[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-qodo-raises-70m-to-verify-ai-written-code-zh":3,"tags-qodo-raises-70m-to-verify-ai-written-code-zh":33,"related-lang-qodo-raises-70m-to-verify-ai-written-code-zh":47,"related-posts-qodo-raises-70m-to-verify-ai-written-code-zh":51,"series-tools-68f5abbe-9348-4403-97c5-41dcf47ff6ff":88},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":21,"translated_content":10,"views":22,"is_premium":23,"created_at":24,"updated_at":24,"cover_image":11,"published_at":25,"rewrite_status":26,"rewrite_error":10,"rewritten_from_id":27,"slug":28,"category":29,"related_article_id":30,"status":31,"google_indexed_at":32,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":23},"68f5abbe-9348-4403-97c5-41dcf47ff6ff","Qodo 募資 7000 萬美元盯 AI 程式碼品質","\u003Cp>\u003Ca href=\"\u002Fnews\u002Fvibe-coded-apps-slow-ios-app-store-review-zh\">AI\u003C\u002Fa> 寫 code 的速度很兇。問題也跟著來了。誰來查這些 code？\u003Ca href=\"https:\u002F\u002Fqodo.ai\" target=\"_blank\" rel=\"noopener\">Qodo\u003C\u002Fa> 認為答案不是再做一個更會寫的模型，而是做一層更會查的系統。它最近拿到 7000 萬美元，想把這件事塞進企業流程。\u003C\u002Fp>\u003Cp>講白了，現在不是大家不會寫 code。是 code 寫太快。\u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa> 這類工具一直把產出速度往上推。可是一旦產出量暴增，review 也會爆炸。\u003C\u002Fp>\u003Cp>\u003Ca href=\"\u002Fnews\u002Fqodo-raises-70m-verify-ai-generated-code-zh\">Qodo\u003C\u002Fa> 的賭注很直接。生成是第一步。驗證才是進 production 前的門檻。這個方向聽起來不浪漫，但很務實。企業買單的，通常就是這種東西。\u003C\u002Fp>\u003Ch2>Qodo 為什麼押注驗證\u003C\u002Fh2>\u003Cp>\u003Ca href=\"\u002Fnews\u002Fintuit-qodo-ai-code-review-investor-angle-zh\">Qodo\u003C\u002Fa> 的核心想法很簡單。寫 code 和查 code，是兩件不同的事。模型可以很快生出一個 function。可是它不會自動知道團隊規範，也不會知道舊系統裡有哪些雷。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122235517-i0yl.png\" alt=\"Qodo 募資 7000 萬美元盯 AI 程式碼品質\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這件事在大型 codebase 特別明顯。你改一行，看起來沒事。可它可能會撞到舊模組，或是打破某個內部約定。人類 reviewer 也常漏掉。LLM 只靠上下文窗，更容易漏。\u003C\u002Fp>\u003Cp>Qodo 的做法是把 AI agent 放進 code review、testing、governance。它不是只看 diff。它想看整體影響。這種思路，比單純抓 syntax error 更接近企業需求。\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>募資金額：\u003C\u002Fstrong>7000 萬美元 Series B\u003C\u002Fli>\u003Cli>\u003Cstrong>累計募資：\u003C\u002Fstrong>1.2 億美元\u003C\u002Fli>\u003Cli>\u003Cstrong>成立時間：\u003C\u002Fstrong>2022 年\u003C\u002Fli>\u003Cli>\u003Cstrong>總部：\u003C\u002Fstrong>紐約\u003C\u002Fli>\u003C\u002Ful>\u003Cp>創辦人 Itamar Friedman 的背景也很有意思。他之前共同創辦 \u003Ca href=\"https:\u002F\u002Fwww.visualead.com\" target=\"_blank\" rel=\"noopener\">Visualead\u003C\u002Fa>，後來被 Alibaba 收購。他也在 \u003Ca href=\"https:\u002F\u002Fwww.alibabagroup.com\" target=\"_blank\" rel=\"noopener\">Alibaba\u003C\u002Fa> 管過機器視覺業務。\u003C\u002Fp>\u003Cp>更早之前，他在 \u003Ca href=\"https:\u002F\u002Fwww.mellanox.com\" target=\"_blank\" rel=\"noopener\">Mellanox\u003C\u002Fa> 工作。那家公司後來被 \u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\" target=\"_blank\" rel=\"noopener\">Nvidia\u003C\u002Fa> 收購。Friedman 看過硬體驗證怎麼做，所以他把那套思維搬到軟體世界。這種背景，確實比只做過 SaaS 的創辦人更懂「驗證」這件事。\u003C\u002Fp>\u003Ch2>它想解的，其實是信任問題\u003C\u002Fh2>\u003Cp>現在很多團隊都在用 AI coding 工具。可是信任沒有跟著一起長。這很正常。因為 AI 可以寫得很順，卻不保證它懂你的商業規則。