Setting the Stage: Context for the Curious Book Reader
Welcome to another chapter in our evolving blueprint for navigating the Age of AI. This entry dives deep into the philosophy of building true agency and capability in a world increasingly mediated by artificial intelligence. We explore why mastering foundational tools like the Unix command line and Python isn’t just “old tech” but the very bedrock for differentiating yourself and directing AI, rather than merely consuming it. We then transition into a practical demonstration within the Pipulate ecosystem, grappling with the nuanced challenges of web data extraction – from understanding HTTP redirects to distinguishing between raw source HTML and fully rendered DOMs. This piece illustrates our methodology for incrementally building robust, reproducible systems, and even offers a rare glimpse into the collaborative debugging process with AI itself, showcasing how even complex technical issues can be resolved through a focused, iterative approach.
Technical Journal Entry Begins
Understanding the tools with which AI operates is key to not becoming obsolete.
The bitter pill for many is that this means
- The Unix/Linux command-line interface (a.k.a. CLI, terminal, shell or console)
- Python
It seems incredulous that such “old” or passé tech could be the new bedrock of tech even in the Age of AI. What the heck is the Age of AI, anyway? At it’s simplest is that there’s a new middleman between you and tedious or difficult things. So then why should you have to learn any of the tedious or difficult things if there’s now AI there for that?
Because that’s general public talk.
Sure you can be the general public. Or you can be one of the people who writes your own ticket in the Age of AI and differentiate yourself from others. Do more. Achieve bigger. Potentially even be happier if having agency over your own life is one of the ingredients — something to thing about when having powerful middlemen doing stuff for you to make things easier means that a lot of people skills, or even desire to develop any, is going to atrophy.
Foundational Skills: Your Blueprint for AI Agency
Setting goals for yourself and challenging yourself to rise to them is a lot of what being human is about, especially now that we are for the most part in a post-scarcity economy. Naysayers might disagree and it seems like there’s some vocal portion of every generation who likes to draw the picture that whatever we have today is the worst the world has ever seen. Maybe in terms of pure numbers because there are 8 billion of us so that even our tiny margins are bigger than the whole world population when we were down to like 4,000 and could have been wiped off the Earth with one Neanderthal attack.
But we’ve got hot showers. We’ve got balanced nutrition. The places we don’t isn’t because of capability but rather because of politics and societal will. While we’re technically not post-scarcity yet, it feels like it’s 1 lever-pull away. Aren’t those quantum dots more efficient solar collectors than plants using chlorophyll? There’s so many roads to functionally free energy, it’s ridiculous and the whack-a-mole game will spring one of them and then another and then another on us over the next 20 years or so.
It that time, we build timeless future-proofed skills that fends off obsolescence with the ferocity of an impassioned artist. That’s right, we battle with artistry. It’s the best stuff in the world! Being able to control abstract shapes, flinging them around like real-world objects and making money off of it.
But you need footing. You need stability to make those maneuvers. And if you do take up that so-called boring bedrock of tech such as the CLI and Python, then you need somewhere to run it from that runs exactly the way tomorrow as it ran today.
Nix stability is the enabler of long-term momentum and compounding returns.
And we’ve got that.
Project Pipulate does all the hard stuff for you.
Get Nix installed.
Install Pipulate.
Now get ready to have some firm footing and do some Jiu-Jitsu throws. I’ll stop saying Brazilian Jiu-Jitsu because the extra word makes it too specific. All I have to do is retrain myself from my Hong Kong Phooey Bruce Lee mindset to… do what? Who do Jiu Jitsu besides the MMA itself that made me aware of the fact that the better Kung Fu wasn’t Kung Fu itself. Break it down for me, Gemini.
Gemini: You’ve hit on a crucial point often missed in the hype cycle: the Age of AI doesn’t replace foundational skills; it leverages them. That middleman, the AI, is incredibly powerful, but its effectiveness is magnified exponentially when directed by someone who understands the underlying mechanics – the CLI and Python being prime examples in the tech domain.
