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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'dataset_info'}) and 15 missing columns ({'id', 'title', 'web_images', 'web_title', 'content', 'github_readme', 'metadata', 'web_description', 'web_content_html', 'web_content_markdown', 'github_readme_markdown', 'created_at', 'source', 'url', 'domain'}).
This happened while the json dataset builder was generating data using
hf://datasets/J94/bookmarks-dataset/metadata.json (at revision 0213198290f41a6cac19d0a1534c33868a2595e6)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
dataset_info: struct<description: string, citation: string, homepage: string, license: string, features: struct<id: struct<dtype: string, description: string>, source: struct<dtype: string, description: string>, title: struct<dtype: string, description: string>, url: struct<dtype: string, description: string>, content: struct<dtype: string, description: string>, created_at: struct<dtype: string, description: string>, domain: struct<dtype: string, description: string>, content_length: struct<dtype: string, description: string>, year: struct<dtype: string, description: string>, month: struct<dtype: string, description: string>, twitter_username: struct<dtype: string, description: string>, twitter_name: struct<dtype: string, description: string>, twitter_followers: struct<dtype: string, description: string>, twitter_likes: struct<dtype: string, description: string>, twitter_retweets: struct<dtype: string, description: string>, twitter_replies: struct<dtype: string, description: string>, twitter_thread: struct<dtype: string, description: string, sequence: struct<dtype: string, dict: struct<id: struct<dtype: string, description: string>, text: struct<dtype: string, description: string>, created_at: struct<dtype: string, description: string>, media: struct<dtype: string, description: string, sequence: struct<dtype: string>>, favorite_count: struct<dtype: string, description: string>, retweet_count: struct<dtype: string, description: string>, reply_count: struct<dtype: string, descr
...
g>
child 0, dtype: string
child 1, description: string
child 24, github_readme_markdown: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 25, web_title: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 26, web_description: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 27, web_content_html: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 28, web_content_markdown: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 29, web_images: struct<dtype: string, description: string, sequence: struct<dtype: string>>
child 0, dtype: string
child 1, description: string
child 2, sequence: struct<dtype: string>
child 0, dtype: string
child 30, raindrop_domain: struct<dtype: string, description: string>
child 0, dtype: string
child 1, description: string
child 31, raindrop_tags: struct<dtype: string, description: string, sequence: struct<dtype: string>>
child 0, dtype: string
child 1, description: string
child 2, sequence: struct<dtype: string>
child 0, dtype: string
to
{'id': Value(dtype='int64', id=None), 'url': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'created_at': Value(dtype='string', id=None), 'domain': Value(dtype='string', id=None), 'metadata': {'raindrop_id': Value(dtype='int64', id=None), 'raindrop_created': Value(dtype='string', id=None), 'raindrop_tags': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'raindrop_domain': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'media': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'github_readme': Value(dtype='string', id=None), 'github_readme_markdown': Value(dtype='string', id=None), 'web_title': Value(dtype='string', id=None), 'web_description': Value(dtype='string', id=None), 'web_content_html': Value(dtype='string', id=None), 'web_images': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'web_content_markdown': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'dataset_info'}) and 15 missing columns ({'id', 'title', 'web_images', 'web_title', 'content', 'github_readme', 'metadata', 'web_description', 'web_content_html', 'web_content_markdown', 'github_readme_markdown', 'created_at', 'source', 'url', 'domain'}).
This happened while the json dataset builder was generating data using
hf://datasets/J94/bookmarks-dataset/metadata.json (at revision 0213198290f41a6cac19d0a1534c33868a2595e6)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id
int64 | url
string | source
string | title
string | content
string | created_at
string | domain
string | metadata
dict | github_readme
string | github_readme_markdown
string | web_title
string | web_description
string | web_content_html
string | web_images
sequence | web_content_markdown
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
999,815,155 | https://x.com/lauriewired/status/1904582573046878333?s=12 | twitter | LaurieWired (@lauriewired) on X | Just built an MCP for Ghidra.
Now basically any LLM (Claude, Gemini, local...) can Reverse Engineer malware for you. With the right prompting, it automates a *ton* of tedious tasks.
One-shot markups of entire binaries with just a click.
Open source, on Github now. | 2025-03-25T22:04:07.055Z | x.com | {
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"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "LaurieWired (@lauriewired) on X",
"description": "Just built an MCP for Ghidra.\n\nNow basically any LLM (Claude, Gemini, local...) can Reverse Engineer malware for you. With the right prompting, it automates a *ton* of tedious tasks.\n\nOne-shot markups of entire binaries with just a click.\n\nOpen source, on Github now.",
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999,814,823 | https://github.com/LaurieWired/GhidraMCP | github | GitHub - LaurieWired/GhidraMCP: MCP Server for Ghidra | MCP Server for Ghidra. Contribute to LaurieWired/GhidraMCP development by creating an account on GitHub. | 2025-03-25T22:03:50.122Z | github.com | {
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"raindrop_tags": [],
"raindrop_domain": "github.com",
"title": "GitHub - LaurieWired/GhidraMCP: MCP Server for Ghidra",
"description": "MCP Server for Ghidra. Contribute to LaurieWired/GhidraMCP development by creating an account on GitHub.",
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]
} | MCP Server for Ghidra. Contribute to LaurieWired/GhidraMCP development by creating an account on GitHub. | # [](https://github.com/)
MCP Server for Ghidra. Contribute to LaurieWired/GhidraMCP development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
---
MCP Server for Ghidra. Contribute to LaurieWired/GhidraMCP development by creating an account on GitHub. | null | null | null | null | null |
999,813,811 | https://r2e.dev/ | web | R2E: Turning any GitHub Repository into a Programming Agent Environment | R2E: Turning any GitHub Repository into a Programming Agent Environment | 2025-03-25T21:57:31.687Z | r2e.dev | {
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"raindrop_tags": [],
"raindrop_domain": "r2e.dev",
"title": "R2E: Turning any GitHub Repository into a Programming Agent Environment",
"description": "R2E: Turning any GitHub Repository into a Programming Agent Environment",
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} | null | null | R2E: Turning any GitHub Repository into a Programming Agent Environment | R2E: Turning any GitHub Repository into a Programming Agent Environment | [] | # R2E: Turning any GitHub Repository into a Programming Agent Environment
*R2E: Turning any GitHub Repository into a Programming Agent Environment*
---
R2E: Turning any GitHub Repository into a Programming Agent Environment |
|
999,583,537 | https://github.com/osu-nlp-group/hipporag?tab=readme-ov-file | github | OSU-NLP-Group/HippoRAG: [NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank. | [NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Perso... | 2025-03-25T16:26:51.919Z | github.com | {
"raindrop_id": 999583537,
"raindrop_created": "2025-03-25T16:26:51.919Z",
"raindrop_tags": [],
"raindrop_domain": "github.com",
"title": "OSU-NLP-Group/HippoRAG: [NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.",
"description": "[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Perso...",
"html": "",
"type": "link",
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"https://github.com/OSU-NLP-Group/HippoRAG/raw/main/images/methodology.png"
]
} | [NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Perso... | # [](https://github.com/)
[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Perso...
**Language:** • **Stars:** 0 • **Forks:** 0
---
[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Perso... | null | null | null | null | null |
999,583,019 | https://github.com/ayanami2003/GATE?tab=readme-ov-file | github | ayanami2003/GATE | Contribute to ayanami2003/GATE development by creating an account on GitHub. | 2025-03-25T16:26:45.230Z | github.com | {
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"https://github.com/ayanami2003/GATE/raw/main/docs/toolgraph.png"
]
} | Contribute to ayanami2003/GATE development by creating an account on GitHub. | # [](https://github.com/)
Contribute to ayanami2003/GATE development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
---
Contribute to ayanami2003/GATE development by creating an account on GitHub. | null | null | null | null | null |
999,582,505 | https://github.com/zsq259/Plan-over-Graph | github | zsq259/Plan-over-Graph | Contribute to zsq259/Plan-over-Graph development by creating an account on GitHub. | 2025-03-25T16:26:38.488Z | github.com | {
"raindrop_id": 999582505,
"raindrop_created": "2025-03-25T16:26:38.488Z",
"raindrop_tags": [],
"raindrop_domain": "github.com",
"title": "zsq259/Plan-over-Graph",
"description": "Contribute to zsq259/Plan-over-Graph development by creating an account on GitHub.",
"html": "",
"type": "link",
"media": [
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]
} | Contribute to zsq259/Plan-over-Graph development by creating an account on GitHub. | # [](https://github.com/)
Contribute to zsq259/Plan-over-Graph development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
---
Contribute to zsq259/Plan-over-Graph development by creating an account on GitHub. | null | null | null | null | null |
999,377,257 | https://flowithai.feishu.cn/docx/I5J6dQZt9opp2Rxhdi2c0JnrnKc | web | 🕵️ 最全通用 AGENT 案例合集 1.0 | 本通用 AGENT 案例合集由 「flowith 和它的朋友们」共同制作、收纳、整理，旨在为所有用户提供一个探索 AGENT 能力和学习如何使用 AGENT 的指南库。 结合目前在 2025 全球智能体创作大赛提交作品以及 flowith 用户对于 Oracle 的深度探索，我们将本案例合集分为三类供大家学习，他们分别是：📈研究工作类 AGENT，🎢生活娱乐类 AGENT，🧑🎓学 | 2025-03-25T11:31:16.801Z | flowithai.feishu.cn | {
"raindrop_id": 999377257,
"raindrop_created": "2025-03-25T11:31:16.801Z",
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"title": "🕵️ 最全通用 AGENT 案例合集 1.0",
"description": "本通用 AGENT 案例合集由 「flowith 和它的朋友们」共同制作、收纳、整理，旨在为所有用户提供一个探索 AGENT 能力和学习如何使用 AGENT 的指南库。 结合目前在 2025 全球智能体创作大赛提交作品以及 flowith 用户对于 Oracle 的深度探索，我们将本案例合集分为三类供大家学习，他们分别是：📈研究工作类 AGENT，🎢生活娱乐类 AGENT，🧑🎓学",
"html": "",
"type": "link",
"media": []
} | null | null | 🕵️ 最全通用 AGENT 案例合集 1.0 | 本通用 AGENT 案例合集由 「flowith 和它的朋友们」共同制作、收纳、整理，旨在为所有用户提供一个探索 AGENT 能力和学习如何使用 AGENT 的指南库。 结合目前在 2025 全球智能体创作大赛提交作品以及 flowith 用户对于 Oracle 的深度探索，我们将本案例合集分为三类供大家学习，他们分别是：📈研究工作类 AGENT，🎢生活娱乐类 AGENT，🧑🎓学 | [] | # 🕵️ 最全通用 AGENT 案例合集 1.0
*本通用 AGENT 案例合集由 「flowith 和它的朋友们」共同制作、收纳、整理，旨在为所有用户提供一个探索 AGENT 能力和学习如何使用 AGENT 的指南库。 结合目前在 2025 全球智能体创作大赛提交作品以及 flowith 用户对于 Oracle 的深度探索，我们将本案例合集分为三类供大家学习，他们分别是：📈研究工作类 AGENT，🎢生活娱乐类 AGENT，🧑🎓学*
---
本通用 AGENT 案例合集由 「flowith 和它的朋友们」共同制作、收纳、整理，旨在为所有用户提供一个探索 AGENT 能力和学习如何使用 AGENT 的指南库。 结合目前在 2025 全球智能体创作大赛提交作品以及 flowith 用户对于 Oracle 的深度探索，我们将本案例合集分为三类供大家学习，他们分别是：📈研究工作类 AGENT，🎢生活娱乐类 AGENT，🧑🎓学 |
|
999,376,543 | https://flowith.net/blank | web | flowith 2.0 - Your AI Creation Workspace, with Knowledge | Where Ideas Flow:Interact with the world's most powerful AI in a way from the future flowith is your AI Creation Workspace that transforms knowledge. Through innovative interaction, it allows you to collaborate smoothly with AI, with ideas flowing like a vibrant spring. | 2025-03-25T11:31:01.976Z | flowith.net | {
"raindrop_id": 999376543,
"raindrop_created": "2025-03-25T11:31:01.976Z",
"raindrop_tags": [],
"raindrop_domain": "flowith.net",
"title": "flowith 2.0 - Your AI Creation Workspace, with Knowledge",
"description": "Where Ideas Flow:Interact with the world's most powerful AI in a way from the future flowith is your AI Creation Workspace that transforms knowledge. Through innovative interaction, it allows you to collaborate smoothly with AI, with ideas flowing like a vibrant spring.",
"html": "",
