We've overhauled the Anthropic Console to serve as one place to build, test, and iterate on prompts with Claude. And now you can share prompts with your teammates, too.
CODE
-
LangChain releases MCP adapters for LangGraph integration
By
–
if you're looking to use langgraph mcp… we released something a week or so ago! https://
changelog.langchain.com/announcements/
mcp-adapters-for-langchain-and-langgraph
… -
Flowise Now Supports MCP Protocol for LLM Integration
By
–
Today, we're supporting MCP in Flowise!
— FlowiseAI (@FlowiseAI) 6 mars 2025
There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy – just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications.
In fact,… pic.twitter.com/XXiOadDRszToday, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy – just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact,
-
Flowise AI Opening Public Access Next Month
By
–
send us an email to support@flowiseai.com, or actually we are opening public access next month!
-
Codeium Windsurf Wave 4 launches with AI preview and automation features
By
–
Codeium released Windsurf Wave 4 with new features, including:
— Rowan Cheung (@rowancheung) 6 mars 2025
—AI previews for rapid app iteration
—Tab-to-import functionality
—Suggested actions
—Linter integration
—Model choices for enterprise adminspic.twitter.com/DWDDnA66N7Codeium released Windsurf Wave 4 with new features, including: —AI previews for rapid app iteration
—Tab-to-import functionality —Suggested actions
—Linter integration
—Model choices for enterprise admins -
RL – An Introduction to Reinforcement Learning Book
By
–
RL – An Introduction http://
incompleteideas.net/book/the-book-
2nd.html
… -
HeadInfer: Long-Context LLM Inference on Consumer GPUs
By
–
HeadInfer: Unlocking Long-Context LLM Inference on Consumer GPUs (Million-level Tokens)
*long-context inputs require large GPU memory.
*A standard LLM like Llama-3–8B requires 207GB of GPU memory for 1 million tokens — far beyond the capabilities of consumer GPUs like the RTX -
ChatGPT’s code interpreter feature renamed multiple times
By
–
It’s a ChatGPT feature originally (and best) known as Code Interpreter, then renamed to Advanced Data Analysis, and now it seems to have no official name in the docs. It’s the thing that powers the features described here:
-
Code Quality Matters Less Than Working Products
By
–
If code I (ai) generate = working, secure, scalable product, it truly doesn’t matter. I’ve no idea how good figmas code is vs @levelsio – I can assume but who gives a fuck. If it works it works
-
o3-mini gets C.I., first fast model to use it for subtasks
By
–
o3-mini getting C.I. is almost as big a deal as 4.5 getting it IMO because it’s the first speedy model that can use it. Also it’s a use case where you routinely know in advance some subtask will be easy but token-hungry (“That’s good, but rename those variables to…”)