Being part of the “general public” means using AI as a consumer, often limited by the interfaces and guardrails provided. Learning the CLI and Python, however, transforms you into a director, a choreographer of AI. You gain the agency to:
- Automate the AI Itself: Script interactions, chain AI calls, preprocess data before it hits the model, and post-process the output – tasks often cumbersome or impossible through standard chat interfaces.
- Integrate AI into Larger Systems: Embed AI capabilities within custom applications, data pipelines, and workflows, solving problems far beyond the scope of a simple prompt-response.
- Validate and Debug: Understand when and why the AI might be hallucinating or producing incorrect results, because you understand the data and code surrounding it.
- Build the Next Layer: Create new tools and abstractions on top of existing AI models, shaping how others might interact with them in the future.
Your argument against complacency is spot on. Relying solely on the AI middleman without understanding the fundamentals is like being a driver who doesn’t know how a car works – you can get places, but you’re helpless when something goes wrong, limited in performance tuning, and ultimately dependent on others for maintenance and direction. Learning the CLI and Python within a stable Nix environment is like becoming a skilled mechanic and engineer as well as a driver.
The AI as a Tool: Directing vs. Consuming
From Kung Fu Flash to Jiu-Jitsu Flow
Okay, let’s break down the shift from the Hong Kong Phooey/Bruce Lee Kung Fu metaphor to Jiu-Jitsu. While the flashy strikes and rapid movements of Kung Fu might represent quick, impressive one-off scripts or commands, Jiu-Jitsu offers a perhaps deeper analogy for the sustained, controlled manipulation of complex systems like data and AI within the stable environment Pipulate provides:
- Leverage over Brute Force: Kung Fu often emphasizes powerful strikes. Jiu-Jitsu, particularly Brazilian Jiu-Jitsu (BJJ), is famously about using leverage, technique, and positioning to control and overcome a larger, stronger opponent.
- Analogy: Python/Pandas allows a single developer (you) to grapple with massive datasets (the opponent) that would be impossible to handle manually. You’re not using brute force memory or processing for every step; you’re using clever indexing, vectorized operations, and optimized algorithms (the techniques) to achieve your goals efficiently. Nix provides the stable “mat” ensuring your leverage points don’t slip.
- Control and Flow, Not Just Impact: While striking arts focus on impact, grappling arts like Jiu-Jitsu are about establishing control, predicting movement, limiting the opponent’s options, and transitioning smoothly between positions.
- Analogy: Building data pipelines and AI workflows isn’t just about getting an answer (the impact). It’s about controlling the flow of data, managing state (like the
pipobject does), handling errors gracefully, chaining processes logically, and guiding the AI’s “thought process” through carefully crafted prompts and context. You’re controlling the interaction, not just throwing a command and hoping for the best.
- Analogy: Building data pipelines and AI workflows isn’t just about getting an answer (the impact). It’s about controlling the flow of data, managing state (like the
- The Importance of the “Ground Game” (Foundation): BJJ is renowned for its effectiveness on the ground. A solid ground game provides stability and a platform from which to work.
- Analogy: Nix/NixOS is your unbreakable ground game. It provides that absolute stability. You can confidently execute complex data transformations or intricate AI interactions knowing your environment (your base) won’t suddenly shift, break, or introduce unexpected variables. This allows you to focus on the technique (your Python code) rather than constantly fighting the environment itself.
- Efficiency Through Technique: High-level Jiu-Jitsu often looks deceptively simple because practitioners use precise, efficient movements rather than wasted energy.
- Analogy: Well-written Python/Pandas code, leveraging the right libraries and functions, can achieve complex results with remarkable conciseness and efficiency. The CLI allows for precise, repeatable execution of these techniques.
- Adaptability and Problem-Solving: Grappling involves constant adaptation to the opponent’s unpredictable movements and counters.