\u003C\u002Fp>\u003Cp>你可能會想問，既然有 code review，為什麼還需要另一層？原因很簡單。人類 reviewer 會累。AI reviewer 也會累，只是它的累表現成幻覺、漏檢，或是過度自信。\u003C\u002Fp>\u003Cp>Qodo 想做的是把「公司自己的標準」放進系統。像是歷史決策、命名規則、架構習慣、風險邊界。這些東西不是一般模型訓練資料裡就會有的。\u003C\u002Fp>\u003Cblockquote>“Generating systems and verifying systems require very different approaches (different tools, different thinking).” — Itamar Friedman\u003C\u002Fblockquote>\u003Cp>這句話很直接。意思就是，會寫不代表會查。你可以把它理解成：產 code 是創作，驗 code 是審核。兩者用的腦袋本來就不同。\u003C\u002Fp>\u003Cp>Qodo 也想處理另一個現實問題。AI 工具產出太多了。review 團隊如果被雜訊淹沒，最後只會一路按掉提醒。那樣就失去意義。工具要夠準，工程師才會持續用。\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Martian Code Review Bench：\u003C\u002Fstrong>64.3%\u003C\u002Fli>\u003Cli>\u003Cstrong>領先第二名：\u003C\u002Fstrong>超過 10 分\u003C\u002Fli>\u003Cli>\u003Cstrong>領先 Claude Code Review：\u003C\u002Fstrong>25 分\u003C\u002Fli>\u003Cli>\u003Cstrong>Qodo 2.0：\u003C\u002Fstrong>多 agent code review 系統\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這組數字很重要。benchmark 不是全部，但它至少說明一件事。Qodo 不是只在講故事。它在 code review 這塊，確實打出了可量化的成績。\u003C\u002Fp>\u003Ch2>Qodo 跟其他工具差在哪\u003C\u002Fh2>\u003Cp>現在市場上，AI coding 工具很多。大多數產品都在拼速度。autocomplete 更快。寫 function 更快。補註解更快。這些都很有用，但它們解的是「產出」問題。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122223524-tcg1.png\" alt=\"Qodo 募資 7000 萬美元盯 AI 程式碼品質\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Qodo 走的是另一條路。它解的是「准不准進去」的問題。這條路比較慢，也比較難賣。可是如果企業真的在意 production 安全，這條路反而比較值錢。\u003C\u002Fp>\u003Cp>它的競爭對手不只是一個。像 \u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> 和 \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa> 都在往 coding 走。只是它們的重點還是模型能力。Qodo 則是把焦點放在企業驗證流程。\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Qodo 客戶：\u003C\u002Fstrong>Nvidia、Walmart、Red Hat\u003C\u002Fli>\u003Cli>\u003Cstrong>其他客戶：\u003C\u002Fstrong>Intuit、Texas Instruments、Monday.com、JFrog\u003C\u002Fli>\u003Cli>\u003Cstrong>產品重點：\u003C\u002Fstrong>多 agent review\u003C\u002Fli>\u003Cli>\u003Cstrong>風險處理：\u003C\u002Fstrong>跨檔案問題、邏輯 bug、組織規則\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這種定位有一個好處。它更容易進 enterprise。因為企業不是只想要一個聰明助手。企業想要的是能接進流程、能配合 policy、能留下紀錄的工具。\u003C\u002Fp>\u003Cp>說得更白一點，AI coding 工具很多都像副駕駛。Qodo 想當 gatekeeper。這兩個角色差很多。副駕駛幫你開快。gatekeeper 決定你能不能上路。\u003C\u002Fp>\u003Ch2>這筆錢代表什麼\u003C\u002Fh2>\u003Cp>這輪 7000 萬美元，不只是 Qodo 自己的事。它也反映 AI coding 市場正在變。第一波大家比誰寫得快。下一波會比誰查得準。\u003C\u002Fp>\u003Cp>這個轉向很合理。因為 code 量一多，review 成本就會上升。你省下 30 分鐘寫 code，卻花 2 小時查 bug，那就沒什麼好炫耀的。\u003C\u002Fp>\u003Cp>Friedman 把這件事稱作從 stateless AI 走向 stateful systems。這句話有點學術味，但意思不難懂。企業軟體需要記憶、規則、上下文。不是只會吐字串就夠了。\u003C\u002Fp>\u003Cp>我自己的看法很直白。接下來 12 到 18 個月，真正值錢的 AI 工具，不會只是會寫。會查、會擋、會記錄，才會進到預算表。\u003C\u002Fp>\u003Cp>這也會改變工程團隊的工作方式。未來不是每個人都拿 AI 亂生 code。比較像是先產出，再交給驗證層過濾。速度還是重要，但信任會更重要。\u003C\u002Fp>\u003Ch2>台灣團隊可以怎麼看\u003C\u002Fh2>\u003Cp>如果你在台灣做軟體，這件事其實很有感。很多公司已經在用 AI coding。只是 review 流程沒有跟上。結果就是 code 變多，review 壓力也變大。\u003C\u002Fp>\u003Cp>尤其是金融、電商、製造、SaaS 這些場景。只要碰到權限、金流、資料流，漏一個 bug 都很痛。這時候不是看誰寫得快，而是看誰能把風險壓下來。\u003C\u002Fp>\u003Cp>所以 Qodo 這種產品，真正的價值不在「更會寫」。而在「幫你少踩雷」。如果它真的能把 review 時間壓下來，同時維持品質，那就會很有市場。\u003C\u002Fp>\u003Cp>我會盯三件事。第一，benchmark 能不能落地。第二，false positive 會不會太多。第三，能不能真的吃進企業既有流程。這三個過不了，再會講也沒用。