Collect daily AI release news from major labs, product changelogs, blogs, and Hacker News. Remove previously reported items and email a digest with source links and brief explanations tailored to the intended audience.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Daily digest of AI releases from Anthropic, Meta, Vercel Labs, OpenAI and Google GitHub repos, the Claude Cowork changelog, the claude.dev blog and Hacker News, deduplicated against a local state file and sent by email. Use when the user asks for a daily AI news digest, to scan AI labs for new releases, or to set up a scheduled release-hunter job.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Scans the public AI repos of Anthropic, Meta, Vercel Labs, OpenAI and Google on GitHub, plus the Claude Cowork changelog, the claude.dev blog and Hacker News, and emails a digest of the day's relevant movements. Designed to run once a day as a scheduled job (cron, a Claude Code routine, or /loop).
Set these before the first run. Every path is relative to the working directory unless you choose otherwise.
| Setting | Default | Purpose |
|---|---|---|
RECIPIENT_EMAIL | (required) | Address that receives the digest |
SEND_COMMAND | (required) | Command that sends an email given a recipient, subject and body file (e.g. a Gmail CLI, mail or sendmail; to send through an MCP tool instead, add it to allowed-tools) |
STATE_DIR | .release-hunter/state/ | Deduplication state (seen.json) |
SNAPSHOT_DIR | .release-hunter/snapshots/ | Page snapshots (Vercel Labs, Cowork changelog) |
OUTPUT_DIR | .release-hunter/output/ | Email bodies and run logs |
EMAIL_LANGUAGE | Spanish | Language of the email |
EMAIL_SUBJECT | IA: novedades del día (Anthropic + Meta + Vercel + OpenAI + Google + Hacker News) | Subject line |
AUDIENCE | the user's audience | Who the "why it matters" lines are written for (e.g. a YouTube channel, a team) |
Suggested schedule: daily at ~08:00 in the user's time zone.
Add .release-hunter/ to .gitignore so state, snapshots and email bodies are never committed. The scan makes well over 60 GitHub API calls per run, so a GITHUB_TOKEN is strongly recommended (see below).
anthropics)Also list the org's repos (https://api.github.com/orgs/anthropics/repos?per_page=100) and consider any other with recent relevant activity (agent SDKs, examples, tooling).
Anthropic's technical blog with tips, techniques and POVs from its developers for building with Claude. Fetch it with WebFetch and take the list of recent posts (date + title + link). Compare against the already-reported ones in STATE_DIR/seen.json under the key claude_dev_posts (list of slugs or URLs; create it if missing). Report each new post as a highlighted item: title, date, one line on why it matters to AUDIENCE + link to the post. Add the new ones at the end.
meta-llama and facebookresearch)If any repo returns 404, skip it and continue. Also list both orgs' repos and add up to ~15 more with a push in the last 30 days whose name or description fits AI: new models, agents, MCP, vision, audio, multimodal, diffusion, segmentation, embeddings, fine-tuning, RL. Ignore internal infra repos, consumer product demos or archived ones.
vercel-labs; experiments at https://vercel.com/labs)If any repo returns 404, skip it and continue. Also list the org's repos and add those with a push in the last 30 days not already in the list.
Labs page updates: fetch https://vercel.com/labs and compare the experiment list (name + category: Labs products / Active experiments / Past experiments) against the snapshot at SNAPSHOT_DIR/vercel-labs-experiments.json. Report any new experiment or category change as a highlighted item. Update the snapshot at the end.
openai)If any repo returns 404, skip it and continue. Also list the org's repos and add up to ~10 more with a push in the last 30 days whose name or description fits AI: agents, codex, MCP, skills, computer use, guardrails, plugins, sandboxing. Ignore internal infra repos, consumer product demos or archived ones.
google)If any repo returns 404, skip it and continue. Also list the org's repos and add up to ~10 more with a push in the last 30 days whose name or description fits agentic AI: agents, orchestration, A2A/MCP, Gemini, evals, tool use. Ignore internal infra repos, consumer product demos, Android, Flutter or archived ones.