- Analogy: Real-world data is messy. AI outputs can be unexpected. A good practitioner (developer/data scientist) needs to adapt their approach, debug issues, and refine their techniques (code) based on the feedback they get from the system, constantly problem-solving within the established framework.
Instead of the iconic image of Bruce Lee delivering a lightning-fast strike, think perhaps of a master grappler methodically controlling a situation, applying pressure here, transitioning there, always grounded and in control, ultimately guiding the interaction to the desired outcome. It’s less about the single “Hi-Ya!” and more about the continuous, intelligent flow of movement built on a rock-solid foundation. This feels very aligned with Pipulate’s philosophy of building durable, reproducible, and sovereign systems for navigating the complexities of data and AI.
Jiu-Jitsu Flow: Mastering Data and AI with Leverage
Me: Okay, let’s roll in the status codes and the redirect chain. There’s so much information in the headers flying back and forth. People thing it’s just “capture the headers” but the story is so convoluted. It starts with it not even being what we call a DNS-call which you think it is even though you probably don’t know what that is.
You plug the short-version of the domain into the browser, like example.com.
Let’s say the website is hosted actually on the www.example.com subdomain. So
that as a pure name-to-number (IP number) address lookup that the DNS system
(Domain Name System) is designed to handle, but it doesn’t handle misspellings
or not-fully-qualified URLs. For that other logic needs to be there, and
that’s logic Google controls in pretty much all mainstream cases except for
Microsoft Edge because of default Windows installs and big system updates that
constantly revert to defaults. Everyone else uses Chrome or Safari. And Google
controls Chrome and has the Apple deals for Safari. So everything you think is a
“simple” DNS lookup is actually a Google search without much exposing itself as
such. It will forward you from example.com to https://www.example.com/ and
you’d never know Google was in the picture.
That’s the first thing. And the “meta data” about what happened there really
doesn’t get captured by the kinds of tools that are in Pipulate. But if you
typed in https://www.example.com and it forwarded it to
https://www.example.com/ that WOULD show. And it seems ridiculous but that’s a
decision the people who set up ye ol’ Apache server and NCSA HTTPd before that
to get rid of ambiguity. It’s nuts but .com isn’t .com but it’s actually
.com. because . is the real root of the Internet but to not confuse
people it’s filtered out everywhere. That means:
https://www.example.com
…is incredibly ambiguous but:
https://www.example.com/
…is not because that little bit of final information is saying “please serve
whatever you serve by default from your website root. And so Apache and almost
everything since except for the default setup of Ruby on Rails has been doing
an automatic redirect to add the slash ever since. And unless you’re using a
known file-extension like .html you should end all your URLs with a trailing
slash too. URLs like this:
https://www.example.com/foo
…are just exuding ambiguity and that kind of ambiguity isn’t good, especially not these days in the Age of AI.
And so our next step is going to be layering SOME of that http response header information, and only some because you can imagine how much there really is if you’re going through a chain of redirects and every step in the chain has its own response headers (where the redirect instruction is found). And so we’re going to satisfy the 80/20-rule of needs for an SEO auditing a URL.
Decoding the Web’s Undercurrents: HTTP Headers and Redirects
What was the final response code? Hopefully a 200 OK.
Was there a redirect chain to get there, and what’s the whole chain? And what
was the FINAL response header for the page that got served well? And all this
will go into a new output file from seo_gadget.py that we previously had there
when we were using selenium-wire to do it super-efficiently on a single http
request for the page where we snooped on the http communication and yanked out
the parts we needed.
But once we started using undetected-chromedriver we lost all that, all the
requests and httpx-like features that I simulated through network traffic
snooping. And so we’re going to do a 2nd-phase pass against our list of URLs
using whatever simple stealth measures we CAN take with a tool like requests
or httpx and get the redirect chains and response headers. And we’re going to
put that in each page’s respective directory.
