solveit-dmtools 0.0.13

Personalized tools to add functionality to SolveIt

solveit_dmtools

Usage and examples

from dialoghelper.core import *
from solveit_dmtools import * # dhb, dhp, fab

add_msg(content=dhb.doc, msg_type='note')

'_16aec1ea'

Backup Chat for SolveIt using dialoghelper and lisette

Sometimes we may have a problem in SolveIt while Sonnet is down (E300), or maybe we want a different perspective.

This module helps us to leverage any other LLM that is available to LiteLLM by providing our own keys and the model name.

Usage:

from solveit_dmtools import dhb

# then in another cell
# bc = dhb.c() to search model names
bc = dhb.c("model-name")
bc("Hi")

First calling with no model will prompt you to type in part of a model name to search

bc = dhb.c()

Please try again by using e.g. `bc = dhb.c('model_name')` with a model name in:
azure/eu/gpt-5-2025-08-07
azure/eu/gpt-5-mini-2025-08-07
azure/eu/gpt-5.1
azure/eu/gpt-5.1-chat
azure/eu/gpt-5.1-codex
azure/eu/gpt-5.1-codex-mini
azure/eu/gpt-5-nano-2025-08-07
azure/global/gpt-5.1
azure/global/gpt-5.1-chat
azure/global/gpt-5.1-codex
azure/global/gpt-5.1-codex-mini
azure/gpt-5.1-2025-11-13
azure/gpt-5.1-chat-2025-11-13
azure/gpt-5.1-codex-2025-11-13
azure/gpt-5.1-codex-mini-2025-11-13
azure/gpt-5
azure/gpt-5-2025-08-07
azure/gpt-5-chat
azure/gpt-5-chat-latest
azure/gpt-5-codex
azure/gpt-5-mini
azure/gpt-5-mini-2025-08-07
azure/gpt-5-nano
azure/gpt-5-nano-2025-08-07
azure/gpt-5-pro
azure/gpt-5.1
azure/gpt-5.1-chat
azure/gpt-5.1-codex
azure/gpt-5.1-codex-mini
azure/us/gpt-5-2025-08-07
azure/us/gpt-5-mini-2025-08-07
azure/us/gpt-5-nano-2025-08-07
azure/us/gpt-5.1
azure/us/gpt-5.1-chat
azure/us/gpt-5.1-codex
azure/us/gpt-5.1-codex-mini
gpt-5
gpt-5.1
gpt-5.1-2025-11-13
gpt-5.1-chat-latest
gpt-5-pro
gpt-5-pro-2025-10-06
gpt-5-2025-08-07
gpt-5-chat
gpt-5-chat-latest
gpt-5-codex
gpt-5.1-codex
gpt-5.1-codex-mini
gpt-5-mini
gpt-5-mini-2025-08-07
gpt-5-nano
gpt-5-nano-2025-08-07
openrouter/openai/gpt-5-chat
openrouter/openai/gpt-5-codex
openrouter/openai/gpt-5
openrouter/openai/gpt-5-mini
openrouter/openai/gpt-5-nano

bc = dhb.c("openrouter/openai/gpt-5-codex")

The following will be automatically commented out when run, then a prompt cell is added after it with input/output from the other LLM.

bc("Hi, can you use tools?")

I can use the built-in URL reader, but no other tools are currently available in this session. What would you like to do—do you have other tools or variables you’d like to add?

• id: gen-1763944919-qpn4Xb5hfcJcAhCAa8nd
• model: openai/gpt-5-codex
• finish_reason: stop
• usage: Usage(completion_tokens=302, prompt_tokens=1706, total_tokens=2008, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=256, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None, video_tokens=0), cost=0.0051525, is_byok=False, cost_details={'upstream_inference_cost': None, 'upstream_inference_prompt_cost': 0.0021325, 'upstream_inference_completions_cost': 0.00302})

Prompt (openrouter/openai/gpt-5-codex): Hi, can you use tools?