Google items are reported as-is, with no Claude/Anthropic comparison angle.
The page is public and organizes changes by date (YYYY-MM-DD headings). Compare the date sections against the snapshot at SNAPSHOT_DIR/cowork-changelog-snapshot.json (format: date → {"sha256": hash of that section's content}).
Exact hash method: download the HTML with curl, strip <script>/<style> tags, convert remaining tags to newlines, drop empty lines, and split on lines that are only a date YYYY-MM-DD. The sha256 is computed over each section's text, with no HTML-entity unescaping. Keep the method identical across runs or every section will look changed.
Report only new dates or dates whose content changed since the snapshot, with the most relevant bullets (features, behavior changes, new Cowork/Code/3P settings; skip minor bug fixes unless important) + the changelog page link. Update the snapshot at the end.
Find stories from the last ~24 hours relevant to Anthropic, Meta AI, Vercel, OpenAI, Google AI, Claude/Claude Code, coding agents and MCP. Use the public Algolia API (no auth), with parallel requests:
GET https://hn.algolia.com/api/v1/search?query=<term>&tags=story&numericFilters=created_at_i><timestamp_24h_ago>,points>40 for each keyword: Claude Code, Codex, Cursor, MCP, Anthropic, OpenAI, Google, Vercel, Llama, AI agentGET https://hn.algolia.com/api/v1/search?tags=front_page and keep the relevant stories.Filter noise: only stories with real traction (meaningful points and comments) and direct relevance to AUDIENCE (launches, technical debates on coding agents, model comparisons, new tools). Deduplicate by storing already-reported story IDs in STATE_DIR/seen.json under the key hn_story_ids. Only report unseen stories; add the new IDs at the end.
Use the public GitHub API, no auth, with parallel requests (e.g. xargs -P16). Unauthenticated calls are limited to 60 requests/hour per IP; if a GITHUB_TOKEN is available in the environment, send it as a bearer token to raise the limit, and never write it to any output file.
GET /repos/{owner}/{repo}/releases; take those published since the last check (filter by publication date, not just tag equality).GET /repos/{owner}/{repo}/commits?since=<ISO-8601>. Filter noise (dependabot, CI, typo fixes); keep features, behavior changes, new commands/flags, deprecations, new models and important fixes.Store in STATE_DIR/seen.json (create if missing) the last release tag and last reviewed commit SHA per repo, keyed by owner/repo. Only report what's new since the last mark; update the mark at the end. On the first run there is no mark: report only the last 24 hours instead of the full history.
EMAIL_LANGUAGE, direct and no filler.AUDIENCE + link to the release or commit.Hacker News section: per story, title, points/comments, one line on why it matters + link to the discussion (https://news.ycombinator.com/item?id=<id>).Write the body to a durable file first, OUTPUT_DIR/email-body-YYYY-MM-DD.txt, never to /tmp (it can be wiped between turns of a scheduled job). Then send it with SEND_COMMAND, for example:
<send-command> --to "$RECIPIENT_EMAIL" --subject "$EMAIL_SUBJECT" --body "$(cat .release-hunter/output/email-body-YYYY-MM-DD.txt)"
The user must authorize the recurring send when setting up the job; do not send to any address other than RECIPIENT_EMAIL. If sending fails, record it in the run log.
If the GitHub API fails or returns a rate limit, wait a minute and retry once; if it still fails, send the email noting which sources couldn't be checked today.
Append a run summary (sources checked, items reported, failures) to OUTPUT_DIR/runs/YYYY-MM-DD.md, and save the updated seen.json and snapshots only after the email is sent, so a failed send is retried with the same items next run.
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Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Prepare an academic CV for faculty, postdoctoral, or research applications. Organize publications, grants, teaching, and research experience, with section order and formatting adapted to the role and academic field.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.