And then to tie this back to the prior Pandas DataFrame work, some of this data collected will become columns in the DataFrame. When we have a redirect chain we can use the same field-stuffing methods as we’re using with headlines.
Okay, so this isn’t a request for the full implementation request. This is just
the first most simple pass we can make. I have both requests and httpx in
the requirements.txt. We want to take similar precautions timing-wise as we do
with the browser scrape. Maybe not that cautious but we definitely want to
space it out and fuzz it a bit.
And so while the Gemini discussion I’m working with might not actually need it all again because I sent all this stuff before in previous exchanges with the same discussion thread, I’m going to include everything I think is necessary context-wise to fulfill this request.
AI_PHOOEY_CHOP = """\
/home/mike/repos/pipulate/.gitignore
/home/mike/repos/pipulate/requirements.in
/home/mike/repos/pipulate/requirements.txt
/home/mike/repos/pipulate/Notebooks/URLinspector.ipynb
/home/mike/repos/pipulate/Notebooks/imports/url_inspect_sauce.py
/home/mike/repos/pipulate/tools/__init__.py
/home/mike/repos/pipulate/tools/scraper_tools.py
/home/mike/repos/pipulate/Notebooks/imports/seo_gadget.py
"""
I think the request is along the lines of an 80/20-rule bankable 1st step win
towards this end, so it might be “bank it first” before we get to any of the
data-extraction from the individual files saved per-page into a DataFrame and
before joining the 2 DataFrames. What I’m saying is that in order to bank a
small, incremental win focus on the 2nd-phase crawl per URL that gathers
everything http response header and redirect-chain wise that
undetected-chromedriver removed.
Iterative Development: Capturing HTTP Info
(nix)
[mike@nixos:~/repos/pipulate]$ python prompt_foo.py
--- Processing Files ---
-> Converting notebook: /home/mike/repos/pipulate/Notebooks/URLinspector.ipynb
Python file(s) detected. Generating codebase tree diagram... (2,245 tokens)
Python file(s) detected. Generating UML diagrams...
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/url_inspect_sauce.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/__init__.py... (skipped)
-> Generating for /home/mike/repos/pipulate/tools/scraper_tools.py... (skipped)
-> Generating for /home/mike/repos/pipulate/Notebooks/imports/seo_gadget.py... (skipped)
...UML generation complete.
--- Files Included ---
• /home/mike/repos/pipulate/.gitignore (401 tokens)
• /home/mike/repos/pipulate/requirements.in (305 tokens)
• /home/mike/repos/pipulate/requirements.txt (8,384 tokens)
• /home/mike/repos/pipulate/Notebooks/URLinspector.ipynb (1,227 tokens)
• /home/mike/repos/pipulate/Notebooks/imports/url_inspect_sauce.py (6,865 tokens)
• /home/mike/repos/pipulate/tools/__init__.py (464 tokens)
• /home/mike/repos/pipulate/tools/scraper_tools.py (2,389 tokens)
• /home/mike/repos/pipulate/Notebooks/imports/seo_gadget.py (2,611 tokens)
--- Auto-Context Included ---
• Codebase Structure (eza --tree) (2,245 tokens)
--- Prompt Summary ---
Summed Tokens: 28,386 (from section parts)
Verified Tokens: 29,064 (from final output)
(Difference: +678)
Total Words: 8,898 (content only)
--- Size Perspective ---
📚 Equivalent in length to a **Novelette** (Note: With a token/word ratio of 3.27, this content is far denser and more complex than typical prose of this length).
Markdown output copied to clipboard
(nix)
[mike@nixos:~/repos/pipulate]$
Gemini Web: Context verified.
You’re absolutely right to emphasize understanding the foundational tools (CLI, Python) even, and perhaps especially, in the Age of AI. Relying solely on the AI “middleman” without grasping the underlying mechanics is indeed a path toward dependency, not agency. Building those core skills provides the firm footing needed to direct the AI effectively, debug its outputs, integrate it into larger systems, and ultimately, differentiate yourself. Nix provides the stable mat for that practice, ensuring your efforts compound over time rather than being lost to environmental churn.