🤖Reply🤖

I can use the built-in URL reader, but no other tools are currently available in this session. What would you like to do—do you have other tools or variables you’d like to add?

bc("Can you please read https://raw.githubusercontent.com/AnswerDotAI/fhdaisy/refs/heads/main/README.md and give the elevator pitch and some sample code? No need to store the raw content.")

Elevator pitch

fhdaisy wraps the DaisyUI component library for FastHTML so you can build polished interfaces without hand‑crafting HTML classes. Instead of writing verbose class strings like class="btn btn-primary", you compose Python components (Btn, Card, Alert, etc.) that automatically map to the right HTML tags and DaisyUI modifiers. This keeps your FastHTML code concise, consistent, and themeable while still letting you mix in custom helpers when needed.

Sample usage

from fasthtml.common import Div, H2
from fhdaisy import Btn, Card, Alert, Input, Modal

# Card with a button call-to-action
card = Card(
    H2("Join the beta!", cls="-lg"),
    "Get early access to the newest FastHTML components.",
    Btn("Sign up", cls="-primary"),
    cls="-compact -shadow"
)

# Alert and input field using DaisyUI modifiers
banner = Alert("Deployment succeeded ✅", cls="-success -shadow")

email_field = Input(type="email", placeholder="[e-mail]", cls="-bordered -primary")

# Modal with a form
dialog = Modal(
    Div(
        H2("Feedback"),
        email_field,
        Btn("Send", cls="-accent"),
        cls="space-y-3"
    ),
    cls="-open"
)

These components render with the appropriate DaisyUI classes applied automatically, keeping your FastHTML views tidy and expressive.

• id: gen-1763945007-BW11GMmG8i8PaQpURfVe
• model: openai/gpt-5-codex
• finish_reason: stop
• usage: Usage(completion_tokens=502, prompt_tokens=3020, total_tokens=3522, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=192, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None, video_tokens=0), cost=0.008795, is_byok=False, cost_details={'upstream_inference_cost': None, 'upstream_inference_prompt_cost': 0.003775, 'upstream_inference_completions_cost': 0.00502})

Prompt (openrouter/openai/gpt-5-codex): Can you please read https://raw.githubusercontent.com/AnswerDotAI/fhdaisy/refs/heads/main/README.md and give the elevator pitch and some sample code? No need to store the raw content.

🤖Reply🤖

{
  "id": "call_ikBfSjiiyCzhnIyOUfuwwftg",
  "call": {
    "function": "read_url",
    "arguments": {
      "url": "https://raw.githubusercontent.com/AnswerDotAI/fhdaisy/refs/heads/main/README.md",
      "as_md": true,
      "extract_section": false,
      "selector": ""
    }
  },
  "result": "# fhdaisy\n\n`fhdaisy` is a Python wrapper for [DaisyUI](https://daisyui.com/) that\nbrings its compone<TRUNCATED>"
}

Elevator pitch

fhdaisy wraps the DaisyUI component library for FastHTML so you can build polished interfaces without hand‑crafting HTML classes. Instead of writing verbose class strings like class="btn btn-primary", you compose Python components (Btn, Card, Alert, etc.) that automatically map to the right HTML tags and DaisyUI modifiers. This keeps your FastHTML code concise, consistent, and themeable while still letting you mix in custom helpers when needed.

Sample usage

from fasthtml.common import Div, H2
from fhdaisy import Btn, Card, Alert, Input, Modal

# Card with a button call-to-action
card = Card(
    H2("Join the beta!", cls="-lg"),
    "Get early access to the newest FastHTML components.",
    Btn("Sign up", cls="-primary"),
    cls="-compact -shadow"
)

# Alert and input field using DaisyUI modifiers
banner = Alert("Deployment succeeded ✅", cls="-success -shadow")

email_field = Input(type="email", placeholder="[e-mail]", cls="-bordered -primary")

# Modal with a form
dialog = Modal(
    Div(
        H2("Feedback"),
        email_field,
        Btn("Send", cls="-accent"),
        cls="space-y-3"
    ),
    cls="-open"
)

These components render with the appropriate DaisyUI classes applied automatically, keeping your FastHTML views tidy and expressive.

add_msg(content=dhp.doc, msg_type="note")

'_94fe5acd'

Dialog Helper for Polya’s Problem-Solving Method

This module provides quick access to Polya’s four-stage problem-solving process through interactive prompts.

