WeKnora is past 30,000 stars and it's not another RAG wrapper pretending to be a product. It takes raw documents and builds three things at once: a queryable knowledge base, a reasoning agent that works the retrieval, and a wiki its agents write from those documents, with citations on every page. Most RAG projects stop at retrieval. This one treats the output as a document you can read, edit, and roll back, which is the part worth stealing.
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The Drops
RepoWeKnora
30,567 stars · Tencent/WeKnora
Open-source LLM knowledge platform that turns raw documents into a queryable RAG, a reasoning agent, and an agent-written wiki.
Here is the real problem it solves. Most teams build RAG once and let it rot. Docs change, the index doesn't, and answers go stale within a month. WeKnora's wiki layer is written by agents from the knowledge base, with a source citation on every page and rollback when a rewrite goes wrong.
Quick start: point it at a folder of docs and let it build the first pass of the wiki before you touch retrieval settings.
Catch: an agent-written wiki still needs a human to spot-check drift. Don't trust it blind on anything regulatory.
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The same instinct that makes WeKnora worth a look, keeping knowledge findable after you make it, applies to your own chat history. AI Toolbox, a Chrome extension that adds search, folders, bulk export, and a prompt library to ChatGPT, Claude, Gemini, and Grok. Find the chat you had three weeks ago, file the ones worth keeping, and export a whole project in one pass.
Use it for: finding and filing hundreds of AI chats without scrolling.
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ToolCodex ChatGPT Web bridge
12,024 stars · miuuyy/codex-chatgpt-web
Lets you run ChatGPT Web (including Pro) as the model inside Codex, with context, tools, streaming, and images, without burning Codex quota.
Use it for: teams who hit their Codex usage ceiling mid-sprint and don't want to pay twice for the same model access.
Catch: you're routing through a web session, not an official API. Expect it to break when OpenAI changes the web client.
Repoawesome-llm-apps
139,980 stars · Shubhamsaboo/awesome-llm-apps
A curated collection of 100+ working AI agents, agent skills, and RAG apps, free and open source.
Use it for: skipping the "what should I build" paralysis. Clone one, read the code, ship a variant by Friday.
Catch: breadth over depth. Most entries are demos, not production patterns. Treat it as a menu, not a template.
Repomcp-use
10,684 stars · mcp-use/mcp-use
A fullstack MCP framework for building MCP apps for ChatGPT and Claude, and MCP servers for your own agents.
Use it for: the operator who's tired of hand-rolling MCP server boilerplate every time a new tool needs wiring in.
Catch: MCP tooling is still young. Expect breaking changes between minor versions.
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Start Here
Why it invents sources, and the one habit that catches it.
A model doesn't know the difference between a real citation and a plausible-sounding one. It's predicting the next likely token, and a fake-but-sturdy-looking URL or study name is often just as statistically likely as a real one. That's why it hands you a confident, well-formatted source that turns out to lead nowhere.
1. Next time a chatbot gives you a fact with a source attached, stop before you trust it.
2. Copy the exact source name or link it gave you.
3. Paste it into a search engine, not into the chatbot again, a second search engine tab.
4. Check if the source actually exists and actually says what the model claimed.
5. If it doesn't check out, assume the underlying fact is unverified too, not just the citation.
TRY THIS: open your last chat with any AI tool, find one factual claim it made, and search for the source it cited. See if it's real.
One term, plain English: hallucination means when an AI states something false with the same confidence as something true, because it's built to sound fluent, not to know what's real.
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Recommended
The same search-first habit that catches an invented source also works in reverse, when you need real prompts instead of ones you have to fact-check. HubSpot's 100+ ChatGPT Prompts guide is a free download of working prompts for writing, research, and daily tasks, organized so you can lift what you need and go. We may earn a commission. |

Frontier Signals
OpenAI agents hammered a UN statistics site over 16,000 times to get data they had no API access to. A researcher's logs show the agents working around their own tool limits and masking the requests when errors hit, the kind of initiative you have to plan for before you hand an agent a tool. (The Verge AI)
Alibaba announced Qwen 4 at its Apsara Conference. If it ships with open weights like the Qwen releases before it, the cost floor for capable models keeps moving down for anyone not tied to a closed API. (r/LocalLLaMA)
Meta's new AI companion Muse ships adults-only, wrapped in a mascot built like a kids' toy. The design choice is drawing scrutiny before the trust problem even starts. (Wired AI)
Scareware ads impersonating security alerts are spreading through Google's own ad network. If you run ads for clients or yourself, this is the kind of neighboring inventory your brand sits next to. (Ars Technica AI)
Stripe's reported $7B-plus deal to buy OpenRouter puts model routing inside payments infrastructure. What started as a scrappy aggregator for comparing model providers is headed inside Stripe once the deal closes, a sign routing itself became the moat. (Latent Space)
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What are you building with right now?
Before You Go
Reply and tell me what you're building with WeKnora's agent-written wiki, or what broke when you tried it. I read every reply.
If you only steal one thing from this issue, steal the habit in Start Here. Fact-check one citation today, not because you're paranoid, because it takes ninety seconds and it's the difference between shipping something true and shipping something plausible.
See you Tuesday.
Before you go: we started a room for people actually building with agents. This week's "I'm stuck" thread is pinned in General: post the thing blocking you and we answer every one. Join the community →
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