Report by The Next New Thing.
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Brand-new (created April 2026) and already at 8.1k stars, carried by a sharp hook — "one person + Claude Code = an investment research team" — and a Trendshift badge. The "four masters debate each other" angle and eye-catching real-money return claims do the marketing; the returns are unaudited author claims, and this is a prompt/skill pack, not runnable software or financial advice.
View on GitHub →Runs Buffett, Munger, Duan Yongping, and Li Lu as opposing viewpoints that generate real tension, not one blended answer.
Deep research, earnings, industry screening, holdings, and thinking tools, each invoked as a slash command.
/investment-team spins up 4 agents that each search and cross-check, then a lead synthesizes.
A non-engineer can point Claude Code at this and get consistently structured, decision-ready company research instead of a generic AI essay.
Warren Buffett — legendary chairman & CEO of Berkshire Hathaway who popularized value investing. Brings the analysis of a business’s moat, its valuation, and a strict margin of safety.
Charlie Munger — Buffett’s late partner, famed for a “latticework of mental models.” Brings inversion thinking (how a business could fail), spotting cognitive biases, and strict quick-kill checklists.
Duan Yongping — Chinese entrepreneur behind BBK / OPPO / vivo, the “Warren Buffett of China.” Brings a focus on the business essence (models, two-sided network effects) and management culture.
Li Lu — founder of Himalaya Capital, backed by Munger himself. Brings assessment of civilizational trends and long-term certainty over a 10-year horizon.
I pointed AI Berkshire's private-company-research framework at SpaceX — the four-master lens (Buffett · Munger · Duan Yongping · Li Lu), a six-dimensional scorecard, five valuation methods, and an inverse-risk check on every bull point.
The verdict: A+ business, C valuation — monitor, don't chase. The best assets in space, at a price that already assumes it wins. Read the full styled report online.
A mature, long-running project (created 2019) that pushes right up to today and trends on durability rather than novelty. Its differentiator — hiding who talks to whom, not just message content — keeps drawing privacy audiences, and the project points to a Trail of Bits audit and Privacy Guides / Whonix listings (attribute these as the project's claims).
View on GitHub →Its central claim: users have no assigned ID (not even a random one), so the connection graph is hidden from servers.
End-to-end encryption with an extra layer, protecting message content plus metadata.
Native Android and iOS apps plus a terminal/CLI app on Linux, macOS, and Windows.
For a creator or founder who cares about not leaking their contact network, this is the leading example of a messenger built to hide metadata, not just message text.
A Hacker News thread where a commenter challenges SimpleX's no-Tor-by-default / IP-metadata story as overhyped — and founder epoberezkin replies directly, defending the design. A clean skeptic-vs-maintainer exchange worth reading before you repeat any privacy claim.
Open SimpleX → New contact → Scan QR code. No phone number or username needed.
We aim to bring this man in for questioning — on camera.
If you know him, tag him. If you are him… let's talk.
Very new (late March 2026) and already the highest star count in the batch at 30.8k — a strong marketing-driven-growth signal, though the repo is heavily documented and pushed daily. The README leans on a "#1 Repository of the Day" badge and a wall of sub-$2 example videos (both self-reported), and courts autonomous coding agents directly.
View on GitHub →Builds a corpus from free stock/archives and edits real motion clips, not only animated stills.
Paste a clip and it analyzes transcript, pacing, and style, then returns 2–3 concepts plus cost estimates.
Optional keys for FAL, Pexels/Pixabay, Suno, ElevenLabs, OpenAI, Google and more — more keys, more tools.
A non-engineer creator could describe a video in one sentence and have their AI assistant produce a captioned, narrated, music-scored clip for pocket change.
A pipeline-driven orchestration system, not a single video model — it coordinates cloud APIs, local GPU models, free stock archives, and programmatic code renderers to assemble the final cut.
I've used Zapier across multiple businesses for over 10 years. They've been doing automation reliably long before this moment — and the MCP service brings that same reliability to any AI agent you're building with.
Try Zapier MCP →Exposes 8,000+ tools as MCP resources any agent can call.
Claude Code, OpenCode, Cursor — anything that speaks MCP.
Enable the tools you want in Zapier, grab the MCP endpoint, paste it into your agent.
Zapier handles auth, rate limits, and reliability for you.
Brand-new (created June 2026) and already past 71k stars — an always-on plugin for Claude Code, Codex, and GitHub Copilot CLI that adds a YAGNI "laziness ladder" the agent climbs before writing code. The pitch: the best code is the code you never wrote. The eye-catching "~54% less code, up to 94%" figures are the author's own benchmarks — treat them as claims.
View on GitHub →Before coding, the agent stops at the first rung that holds: does it need to exist? already in the codebase? stdlib? native feature? installed dep? one line?