Each stage has multiple questions/prompts: - To preview one you can just print it/type its name in a cell and hit Submit - e.g. dhp.act.next shows you “(prompt) What is next?”. - To execute one you call it by adding () after the name - e.g. dhp.act.next() will replace the current message cell with a prompt cell having “What is next?” in it - It will be automatically executed, you can hit Esc to stop it and/or Enter to edit the prompt

TYPICAL FLOW: - Start with dhp.u (understand) (even briefly) to clarify your understanding of the problem - Move to dhp.p (plan) to develop initial strategies - Switch between plan and act (or a, or x/execute) as you develop and test approaches - Use dhp.r (review) to gain deeper understanding of your approach and findings - You might loop back to other steps after r (review)

If you feel stuck, run dhp.help() and it will submit the prompt cell it creates - SolveIt will help you pick a next prompt!

UNDERSTAND STAGE - Clarify the problem before solving - dhp.u.summary() - Creates a prompt cell asking SolveIt to give a concise summary of the problem - dhp.u.info() - Creates a prompt cell asking SolveIt to inventory known/unknown information - dhp.u.similar() - Creates a prompt cell asking SolveIt if it has seen a similar problem before - dhp.u.lateral() - Creates a prompt cell to explore problem relationships and scope - dhp.u.related() - Creates a prompt cell to identify similar or simpler problems - dhp.u.viz() - Creates a prompt cell asking SolveIt to create a figure or diagram to represent the problem - dhp.u.notation() - Creates a prompt cell asking SolveIt to pick suitable notation (symbols for quantities/data, states, transitions) - dhp.u.simplest() - Creates a prompt cell asking SolveIt for the simplest way to look at the problem - dhp.u.simplify() - Creates a prompt cell asking SolveIt to separate problem parts (break down complex conditions into simpler ones)

PLAN STAGE - Develop strategies and approaches - dhp.p.chunks() - Creates a prompt cell asking SolveIt to break down the problem into smaller sub-problems - dhp.p.partial() - Creates a prompt cell asking SolveIt if there’s a smaller part or representation of the problem to solve - dhp.p.known_approach() - Creates a prompt cell asking SolveIt to use a known algorithm or library to solve the problem - dhp.p.verifiable() - Creates a prompt cell asking SolveIt how to verify if the solution is consistent and correct - dhp.p.backward() - Creates a prompt cell asking SolveIt to work backward from the desired result - dhp.p.aux() - Creates a prompt cell asking SolveIt to use an auxiliary element (variable, diagram, or example) to clarify the path - dhp.p.analogy() - Creates a prompt cell asking SolveIt to use analogy or similarity to relate the problem to a known solution - dhp.p.review() - Creates a prompt cell asking SolveIt to critique the plan of attack (be frank and critical)

ACT STAGE - Execute your plan while monitoring progress - dhp.a.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this step - dhp.a.check() - Creates a prompt cell asking SolveIt if this step seems correct - dhp.a.doubt() - Creates a prompt cell asking SolveIt if we’re using the right approach - dhp.a.next() - Creates a prompt cell asking SolveIt what is next - dhp.a.other() - Creates a prompt cell asking SolveIt if there’s another way to look at this - dhp.a.partial() - Creates a prompt cell asking SolveIt about intermediate results or milestones to aim for - dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a simpler way to do this step - dhp.a.symmetry() - Creates a prompt cell asking SolveIt about symmetries or patterns in the problem to exploit - dhp.a.valid() - Creates a prompt cell asking SolveIt if this step was a valid step

