your real browser sees exactly what you see, with your cookies, so it does not get a stripped-down or rate-limited view. Second, Fazm reads the page through the browser and the macOS accessibility tree rather than guessing from screenshots, so it can pull the actual model names, paper titles, and star counts as text instead of OCR-ing them off pixels. That is the difference between a digest you can trust and one you have to double-check.

Yes. Routines live in a local SQLite store (cron_jobs and cron_runs tables), so they survive an app close and a full restart. When Fazm relaunches, the scheduler picks up where it left off and fires any job that is due. Chats themselves persist the same way, keyed by task, so the digest history is still there when you reopen the window. Nothing about the routine depends on the app having stayed open overnight.

Yes. Each routine has a sessionMode field set to either 'new' or 'resume'. With 'resume' the routine keeps firing inside the same conversation, so the agent can remember what it told you yesterday and flag what is genuinely new versus a re-post. With 'new' every run is a clean slate. For a daily AI-release digest, resume is usually what you want, because the value is in 'what changed since yesterday,' not 'everything that exists today.'

It is the harness. Fazm wraps Claude Code (and Codex) via the Agent Client Protocol and brings your own Claude Pro or Max account, so the reasoning is the same agent loop you already use. What Fazm adds is the native macOS surface around it: the routine scheduler, persistent sessions, browser and native-app control through accessibility APIs, and voice input. For a digest task, the model reads and summarizes; Fazm is the part that opens the pages, runs it on time, and keeps the history.