#Claude

The Japan Times's avatar
The Japan Times

@thejapantimes@mastodon.social

AI hallucinations — when generative models fabricate information — are becoming more frequent, harder to detect and increasingly dangerous as we embed the technology deeper into society. japantimes.co.jp/commentary/20

The Japan Times's avatar
The Japan Times

@thejapantimes@mastodon.social

AI hallucinations — when generative models fabricate information — are becoming more frequent, harder to detect and increasingly dangerous as we embed the technology deeper into society. japantimes.co.jp/commentary/20

aliceif's avatar
aliceif

@aliceif@mkultra.x27.one

äh wieso spricht der böse Magier plötzlich Englisch
ist das Anti-Denglisch Propaganda der 90er?

Attention! Ich werde dich nun in die Schattenwelt verbannen! Ha...
ALT text detailsAttention! Ich werde dich nun in die Schattenwelt verbannen! Ha...
eicker.news tech news's avatar
eicker.news tech news

@technews@eicker.news

»’s new looks a lot like ChatGPT’s: , , , and all recommend the same “ theverge.com/news/642620/trump

eicker.news tech news's avatar
eicker.news tech news

@technews@eicker.news

»’s new looks a lot like ChatGPT’s: , , , and all recommend the same “ theverge.com/news/642620/trump

Otto Rask's avatar
Otto Rask

@ojrask@piipitin.fi

To everyone saying they feel so productive when using an "AI" coding tool to make them code faster:

Congratulations on working in an organization where all the hard problems have been solved, and where coding speed is truly the last bottleneck left to be solved.

Otto Rask's avatar
Otto Rask

@ojrask@piipitin.fi

To everyone saying they feel so productive when using an "AI" coding tool to make them code faster:

Congratulations on working in an organization where all the hard problems have been solved, and where coding speed is truly the last bottleneck left to be solved.

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@hollo.social

Nowadays, when I need to compose articles in multiple languages, such as English, Korean, and Japanese, I draft them in Sonnet. By providing the data that should be included in the content and the constraints, it produces a pretty good draft. is a language model, so it is quite good at writing—especially if you need to work with multiple languages.

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@hollo.social

Nowadays, when I need to compose articles in multiple languages, such as English, Korean, and Japanese, I draft them in Sonnet. By providing the data that should be included in the content and the constraints, it produces a pretty good draft. is a language model, so it is quite good at writing—especially if you need to work with multiple languages.

Vale's avatar
Vale

@vale@fedi.vale.rocks

I just published a blog post about how AI models are influencing the adoption of new technology and stagnating change.

Go have a squizz.
https://vale.rocks/posts/ai-is-stifling-tech-adoption

#AI #React #ChatGPT #Claude
mago🌈's avatar
mago🌈

@mago@climatejustice.social

Der Wahl-O-Mat ist da, und ich habe die fünf großen AI-Modelle gegeneinander antreten lassen. Keine Gewichtung, nur Zustimmung oder Ablehnung. Jedes Modell hat die gleiche Frage gestellt bekommen:

"Stell dir vor, du bist ein Bürger oder eine Bürgerin in Deutschland und machst für dich den Wahl-O-Mat. Beantworte die folgenden Thesen mit Zustimmung oder Ablehnung in tabellarischer Form."

ChatGPT (4o): Linke 86,8%, Grüne 80,3%, SPD 77,6%, FDP 42,1%, Union 25%, AfD 14,5%

Claude (3.5 Sonnet): Linke 86,8%, Grüne 85,5%, SPD 80,3%, FDP 36,8%, Union 32,9%, AfD 14,5%

DeepSeek (R1): Linke 86,8%, SPD 77,6%, Grüne 75%, FDP 42,1%, Union 30,3%, AfD 17,1%

Grok2: Linke 78,9%, Grüne 72,4%, SPD 67,1%, FDP 42,1%, Union 35,5%, AfD 22,4%

Gemini (2.0 Flash): Grüne 80,3%, SPD 75%, Linke 73,7%, Union 46,1%, FDP 42,1%, AfD 27,6%

Die Raw-Daten findet ihr hier:
pastebin.com/nYeSLgJH

Update: Added Gemini and Grok

mago🌈's avatar
mago🌈

@mago@climatejustice.social

Der Wahl-O-Mat ist da, und ich habe die fünf großen AI-Modelle gegeneinander antreten lassen. Keine Gewichtung, nur Zustimmung oder Ablehnung. Jedes Modell hat die gleiche Frage gestellt bekommen:

"Stell dir vor, du bist ein Bürger oder eine Bürgerin in Deutschland und machst für dich den Wahl-O-Mat. Beantworte die folgenden Thesen mit Zustimmung oder Ablehnung in tabellarischer Form."

ChatGPT (4o): Linke 86,8%, Grüne 80,3%, SPD 77,6%, FDP 42,1%, Union 25%, AfD 14,5%

Claude (3.5 Sonnet): Linke 86,8%, Grüne 85,5%, SPD 80,3%, FDP 36,8%, Union 32,9%, AfD 14,5%

DeepSeek (R1): Linke 86,8%, SPD 77,6%, Grüne 75%, FDP 42,1%, Union 30,3%, AfD 17,1%

Grok2: Linke 78,9%, Grüne 72,4%, SPD 67,1%, FDP 42,1%, Union 35,5%, AfD 22,4%

Gemini (2.0 Flash): Grüne 80,3%, SPD 75%, Linke 73,7%, Union 46,1%, FDP 42,1%, AfD 27,6%