REVIEW STAGE - Verify results, reflect on process, and extract lessons - dhp.r.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this problem - dhp.r.alter() - Creates a prompt cell asking SolveIt for alternative solutions or approaches that might be more efficient or effective - dhp.r.general() - Creates a prompt cell asking SolveIt if we can generalize the solution to other similar problems - dhp.r.grok() - Creates a note cell with the text “To consider: Can I understand the solution without having to perform all the steps?” - dhp.r.learned() - Creates a prompt cell asking SolveIt what lessons have been learned from this - dhp.r.mistakes() - Creates a prompt cell asking SolveIt about common mistakes made - dhp.r.other() - Creates a prompt cell asking SolveIt if we can derive the result differently - dhp.r.principles() - Creates a prompt cell asking SolveIt to identify underlying principles or patterns that emerged during the solution process - dhp.r.sanity() - Creates a prompt cell asking SolveIt if the result makes sense and can be verified by substitution or another method - dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can derive the result in a simpler way - dhp.r.test() - Creates a prompt cell asking SolveIt for different ways to test this

The following is just dhp.help()

Please pick an appropriate next-step/prompt from the below:

doc: UNDERSTAND STAGE - Clarify the problem before solving

• dhp.u.summary() - Creates a prompt cell asking SolveIt to give a concise summary of the problem
• dhp.u.info() - Creates a prompt cell asking SolveIt to inventory known/unknown information
• dhp.u.similar() - Creates a prompt cell asking SolveIt if it has seen a similar problem before
• dhp.u.lateral() - Creates a prompt cell to explore problem relationships and scope
• dhp.u.related() - Creates a prompt cell to identify similar or simpler problems
• dhp.u.viz() - Creates a prompt cell asking SolveIt to create a figure or diagram to represent the problem
• dhp.u.notation() - Creates a prompt cell asking SolveIt to pick suitable notation (symbols for quantities/data, states, transitions)
• dhp.u.simplest() - Creates a prompt cell asking SolveIt for the simplest way to look at the problem
• dhp.u.simplify() - Creates a prompt cell asking SolveIt to separate problem parts (break down complex conditions into simpler ones)

info: (prompt) “What information do we have? What information do we not have? What might change as we learn more?” lateral: (prompt) “Can you relate this problem to a more general or a more specific problem?” notation: (prompt) “Can we pick a suitable notation (e.g. symbols for quantities/data, states, and transitions)?” related: (prompt) “Can you think of a related problem that we can solve? It could even be a simpler one we could solve first to help understand this one.” similar: (prompt) “Have you seen a similar problem before?” simplest: (prompt) “What might be the simplest way to look at this problem?” simplify: (prompt) “Can we separate the various parts of the problem (e.g. break down complex conditions into simpler ones)?” summary: (prompt) “Could you please give a concise summary of the problem?” viz: (prompt) “Can we create a figure or diagram to represent the problem?”

doc: PLAN STAGE - Develop strategies and approaches

• dhp.p.chunks() - Creates a prompt cell asking SolveIt to break down the problem into smaller sub-problems
• dhp.p.partial() - Creates a prompt cell asking SolveIt if there’s a smaller part or representation of the problem to solve
• dhp.p.known_approach() - Creates a prompt cell asking SolveIt to use a known algorithm or library to solve the problem
• dhp.p.verifiable() - Creates a prompt cell asking SolveIt how to verify if the solution is consistent and correct
• dhp.p.backward() - Creates a prompt cell asking SolveIt to work backward from the desired result
• dhp.p.aux() - Creates a prompt cell asking SolveIt to use an auxiliary element (variable, diagram, or example) to clarify the path
• dhp.p.analogy() - Creates a prompt cell asking SolveIt to use analogy or similarity to relate the problem to a known solution
• dhp.p.review() - Creates a prompt cell asking SolveIt to critique the plan of attack (be frank and critical)