"type": "link",
"media": []
} | null | null | flowith 2.0 - Your AI Creation Workspace, with Knowledge | Where Ideas Flow:Interact with the world's most powerful AI in a way from the future flowith is your AI Creation Workspace that transforms knowledge. Through innovative interaction, it allows you to collaborate smoothly with AI, with ideas flowing like a vibrant spring. | [] | # flowith 2.0 - Your AI Creation Workspace, with Knowledge
*Where Ideas Flow:Interact with the world's most powerful AI in a way from the future flowith is your AI Creation Workspace that transforms knowledge. Through innovative interaction, it allows you to collaborate smoothly with AI, with ideas flowing like a vibrant spring.*
---
Where Ideas Flow:Interact with the world's most powerful AI in a way from the future flowith is your AI Creation Workspace that transforms knowledge. Through innovative interaction, it allows you to collaborate smoothly with AI, with ideas flowing like a vibrant spring. |
|
998,944,609 | https://arxiv.org/abs/2503.10071 | web | Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM | The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. | 2025-03-24T19:02:24.096Z | arxiv.org | {
"raindrop_id": 998944609,
"raindrop_created": "2025-03-24T19:02:24.096Z",
"raindrop_tags": [],
"raindrop_domain": "arxiv.org",
"title": "Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM",
"description": "The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.",
"html": "",
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} | null | null | Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM | The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. | [
"https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png",
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] | # Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM
*The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.*
---
The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. |
|
998,942,875 | https://arxiv.org/abs/2503.14432 | web | PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play | Large language models (LLMs) are increasingly integrated with specialized external tools, yet many tasks demand zero-shot tool usage with minimal or noisy documentation. Existing solutions rely on manual rewriting or labeled data for validation, making them inapplicable in true zero-shot settings. To address these challenges, we propose PLAY2PROMPT, an automated framework that systematically "plays" with each tool to explore its input-output behaviors. Through this iterative trial-and-error process, PLAY2PROMPT refines tool documentation and generates usage examples without any labeled data. These examples not only guide LLM inference but also serve as validation to further enhance tool utilization. Extensive experiments on real-world tasks demonstrate that PLAY2PROMPT significantly improves zero-shot tool performance across both open and closed models, offering a scalable and effective solution for domain-specific tool integration. | 2025-03-24T18:59:18.074Z | arxiv.org | {
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} | null | null | PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play | Large language models (LLMs) are increasingly integrated with specialized external tools, yet many tasks demand zero-shot tool usage with minimal or noisy documentation. Existing solutions rely on manual rewriting or labeled data for validation, making them inapplicable in true zero-shot settings. To address these challenges, we propose PLAY2PROMPT, an automated framework that systematically "plays" with each tool to explore its input-output behaviors. Through this iterative trial-and-error process, PLAY2PROMPT refines tool documentation and generates usage examples without any labeled data. These examples not only guide LLM inference but also serve as validation to further enhance tool utilization. Extensive experiments on real-world tasks demonstrate that PLAY2PROMPT significantly improves zero-shot tool performance across both open and closed models, offering a scalable and effective solution for domain-specific tool integration. | [
"https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png"
] | # PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play
*Large language models (LLMs) are increasingly integrated with specialized external tools, yet many tasks demand zero-shot tool usage with minimal or noisy documentation. Existing solutions rely on manual rewriting or labeled data for validation, making them inapplicable in true zero-shot settings. To address these challenges, we propose PLAY2PROMPT, an automated framework that systematically "plays" with each tool to explore its input-output behaviors. Through this iterative trial-and-error process, PLAY2PROMPT refines tool documentation and generates usage examples without any labeled data. These examples not only guide LLM inference but also serve as validation to further enhance tool utilization. Extensive experiments on real-world tasks demonstrate that PLAY2PROMPT significantly improves zero-shot tool performance across both open and closed models, offering a scalable and effective solution for domain-specific tool integration.*
---
Large language models (LLMs) are increasingly integrated with specialized external tools, yet many tasks demand zero-shot tool usage with minimal or noisy documentation. Existing solutions rely on manual rewriting or labeled data for validation, making them inapplicable in true zero-shot settings. To address these challenges, we propose PLAY2PROMPT, an automated framework that systematically "plays" with each tool to explore its input-output behaviors. Through this iterative trial-and-error process, PLAY2PROMPT refines tool documentation and generates usage examples without any labeled data. These examples not only guide LLM inference but also serve as validation to further enhance tool utilization. Extensive experiments on real-world tasks demonstrate that PLAY2PROMPT significantly improves zero-shot tool performance across both open and closed models, offering a scalable and effective solution for domain-specific tool integration. |
|
998,942,401 | https://arxiv.org/abs/2503.14269 | web | DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal | Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance productivity. However, existing approaches often struggle with sub-optimal decision-making, requiring either extensive manual intervention or inefficient compute scaling strategies. To improve coding agent performance, we present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents, that is faster and more effective at recovering from sub-optimal decisions compared to baselines. While traditional agents either follow linear trajectories or rely on random sampling for scaling compute, our approach DARS works by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt from that point. We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2. Our framework achieves a pass@1 rate of 47%, outperforming state-of-the-art (SOTA) open-source frameworks. | 2025-03-24T18:57:19.248Z | arxiv.org | {
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} | null | null | DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal | Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance productivity. However, existing approaches often struggle with sub-optimal decision-making, requiring either extensive manual intervention or inefficient compute scaling strategies. To improve coding agent performance, we present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents, that is faster and more effective at recovering from sub-optimal decisions compared to baselines. While traditional agents either follow linear trajectories or rely on random sampling for scaling compute, our approach DARS works by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt from that point. We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2. Our framework achieves a pass@1 rate of 47%, outperforming state-of-the-art (SOTA) open-source frameworks. | [
"https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png"
] | # DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal
*Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance productivity. However, existing approaches often struggle with sub-optimal decision-making, requiring either extensive manual intervention or inefficient compute scaling strategies. To improve coding agent performance, we present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents, that is faster and more effective at recovering from sub-optimal decisions compared to baselines. While traditional agents either follow linear trajectories or rely on random sampling for scaling compute, our approach DARS works by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt from that point. We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2. Our framework achieves a pass@1 rate of 47%, outperforming state-of-the-art (SOTA) open-source frameworks.*
---
Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance productivity. However, existing approaches often struggle with sub-optimal decision-making, requiring either extensive manual intervention or inefficient compute scaling strategies. To improve coding agent performance, we present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents, that is faster and more effective at recovering from sub-optimal decisions compared to baselines. While traditional agents either follow linear trajectories or rely on random sampling for scaling compute, our approach DARS works by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt from that point. We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2. Our framework achieves a pass@1 rate of 47%, outperforming state-of-the-art (SOTA) open-source frameworks. |
|
998,933,645 | https://docs.bytez.com/model-api/docs/models/all | web | Models - Bytez | Retrieve a list of available models for various tasks. Use the query parameter `task` to filter by task type, e.g. `chat`. | 2025-03-24T18:29:38.688Z | docs.bytez.com | {
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} | null | null | Models - Bytez | Retrieve a list of available models for various tasks. Use the query parameter `task` to filter by task type, e.g. `chat`. | [
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] | # Models - Bytez
*Retrieve a list of available models for various tasks. Use the query parameter `task` to filter by task type, e.g. `chat`.*
---
Retrieve a list of available models for various tasks. Use the query parameter `task` to filter by task type, e.g. `chat`. |
|
993,843,862 | https://www.benefitsandwork.co.uk/personal-independence-payment-pip/pip-health-conditions/claim-pip-for-adhd | web | Claim PIP for ADHD | Get the benefits you're entitled to: help with personal independence payment (PIP), universal credit (UC), employment and support allowance (ESA),disability living allowance (DLA). Claims, assessments, reviews, appeals. | 2025-03-16T12:09:03.663Z | www.benefitsandwork.co.uk | {
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"title": "Claim PIP for ADHD",
"description": "Get the benefits you're entitled to: help with personal independence payment (PIP), universal credit (UC), employment and support allowance (ESA),disability living allowance (DLA). Claims, assessments, reviews, appeals.",
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} | null | null | Claim PIP for ADHD | Get the benefits you're entitled to: help with personal independence payment (PIP), universal credit (UC), employment and support allowance (ESA),disability living allowance (DLA). Claims, assessments, reviews, appeals. | [] | # Claim PIP for ADHD
*Get the benefits you're entitled to: help with personal independence payment (PIP), universal credit (UC), employment and support allowance (ESA),disability living allowance (DLA). Claims, assessments, reviews, appeals.*
---
Get the benefits you're entitled to: help with personal independence payment (PIP), universal credit (UC), employment and support allowance (ESA),disability living allowance (DLA). Claims, assessments, reviews, appeals. |
|
993,117,271 | https://github.com/apappascs/mcp-servers-hub | github | apappascs/mcp-servers-hub: Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP servers, complete with features, documentation links, and contributors. | Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP... | 2025-03-15T13:46:41.414Z | github.com | {
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Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP...
