Building a lot of stuff since past 1 year Go2Billing — ₹60K/month generating billing SaaS go2billingsoftware.com AutoRAG — Upload any document, get a self-improving AI knowledge base. deepanshuverma.site/rag-anyt… Rest of my live SaaS products → deepanshuverma.site

awesome-autoresearch 更新，新增 6 条目。 Trace2Evolve — 把 autoresearch 用在客服 agent 的自我进化上。自动生成困难 case、评分 trace、分类失败原因，只在 benchmark 和可靠性门禁都通过时才保留改进。跨零售和航司任务都有验证。 bob-autoresearch — Karpathy 的 autoresearch 移植到了 IBM Bob Shell。用贪吃蛇 AI 做 demo，10-15 轮 keep/discard 迭代就从随机乱跑到 Hamilton 路径满分。 claude-code-nightly — 让你的 Claude Code 配置每晚自己进化。从会话历史中自动挖掘 eval suite，然后用 Karpathy 式循环改进 CLAUDE.md、hooks、skills 和 memory，git 回滚保护。 AutoRAG Research Lab — 用 Karpathy mode 跑 RAG 流水线调优。Planner → Worker → Evaluator 多 agent 协作，Docker 沙箱隔离，语义无操作检测防假迭代。 Google Play Description Autoresearcher — autoresearch 应用到 ASO。每轮跑 ~25 次关键词密度实验，只保留能提升匹配分数的 Google Play 描述版本。 📊 222 实现 114 讨论 = 336 条目 🔗 github.com/yibie/awesome-aut…

@Cloudflare (member company) breaks down how they use Workers AI, AutoRAG, MCP, Durable Objects, Queues, Vectorize, and evals for internal tooling. The pattern: build AI tools close to the runtime, data, and permissions layer. Read on: bit.ly/4dk6xQI

Building a lot of stuff since past 1 year AutoRAG — Upload any document, get a self-improving AI knowledge base instantly. deepanshuverma.site/rag-anyt… Checkout rest SAAS Products here: deepanshuverma.site

I recorded two podcast episodes today. For the second episode, he created "K-Skill" for Koreans and reached 4.9k Number of GitHub stars in just one month. And he's also the developer who created AutoRAG. This person is truly committed to the open-source ecosystem. He says that artificial intelligence (AI) is more popular in Korea than ever before! I'll post it soon. I'll also upload English subtitles for English-speaking viewers. Don't worry.

As a vibr coder, this's all you need Tech stack 📌 > Research & Discovery – Perplexity AI, Grok > UI Component Generator – v0 > App & Website Builder – Lovable, Replit > Mobile Apps Builder – Rork > AI IDE – Cursor, Claude, Codex > Agentic Workflow – n8n, LangGraph > AutoRAG – LlamaIndex, Flowise > Database – Supabase > Authentication – Stack Auth > Subscriptions – RevenueCat > Documentation – Mintlify, NotebookLM > Deployment – Vercel, Railway > Fast Collaborative Editor – Zed Industries > Image Generation – Recraft, Gemini

For a future iOS app backend, I’d treat Cloudflare as: -> Workers API -> D1 for app DB -> R2 for files/uploads -> Queues for async jobs -> Durable Objects for realtime/user/session state -> Workflows for durable multi-step jobs -> AI Gateway for LLM calls -> Vectorize/AutoRAG for retrieval -> Browser Rendering for web automation/crawling -> Containers/Sandboxes for heavier compute -> Dynamic Workers only when we need safe runtime code execution

@CloudflareDev AI Search (fka AutoRAG), now has wrangler CLI commands. Use it to create new resources or manage your existing ones. Let us know if there are any other commands you want to see us add.

【テックブログ更新⚡️】 今週の週刊Cloudflareです！ AI SearchにOpenAI互換のREST APIエンドポイントが追加されました✨ 従来のAutoRAG APIエンドポイントからの移行が推奨されていますが、旧版も引き続きサポートされます！ zenn.dev/gemcook/articles/we… @asahiXXXXXXXXX #gemcook #zenn #cloudflare

Cloudflare's AI Search (AutoRAG) is now manageable via Wrangler with a new set of commands, it was only possible in the dashboard before Tthere's a nice little step-by-step guide when using the creation command, lots of love for Wrangler today with the Workflows updates earlier!

You can now embed AI-powered search on any website using Cloudflare's AI Search (formerly AutoRAG) with new component support! There's 4 supported components (search bar, search modal, chat bubble, chat page) with a range of configuration options via props framework support

This is something I have built that use AutoRAG (AI Search) x.com/harshil1712/status/203… I also have another app that uses it. AI Search simplifies the whole RAG pipeline, that’s what I love about it

Hi Georgios! I work on AI Search (fka Autorag). What are you trying to build?

i want instantly indexxed, co-located, low-latency full text search. not vector search. autorag would introduce extra async trips with s3 and vectorize. and i dont really need embeddings. so something more like rg thats works on data in a durable object

Why would you not use autorag for search?

It’s a waste of time. Most AutoRAG tools fail because they are still stuck in: chunk make embeddings do retrieval. You’re better off defining some metadata tags for pages, storing them in a database and making page summaries optimized for BM25 for fast retrieval.

anyone using Cloudflare's AutoRAG and wanna sell me on it?

its absurd how cheap cloudflare is we're like ~$300/mo on total spend right now, which is our MCP some other stuff, and im pretty sure a large chunk of that spend is from autorag usage

とてつもなく新しい考え、というわけではなく、すでにこれはAgentic-RagとしてAzureでも、ReActなどでも先駆けがあり、実際にこれは次に採用されてくるはずです。 が、これには非常に大きな課題が今はあるため、まだ導入されていないというのが、 Agentic にすると、改善できる一方で 最悪ケース が一気に悪化します。 データ未整備：取得しても “使える根拠” にならない（フォーマット不統一・版管理・メタ欠落） チャンク不適：ツールが「読むべき単位」を誤ると、全文読み込み連打＝コスト爆発 迷走/揺らぎ：探索系は局所最適・ループ・再試行で挙動がブレる（監査/再現性に弱い） つまり「平均精度」より先に P99コストとP99挙動 を潰さないと採用されない可能性が今の状況では高いというのがヒアリング結果です。 ここを厳密に制御するためのダイナミックなツールセレクトをここ数ヶ月で研究中です。 AutoRAGなどが近い思想を持っていますかも、汎用的かつ再現性ある精度、というのには、運用が絡む前提のカスタマイズが必須なのが今なので、その汎用化、つまりデータ側のセレクトと、A-RAGのツールセレクトのポリシーがセットでパッケージ化されて回るようになると、一気にスタンダードになると考えます。

感覺很像 cloudflare autorag agent 版