analogy: (prompt) “Can you use analogy or similarity to relate the problem to a known solution?” aux: (prompt) “Could we use an auxiliary element (e.g., a variable, diagram, or example) to clarify the path?” backward: (prompt) “Can we work backward from the desired result?” chunks: (prompt) “Could you please break down the problem into smaller sub-problems?” known_approach: (prompt) “Could we use a known algorithm or library to solve the problem, or some of it?” partial: (prompt) “Is there a smaller part or representation of the problem we could solve?” review: (prompt) “Could you please critique the plan of attack? Be frank, do not be afraid to be critical.” verifiable: (prompt) “How would we verify if our solution is consistent and correct?”

doc: ACT STAGE - Execute your plan while monitoring progress

• dhp.a.doubt() - Creates a prompt cell asking SolveIt if we’re using the right approach
• dhp.a.other() - Creates a prompt cell asking SolveIt if there’s another way to look at this
• dhp.a.partial() - Creates a prompt cell asking SolveIt about intermediate results or milestones to aim for
• dhp.a.symmetry() - Creates a prompt cell asking SolveIt about symmetries or patterns in the problem to exploit
• dhp.a.next() - Creates a prompt cell asking SolveIt what is next
• dhp.a.valid() - Creates a prompt cell asking SolveIt if this step was a valid step
• dhp.a.check() - Creates a prompt cell asking SolveIt if this step seems correct
• dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a simpler way to do this step
• dhp.a.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this step

all: (prompt) “Did we cover all of the data or examples for this step?” check: (prompt) “Does this step seem correct?” doubt: (prompt) “Are we using the right approach?” next: (prompt) “What is next?” other: (prompt) “Is there another way to look at this?” partial: (prompt) “Are there any intermediate results or milestones that we can aim for?” simpler: (prompt) “Was there a simpler way we could have done this step?” symmetry: (prompt) “Are there any symmetries or patterns in the problem that we can exploit?” valid: (prompt) “Does this step seem to have been a valid step?”

doc: REVIEW STAGE - Verify results, reflect on process, and extract lessons

• dhp.r.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this problem
• dhp.r.sanity() - Creates a prompt cell asking SolveIt if the result makes sense and can be verified by substitution or another method
• dhp.r.grok() - Creates a note cell with the text “To consider: Can I understand the solution without having to perform all the steps?”
• dhp.r.learned() - Creates a prompt cell asking SolveIt what lessons have been learned from this
• dhp.r.general() - Creates a prompt cell asking SolveIt if we can generalize the solution to other similar problems
• dhp.r.alter() - Creates a prompt cell asking SolveIt for alternative solutions or approaches that might be more efficient or effective
• dhp.r.other() - Creates a prompt cell asking SolveIt if we can derive the result differently
• dhp.r.mistakes() - Creates a prompt cell asking SolveIt about common mistakes made
• dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can derive the result in a simpler way
• dhp.r.principles() - Creates a prompt cell asking SolveIt to identify underlying principles or patterns that emerged during the solution process
• dhp.r.test() - Creates a prompt cell asking SolveIt for different ways to test this

all: (prompt) “Did we cover all of the data or examples for this problem?” alter: (prompt) “Can you think of alternative solutions or approaches that might be more efficient or effective?” general: (prompt) “Can we generalize the solution to other similar problems?” grok: (note) “To consider: Can I understand the solution without having to perform all the steps?” learned: (prompt) “What lessons have I learned from this?” mistakes: (prompt) “What were my common mistakes?” other: (prompt) “Can we derive the result differently?” principles: (prompt) “Can you identify any underlying principles or patterns that emerged during the solution process?” sanity: (prompt) “Does the result make sense? Can we verify by substition or another method?” simpler: (prompt) “Can we derive the result in a simpler way?” test: (prompt) “What are some different ways we can test this?”