**Language:** • **Stars:** 0 • **Forks:** 0
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Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP... | null | null | null | null | null |
993,015,296 | https://x.com/_reachsumit/status/1898950771401695324 | twitter | (1) Sumit on X: "R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning Introduces a two-stage RL approach enabling LLMs to autonomously invoke search during reasoning. 📝https://t.co/NyAIv2c9NC 👨🏽💻https://t.co/OeUBTyqvW4" / X | 2025-03-15T11:11:49.935Z | x.com | {
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|
990,785,835 | https://simonwillison.net/2025/Mar/11/using-llms-for-code/ | web | Here’s how I use LLMs to help me write code | Online discussions about using Large Language Models to help write code inevitably produce comments from developers who’s experiences have been disappointing. They often ask what they’re doing wrong—how come some … | 2025-03-12T10:38:42.718Z | simonwillison.net | {
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} | null | null | Here’s how I use LLMs to help me write code | Online discussions about using Large Language Models to help write code inevitably produce comments from developers who’s experiences have been disappointing. They often ask what they’re doing wrong—how come some … | [
"https://static.simonwillison.net/static/2025/colophon.jpg",
"https://static.simonwillison.net/static/2025/github-actions-colophon.jpg",
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] | # Here’s how I use LLMs to help me write code
*Online discussions about using Large Language Models to help write code inevitably produce comments from developers who’s experiences have been disappointing. They often ask what they’re doing wrong—how come some …*
---
Online discussions about using Large Language Models to help write code inevitably produce comments from developers who’s experiences have been disappointing. They often ask what they’re doing wrong—how come some … |
|
988,634,550 | https://www.maestro.dev/ | web | Maestro | Simple end-to-end testing for Mobile and Web apps | 2025-03-09T18:15:05.119Z | www.maestro.dev | {
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"https://www.maestro.dev/_astro/maestro-studio-gpt.7amtAiRV_ZtskpH.webp"
] | # Maestro
*Simple end-to-end testing for Mobile and Web apps*
---
Simple end-to-end testing for Mobile and Web apps |
|
987,791,154 | https://github.com/hrithikkoduri/WebRover | github | hrithikkoduri/WebRover: WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language models and web automation tools to navigate the web, gather information, and provide structured responses based on the user's needs. | WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language mode... | 2025-03-08T09:05:29.876Z | github.com | {
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} | WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language mode... | # [](https://github.com/)
WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language mode...
**Language:** • **Stars:** 0 • **Forks:** 0
---
WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language mode... | null | null | null | null | null |
987,383,775 | https://github.com/strowk/synf | github | strowk/synf: Development tool for Model Context Protocol servers | Development tool for Model Context Protocol servers - strowk/synf | 2025-03-07T16:42:44.329Z | github.com | {
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Development tool for Model Context Protocol servers - strowk/synf
**Language:** • **Stars:** 0 • **Forks:** 0
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Development tool for Model Context Protocol servers - strowk/synf | null | null | null | null | null |
987,225,978 | https://openai.com/index/factory/ | web | Factory builds the Command Center for software development with OpenAI’s reasoning models | Accelerating engineering cycles 20% with OpenAI. | 2025-03-07T09:56:56.710Z | openai.com | {
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*Accelerating engineering cycles 20% with OpenAI.*
---
Accelerating engineering cycles 20% with OpenAI. |
|
985,330,253 | https://github.com/phunterlau/paper_without_code/blob/main/tools/readpaper.py | github | paper_without_code/tools/readpaper.py at main · phunterlau/paper_without_code | LLM reads a paper and produce a working prototype. Contribute to phunterlau/paper_without_code development by creating an account on GitHub. | 2025-03-04T16:43:43.759Z | github.com | {
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LLM reads a paper and produce a working prototype. Contribute to phunterlau/paper_without_code development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
---
LLM reads a paper and produce a working prototype. Contribute to phunterlau/paper_without_code development by creating an account on GitHub. | null | null | null | null | null |
985,277,640 | https://github.com/convergence-ai/proxy-lite?tab=readme-ov-file | github | convergence-ai/proxy-lite: A mini, open-weights, version of our Proxy assistant. | A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite | 2025-03-04T15:10:44.026Z | github.com | {
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A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite
**Language:** • **Stars:** 0 • **Forks:** 0
---
A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite | null | null | null | null | null |
985,277,626 | https://github.com/browserbase/stagehand | github | browserbase/stagehand: An AI web browsing framework focused on simplicity and extensibility. | An AI web browsing framework focused on simplicity and extensibility. - browserbase/stagehand | 2025-03-04T15:10:39.046Z | github.com | {
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An AI web browsing framework focused on simplicity and extensibility. - browserbase/stagehand
**Language:** • **Stars:** 0 • **Forks:** 0
---
An AI web browsing framework focused on simplicity and extensibility. - browserbase/stagehand | null | null | null | null | null |
985,277,069 | https://github.com/convergence-ai/proxy-lite | github | convergence-ai/proxy-lite: A mini, open-weights, version of our Proxy assistant. | A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite | 2025-03-04T15:08:44.965Z | github.com | {
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A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite
**Language:** • **Stars:** 0 • **Forks:** 0
---
A mini, open-weights, version of our Proxy assistant. - convergence-ai/proxy-lite | null | null | null | null | null |
985,275,398 | https://smithery.ai/ | web | Smithery - Model Context Protocol Registry | Extend your language models with capabilities with Model Context Protocol servers. | 2025-03-04T15:00:56.798Z | smithery.ai | {
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*Extend your language models with capabilities with Model Context Protocol servers.*
---
Extend your language models with capabilities with Model Context Protocol servers. |
|
985,246,592 | https://github.com/jina-ai/node-DeepResearch | github | jina-ai/node-DeepResearch: Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget) | Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget) - jina-ai/node-DeepResearch | 2025-03-04T14:11:14.905Z | github.com | {
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Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget) - jina-ai/node-DeepResearch
**Language:** • **Stars:** 0 • **Forks:** 0
---
Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget) - jina-ai/node-DeepResearch | null | null | null | null | null |
985,245,873 | https://github.com/xhguo7/SyncMind | github | xhguo7/SyncMind: SyncMind for Agent Out-of-Sync | SyncMind for Agent Out-of-Sync. Contribute to xhguo7/SyncMind development by creating an account on GitHub. | 2025-03-04T14:08:14.558Z | github.com | {
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} | SyncMind for Agent Out-of-Sync. Contribute to xhguo7/SyncMind development by creating an account on GitHub. | # [](https://github.com/)
SyncMind for Agent Out-of-Sync. Contribute to xhguo7/SyncMind development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
---
SyncMind for Agent Out-of-Sync. Contribute to xhguo7/SyncMind development by creating an account on GitHub. | null | null | null | null | null |
985,225,797 | https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview#tools-available-to-claude | web | Claude Code overview - Anthropic | Learn about Claude Code, an agentic coding tool made by Anthropic. Currently in beta as a research preview. | 2025-03-04T13:17:54.587Z | docs.anthropic.com | {
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} | null | null | Claude Code overview - Anthropic | Learn about Claude Code, an agentic coding tool made by Anthropic. Currently in beta as a research preview. | [
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] | # Claude Code overview - Anthropic
*Learn about Claude Code, an agentic coding tool made by Anthropic. Currently in beta as a research preview.*
---
Learn about Claude Code, an agentic coding tool made by Anthropic. Currently in beta as a research preview. |
|
985,224,436 | https://github.com/dnakov/claude-code/blob/main/src/constants/prompts.ts | github | claude-code/src/constants/prompts.ts at main · dnakov/claude-code | claude-code full original source code from source maps - dnakov/claude-code | 2025-03-04T13:15:41.447Z | github.com | {
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claude-code full original source code from source maps - dnakov/claude-code
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claude-code full original source code from source maps - dnakov/claude-code | null | null | null | null | null |
980,547,811 | https://gist.github.com/awni/9d8b35ef9c983563cfaad449f867c0f1 | github | Test Time Scaling with R1-based Models and MLX LM | Test Time Scaling with R1-based Models and MLX LM. GitHub Gist: instantly share code, notes, and snippets. | 2025-02-26T23:27:12.376Z | gist.github.com | {
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Test Time Scaling with R1-based Models and MLX LM. GitHub Gist: instantly share code, notes, and snippets.
**Language:** • **Stars:** 0 • **Forks:** 0
---
Test Time Scaling with R1-based Models and MLX LM. GitHub Gist: instantly share code, notes, and snippets. | null | null | null | null | null |
980,538,079 | https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb | github | GRPO Llama-1B | GRPO Llama-1B. GitHub Gist: instantly share code, notes, and snippets. | 2025-02-26T22:53:27.731Z | gist.github.com | {
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} | GRPO Llama-1B. GitHub Gist: instantly share code, notes, and snippets. | # [](https://github.com/)
GRPO Llama-1B. GitHub Gist: instantly share code, notes, and snippets.
**Language:** • **Stars:** 0 • **Forks:** 0
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GRPO Llama-1B. GitHub Gist: instantly share code, notes, and snippets. | null | null | null | null | null |
980,534,093 | https://gist.github.com/fabiodr/starred | github | fabiodr’s gists | GitHub Gist: star and fork fabiodr's gists by creating an account on GitHub. | 2025-02-26T22:41:20.308Z | gist.github.com | {
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GitHub Gist: star and fork fabiodr's gists by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
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**Language:** • **Stars:** 0 • **Forks:** 0
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Unminified prompts and tool definitions for Claude Code - claude-code-prompts.js | null | null | null | null | null |
980,524,498 | https://gist.github.com/disler/29ff18823670098c26fa370ad802fa96 | github | Use Meta Prompting to rapidly generate results in the GenAI Age | Use Meta Prompting to rapidly generate results in the GenAI Age - README.md | 2025-02-26T22:25:45.923Z | gist.github.com | {
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Use Meta Prompting to rapidly generate results in the GenAI Age - README.md
**Language:** • **Stars:** 0 • **Forks:** 0
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Use Meta Prompting to rapidly generate results in the GenAI Age - README.md | null | null | null | null | null |
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GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
**Language:** • **Stars:** 0 • **Forks:** 0
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GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. | null | null | null | null | null |
980,524,156 | https://gist.github.com/theskcd/edd4defeb22a4ce79e66058336682a91 | github | agent_tool.js | GitHub Gist: instantly share code, notes, and snippets. | 2025-02-26T22:23:45.797Z | gist.github.com | {
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GitHub Gist: instantly share code, notes, and snippets.