Validation, data-loss handling, security, and accessibility are never on the chopping block — it trims over-building, not safety.
Installs as an always-on skill for Claude Code, Codex, and GitHub Copilot CLI.
Author's agentic benchmark (a headless Claude Code session editing a real FastAPI + React repo) reports the biggest cuts where agents over-build — their numbers, not audited.
If your AI agent over-engineers — installing a library and writing a wrapper for what a one-liner would do — this bolts a "do less" discipline onto it without dropping the safety guards.
The flagship repo of the Nextcloud org you linked — a decade-old (2016), 36k-star institution, still pushed daily. Self-host your files, contacts, calendars, mail, and video chat on a server you control, and extend it with hundreds of apps from its own App Store. AGPL-3.0, with a HackerOne bounty program and two-factor auth.
View on GitHub →Store files, contacts, and calendars on a server you choose and keep them synced across every device.
Extend with Calendar, Contacts, Mail, and Talk video chat from the Nextcloud App Store.
Built-in encryption, two-factor authentication, and a HackerOne bug-bounty program.
Self-install on your own hardware, buy a preinstalled device, or pick a hosting provider.
If you want the convenience of Google's suite without handing Google your data, Nextcloud is the mature, self-hosted alternative — files, calendar, contacts, and chat under your own roof.
11.5k stars for a self-hostable AI email assistant that organizes your inbox and pre-drafts replies in your own tone. It bulk-unsubscribes, blocks cold emails, and you can chat with it from Slack or Telegram. Built on Next.js / Tailwind / Prisma; run the hosted app at getinboxzero.com or self-host with a single CLI command.
View on GitHub →Organizes your inbox and pre-drafts replies in your own tone and style.
Describe how the AI should handle your inbox in plain English; "Reply Zero" tracks what still needs a response.
One-click bulk unsubscribe, bulk archive, and an automatic cold-email blocker.
Manage your inbox on the go; self-host with npx @inbox-zero/cli or use the hosted app.
A privacy-friendly, self-hostable take on AI email triage — the parts of Superhuman / Fyxer you'd pay for, open-sourced and rule-driven.
New (April 2026) and already 24k stars, riding the "give agents persistent design context" idea at the moment agentic coding is hot. It ships a real working CLI, a tagged 0.3.0 release, and carries the Google Labs Code name — but that GitHub org is not verified, so treat it as "from the Google Labs Code org," not an official Google product. Format is explicitly alpha.
View on GitHub →YAML front matter holds the exact token values; markdown body explains intent, giving agents both.
npx design.md lint outputs structured JSON findings, including WCAG contrast and broken-reference checks.
design.md diff compares two versions and flags token-level and prose changes as regressions.
A founder can hand their brand's colors and type to an AI agent as one file and get UIs that actually match, instead of re-explaining the look every session.
One of the liveliest HN threads on the format — opinionated takes on whether a markdown design spec really replaces design tools, and where an agent-readable style sheet actually helps. Read it for the pushback, not just the hype.
Only ~4 months old and already 23.7k stars, pushed today with a v0.8.1 release. It hits three hot buttons at once — MCP, AI coding agents, and token cost — behind a benchmark-heavy README (all vendor claims; the cited arXiv ID looks future-dated, so don't call it peer-reviewed). The "single static binary, zero dependencies, 100% local" story is exactly what agent users want.
View on GitHub →README claims it indexes the Linux kernel (28M LOC) in 3 minutes and answers structural queries in under 1ms.
README claims ~99% fewer tokens on structural queries vs file-by-file grep.
tree-sitter parsing plus "Hybrid LSP" type resolution for 11 languages.
If you build with AI coding agents, this is the kind of tool that makes them noticeably faster and cheaper by giving them a map of your code instead of making them read every file.
The youngest of the batch (created April 2026) yet already 4.7k stars, pushed today. Its tagline "Kill all the slop. Raise clean PR." lands squarely on today's anxiety about AI-generated code quality, and it's agent-agnostic across Claude, Codex, Copilot and others. (The v1.31.2 version number is fast automated CI releasing, not years of maturity.)
View on GitHub →git push no-mistakes runs an isolated review → test → docs → lint → PR pipeline without touching your working tree.
Works with claude, codex, opencode, copilot, or any agent via a common interface.
Safe mechanical fixes auto-apply; anything touching intent is escalated to approve / fix / skip.
Think of it as a bouncer for your code — an AI checkpoint that catches messy or broken changes before they ever reach your team's repo.
Normally you push straight to GitHub and open a PR — and if you made a mistake, your team's tests fail or a reviewer has to catch it. no-mistakes puts an AI checkpoint in front of that:
1. Push to the safety net — run git push no-mistakes instead of pushing straight to GitHub.
2. It tests in secret — copies your code to a hidden temporary workspace so your current work isn’t disturbed.
3. AI validation runs — checks for bugs, runs tests, fixes formatting, updates docs.
4. Fixes what it can, asks about the rest — auto-fixes the trivial stuff; for bigger issues it asks you to fix / approve / skip.