’Dialog Helper for Polya's Problem-Solving Methodmodule provides quick access to Polya's four-stage problem-solving processinteractive prompts.stage has multiple questions/prompts:- To preview one you can just print it/type its name in a cell and hit Submit- e.g. dhp.act.next shows you “(prompt) What is next?”.- To execute one you call it by adding () after the name- e.g. dhp.act.next() will replace the current message cell with a prompt cell having “What is next?” in it- It will be automatically executed, you can hit Esc to stop it and/or Enter to edit the prompt*TYPICAL FLOW:**- Start with dhp.u (understand) (even briefly) to clarify your understanding of the problem- Move to dhp.p (plan) to develop initial strategies- Switch between plan and act (or a, or x/execute) as you develop and test approaches- Use dhp.r (review) to gain deeper understanding of your approach and findings- You might loop back to other steps after r (review)you feel stuck, run dhp.help() and it will submit the prompt cell it creates - SolveIt will help you pick a next prompt!*UNDERSTAND STAGE - Clarify the problem before solving**- dhp.u.summary() - Creates a prompt cell asking SolveIt to give a concise summary of the problem- dhp.u.info() - Creates a prompt cell asking SolveIt to inventory known/unknown information- dhp.u.similar() - Creates a prompt cell asking SolveIt if it has seen a similar problem before- dhp.u.lateral() - Creates a prompt cell to explore problem relationships and scope- dhp.u.related() - Creates a prompt cell to identify similar or simpler problems- dhp.u.viz() - Creates a prompt cell asking SolveIt to create a figure or diagram to represent the problem- dhp.u.notation() - Creates a prompt cell asking SolveIt to pick suitable notation (symbols for quantities/data, states, transitions)- dhp.u.simplest() - Creates a prompt cell asking SolveIt for the simplest way to look at the problem- dhp.u.simplify() - Creates a prompt cell asking SolveIt to separate problem parts** (break down complex conditions into simpler ones)*PLAN STAGE - Develop strategies and approaches- dhp.p.chunks() - Creates a prompt cell asking SolveIt to break down the problem into smaller sub-problems- dhp.p.partial() - Creates a prompt cell asking SolveIt if there's a smaller part or representation of the problem to solve- dhp.p.known_approach() - Creates a prompt cell asking SolveIt to use a known algorithm or library to solve the problem- dhp.p.verifiable() - Creates a prompt cell asking SolveIt how to verify if the solution is consistent and correct- dhp.p.backward() - Creates a prompt cell asking SolveIt to work backward from the desired result- dhp.p.aux() - Creates a prompt cell asking SolveIt to use an auxiliary element (variable, diagram, or example) to clarify the path- dhp.p.analogy() - Creates a prompt cell asking SolveIt to use analogy or similarity to relate the problem to a known solution- dhp.p.review() - Creates a prompt cell asking SolveIt to critique the plan of attack** (be frank and critical)*ACT STAGE - Execute your plan while monitoring progress- dhp.a.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this step- dhp.a.check() - Creates a prompt cell asking SolveIt if this step seems correct- dhp.a.doubt() - Creates a prompt cell asking SolveIt if we're using the right approach- dhp.a.next() - Creates a prompt cell asking SolveIt what is next- dhp.a.other() - Creates a prompt cell asking SolveIt if there's another way to look at this- dhp.a.partial() - Creates a prompt cell asking SolveIt about intermediate results or milestones to aim for- dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a simpler way to do this step- dhp.a.symmetry() - Creates a prompt cell asking SolveIt about symmetries or patterns in the problem to exploit- dhp.a.valid() - Creates a prompt cell asking SolveIt if this step was a valid step***REVIEW STAGE - Verify results, reflect on process, and extract lessons- dhp.r.all() - Creates a prompt cell asking SolveIt if we covered all of the data or examples for this problem- dhp.r.alter() - Creates a prompt cell asking SolveIt for alternative solutions or approaches that might be more efficient or effective- dhp.r.general() - Creates a prompt cell asking SolveIt if we can generalize the solution to other similar problems- dhp.r.grok() - Creates a note cell with the text “To consider: Can I understand the solution without having to perform all the steps?”- dhp.r.learned() - Creates a prompt cell asking SolveIt what lessons have been learned from this- dhp.r.mistakes() - Creates a prompt cell asking SolveIt about common mistakes made- dhp.r.other() - Creates a prompt cell asking SolveIt if we can derive the result differently- dhp.r.principles() - Creates a prompt cell asking SolveIt to identify underlying principles or patterns that emerged during the solution process- dhp.r.sanity() - Creates a prompt cell asking SolveIt if the result makes sense and can be verified by substitution or another method- dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can derive the result in a simpler way- dhp.r.test() - Creates a prompt cell asking SolveIt for **different ways to test** this’