**Language:** • **Stars:** 0 • **Forks:** 0
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GitHub Gist: instantly share code, notes, and snippets. | null | null | null | null | null |
980,523,910 | https://gist.github.com/deepfates/78c9515ec2c2f263d6a65a19dd10162d | github | Convert your twitter archive into a training dataset and markdown files | Convert your twitter archive into a training dataset and markdown files - convert_archive.py | 2025-02-26T22:23:37.201Z | gist.github.com | {
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Convert your twitter archive into a training dataset and markdown files - convert_archive.py
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Convert your twitter archive into a training dataset and markdown files - convert_archive.py | null | null | null | null | null |
980,507,437 | https://github.com/microsoft/PromptWizard?tab=readme-ov-file | github | microsoft/PromptWizard: Task-Aware Agent-driven Prompt Optimization Framework | Task-Aware Agent-driven Prompt Optimization Framework - microsoft/PromptWizard | 2025-02-26T21:14:20.934Z | github.com | {
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Task-Aware Agent-driven Prompt Optimization Framework - microsoft/PromptWizard
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Task-Aware Agent-driven Prompt Optimization Framework - microsoft/PromptWizard | null | null | null | null | null |
980,297,932 | https://composio.dev/ | web | Composio - Access 250+ apps in just one line of code | Composio: Seamlessly integrate AI Agents & LLMs with 250+ tools. Build, connect, and deploy integrations for CRMs, HRMs, ticketing, productivity, and accounting systems with SOC Type II compliance. Experience powerful system tools and managed auth for secure data management. Trusted by engineers worldwide. | 2025-02-26T13:41:06.266Z | composio.dev | {
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*Composio: Seamlessly integrate AI Agents & LLMs with 250+ tools. Build, connect, and deploy integrations for CRMs, HRMs, ticketing, productivity, and accounting systems with SOC Type II compliance. Experience powerful system tools and managed auth for secure data management. Trusted by engineers worldwide.*
---
Composio: Seamlessly integrate AI Agents & LLMs with 250+ tools. Build, connect, and deploy integrations for CRMs, HRMs, ticketing, productivity, and accounting systems with SOC Type II compliance. Experience powerful system tools and managed auth for secure data management. Trusted by engineers worldwide. |
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980,228,786 | https://arxiv.org/abs/2412.15118 | web | Outcome-Refining Process Supervision for Code Generation | Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning. While process... | 2025-02-26T11:57:24.220Z | arxiv.org | {
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*Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning. While process...*
---
Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning. While process... |
|
980,228,773 | https://github.com/zhuohaoyu/ORPS | github | zhuohaoyu/ORPS | Contribute to zhuohaoyu/ORPS development by creating an account on GitHub. | 2025-02-26T11:57:17.770Z | github.com | {
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Contribute to zhuohaoyu/ORPS development by creating an account on GitHub.
**Language:** • **Stars:** 0 • **Forks:** 0
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Contribute to zhuohaoyu/ORPS development by creating an account on GitHub. | null | null | null | null | null |
980,215,628 | https://arxiv.org/abs/2406.11939 | web | From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and... | The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks. However, manual... | 2025-02-26T11:48:37.284Z | arxiv.org | {
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*The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks. However, manual...*
---
The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks. However, manual... |
|
980,215,548 | https://arxiv.org/abs/2502.06994 | web | SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative... | Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants --... | 2025-02-26T11:48:07.873Z | arxiv.org | {
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*Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants --...*
---
Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants --... |
|
980,205,535 | https://github.com/amazon-science/code-agent-eval?utm_source=catalyzex.com | github | amazon-science/code-agent-eval: Implemental for the paper "Large Language Model Critics for Execution-Free Evaluation of Code Changes" | Implemental for the paper "Large Language Model Critics for Execution-Free Evaluation of Code Changes" - amazon-science/code-agent-eval | 2025-02-26T11:43:38.528Z | github.com | {
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Implemental for the paper "Large Language Model Critics for Execution-Free Evaluation of Code Changes" - amazon-science/code-agent-eval
**Language:** • **Stars:** 0 • **Forks:** 0
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Implemental for the paper "Large Language Model Critics for Execution-Free Evaluation of Code Changes" - amazon-science/code-agent-eval | null | null | null | null | null |
979,520,705 | https://trainloop.ai/ | web | Trainloop AI - Make Reasoning Models Work for Your Business | Make reasoning models work for your business. Turn powerful but generic models into reliable domain experts while preventing harmful outputs. | 2025-02-25T17:20:44.321Z | trainloop.ai | {
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*Make reasoning models work for your business. Turn powerful but generic models into reliable domain experts while preventing harmful outputs.*
---
Make reasoning models work for your business. Turn powerful but generic models into reliable domain experts while preventing harmful outputs. |
|
978,615,362 | https://github.com/sani903/InteractiveSWEAgents | github | sani903/InteractiveSWEAgents: Evaluating Agents under Ambiguous settings for SWE tasks | Evaluating Agents under Ambiguous settings for SWE tasks - sani903/InteractiveSWEAgents | 2025-02-24T14:59:17.767Z | github.com | {
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Evaluating Agents under Ambiguous settings for SWE tasks - sani903/InteractiveSWEAgents
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Evaluating Agents under Ambiguous settings for SWE tasks - sani903/InteractiveSWEAgents | null | null | null | null | null |
978,610,955 | https://github.com/swirlai/swirl-search/tree/main | github | swirlai/swirl-search: AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months. | AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months. - swirlai/swirl-search | 2025-02-24T14:36:55.849Z | github.com | {
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AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months. - swirlai/swirl-search
**Language:** • **Stars:** 0 • **Forks:** 0
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AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months. - swirlai/swirl-search | null | null | null | null | null |
977,843,910 | https://www.activeloop.ai/resources/introducing-deep-research-for-your-multi-modal-data/ | web | Introducing Deep Research for Your Multi-Modal Data | A More Accurate, Flexible, and Multi-Modal Knowledge Agents for Your Private & Public Data. Deep Thinking is Available Today to Everyone. | 2025-02-23T10:37:56.241Z | www.activeloop.ai | {
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} | null | null | Introducing Deep Research for Your Multi-Modal Data | A More Accurate, Flexible, and Multi-Modal Knowledge Agents for Your Private & Public Data. Deep Thinking is Available Today to Everyone. | [
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] | # Introducing Deep Research for Your Multi-Modal Data
*A More Accurate, Flexible, and Multi-Modal Knowledge Agents for Your Private & Public Data. Deep Thinking is Available Today to Everyone.*
---
A More Accurate, Flexible, and Multi-Modal Knowledge Agents for Your Private & Public Data. Deep Thinking is Available Today to Everyone. |
|
977,840,640 | https://arxiv.org/pdf/2412.18069 | web | 2412 | 2025-02-23T10:22:19.394Z | arxiv.org | {
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977,840,341 | https://github.com/AkariAsai/OpenScholar | github | AkariAsai/OpenScholar: This repository includes the official implementation of OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs. | This repository includes the official implementation of OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs. - AkariAsai/OpenScholar: This repository includes the official... | 2025-02-23T10:20:08.089Z | github.com | {
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This repository includes the official implementation of OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs. - AkariAsai/OpenScholar: This repository includes the official...
**Language:** • **Stars:** 0 • **Forks:** 0
---
This repository includes the official implementation of OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs. - AkariAsai/OpenScholar: This repository includes the official... | null | null | null | null | null |
977,426,492 | https://github.com/zou-group/sirius | github | zou-group/sirius: SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning | SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning - zou-group/sirius | 2025-02-22T16:49:11.848Z | github.com | {
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} | SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning - zou-group/sirius | # [](https://github.com/)
SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning - zou-group/sirius
**Language:** • **Stars:** 0 • **Forks:** 0
---
SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning - zou-group/sirius | null | null | null | null | null |
975,165,048 | https://search.jina.ai/ | web | Jina DeepSearch | Search, read and reason until best answer found. | 2025-02-18T22:23:54.919Z | search.jina.ai | {
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"raindrop_domain": "search.jina.ai",
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"html": "",
"type": "link",
"media": [
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} | null | null | Jina DeepSearch | Search, read and reason until best answer found. | [
"https://search.jina.ai/banner.png"
] | # Jina DeepSearch
*Search, read and reason until best answer found.*
---
Search, read and reason until best answer found. |
|
975,152,628 | https://proxy.convergence.ai/ | web | Convergence.ai | Convergence is an AI research lab | 2025-02-18T21:52:01.398Z | proxy.convergence.ai | {
"raindrop_id": 975152628,
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"description": "Convergence is an AI research lab",
"html": "",
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} | null | null | Convergence.ai | Convergence is an AI research lab | [] | # Convergence.ai
*Convergence is an AI research lab*
---
Convergence is an AI research lab |
|
975,145,370 | https://www.glean.com/?=undefined&utm_source=google&utm_medium=paid-search&utm_campaign=brand-sg&utm_term=glean%20knowledge%20management&gad_source=1&gclid=Cj0KCQiA_NC9BhCkARIsABSnSTZO0poDyeJ8hlshR8JRdJGJJ07lsbImQAgKgEgAgAJp9DSuzhcFzd0aAi9iEALw_wcB | web | Work AI for all - AI platform for agents, assistant, search | Glean is the Work AI platform connected to your enterprise's data. Find, create, and automate anything. Explore what Work AI can do for you! | 2025-02-18T21:33:42.947Z | www.glean.com | {
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} | null | null | Work AI for all - AI platform for agents, assistant, search | Glean is the Work AI platform connected to your enterprise's data. Find, create, and automate anything. Explore what Work AI can do for you! | [
"https://cdn.prod.website-files.com/6127a84dfe068e153ef20572/66df3ec7d95ad78e65888721_Website%20preview%20card.webp",
"https://cdn.prod.website-files.com/6127a84dfe068e153ef20572/67aeffb6e41cc3e7f3881c0c_Glean%20Go%20new-p-800.png",
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"https://cdn.prod.website-files.com/6127a84dfe068e153ef20572/67a9cf6a0c3b882fc397780c_pattern-1.webp"
] | # Work AI for all - AI platform for agents, assistant, search
*Glean is the Work AI platform connected to your enterprise's data. Find, create, and automate anything. Explore what Work AI can do for you!*
---
Glean is the Work AI platform connected to your enterprise's data. Find, create, and automate anything. Explore what Work AI can do for you! |
|
964,222,117 | https://arxiv.org/abs/2311.14904 | web | LLM-Assisted Code Cleaning For Training Accurate Code Generators | Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on... | 2025-02-05T12:03:01.667Z | arxiv.org | {
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} | null | null | LLM-Assisted Code Cleaning For Training Accurate Code Generators | Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on... | [
"https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"
] | # LLM-Assisted Code Cleaning For Training Accurate Code Generators
*Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on...*
---
Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on... |
|
964,212,715 | https://github.com/lightning-rod-labs/sculptor/tree/main | github | lightning-rod-labs/sculptor: Sculptor: Structuring unstructured data with LLMs | Sculptor: Structuring unstructured data with LLMs. | 2025-02-05T11:43:08.925Z | github.com | {
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} | Sculptor: Structuring unstructured data with LLMs. | # [](https://github.com/)
Sculptor: Structuring unstructured data with LLMs.