5. Clean release — only once every check is green does it push and open a clean, mistake-free PR.
Why use it: no broken builds, less time lost to silly mistakes, and it sits in front of AI assistants (Claude Code, Copilot) to catch bugs they introduce.
On the Show HN thread, commenters ask why not just review at PR time or use pre-commit hooks. Creator akane8 defends blocking slop before it becomes a PR and explains why hooks feel jarring — a genuine "does this actually help?" back-and-forth.
The highest star count of the batch's newcomers at 24.4k on a punchy, demoable premise (point at a URL, get a clean codebase). But it's the stale one: last push 2026-06-01 (~1 month ago) with release v0.3.1 back in March — high stars, cooling commits. It's a template, so the AI agent you bring does the work, and its own README flags ToS/impersonation risks.
View on GitHub →Run /clone-website
Recon → foundation → component specs (exact computed styles) → parallel builders in git worktrees → visual-diff QA.
Next.js 16, React 19, TypeScript strict, shadcn/ui, Tailwind v4, Lucide icons.
If you own a site stuck on WordPress or Webflow, or lost the source, this lets an AI agent rebuild it as a clean, modern codebase you control.
Give it a URL and AI reverse-engineers it into a clean Next.js codebase.
✅ Insanely high fidelity — it auto-screenshots, analyzes responsive layouts, and extracts CSS colors, fonts, spacing, even interactive states (clicks/hovers/scrolls).
✅ Cutting-edge stack — generated code is Next.js 16 + React 19 + shadcn/ui + Tailwind v4; super clean, no messy junk.
✅ Broad compatibility — works with Claude Code, Cursor, GitHub Copilot, and more.
How to use: type /clone-website [URL] in your terminal, run npm install, open in Cursor. “If you want to replicate a website’s design or do competitive analysis, this tool cranks your efficiency to the max.”
A fresh repo (mid-April 2026) that pulled ~9.2k stars in about ten weeks, riding interest in feed-forward 3D models, and backed by Ant Group's Robbyant. It's actively maintained with benchmark and long-video demo releases — but all speed/SOTA numbers are author claims, and the linked paper ID can't be verified, so call it "a linked paper," not peer-reviewed.
View on GitHub →README claims one streaming architecture unifying coordinate grounding, geometric cues, and drift correction.
README states ~20 FPS at 518×378 on sequences over 10,000 frames, via paged KV-cache attention.
Three model variants hosted on Hugging Face and ModelScope; the "long" variant is for large scenes.
This is the kind of camera-to-3D-world tech that will quietly power robots and AR — impressive, but firmly a researcher/engineer tool, not something a founder installs today.
The highest-scoring HN thread on LingBot-Map — practitioners weighing the geometric-context-transformer approach and the streaming-speed claims for real-time 3D. The linked paper is the team's own, so treat the benchmarks as claims.
The elder of the slate — created March 2015, a 10-year institution rather than a launch, that recurs on trending lists because it's continuously updated (pushed today) and endlessly useful. The README credits 1,600+ contributors (its own claim), and 128k stars reflect a decade of accumulated goodwill.
View on GitHub →Inclusion rules require a genuine free tier, not a trial; self-hosted software is out of scope.
The maintainer won't list services that gate TLS behind paid tiers, and treats SSO as a plus.
Dozens of sections: cloud always-free limits, CI/CD, CDN, DNS, email, storage, monitoring, generative AI.
If you're a founder or creator building without paying for infrastructure, this is one of the single most useful bookmarks on GitHub — a map of what you can run for free.
The marquee HN thread — 967 points, 197 comments — on the free-tiers list, with developers trading favorite always-free services and debating what belongs. A decade-strong "people love this" signal for the veteran of the week.
A new repo (mid-March 2026) that hit ~10.2k stars in ~3.5 months by riding the multi-agent coding boom. It ships extremely fast — the latest release at capture was v1.4.115, published the same day — and leans on a "we ship daily" narrative. Note 912 open issues on a repo this young: frame it as very active, not a defect.
View on GitHub →Fan one prompt across five agents, each in its own isolated git worktree, then compare and merge the winner.
Claude Code, Codex, Cursor, Copilot, OpenCode, Goose and many more — plus any terminal CLI agent.
iOS and Android apps let you monitor and steer agents and get notified when one finishes.
If you're a non-engineer builder leaning on AI coding agents, Orca is the "mission control" that lets you run several at once and pick the best result.
The Show HN thread for Orca — a real "what people said" discussion of running many coding agents in parallel worktrees, the Ghostty-class terminal, and whether the multi-agent workflow actually pays off.
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