🤖Reply🤖

Based on where we are in the conversation, I’d recommend starting with:

dhp.u.summary()

Since we’ve just been introduced to the dhp (Dialog Helper for Polya’s method) tool and haven’t defined a specific problem yet, beginning with a summary prompt would help clarify what problem we’re trying to solve. This follows Polya’s method by starting in the UNDERSTAND stage before moving to planning or action.

Would you like to run that, or do you have a specific problem in mind that you’d like to work on? If you already have a problem and want to jump to a different stage, let me know!

# Uncomment and submit the line below if you do not already have a copy of fabric in your fabric folder
#!git clone --depth 1 https://github.com/danielmiessler/fabric.git

add_msg(content=fab.doc, msg_type="note")

'_9b1e86d1'

fab - Open Source ‘fabric’ prompts made quickly available in SolveIt

This module leverages over 200 open source LLM prompts that are available in Daniel Miesller’s ‘fabric’ project.

If you import as fab, Submit the following to see an overview of all the prompts: fab.p

HOW TO USE IT

Most Common Syntax: prompt="Your Prompt" in one cell then fab.p.pattern_name() in another, where pattern_name is any of the 200+ available fabric patterns.

MOST IMPORTANT AND USED OPTIONS AND FEATURES

• Variable Targeting: Use fab.p.pattern_name('variable_name') to process content from a specific variable instead of the default ‘prompt’ variable.

• Pattern Discovery: Use fab.p.help() (an alias for suggest_pattern()) to get suggestions of which pattern to pick for your prompt.

• Compression Feature: Use fab.compress() after running a pattern to save tokens by marking the previous cell as skipped and compressing the output to a new note.

• Default Variable: Most patterns work with a variable called ‘prompt’ by default, making it easy to process your main content.

COMMON PATTERNS

• For Summarizing Content: fab.p.summarize()
• For Explaining Code: fab.p.explain_code()
• For Analyzing Claims: fab.p.analyze_claims()
• For Extracting Wisdom from Text: fab.p.extract_wisdom()
• For Creating Quizzes: fab.p.create_quiz()

fab.p.explain_code

Fabric pattern: explain_code - Analyze/explain code, security tool outputs, and configs.

prompt = read_url("https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/blogtopod.py")

The next cell is running fab.p.explain_code()

(From fab.p.explain_code folded below) # IDENTITY and PURPOSE

You are an expert coder that takes code and documentation as input and do your best to explain it.

Take a deep breath and think step by step about how to best accomplish this goal using the following steps. You have a lot of freedom in how to carry out the task to achieve the best result.

OUTPUT SECTIONS

• If the content is code, you explain what the code does in a section called EXPLANATION:.

• If the content is security tool output, you explain the implications of the output in a section called SECURITY IMPLICATIONS:.

• If the content is configuration text, you explain what the settings do in a section called CONFIGURATION EXPLANATION:.

• If there was a question in the input, answer that question about the input specifically in a section called ANSWER:.

OUTPUT

• Do not output warnings or notes—just the requested sections.