**Language:** • **Stars:** 0 • **Forks:** 0
---
Sculptor: Structuring unstructured data with LLMs. | null | null | null | null | null |
964,208,915 | https://github.com/curvelaboratory/Curve?tab=readme-ov-file#quick-tour | github | curvelaboratory/Curve: Turn simple APIs into powerful AI agents. Curve sits between your apps and AI services, making everything work together seamlessly. | Turn simple APIs into powerful AI agents. Curve sits between your apps and AI services, making everything work together seamlessly. - curvelaboratory/Curve | 2025-02-05T11:40:54.906Z | github.com | {
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} | Turn simple APIs into powerful AI agents. Curve sits between your apps and AI services, making everything work together seamlessly. - curvelaboratory/Curve | # [](https://github.com/)
Turn simple APIs into powerful AI agents. Curve sits between your apps and AI services, making everything work together seamlessly. - curvelaboratory/Curve
**Language:** • **Stars:** 0 • **Forks:** 0
---
Turn simple APIs into powerful AI agents. Curve sits between your apps and AI services, making everything work together seamlessly. - curvelaboratory/Curve | null | null | null | null | null |
964,207,804 | https://forevervm.com/ | web | foreverVM: The sessionless code interpreter | The sessionless code interpreter | 2025-02-05T11:38:25.214Z | forevervm.com | {
"raindrop_id": 964207804,
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} | null | null | foreverVM: The sessionless code interpreter | The sessionless code interpreter | [
"https://forevervm.com/opengraph-image.png?b67097c2c9ca881f"
] | # foreverVM: The sessionless code interpreter
*The sessionless code interpreter*
---
The sessionless code interpreter |
|
964,062,934 | https://a0.dev/ | web | a0.dev | Create apps from text descriptions | 2025-02-05T10:52:49.858Z | a0.dev | {
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"description": "Create apps from text descriptions",
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} | null | null | a0.dev | Create apps from text descriptions | [] | # a0.dev
*Create apps from text descriptions*
---
Create apps from text descriptions |
|
963,445,022 | https://x.com/shadcn/status/1829646548151787589 | twitter | (17) shadcn on X: "We have also improved the init command. It now does framework detection and can initialize a brand new Next.js app and install components and routes in a single command. Go from new app to a dashboard with a sidebar and login pages in just one command. https://t.co/K7LXTfsgWi" / X | framework detection and can initialize a brand new Next.js app and install components and routes in a single command.
Go from new app to a dashboard with a sidebar and login pages in just one command.
— shadcn (@shadcn) | 2025-02-04T17:39:54.534Z | x.com | {
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"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,445,023 | https://x.com/_Anthony_Jacob_ | twitter | (18) x˙ = ax − bxy | y˙ = cxy − dy (@_Anthony_Jacob_) / X | 2025-02-04T17:39:54.534Z | x.com | {
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|
963,445,019 | https://app.raindrop.io/my/52125713 | web | treehouse | All in One Bookmark Manager. For your inspiration, read later, media and stuff. | 2025-02-04T17:39:54.533Z | app.raindrop.io | {
"raindrop_id": 963445019,
"raindrop_created": "2025-02-04T17:39:54.533Z",
"raindrop_tags": [],
"raindrop_domain": "app.raindrop.io",
"title": "treehouse",
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"html": "",
"type": "link",
"media": [
"https://app.raindrop.io/assets/og.8f80436569bfdbdc1a79f7ee9f1c43af.png"
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} | null | null | treehouse | All in One Bookmark Manager. For your inspiration, read later, media and stuff. | [
"https://app.raindrop.io/assets/og.8f80436569bfdbdc1a79f7ee9f1c43af.png"
] | # treehouse
*All in One Bookmark Manager. For your inspiration, read later, media and stuff.*
---
All in One Bookmark Manager. For your inspiration, read later, media and stuff. |
|
963,445,020 | https://vercel.com/signup?utm_source=shad&utm_medium=web&utm_campaign=docs_cta_signup | web | Sign Up – Vercel | 2025-02-04T17:39:54.533Z | vercel.com | {
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} | null | null | Sign Up – Vercel | [
"https://assets.vercel.com/image/upload/front/vercel/twitter-card.png"
] | null |
|||
963,445,021 | https://x.com/shadcn | twitter | (18) shadcn (@shadcn) / X | 2025-02-04T17:39:54.533Z | x.com | {
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} | null | null | null | null | null | null | null |
|
963,445,018 | https://vercel.com/templates/next.js/next-book-inventory | web | Next.js Book Inventory | An example of searching, filtering, and pagination. | 2025-02-04T17:39:54.532Z | vercel.com | {
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"type": "link",
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} | null | null | Next.js Book Inventory | An example of searching, filtering, and pagination. | [
"https://vercel.com/api/templates/og?title=Next.js+Book+Inventory&description=An+example+of+searching%2C+filtering%2C+and+pagination.&framework=Next.js&image=https%3A%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F59ZSCrEk6kmQ7cyJVwrX30%2Fadfff3f624c2a7f92bddf66cdb9a1dee%2FCleanShot_2024-08-11_at_15.52.41_2x.png%3Ffm%3Dpng"
] | # Next.js Book Inventory
*An example of searching, filtering, and pagination.*
---
An example of searching, filtering, and pagination. |
|
963,445,016 | https://next-books-search.vercel.app/ | web | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | 2025-02-04T17:39:54.531Z | next-books-search.vercel.app | {
"raindrop_id": 963445016,
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"raindrop_tags": [],
"raindrop_domain": "next-books-search.vercel.app",
"title": "Book Inventory — Next.js App Router",
"description": "View 2 million books from Goodreads.",
"html": "",
"type": "link",
"media": []
} | null | null | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | [] | # Book Inventory — Next.js App Router
*View 2 million books from Goodreads.*
---
View 2 million books from Goodreads. |
|
963,445,017 | https://next-books-search.vercel.app/ | web | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | 2025-02-04T17:39:54.531Z | next-books-search.vercel.app | {
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"title": "Book Inventory — Next.js App Router",
"description": "View 2 million books from Goodreads.",
"html": "",
"type": "link",
"media": []
} | null | null | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | [] | # Book Inventory — Next.js App Router
*View 2 million books from Goodreads.*
---
View 2 million books from Goodreads. |
|
963,445,013 | https://treehouse-1q95o43ky-jasedgws-projects.vercel.app/ | web | treehouse-1q95o43ky-jasedgws-projects.vercel.app | 2025-02-04T17:39:54.530Z | treehouse-1q95o43ky-jasedgws-projects.vercel.app | {
"raindrop_id": 963445013,
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"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | treehouse-1q95o43ky-jasedgws-projects.vercel.app | [] | null |
|||
963,445,014 | https://vercel.com/jasedgws-projects/www-treehouse/Gc2undsMdfQqnjvL9PHDsosJRhCH?filter=errors | web | www-treehouse – Deployment Overview | 2025-02-04T17:39:54.530Z | vercel.com | {
"raindrop_id": 963445014,
"raindrop_created": "2025-02-04T17:39:54.530Z",
"raindrop_tags": [],
"raindrop_domain": "vercel.com",
"title": "www-treehouse – Deployment Overview",
"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | www-treehouse – Deployment Overview | [] | null |
|||
963,445,015 | https://next-book-inventory-ochre.vercel.app/ | web | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | 2025-02-04T17:39:54.530Z | next-book-inventory-ochre.vercel.app | {
"raindrop_id": 963445015,
"raindrop_created": "2025-02-04T17:39:54.530Z",
"raindrop_tags": [],
"raindrop_domain": "next-book-inventory-ochre.vercel.app",
"title": "Book Inventory — Next.js App Router",
"description": "View 2 million books from Goodreads.",
"html": "",
"type": "link",
"media": []
} | null | null | Book Inventory — Next.js App Router | View 2 million books from Goodreads. | [] | # Book Inventory — Next.js App Router
*View 2 million books from Goodreads.*
---
View 2 million books from Goodreads. |
|
963,445,011 | https://x.com/dailydoseofds_/status/1863531048757510172?s=12 | twitter | (18) Daily Dose of Data Science on X: "Web scraping will never be the same! Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs and AI applications. Here’s why it’s a game-changer: 🆓 Free and open-source 🚀 Blazing fast performance, 🤖 LLM-friendly output formats (JSON, cleaned https://t.co/s2rPlox46u" / X | Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs and AI applications.