\u003C\u002Fp>\u003Cp>如果你是工程主管，現在就該問一個問題：團隊用 AI 生 code 之後，誰負責驗證？如果答案還是「人工慢慢看」，那你的流程很快就會卡住。這不是危言聳聽，這是現場會發生的事。\u003C\u002Fp>\u003Cp>Qodo 把 7000 萬美元押在這個洞上。接下來要看的是，它能不能從 benchmark 走進真實 codebase。這才是最硬的考題。\u003C\u002Fp>","Qodo 募資 7000 萬美元，主打 AI 程式碼驗證。它押注的不是生成速度，而是企業能不能放心把 AI 寫的 code 丟進 production。","techcrunch.com","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F03\u002F30\u002Fqodo-bets-on-code-verification-as-ai-coding-scales-raises-70m\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122235517-i0yl.png",[13,14,15,16,17,18,19,20],"Qodo","AI code review","程式碼驗證","AI coding","企業軟體","LLM","code 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定價其實比看起來更便宜","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778869845081-j4m7.png","2026-05-15T18:30:25.797639+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":29},"68e4be16-dc38-4524-a6ea-5ebe22a6c4fb","why-vidhub-huiyuan-hutong-bushi-quan-shebei-tongyong-zh","為什麼 VidHub 會員互通不是「買一次全設備通用」","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778789450987-advz.png","2026-05-14T20:10:24.048988+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":29},"7a1e174f-746b-4e82-a0e3-b2475ab39747","why-buns-zig-to-rust-experiment-is-right-zh","為什麼 Bun 的 Zig-to-Rust 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Copilot","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778742645084-dao9.png","2026-05-14T07:10:29.371901+00:00",{"id":83,"slug":84,"title":85,"cover_image":86,"image_url":86,"created_at":87,"category":29},"4adef3ab-9f07-4970-91cf-77b8b581b348","why-databricks-model-serving-is-right-default-zh","為什麼 Databricks Model Serving 是生產推論的正確預設","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778692245329-a2wt.png","2026-05-13T17:10:30.659153+00:00",[89,94,99,104,109,114,119,124,129,134],{"id":90,"slug":91,"title":92,"created_at":93},"de769291-4574-4c46-a76d-772bd99e6ec9","googles-biggest-gemini-launches-in-2026-zh","Google 2026 最大 Gemini 盤點","2026-03-26T07:26:39.21072+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"855cd52f-6fab-46cc-a7c1-42195e8a0de4","surepath-real-time-mcp-policy-controls-zh","SurePath 推出即時 MCP 政策控管","2026-03-26T07:57:40.77233+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"9b19ab54-edef-4dbd-9ce4-a51e4bae4ebb","mcp-in-2026-the-ai-tool-layer-teams-use-zh","2026 年 MCP：團隊真的在用的 AI 工具層","2026-03-26T08:01:46.589694+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"af9c46c3-7a28-410b-9f04-32b3de30a68c","prompting-in-2026-what-actually-works-zh","2026 提示工程，真正有用的是什麼","2026-03-26T08:08:12.453028+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"05553086-6ed0-4758-81fd-6cab24b575e0","garry-tan-open-sources-claude-code-toolkit-zh","Garry Tan 開源 Claude Code 工具包","2026-03-26T08:26:20.068737+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"042a73a2-18a2-433d-9e8f-9802b9559aac","github-ai-projects-to-watch-in-2026-zh","2026 必看 20 個 GitHub AI 專案","2026-03-26T08:28:09.619964+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"a5f94120-ac0d-4483-9a8b-63590071ac6a","claude-code-vs-cursor-2026-zh","Claude Code 與 Cursor 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