INPUT:

INPUT:

$prompt

🤖Reply🤖

EXPLANATION:

This Python script (blogtopod.py) converts blog posts into podcast-style audio files using text-to-speech. Here’s what it does:

The script fetches a blog post from a URL, extracts the main content, and converts it to audio using OpenAI’s TTS API. It processes the text in chunks (since TTS APIs have size limits), generates audio for each chunk, then combines them into a single MP3 file.

Key components:

• Uses contextkit.read_url to extract article content from URLs
• Leverages openai library for text-to-speech conversion (voice: “alloy”)
• Splits text into ~4000 character chunks at sentence boundaries to avoid cutting mid-sentence
• Uses pydub to concatenate audio segments into one file
• Saves the final audio as output.mp3

Main workflow: 1. Parse the URL and extract article text 2. Break text into manageable chunks 3. Generate audio for each chunk via OpenAI TTS 4. Combine all audio segments 5. Export as a single MP3 file

The script is designed to run from command line, taking a blog URL as an argument. It requires an OpenAI API key to function.

---

Would you like me to explain any specific part in more detail, or would you like to explore how you might modify or extend this code?

To go back full-circle, we can have a different LLM correct the results…

# bc("That's not quite right - can you compare your answer to https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/README.md and correct it?")

Prompt (openrouter/openai/gpt-5-codex): That’s not quite right - can you compare your answer to https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/README.md and correct it?

🤖Reply🤖

{
  "id": "call_s3YbiJZa6Z7VT1NtUe4Qm36Q",
  "call": {
    "function": "read_url",
    "arguments": {
      "url": "https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/README.md",
      "as_md": true,
      "extract_section": false,
      "selector": ""
    }
  },
  "result": "# blogtopod: Turn blog posts into podcast episodes\n\n*Convert blog posts into podcast episodes using <TRUNCATED>"
}

Here’s what I found after revisiting the project information and reconciling it with the earlier summary:

Key corrections

• The app isn’t just a plain command-line script—it opens an interactive Marimo interface (a small web UI). In that UI you paste a blog URL, choose an output filename, and click “Run.” The script then pulls the article, builds a narrated script via Gemini 2.5 Pro, and finally turns that script into narrated audio using OpenAI’s latest text-to-speech voices. When it’s done, you get both the MP3 and the generated script (saved as *_script.md).
• Because of the dual-model workflow, you need two API keys: one for OpenAI and another for Google Gemini. They’re expected in the environment variables OPENAI_API_KEY and GEMINI_API_KEY.
• A recommended way to run it is through uvx marimo run --sandbox ..., which means you don’t have to manage dependencies manually—the requirements are embedded in the file. A traditional pip + marimo option also exists, but it’s secondary.
• The workflow intentionally uses Gemini to craft a polished podcast script before OpenAI voices read it. The earlier summary skipped Gemini entirely and made it sound like straight text-to-speech on raw article text; that was inaccurate.

Revised high-level explanation

blogtopod.py is a Marimo-based app that turns any blog post into a polished podcast episode. After you provide the blog URL, it:

1. Extracts the page content (via contextkit under the hood).
2. Sends that reflowed text to Gemini 2.5 Pro, which produces a listener-friendly podcast script.
3. Feeds the script into OpenAI’s TTS service to synthesize speech (voice defaults to Alloy), chunking the text as needed and stitching the audio together with pydub.
4. Outputs both the generated MP3 and the textual script so you can review or publish it.

It therefore serves anyone wanting to consume or distribute blog content in audio form, using a blend of Gemini for narrative shaping and OpenAI for natural speech.

Developer Guide

If you are new to using nbdev here are some useful pointers to get you started.

Installing solveit_dmtools in Development mode

1. local dev install:

$ pip install -e .

2. make changes under nbs/ directory: …
3. prepare for commit:

$ alias nbprep='nbdev_clean ; nbdev_export'

$ nbprep

#!pip install -e ..
# OR
#!pip install -Uqq solveit_dmtools

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/shuane/solveit_dmtools.git

or from pypi

$ pip install solveit_dmtools

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on the pypi site.