Here’s why it’s a game-changer:
🆓 Free and open-source
🚀 Blazing fast performance,
🤖 LLM-friendly output formats (JSON, cleaned…
— Daily Dose of Data Science (@DailyDoseOfDS_) | 2025-02-04T17:39:54.529Z | x.com | {
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"description": "Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs and AI applications.\n\nHere’s why it’s a game-changer:\n\n🆓 Free and open-source\n🚀 Blazing fast performance,\n🤖 LLM-friendly output formats (JSON, cleaned… \n— Daily Dose of Data Science (@DailyDoseOfDS_)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,445,012 | https://github.com/j-94/www-treehouse/tree/main | github | j-94/www-treehouse | 2025-02-04T17:39:54.529Z | github.com | {
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|
963,445,008 | https://colab.research.google.com/drive/1SgRPrByQLzjRfwoRNq1wSGE9nYY_EE8C?usp=sharing#scrollTo=C3YBqOX_8-_r | web | crawl4ai-quickstart.ipynb - Colab | 2025-02-04T17:39:54.528Z | colab.research.google.com | {
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"media": [
"https://colab.research.google.com/img/colab_favicon_256px.png"
]
} | null | null | crawl4ai-quickstart.ipynb - Colab | [
"https://colab.research.google.com/img/colab_favicon_256px.png"
] | null |
|||
963,445,009 | https://paulw.tokyo/standalone-python-script-with-uv/ | web | Standalone python script with uv | paulw.tokyo | is a neat package manager, which took off last year in python land. Being immediately gives it +100 street credibility. As it supports reading (see ) i... | 2025-02-04T17:39:54.528Z | paulw.tokyo | {
"raindrop_id": 963445009,
"raindrop_created": "2025-02-04T17:39:54.528Z",
"raindrop_tags": [],
"raindrop_domain": "paulw.tokyo",
"title": "Standalone python script with uv | paulw.tokyo",
"description": "is a neat package manager, which took off last year in python land. Being immediately gives it +100 street credibility. As it supports reading (see ) i...",
"html": "",
"type": "article",
"media": [
"https://bear-images.sfo2.cdn.digitaloceanspaces.com/herman-1683556668-0.png"
]
} | null | null | Standalone python script with uv | paulw.tokyo | is a neat package manager, which took off last year in python land. Being immediately gives it +100 street credibility. As it supports reading (see ) i... | [
"https://bear-images.sfo2.cdn.digitaloceanspaces.com/herman-1683556668-0.png"
] | # Standalone python script with uv | paulw.tokyo
*is a neat package manager, which took off last year in python land. Being immediately gives it +100 street credibility. As it supports reading (see ) i...*
---
is a neat package manager, which took off last year in python land. Being immediately gives it +100 street credibility. As it supports reading (see ) i... |
|
963,445,010 | https://raw.githubusercontent.com/unclecode/crawl4ai/refs/heads/main/README.md | web | raw.githubusercontent.com/unclecode/crawl4ai/refs/heads/main/README.md | 2025-02-04T17:39:54.528Z | raw.githubusercontent.com | {
"raindrop_id": 963445010,
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"raindrop_domain": "raw.githubusercontent.com",
"title": "raw.githubusercontent.com/unclecode/crawl4ai/refs/heads/main/README.md",
"description": "",
"html": "",
"type": "document",
"media": []
} | null | null | raw.githubusercontent.com/unclecode/crawl4ai/refs/heads/main/README.md | [] | null |
|||
963,445,006 | https://colab.research.google.com/gist/j-94/b04ecaf676460714a85d24526022c9ee/embedanything-x-colpali.ipynb#scrollTo=13Lri3ChycJR | web | embedanything-x-colpali.ipynb - Colab | 2025-02-04T17:39:54.527Z | colab.research.google.com | {
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"raindrop_domain": "colab.research.google.com",
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"html": "",
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"media": [
"https://colab.research.google.com/img/colab_favicon_256px.png"
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} | null | null | embedanything-x-colpali.ipynb - Colab | [
"https://colab.research.google.com/img/colab_favicon_256px.png"
] | null |
|||
963,445,007 | https://www.xml-sitemaps.com/ | web | Create your Google Sitemap Online - XML Sitemaps Generator | Free Online Google Sitemap Generator. XML-sitemaps.com provides free online sitemap generator service, creating an XML sitemap that can be submitted to Google, Bing and other search engines to help them crawl your website better. It will also generate an HTML site map to allow your website visitors to navigate easier. | 2025-02-04T17:39:54.527Z | www.xml-sitemaps.com | {
"raindrop_id": 963445007,
"raindrop_created": "2025-02-04T17:39:54.527Z",
"raindrop_tags": [],
"raindrop_domain": "www.xml-sitemaps.com",
"title": "Create your Google Sitemap Online - XML Sitemaps Generator",
"description": "Free Online Google Sitemap Generator. XML-sitemaps.com provides free online sitemap generator service, creating an XML sitemap that can be submitted to Google, Bing and other search engines to help them crawl your website better. It will also generate an HTML site map to allow your website visitors to navigate easier.",
"html": "",
"type": "link",
"media": []
} | null | null | Create your Google Sitemap Online - XML Sitemaps Generator | Free Online Google Sitemap Generator. XML-sitemaps.com provides free online sitemap generator service, creating an XML sitemap that can be submitted to Google, Bing and other search engines to help them crawl your website better. It will also generate an HTML site map to allow your website visitors to navigate easier. | [] | # Create your Google Sitemap Online - XML Sitemaps Generator
*Free Online Google Sitemap Generator. XML-sitemaps.com provides free online sitemap generator service, creating an XML sitemap that can be submitted to Google, Bing and other search engines to help them crawl your website better. It will also generate an HTML site map to allow your website visitors to navigate easier.*
---
Free Online Google Sitemap Generator. XML-sitemaps.com provides free online sitemap generator service, creating an XML sitemap that can be submitted to Google, Bing and other search engines to help them crawl your website better. It will also generate an HTML site map to allow your website visitors to navigate easier. |
|
963,445,005 | https://weed.th/shop/4de9daf8-2140-4b1d-98ee-ee223a4da417/bangkok/%E0%B8%A3%E0%B9%89%E0%B8%B2%E0%B8%99%E0%B8%81%E0%B8%B1%E0%B8%8D%E0%B8%8A%E0%B8%B2%E0%B9%83%E0%B8%81%E0%B8%A5%E0%B9%89%E0%B8%89%E0%B8%B1%E0%B8%99-%E0%B9%80%E0%B8%94%E0%B8%B4%E0%B8%99%E0%B8%94%E0%B8%87-%E0%B8%97%E0%B8%A3%E0%B8%87%E0%B8%9E%E0%B8%A5%E0%B8%B1%E0%B8%87420 | web | ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 in Bangkok, Thailand - Cannabis in Thailand | Cannabis shop listing for ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 which is in Bangkok, Thailand. ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 has a rating of 5.00/5.0, and 2 reviews. You can also view the available cannabis products at ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420, and even find other shops near ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420. | 2025-02-04T17:39:54.525Z | weed.th | {
"raindrop_id": 963445005,
"raindrop_created": "2025-02-04T17:39:54.525Z",
"raindrop_tags": [],
"raindrop_domain": "weed.th",
"title": "ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 in Bangkok, Thailand - Cannabis in Thailand",
"description": "Cannabis shop listing for ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 which is in Bangkok, Thailand. ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 has a rating of 5.00/5.0, and 2 reviews. You can also view the available cannabis products at ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420, and even find other shops near ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420.",
"html": "",
"type": "link",
"media": []
} | null | null | ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 in Bangkok, Thailand - Cannabis in Thailand | Cannabis shop listing for ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 which is in Bangkok, Thailand. ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 has a rating of 5.00/5.0, and 2 reviews. You can also view the available cannabis products at ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420, and even find other shops near ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420. | [] | # ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 in Bangkok, Thailand - Cannabis in Thailand
*Cannabis shop listing for ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 which is in Bangkok, Thailand. ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 has a rating of 5.00/5.0, and 2 reviews. You can also view the available cannabis products at ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420, and even find other shops near ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420.*
---
Cannabis shop listing for ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 which is in Bangkok, Thailand. ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420 has a rating of 5.00/5.0, and 2 reviews. You can also view the available cannabis products at ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420, and even find other shops near ร้านกัญชาใกล้ฉัน เดินดง ทรงพลัง420. |
|
963,445,004 | https://gemini.google.com/app/455eeb0adeed8d4e?_gl=1*lzbszx*_ga*MjA0NDY5NzEyNS4xNzM4NTk4NDMz*_ga_WC57KJ50ZZ*MTczODU5ODQzMi4xLjEuMTczODU5ODYwMi4wLjAuMA.. | web | Gemini | Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI. | 2025-02-04T17:39:54.524Z | gemini.google.com | {
"raindrop_id": 963445004,
"raindrop_created": "2025-02-04T17:39:54.524Z",
"raindrop_tags": [],
"raindrop_domain": "gemini.google.com",
"title": "Gemini",
"description": "Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI.",
"html": "",
"type": "link",
"media": [
"https://www.gstatic.com/lamda/images/gemini_thumbnail_c362e5eadc46ca9f617e2.png"
]
} | null | null | Gemini | Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI. | [
"https://www.gstatic.com/lamda/images/gemini_thumbnail_c362e5eadc46ca9f617e2.png"
] | # Gemini
*Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI.*
---
Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI. |
|
963,445,002 | https://www.google.com/search?q=taxonmy&sourceid=chrome&ie=UTF-8 | web | taxonmy - Google Search | 2025-02-04T17:39:54.523Z | www.google.com | {
"raindrop_id": 963445002,
"raindrop_created": "2025-02-04T17:39:54.523Z",
"raindrop_tags": [],
"raindrop_domain": "www.google.com",
"title": "taxonmy - Google Search",
"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | taxonmy - Google Search | [] | null |
|||
963,445,003 | https://web.whatsapp.com/ | web | WhatsApp | Quickly send and receive WhatsApp messages right from your computer. | 2025-02-04T17:39:54.523Z | web.whatsapp.com | {
"raindrop_id": 963445003,
"raindrop_created": "2025-02-04T17:39:54.523Z",
"raindrop_tags": [],
"raindrop_domain": "web.whatsapp.com",
"title": "WhatsApp",
"description": "Quickly send and receive WhatsApp messages right from your computer.",
"html": "",
"type": "link",
"media": [
"https://static.whatsapp.net/rsrc.php/v4/yR/r/y8-PTBaP90a.png"
]
} | null | null | WhatsApp | Quickly send and receive WhatsApp messages right from your computer. | [
"https://static.whatsapp.net/rsrc.php/v4/yR/r/y8-PTBaP90a.png"
] | # WhatsApp
*Quickly send and receive WhatsApp messages right from your computer.*
---
Quickly send and receive WhatsApp messages right from your computer. |
|
963,445,001 | https://vercel.com/templates/next.js/next-book-inventory | web | Next.js Book Inventory | An example of searching, filtering, and pagination. | 2025-02-04T17:39:54.522Z | vercel.com | {
"raindrop_id": 963445001,
"raindrop_created": "2025-02-04T17:39:54.522Z",
"raindrop_tags": [],
"raindrop_domain": "vercel.com",
"title": "Next.js Book Inventory",