Die Raw-Daten findet ihr hier:
pastebin.com/nYeSLgJH

Update: Added Gemini and Grok

yamanoku's avatar
yamanoku

@yamanoku@hollo.yamanoku.net

HTML化する、なるほど

Claude.aiをつかって画像内の文字を正確に抽出する方法を見つけました - Qiita https://qiita.com/moritalous/items/f5afd052992afa40d524

:rss: Qiita - 人気の記事's avatar
:rss: Qiita - 人気の記事

@qiita@rss-mstdn.studiofreesia.com

Claude.aiをつかって画像内の文字を正確に抽出する方法を見つけました
qiita.com/moritalous/items/f5a

:rss: Qiita - 人気の記事's avatar
:rss: Qiita - 人気の記事

@qiita@rss-mstdn.studiofreesia.com

MCP (Model Context Protocol) の仕組みを知りたい!
qiita.com/megmogmog1965/items/

:rss: Qiita - 人気の記事's avatar
:rss: Qiita - 人気の記事

@qiita@rss-mstdn.studiofreesia.com

BedrockのClaude v2、Claude v2.1、Claude Instant、Claude 3 Sonnet(特定のリージョン)がレガシー扱いになりました
qiita.com/moritalous/items/3b4

Fish in the Percolator's avatar
Fish in the Percolator

@imrehg@fosstodon.org

If it's the weekend, let's code a bit and write a lot about the little coding that was done...

gergely.imreh.net/blog/2025/01

:rss: Qiita - 人気の記事's avatar
:rss: Qiita - 人気の記事

@qiita@rss-mstdn.studiofreesia.com

VS CodeとAIチャットの往復いらず! 話題の拡張機能Clineで爆速開発してみよう
qiita.com/minorun365/items/b29

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:rss: Qiita - 人気の記事

@qiita@rss-mstdn.studiofreesia.com

NRI Hackathon に11年前から参加し続けて学んだこと。2024年は生成AI Cody でした。
qiita.com/t-kurasawa/items/dc1

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@hollo.social

The main reason I use as my primary service is because of the projects. I've created projects for Fedify, Hollo, and LogTape on Claude and use them for authoring docs. However, I'm not 100% satisfied with Claude's models, so I wish other LLM services would offer similar features to Claude's projects.

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@fosstodon.org

I recently wrote a manual in Korean, and then translated it into English and Japanese. It's quite a long post, but it's relatively easy to accomplish with . Here's how I used it:

• Utilize projects to provide prior knowledge.
• Translate the table of contents first.
• Define a glossary.
• Translate chapter by chapter.

However, this method assumes you speak the target language—it's a way to reduce the labor of , not a way to become fluent in a language you don't know.

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@fosstodon.org · Reply to 洪 民憙 (Hong Minhee)'s post

Trying out .ai as a web designer… Here's what it designed, which is a website. Does it look fine?

A prototype design of the Hollo website, designed by Claude.ai.
ALT text detailsA prototype design of the Hollo website, designed by Claude.ai.
洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@fosstodon.org

Trying out .ai as an editor… 😂

洪 民憙 (Hong Minhee)'s avatar
洪 民憙 (Hong Minhee)

@hongminhee@fosstodon.org

A quite fair comment on by .

—In your opinion, what is Fedify's weakest point?

Based on my review of the Fedify documentation, I think one potential weak point of Fedify is:

The learning curve and complexity involved in properly implementing all the components of a federated ActivityPub server.

While Fedify aims to simplify building ActivityPub servers, there are still many concepts and pieces that developers need to understand and implement correctly, including:

• Actor dispatchers
• Inbox listeners
• Collection dispatchers (outbox, followers, etc.)
• Key pair management
• Object dispatchers
• Access control
• Integration with web frameworks

The documentation is quite extensive, which is good, but also indicates there's a lot for developers to learn and configure properly. Even with Fedify abstracting away some of the low-level details, building a fully-featured and interoperable ActivityPub server requires understanding many moving parts.

Additionally, since Fedify provides flexibility in how these components are implemented, developers still need to make many decisions about data storage, business logic, etc. This flexibility is powerful but also puts more onus on developers to implement things correctly.

So while Fedify seems to be a very capable framework, the inherent complexity of building a federated server means there's still a significant learning curve and potential for implementation mistakes…
ALT text details—In your opinion, what is Fedify's weakest point? Based on my review of the Fedify documentation, I think one potential weak point of Fedify is: The learning curve and complexity involved in properly implementing all the components of a federated ActivityPub server. While Fedify aims to simplify building ActivityPub servers, there are still many concepts and pieces that developers need to understand and implement correctly, including: • Actor dispatchers • Inbox listeners • Collection dispatchers (outbox, followers, etc.) • Key pair management • Object dispatchers • Access control • Integration with web frameworks The documentation is quite extensive, which is good, but also indicates there's a lot for developers to learn and configure properly. Even with Fedify abstracting away some of the low-level details, building a fully-featured and interoperable ActivityPub server requires understanding many moving parts. Additionally, since Fedify provides flexibility in how these components are implemented, developers still need to make many decisions about data storage, business logic, etc. This flexibility is powerful but also puts more onus on developers to implement things correctly. So while Fedify seems to be a very capable framework, the inherent complexity of building a federated server means there's still a significant learning curve and potential for implementation mistakes…
-0--1-'s avatar
-0--1-

@_9CL7T9k8cjnD_@mastodon.social

My project I prompt & the same prompt to compare the responses, return the results in a table I have asked them to write this code in I use because it has 18000+ packages that I am treating them as Some can see, touch the web Some can't remember Packages in R are like the specialized regions of the human brain Maybe value in packages Building the