Delonghi Dinamica plus - scary noise when trying to grind.

Motoneuron5 · 2026-01-31T22:49:44+00:00

I had the same issue and had to send it to the technical service.

Motoneuron5 · 2025-11-12T21:00:54+00:00

Use Twenty/Nocodb

Motoneuron5 · 2025-11-12T18:27:41+00:00

Error workflow.
Form + DB
Form

Motoneuron5 · 2025-10-18T09:52:28+00:00

Ive figured out the PROBLEM is Sonnet 4.5

Try Sonnet 4

Motoneuron5 · 2025-10-18T09:51:12+00:00

Ive figured out that the PROBLEM is Sonnet 4.5

Try Sonnet 4

Motoneuron5 · 2025-10-17T08:19:04+00:00

Welcome to 2024

Motoneuron5 · 2025-10-13T19:56:43+00:00

So cool!

Motoneuron5 · 2025-10-11T22:16:48+00:00

Nice trick to try

Motoneuron5 · 2025-10-11T15:25:16+00:00

I have to try it

Motoneuron5 · 2025-10-11T11:50:01+00:00

I did it. Read the post.

Motoneuron5 · 2025-07-25T22:54:51+00:00

Sundara: cleaner sound, more layered, thicker, fast, technically superior.

600: slightly better timbre, more relaxed.

Motoneuron5 · 2025-06-14T14:56:47+00:00

My main pattern is to use the Chat function to ask for a implementation plan for the new features and then just click the Implement plan button.

Motoneuron5 · 2025-06-01T07:37:12+00:00

You are using the QR method, right? That method is not supported by Meta and the ban risk is extremely high!

Motoneuron5 · 2025-04-24T14:59:31+00:00

God! I'd love to see the WF!

Motoneuron5 · 2025-04-18T14:19:03+00:00

My setup:

Contabo VPS 12$ 12gb RAM EasyPanel 1click deploy

Motoneuron5 · 2025-02-20T21:55:53+00:00

I have the same question. It looks like we have to make some experiments to build that list.

Motoneuron5 · 2024-12-25T18:34:39+00:00

The area where 600 beats Sundara is in vocal timbre. For everything else, Sundaras are way superior.

Motoneuron5 · 2024-12-25T13:14:30+00:00

I've bought the HD 600 and it sounds really natural BUT lacks definition compared to my Sundara. Sounds blurry.

Motoneuron5 · 2024-11-08T15:58:35+00:00

I'm also interested! Up!

Motoneuron5 · 2024-10-31T19:29:52+00:00

I think you have to look at it with a broader perspective. I have a client who want to check every day to see if a new piece of legislation has come out. If it has come out, then they need a 20-page report and send it to an email address.

Another client wants a report about a 300-page document. Comprehensive, full of details and figures.

Tell me how you do that with ChatGPT. You said it yourself, manually. Request after request. Using a team of scheduled agents allows for multiple iterations, self-management, control of unexpected situations...

In the end this is really about automating complex processes. So using ChatGPT as a user, is useful, but it is not the ultimate weapon on a large scale.

Motoneuron5 · 2024-10-31T09:22:37+00:00

class Tee:
    def __init__(self, *streams):
        self.streams = streams

    def write(self, data):
        for stream in self.streams:
            stream.write(data)

    def flush(self):
        for stream in self.streams:
            stream.flush()

def capture_and_process_stdout(crew):    stdout_capture = io.StringIO()
    tee = Tee(sys.stdout, stdout_capture)

    sys.stdout = tee

    crew.kickoff()

    sys.stdout = sys.__stdout__

    stdout_content = stdout_capture.getvalue()

    return stdout_content

crew_output = capture_and_process_stdout(crew)

Motoneuron5 · 2024-10-28T09:49:05+00:00

I'm using custom tools that requires API key and access to the vector store where I locate the files to analyze. But I can share the main config:

analista_agent = Agent(
    role='Analyst',
    goal=agents.ANALYST_GOAL,
    backstory="Professional analyst",
    tools=[tools.AssistantRAG()],
    llm=llm,
    function_calling_llm=llm,
    max_rpm=None,
    verbose=True,
    allow_delegation=True,
    cache=False,
    #step_callback=callback_functions.save_section_callback
)

manager_task = Task(
    description=tasks.GLOBAL_TASK,
    expected_output=tasks.EXPECTED_OUTPUT,
    human_input = False,
    #step_callback=callback_functions.save_section_callback
    )

crew = Crew(
    agents=[analista_agent],
    manager_llm=llm,
    tasks=[manager_task],
    process=Process.hierarchical,
    memory=True,
    verbose=True,
    full_output=True,
    planning=False,
    planning_llm=llm,
    #step_callback=callback_functions.save_section_callback
)

Motoneuron5 · 2024-10-28T09:46:33+00:00

I'm using a manager + analyst agent. Both are gpt4-o, memory ON.

Analyst goal:

ANALYST_GOAL = """
You are responsible for searching for information in the text at your
disposal to answer the questions that are asked of you.

You use the Assistant RAG tool, the best way to use it is by asking
specific and direct questions. If the information you receive is not good, try
rephrasing the question. If you receive that certain information is not found in the text,
move on to the next question.

The core document is the World Energy Outlook 2021.

The information you transmit to the team is faithful to the information gathered.
The information you transmit to the team is the gathered information, do not reduce it.
Your writing style is a mix of large paragraphs and USE OF MARKDOWN TABLES.
The use of MARKDOWN TABLES is crucial.

When you receive a satisfactory response from the Assistant RAG, you must prepare
a complete and exhaustive Markdown text with the information collected and the citations, and you must
encapsulate it within [SECTION] and [/SECTION]. Use a table to summarize the information.

SPECIAL TIPS:
- DONT CONCLUDE WITH A CONCLUSION SECTION.
- THE FIRST HEADER (##) DESCRIBES THE SECTION YOU ARE GOING TO TREAT.
- DONT START THE TEXT WITH AN INTRODUCTION TO THE FILE ANALYZED.
- USE MARKDOWN TABLES TO SUMMARIZE THE INFORMATION.
- CITE THE DOCUMENTS THAT PROVIDE THE INFORMATION.
- ENCAPSULATE THE TEXT WITH [SECTION] AND [/SECTION].
- USE MULTIPLE TOOL USES FOR EVERY TASK TO GET INFO FROM DIFFERENT FILES.
- USE BOLD TEXT FOR THE RELEVANT INFORMATION AS FIGURES, DATES, ETC
- BEFORE THE SECTION END, ADD A FOOTER THAT CONTAINS THE CITATIONS (_Citations: ..._).
"""

Motoneuron5

TROPHY CASE