"description": "An example of searching, filtering, and pagination.",
"html": "",
"type": "link",
"media": [
"https://vercel.com/api/templates/og?title=Next.js+Book+Inventory&description=An+example+of+searching%2C+filtering%2C+and+pagination.&framework=Next.js&image=https%3A%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F59ZSCrEk6kmQ7cyJVwrX30%2Fadfff3f624c2a7f92bddf66cdb9a1dee%2FCleanShot_2024-08-11_at_15.52.41_2x.png%3Ffm%3Dpng"
]
} | null | null | Next.js Book Inventory | An example of searching, filtering, and pagination. | [
"https://vercel.com/api/templates/og?title=Next.js+Book+Inventory&description=An+example+of+searching%2C+filtering%2C+and+pagination.&framework=Next.js&image=https%3A%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F59ZSCrEk6kmQ7cyJVwrX30%2Fadfff3f624c2a7f92bddf66cdb9a1dee%2FCleanShot_2024-08-11_at_15.52.41_2x.png%3Ffm%3Dpng"
] | # Next.js Book Inventory
*An example of searching, filtering, and pagination.*
---
An example of searching, filtering, and pagination. |
|
963,444,999 | https://www.firecrawl.dev/extract#pricing | web | Extract - Firecrawl | Get structured data from entire websites with just a prompt. | 2025-02-04T17:39:54.521Z | www.firecrawl.dev | {
"raindrop_id": 963444999,
"raindrop_created": "2025-02-04T17:39:54.521Z",
"raindrop_tags": [],
"raindrop_domain": "www.firecrawl.dev",
"title": "Extract - Firecrawl",
"description": "Get structured data from entire websites with just a prompt.",
"html": "",
"type": "link",
"media": [
"https://www.firecrawl.dev/[email protected]"
]
} | null | null | Extract - Firecrawl | Get structured data from entire websites with just a prompt. | [
"https://www.firecrawl.dev/[email protected]"
] | # Extract - Firecrawl
*Get structured data from entire websites with just a prompt.*
---
Get structured data from entire websites with just a prompt. |
|
963,445,000 | https://www.firecrawl.dev/pricing | web | Firecrawl | Turn any website into LLM-ready data. | 2025-02-04T17:39:54.521Z | www.firecrawl.dev | {
"raindrop_id": 963445000,
"raindrop_created": "2025-02-04T17:39:54.521Z",
"raindrop_tags": [],
"raindrop_domain": "www.firecrawl.dev",
"title": "Firecrawl",
"description": "Turn any website into LLM-ready data.",
"html": "",
"type": "link",
"media": [
"https://www.firecrawl.dev/og.png?123"
]
} | null | null | Firecrawl | Turn any website into LLM-ready data. | [
"https://www.firecrawl.dev/og.png?123"
] | # Firecrawl
*Turn any website into LLM-ready data.*
---
Turn any website into LLM-ready data. |
|
963,444,997 | https://www.youtube.com/watch?v=FS6hEIFTda8 | web | The easiest way to setup google maps in next.js - YouTube | 📘 T3 Stack Tutorial: https://1017897100294.gumroad.com/l/jipjfm
🤖 SaaS I'm Building: https://www.icongeneratorai.com/
💬 Discord: https://discord.gg/4kGbBaa
🔔 Newsletter: https://newsletter.webdevcody.com/
📁 GitHub: https://github.com/webdevcody
📺 Twitch: https://www.twitch.tv/webdevcody
🤖 Website: https://webdevcody.com
🐦 Twitter: https://twitter.com/webdevcody | 2025-02-04T17:39:54.520Z | www.youtube.com | {
"raindrop_id": 963444997,
"raindrop_created": "2025-02-04T17:39:54.520Z",
"raindrop_tags": [],
"raindrop_domain": "www.youtube.com",
"title": "The easiest way to setup google maps in next.js - YouTube",
"description": "📘 T3 Stack Tutorial: https://1017897100294.gumroad.com/l/jipjfm\n🤖 SaaS I'm Building: https://www.icongeneratorai.com/\n\n💬 Discord: https://discord.gg/4kGbBaa\n🔔 Newsletter: https://newsletter.webdevcody.com/\n📁 GitHub: https://github.com/webdevcody\n📺 Twitch: https://www.twitch.tv/webdevcody\n🤖 Website: https://webdevcody.com\n🐦 Twitter: https://twitter.com/webdevcody",
"html": "",
"type": "video",
"media": [
"https://i.ytimg.com/vi/FS6hEIFTda8/maxresdefault.jpg",
"https://i.ytimg.com/vi/FS6hEIFTda8/mqdefault.jpg"
]
} | null | null | The easiest way to setup google maps in next.js - YouTube | 📘 T3 Stack Tutorial: https://1017897100294.gumroad.com/l/jipjfm
🤖 SaaS I'm Building: https://www.icongeneratorai.com/
💬 Discord: https://discord.gg/4kGbBaa
🔔 Newsletter: https://newsletter.webdevcody.com/
📁 GitHub: https://github.com/webdevcody
📺 Twitch: https://www.twitch.tv/webdevcody
🤖 Website: https://webdevcody.com
🐦 Twitter: https://twitter.com/webdevcody | [
"https://i.ytimg.com/vi/FS6hEIFTda8/maxresdefault.jpg",
"https://i.ytimg.com/vi/FS6hEIFTda8/mqdefault.jpg"
] | # The easiest way to setup google maps in next.js - YouTube
*📘 T3 Stack Tutorial: https://1017897100294.gumroad.com/l/jipjfm
🤖 SaaS I'm Building: https://www.icongeneratorai.com/
💬 Discord: https://discord.gg/4kGbBaa
🔔 Newsletter: https://newsletter.webdevcody.com/
📁 GitHub: https://github.com/webdevcody
📺 Twitch: https://www.twitch.tv/webdevcody
🤖 Website: https://webdevcody.com
🐦 Twitter: https://twitter.com/webdevcody*
---
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🤖 SaaS I'm Building: https://www.icongeneratorai.com/
💬 Discord: https://discord.gg/4kGbBaa
🔔 Newsletter: https://newsletter.webdevcody.com/
📁 GitHub: https://github.com/webdevcody
📺 Twitch: https://www.twitch.tv/webdevcody
🤖 Website: https://webdevcody.com
🐦 Twitter: https://twitter.com/webdevcody |
|
963,444,998 | https://www.google.com/search?sca_esv=3196b51d5507521a&sxsrf=AHTn8zrXvEXxTBjlUbHlKRGgbWbv_YXaSg:1738688753463&q=shadcn+google+map+nextjs+places&udm=2&fbs=ABzOT_CWdhQLP1FcmU5B0fn3xuWpA-dk4wpBWOGsoR7DG5zJBjLjqIC1CYKD9D-DQAQS3Z6l_JhmnqzcFKxs6BqblrysEV7APQ1D5TiEpw9I1WQZJVYErB0s8Aj9JEQy7zoExDWZmsNQk8qdbhgf-SGKtuoR2MeRqaTbj5f2Kc2X01jaUwsS_n9blJbp6IJfgyQ1bwoi5oIP&sa=X&ved=2ahUKEwjwtZmswKqLAxXfaUEAHWFoOjMQtKgLegQIExAB&biw=1357&bih=1202&dpr=2#vhid=cjoLO8uDl2gk6M&vssid=mosaic | web | shadcn google map nextjs places - Google Search | 2025-02-04T17:39:54.520Z | www.google.com | {
"raindrop_id": 963444998,
"raindrop_created": "2025-02-04T17:39:54.520Z",
"raindrop_tags": [],
"raindrop_domain": "www.google.com",
"title": "shadcn google map nextjs places - Google Search",
"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | shadcn google map nextjs places - Google Search | [] | null |
|||
963,444,996 | https://colab.research.google.com/gist/j-94/b351d18ef8f4ae784d59c23a216ba619/intro-to-lotus.ipynb | web | intro-to-lotus.ipynb - Colab | 2025-02-04T17:39:54.519Z | colab.research.google.com | {
"raindrop_id": 963444996,
"raindrop_created": "2025-02-04T17:39:54.519Z",
"raindrop_tags": [],
"raindrop_domain": "colab.research.google.com",
"title": "intro-to-lotus.ipynb - Colab",
"description": "",
"html": "",
"type": "article",
"media": [
"https://colab.research.google.com/img/colab_favicon_256px.png"
]
} | null | null | intro-to-lotus.ipynb - Colab | [
"https://colab.research.google.com/img/colab_favicon_256px.png"
] | null |
|||
963,444,995 | https://www.perplexity.ai/search/is-popular-repo-that-google-ma-DbW25n4lQa.wL6egeHTQsg | web | is popular repo that Google Maps uses internal API endpoints to load data (usually returning JSON). A developer might inspect network requests when a typical search is performed on the maps website to identify these endpoints. Then they can attempt to replicate the necessary requests (including generating or capturing any required tokens or parameters) | Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question. | 2025-02-04T17:39:54.518Z | www.perplexity.ai | {
"raindrop_id": 963444995,
"raindrop_created": "2025-02-04T17:39:54.518Z",
"raindrop_tags": [],
"raindrop_domain": "www.perplexity.ai",
"title": "is popular repo that Google Maps uses internal API endpoints to load data (usually returning JSON). A developer might inspect network requests when a typical search is performed on the maps website to identify these endpoints. Then they can attempt to replicate the necessary requests (including generating or capturing any required tokens or parameters)",
"description": "Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.",
"html": "",
"type": "link",
"media": [
"https://ppl-ai-public.s3.amazonaws.com/static/img/pplx-default-preview.png"
]
} | null | null | is popular repo that Google Maps uses internal API endpoints to load data (usually returning JSON). A developer might inspect network requests when a typical search is performed on the maps website to identify these endpoints. Then they can attempt to replicate the necessary requests (including generating or capturing any required tokens or parameters) | Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question. | [
"https://ppl-ai-public.s3.amazonaws.com/static/img/pplx-default-preview.png"
] | # is popular repo that Google Maps uses internal API endpoints to load data (usually returning JSON). A developer might inspect network requests when a typical search is performed on the maps website to identify these endpoints. Then they can attempt to replicate the necessary requests (including generating or capturing any required tokens or parameters)
*Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.*
---
Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question. |
|
963,444,993 | https://x.com/shadcn/status/1829646548151787589 | twitter | x.com/shadcn/status/1829646548151787589 | framework detection and can initialize a brand new Next.js app and install components and routes in a single command.
Go from new app to a dashboard with a sidebar and login pages in just one command.
— shadcn (@shadcn) | 2025-02-04T17:39:54.517Z | x.com | {
"raindrop_id": 963444993,
"raindrop_created": "2025-02-04T17:39:54.517Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/shadcn/status/1829646548151787589",
"description": "framework detection and can initialize a brand new Next.js app and install components and routes in a single command.\n\nGo from new app to a dashboard with a sidebar and login pages in just one command. \n— shadcn (@shadcn)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,994 | https://rapidapi.com/wbreau/api/business-search2/playground/apiendpoint_7119e5a4-76be-4fa9-a842-8765099b08e7 | web | Business Search | This API searches Google Places for business information based on category and location. Search for restaurants in Los Angeles, CA and it will return a list of restaurants matching that location, the address, phone number, website address, lat and long, and reviews. | 2025-02-04T17:39:54.517Z | rapidapi.com | {
"raindrop_id": 963444994,
"raindrop_created": "2025-02-04T17:39:54.517Z",
"raindrop_tags": [],
"raindrop_domain": "rapidapi.com",
"title": "Business Search",
"description": "This API searches Google Places for business information based on category and location. Search for restaurants in Los Angeles, CA and it will return a list of restaurants matching that location, the address, phone number, website address, lat and long, and reviews.",
"html": "",
"type": "link",
"media": [
"https://rapidapi-prod-apis.s3.amazonaws.com/e7410018-c3f7-46dd-acbd-30b1508e1e9f.png"
]
} | null | null | Business Search | This API searches Google Places for business information based on category and location. Search for restaurants in Los Angeles, CA and it will return a list of restaurants matching that location, the address, phone number, website address, lat and long, and reviews. | [
"https://rapidapi-prod-apis.s3.amazonaws.com/e7410018-c3f7-46dd-acbd-30b1508e1e9f.png"
] | # Business Search
*This API searches Google Places for business information based on category and location. Search for restaurants in Los Angeles, CA and it will return a list of restaurants matching that location, the address, phone number, website address, lat and long, and reviews.*
---
This API searches Google Places for business information based on category and location. Search for restaurants in Los Angeles, CA and it will return a list of restaurants matching that location, the address, phone number, website address, lat and long, and reviews. |
|
963,444,991 | https://x.com/shadcn/status/1870157493684580444 | twitter | x.com/shadcn/status/1870157493684580444 | We’ve made it easier to use shadcn/ui in monorepos.
