Загрузка видео...

Не удалось загрузить видео

На главную

LLMs landed. The landscape changed. We helped Stack Overflow change with it. From a repository of static knowledge, to the human-validated engine room for the AI era. Redefined. Repositioned. Refreshed. Full case study →

39,061 просмотров • 3 месяцев назад •via X (Twitter)

Комментарии: 13

Фото профиля vasudev
vasudev3 месяцев назад

@StackOverflow i miss the old stack overflow days. browsing at 3am to finish a project...

Фото профиля Nick Venturi
Nick Venturi3 месяцев назад

@StackOverflow hope the copy paste button is still in the same place

Фото профиля Gobind Bakhshi
Gobind Bakhshi3 месяцев назад

@StackOverflow we got stack overflow with aura before GTA 6

Фото профиля Simon Lind
Simon Lind3 месяцев назад

@StackOverflow lovely work. doesn't look like it's live yet though? :)

Фото профиля Lorenzo de Lijser
Lorenzo de Lijser3 месяцев назад

@StackOverflow Love the case study!

Фото профиля Phil Hedayatnia
Phil Hedayatnia3 месяцев назад

@StackOverflow @joweyroden this is brilliant!

Фото профиля Gargarin
Gargarin3 месяцев назад

@StackOverflow Beautiful work

Фото профиля Maxx
Maxx3 месяцев назад

@StackOverflow utterly elite

Фото профиля capitalized time
capitalized time3 месяцев назад

@StackOverflow One of the best in class Great case study and loved how you added the pyramid design tool in it

Фото профиля Shady Red Pilgrim
Shady Red Pilgrim3 месяцев назад

@StackOverflow Amazing work by the Koto team as always. Really nailed the new repositioning for Stack.

Фото профиля Ed
Ed3 месяцев назад

@StackOverflow Love how you utilized the “stack” icon. Great stuff!

Фото профиля rob 🌿
rob 🌿3 месяцев назад

@StackOverflow Nice

Фото профиля Amjad
Amjad3 месяцев назад

@StackOverflow Bangerrrr 🔥🔥🔥🔥

Похожие видео

I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

Muratcan Koylan

85,353 просмотров • 1 год назад