hotin — the whole field, ranked. With receipts.
[hotin repos output: a ranked board of AI repos with scores, tags and badges]
[PyPI]
[downloads]
[zero dependencies]
hotin.ai
One command, one ranked list of what's actually hot in AI today: trending
repos, models, papers and news, ranked daily by cross-source consensus.
Numbers are receipts, badges are verdicts. Want the same thing in a
browser? The live hotin board is what's trending in AI
today, updated daily.
Every row shows why it is there: how fast it is growing, who backed it, where
else it surfaced. Nothing ranks on a single source's say-so.
It works two ways, from one install.
Your model's training data has a cutoff and a web search returns marketing. Ask
an agent what is worth using in AI this week and it guesses. Point it at hotin
and it answers with the same ranked evidence you would have read yourself.
What it does
The zero-key core combines GitHub Trending, curated growth momentum, Hacker News, and npm velocity with no configuration. Add an optional key to unlock Reddit and YouTube (ScrapeCreators, or the official YouTube Data API v3), including curated repo-roundup channels. A “smart money” signal (repos the AI Insiders are backing) is included on a best-effort basis, and an AI-newsletter feed adds an editorial signal. Sources can be temporarily unavailable without taking down the CLI.
Beyond repos, hotin surfaces trending AI models (hotin models) and papers (hotin papers) as their own views, and a short daily hotin brief of what's happening across all of them. Run hotin refresh on a schedule and hotin records a time series, so the board can flag what's genuinely rising and viral (velocity, not just a snapshot) — not just what's big right now.
Install
hotin has zero dependencies, so a plain pip install is safe (nothing to conflict with). Prefer an isolated install, or don't want to install at all?
No package manager, just Python? Grab the single-file hotin.pyz from the latest release and run it:
Developing on a checkout: pip install -e ..
Use it from your AI agent (MCP)
The same board, callable by Claude Code, Cursor, Codex CLI, Gemini CLI, or
anything else that speaks MCP. Pick whichever line is least work for you.
One click. Download hotin.mcpb
and open it. Claude Desktop shows an install dialog and that is the whole
process: no config file to find, no JSON to edit, no terminal. The bundle is
136 KB and carries hotin inside it, so there is nothing to install first --
hotin has zero dependencies, which is what makes that possible. macOS and Linux.
One line. Nothing installed at all, uvx fetches it on demand:
Any other client. Same idea, as config:
Already ran pip install hotin? Then it is "command": "hotin", "args": ["mcp"]
-- the MCP server ships inside the package, there is no second thing to install.
Then ask your agent things it otherwise cannot answer:
"What AI repos are actually worth looking at this week?"
"Is anything notable happening with open-weight models right now?"
"Before we pick a library for this, what is trending and who is backing it?"
Two tools are exposed. hotin_board returns any tab (repos, rising,
insiders, models, papers, news) with the receipts attached, and
hotin_brief returns the daily digest. Answers come back in about a second,
served from the local cache.
It runs on your machine, not ours. Your MCP client starts hotin as a local
child process. It fetches GitHub, Hacker News, npm and Hugging Face directly,
caches to your own disk, and uses your own token if you set one. hotin.ai is
never in the path: the site is a separate rendering of the same tool, not a
service this depends on.
Set GITHUB_TOKEN if you want the insiders signal; everything else works
without any key. A first call on a cold cache takes about 15 seconds while it
fetches; after that answers are served locally in about a second.
Works with every install route: the bundle, uvx, pip, pipx, and the
single-file hotin.pyz.
Claude Code skill
skills/hotin/SKILL.md teaches an agent when to reach
for the board and, more usefully, how to read it: which fields are actual
evidence, and what the board does not know. Copy it in:
It works with or without the MCP server, and is worth having alongside it — the
MCP tool descriptions say what the tools return, the skill says which numbers
mean something and when a row is a lead rather than a finding.
Quick start
Available commands:
Flags: --format text\|json\|md\|html · --limit N (default 20) · --source <name> (repos: one upstream feed instead of the fused board) · --since 30d / --min-stars N (repos filters) · --verbose.
Each repo result presents a score, the owner/repo (clickable), category, and applicable badges: fresh (recently created or active), rising / viral (climbing fast on the recorded time series, viral being the rare accelerating-and-consensus extreme), smart-money (the AI Insiders are backing it), and paper-backed (linked from a trending paper). Consensus across sources is folded into the score itself, not shown as a badge.
Example (real output, top of a live run):
The first line of each result is score, owner/repo (clickable in a real terminal), category, and badges; a dimmed second line shows the human title when it adds context the slug doesn't. fresh reflects recent repository activity. In a live terminal the score and badges are colored. Your output will differ — it reflects what is actually hot when you run it.
Why corroboration, not popularity
The board requires two independent signals. Here is the measurement behind that.
Ranking on "a well-known developer starred it" is the obvious design. It does not
hold up. Measured across 288 tracked repos, growth as a percentage of a repo's own
star count so size is not mistaken for heat:
One star tracks slightly worse than no endorsement. Two or more tracks about
2x the baseline — though at n=21 that is p=0.087, suggestive rather than proven,
and the board rests on the first result rather than the second.
So nothing reaches the board on one source's say-so. It needs corroboration from
an independent signal, and it has to still be true a few hours later.
scripts/measure_insider_signal.py in the site repo re-runs this whenever you
want to check the claim.
Keeping it fresh
“+N in 7d” counts stars gained in the trailing 7 complete UTC days from GitHub’s star-history endpoint. “/day” on the repos tab comes from hotin’s own observation store, seeded from the same endpoint so a first-seen repo has real velocity. A “cooling” badge marks a sharp drop after a previous week with at least 50 stars.
hotin's rising / viral badges and the hotin brief come from a recorded time series, so they get better the more often hotin refresh runs. hotin setup can install a scheduled job for you:
On macOS/Linux this manages a marked block in your crontab; on Windows it creates hotin-refresh scheduled tasks. Either way it runs python -m hotin refresh --quiet, leaving the rest of your schedule untouched.
Data Sources & Terms
hotin's code is licensed under Apache-2.0. That license does not relicense the underlying data returned by GitHub, Hacker News, npm, Reddit, or YouTube: each source's own terms of use apply.
The Reddit and YouTube integrations are unofficial third-party integrations via ScrapeCreators; they are not officially sanctioned by Reddit or YouTube. The smart-money signal is a best-effort read of a public AI-influencer graph and may change or break without notice.
Contributing
Issues and contributions are welcome at the issue tracker.
License
Apache License 2.0 — see LICENSE and NOTICE.
hotin was created by Abe Diaz. If hotin, its ranking approach, or its ideas helped your project, a credit and link back to github.com/abe238/hotinai are appreciated.
Made by @abe238 · hotin.ai