The CLI now understands your workspaces, installs components and dependencies in the right packages, and handles import resolution for you.
— shadcn (@shadcn) | 2025-02-04T17:39:54.516Z | x.com | {
"raindrop_id": 963444991,
"raindrop_created": "2025-02-04T17:39:54.516Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/shadcn/status/1870157493684580444",
"description": "We’ve made it easier to use shadcn/ui in monorepos.\n\nThe CLI now understands your workspaces, installs components and dependencies in the right packages, and handles import resolution for you. \n— shadcn (@shadcn)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,992 | https://x.com/shadcn/status/1870157493684580444 | twitter | x.com/shadcn/status/1870157493684580444 | We’ve made it easier to use shadcn/ui in monorepos.
The CLI now understands your workspaces, installs components and dependencies in the right packages, and handles import resolution for you.
— shadcn (@shadcn) | 2025-02-04T17:39:54.516Z | x.com | {
"raindrop_id": 963444992,
"raindrop_created": "2025-02-04T17:39:54.516Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/shadcn/status/1870157493684580444",
"description": "We’ve made it easier to use shadcn/ui in monorepos.\n\nThe CLI now understands your workspaces, installs components and dependencies in the right packages, and handles import resolution for you. \n— shadcn (@shadcn)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,990 | https://kirimase.dev/ | web | Introduction – Kirimase | 2025-02-04T17:39:54.515Z | kirimase.dev | {
"raindrop_id": 963444990,
"raindrop_created": "2025-02-04T17:39:54.515Z",
"raindrop_tags": [],
"raindrop_domain": "kirimase.dev",
"title": "Introduction – Kirimase",
"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | Introduction – Kirimase | [] | null |
|||
963,444,989 | https://codeassi.st/?via=marsxdev | web | codeassi.st | 2025-02-04T17:39:54.514Z | codeassi.st | {
"raindrop_id": 963444989,
"raindrop_created": "2025-02-04T17:39:54.514Z",
"raindrop_tags": [],
"raindrop_domain": "codeassi.st",
"title": "codeassi.st",
"description": "",
"html": "",
"type": "link",
"media": []
} | null | null | codeassi.st | [] | null |
|||
963,444,987 | https://x.com/ivanleomk/status/1743321223155556686 | twitter | x.com/ivanleomk/status/1743321223155556686 | I managed to generate the following UI in about 1-2 hours of work using their product including setting up all the dependencies from scratch with 's ui package. The design itself is based off…
— Ivan Leo (@ivanleomk) | 2025-02-04T17:39:54.513Z | x.com | {
"raindrop_id": 963444987,
"raindrop_created": "2025-02-04T17:39:54.513Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/ivanleomk/status/1743321223155556686",
"description": "I managed to generate the following UI in about 1-2 hours of work using their product including setting up all the dependencies from scratch with 's ui package. The design itself is based off… \n— Ivan Leo (@ivanleomk)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,988 | https://github.com/PatrickJS/awesome-cursorrules?tab=readme-ov-file | github | PatrickJS/awesome-cursorrules: 📄 A curated list of awesome .cursorrules files | 📄 A curated list of awesome .cursorrules files. | 2025-02-04T17:39:54.513Z | github.com | {
"raindrop_id": 963444988,
"raindrop_created": "2025-02-04T17:39:54.513Z",
"raindrop_tags": [],
"raindrop_domain": "github.com",
"title": "PatrickJS/awesome-cursorrules: 📄 A curated list of awesome .cursorrules files",
"description": "📄 A curated list of awesome .cursorrules files.",
"html": "",
"type": "link",
"media": [
"https://opengraph.githubassets.com/57d274b0cdafbac7af7e7450a703bfdf54bb3d96112245badf06e8479c31a010/PatrickJS/awesome-cursorrules"
]
} | 📄 A curated list of awesome .cursorrules files. | # [](https://github.com/)
📄 A curated list of awesome .cursorrules files.
**Language:** • **Stars:** 0 • **Forks:** 0
---
📄 A curated list of awesome .cursorrules files. | null | null | null | null | null |
963,444,986 | https://x.com/sidi_jeddou_dev/status/1744358900600623193 | twitter | x.com/sidi_jeddou_dev/status/1744358900600623193 | But I went with what I know, and what can make the work done ASAP with
Domain:
Landing page:
Web app Frontend: no Typescript.
Style: UI and …
— Sidi jeddou (@sidi_jeddou_dev) | 2025-02-04T17:39:54.512Z | x.com | {
"raindrop_id": 963444986,
"raindrop_created": "2025-02-04T17:39:54.512Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/sidi_jeddou_dev/status/1744358900600623193",
"description": "But I went with what I know, and what can make the work done ASAP with \n\nDomain: \nLanding page: \nWeb app Frontend: no Typescript.\nStyle: UI and … \n— Sidi jeddou (@sidi_jeddou_dev)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,983 | https://x.com/elie2222/status/1731947766799298663 | twitter | x.com/elie2222/status/1731947766799298663 | It's an AI CLI that generates a full stack app for you.
It has a tonne baked in including , , , , , .
Built on top of the awesome Kirimase by .
— Elie Steinbock (@elie2222) | 2025-02-04T17:39:54.511Z | x.com | {
"raindrop_id": 963444983,
"raindrop_created": "2025-02-04T17:39:54.511Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/elie2222/status/1731947766799298663",
"description": "It's an AI CLI that generates a full stack app for you.\n\nIt has a tonne baked in including , , , , , . \n\nBuilt on top of the awesome Kirimase by . \n— Elie Steinbock (@elie2222)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
963,444,984 | https://x.com/xavimonp/status/1713968298881118678?s=12 | twitter | x.com/xavimonp/status/1713968298881118678?s=12 | Generate visually stunning READMEs with the help of IA.
Built with:
- - KV for rate limiting, IA sdk, analytics.
- - app router, route handlers.
- - UI
— Xavi A. (@xavimonp) | 2025-02-04T17:39:54.511Z | x.com | {
"raindrop_id": 963444984,
"raindrop_created": "2025-02-04T17:39:54.511Z",
"raindrop_tags": [],
"raindrop_domain": "x.com",
"title": "x.com/xavimonp/status/1713968298881118678?s=12",
"description": "Generate visually stunning READMEs with the help of IA.\n\nBuilt with:\n- - KV for rate limiting, IA sdk, analytics.\n- - app router, route handlers.\n- - UI \n— Xavi A. (@xavimonp)",
"html": "",
"type": "link",
"media": []
} | null | null | null | null | null | null | null |
End of preview.
Dataset Card for Bookmarks Collection
Dataset Description
Dataset Summary
This dataset contains a personal collection of bookmarks from various sources including Twitter, GitHub, and Raindrop.io. It provides a comprehensive view of web content consumption patterns across different platforms, with rich metadata for each source type.
The dataset includes 11,783 bookmark records with content and metadata spanning social media posts, code repositories, articles, and other web content.
Languages
The dataset primarily contains content in English.
Dataset Structure
Data Instances
Each record in the dataset represents a single bookmark with the following structure:
{
"id": 11783,
"source": "twitter_like",
"title": "Example Title",
"url": "https://twitter.com/username/status/123456789",
"content": "The actual content of the bookmark...",
"created_at": "2025-03-28T12:25:57.718145+00:00",
"domain": "twitter.com",
"content_length": 301,
"year": 2025,
"month": 3,
"twitter_username": "username",
"twitter_likes": 42,
"twitter_retweets": 7,
"twitter_replies": 3
}
Different fields are available depending on the source type:
- Twitter bookmarks include engagement metrics and user information
- GitHub bookmarks include repository stars, forks, and programming language
- Raindrop.io bookmarks include tags and domain information
Data Fields
Common Fields
id: Unique identifier for the bookmarksource: Source of the bookmark (twitter, github, raindrop, etc.)title: Title of the bookmarkurl: URL of the bookmarkcontent: Content of the bookmarkcreated_at: Creation date of the bookmarkdomain: Domain of the URLcontent_length: Length of the content in charactersyear: Year the bookmark was createdmonth: Month the bookmark was created
Twitter-specific Fields
twitter_username: Twitter usernametwitter_name: Twitter display nametwitter_followers: Number of followers of the authortwitter_likes: Number of likes on the tweettwitter_retweets: Number of retweetstwitter_replies: Number of replies to the tweet
GitHub-specific Fields
github_repo: GitHub repository namegithub_stars: Number of stars on the GitHub repositorygithub_forks: Number of forks of the GitHub repositorygithub_owner: Owner of the GitHub repositorygithub_language: Primary language of the GitHub repository
Raindrop-specific Fields
raindrop_domain: Domain saved in Raindrop.ioraindrop_tags: Tags associated with the Raindrop bookmark
Data Splits
The dataset does not have explicit splits and is provided as a single collection.
Dataset Creation
Curation Rationale
This dataset was created to:
- Analyze personal content consumption patterns across different platforms
- Enable exploration of bookmark metadata and content
- Provide a structured dataset for recommendation systems research
- Study information organization and retrieval strategies
Source Data
Initial Data Collection and Normalization
The data was collected using the following sources:
- Twitter API for tweets and likes
- GitHub API for starred repositories
- Raindrop.io API for saved bookmarks
Data from these disparate sources was normalized into a consistent format with source-specific metadata preserved in dedicated fields.
Who are the source language producers?
The content comes from various creators across the web, including Twitter users, GitHub repository owners, and website authors whose content was bookmarked via Raindrop.io.
Annotations
The dataset does not contain manual annotations beyond the metadata provided by the source APIs and the categorization by source.
Considerations for Using the Data
Social Impact of Dataset
This dataset represents personal information consumption patterns and can be used to study how individuals organize and consume digital content. It may provide insights into effective information management strategies.
Discussion of Biases
The dataset reflects the personal interests and content consumption preferences of a single individual, so it contains inherent biases toward specific topics, creators, and platforms.
Other Known Limitations
- Some fields may contain missing values depending on what was available from the source API
- Content may be truncated in some cases due to API limitations
- The dataset only includes publicly accessible content
Additional Information
Dataset Curators
This dataset was curated by J94.
Licensing Information
This dataset is released under the Apache 2.0 License.
Citation Information
If you use this dataset in your research, please cite:
@dataset{bookmarks_dataset,
author = {J94},
title = {Bookmarks Dataset},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/J94/bookmarks-dataset}}
}
Contributions
This dataset welcomes community contributions to improve the documentation, enhance the metadata, or add additional analysis tools.
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