Facts
I had a lot of issues with spec-driven approaches, agents are too readily producing fluff, large projects have so many specs agents start making mistakes maintaining them. There's a constant consistency tax. In the end every spec is just a bunch of facts, so I decided to leave that and throw away everything else while making it friendlier for agentic use. Introducing facts - skills and CLI for agents to use facts-driven development. https://github.com/av/facts
Facts launched on May 5, 2026 with 73 votes, #546 of 20,538 launches that month and more than 95% of that year's launches.
What it does
Your project has 48 facts, 31 of them implemented: code-backed, verified by command. 12 are specs your agent is working through. 5 are rough drafts you'll refine later. You know all of this because you ran facts check.
Antidote for fluffy specs, a toolkit for fact-driven development with AI agents - av/facts
Read your entire project spec in 30 seconds. Verify it in one command. Your project has 48 facts, 31 of them implemented: code-backed, verified by command. 12 are specs your agent is working through. 5 are rough drafts you'll refine later. You know all of this because you ran facts check . # auth - users authenticate via OAuth2 @implemented - sessions expire after 24 hours @implemented - failed logins rate-limited to 5 per minute @spec - label: bcrypt password hashing with cost factor 12 command: grep -q 'bcrypt.*12' src/auth.ts # data - all timestamps stored in UTC @implemented - soft deletes only, no rows dropped @spec - PII encrypted at rest @draft # api - REST with versioned endpoints…from github.com
Does a similar job
all alternatives →
Agent SkillsMar 2026 · agentskill.sh · ▲190Curated agent-skills: 1,000+ practical skills selected from 63,000+ GitHub skills. Filter by newest/hot/installs. Works with Claude Code, Codex, ChatGPT, and Cursor.
Aviator Agents2025 · aviator.co · ▲97When we asked developers from large engineering teams, almost everyone is using Claude Code, Cursor, or Copilot. But adoption is still inconsistent. Some of us would have 15 agents running in Claude Code at the same time; some would still refuse to use any of them and write code manually. There's a fragmented AI development problem: - Five developers building similar features, and all of them start from scratch - No way to share "here's how we prompt AI to follow our architecture" - No trail of how the AI changes were generated - People leave the company, so does the their knowledge Aviator…
Deep Work PlanJun 2026 · deepworkplan.com · ▲114Models matter. Context matters more. Give your agent a plan.
Spec27Apr 2026 · spec27.ai · ▲34Hi HN! We’re a team of ML validation specialists and we’ve been building /Spec27, a tool for testing whether AI agents still do their job safely and reliably as models, prompts, tools, and surrounding systems change. We started working on this because a lot of current LLM evaluation work seems aimed at scoring general model behavior, while many teams are deploying systems that have a specific mission to fulfill. Many of the tools also assume you have full access to the agent stack and traces so you can place SDKs and Gateways, but a lot of agents are being created on vendor platforms…
Continue – Source-controlled AI checks, enforceable in CIFeb 2026 · docs.continue.dev · ▲44We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product. Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds…
FactIQ – a realtime econ+finance database for AI agentsJul 2026 · github.com · ▲7Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video
Source: Product Hunt launch ↗
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
Dev tools · May 2026 · github.com