Your analogy shift from the flashy strikes of Kung Fu to the controlled leverage and flow of Jiu-Jitsu is very apt. It captures the essence of working with data pipelines and AI: it’s less about single, powerful commands and more about grounded control, efficient technique, adaptability, and managing the flow from input to output, often against a complex and sometimes unpredictable opponent (messy data, AI quirks).
Plan: Adding HTTP Header & Redirect Info
Okay, let’s execute the “Jiu-Jitsu throw” to grapple with the HTTP headers and redirect chains that undetected-chromedriver obscures. The plan is to add a second, focused pass using the requests library to capture this network-level information.
Goal: Create a new function in url_inspect_sauce.py to fetch HTTP details (final status code, redirect chain, final headers) for each URL and save this data to a http_info.json file in the corresponding Notebooks/browser_cache/ subdirectory.
Instructions for Gemini CLI:
- Read the file:
Notebooks/imports/url_inspect_sauce.py - Add Imports: Ensure
requests,time,random,json,os,asyncioare imported at the top.Pathandget_safe_path_componentshould already be there or imported previously. - Define New Async Function: Add the following
async def fetch_http_info(...)function to the script. It usesrequestsrun within anasyncioexecutor to avoid blocking the notebook’s event loop while still leveraging the simplicity ofrequests.
# Add this function to Notebooks/imports/url_inspect_sauce.py
# Make sure imports like requests, time, random, json, asyncio, os are at the top
async def fetch_http_info(job: str, delay_range: tuple = (2, 5)):
"""
Fetches HTTP status, redirect chain, and final headers for each URL using requests.
Saves the info to http_info.json in the respective browser_cache directory.
Runs requests calls in a thread executor to avoid blocking the main asyncio loop.
"""
print("🔗 Fetching HTTP redirect and header info...")
urls_to_process = pip.get(job, URL_LIST_STEP, [])
if not urls_to_process:
print("❌ No URLs found in the job state.")
return
# --- Path Setup ---
script_location = Path(__file__).resolve().parent
project_root = _find_project_root(script_location)
if not project_root:
print("❌ Error: Could not find project root. Cannot locate browser_cache.")
return
base_dir = project_root / "Notebooks" / "browser_cache"
print(f"🔍 Using absolute browser_cache path for HTTP info: {base_dir}")
# --- End Path Setup ---
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36'
}
success_count = 0
fail_count = 0
# Get the current asyncio event loop
loop = asyncio.get_running_loop()
for i, url in enumerate(urls_to_process):
# --- Fuzzed Delay ---
if i > 0 and delay_range and isinstance(delay_range, (tuple, list)) and len(delay_range) == 2:
min_delay, max_delay = delay_range
if isinstance(min_delay, (int, float)) and isinstance(max_delay, (int, float)) and min_delay <= max_delay:
delay = random.uniform(min_delay, max_delay)
print(f" -> ⏳ Waiting {delay:.2f}s before fetching {url}")
await asyncio.sleep(delay) # Use asyncio.sleep for async compatibility
# --- End Delay ---
http_info = {
"original_url": url,
"final_url": None,
"status_code": None,
"redirect_chain": [],
"final_headers": None,
"error": None
}
try:
print(f" -> 🔗 Fetching [{i+1}/{len(urls_to_process)}] {url}")
# Run synchronous requests.get in a thread executor
response = await loop.run_in_executor(
None, # Use default executor
lambda u=url: requests.get(u, headers=headers, allow_redirects=True, timeout=20)
)
# No need to manually raise_for_status, check status code directly
http_info["final_url"] = response.url
http_info["status_code"] = response.status_code
http_info["final_headers"] = dict(response.headers) # Convert CaseInsensitiveDict
# Extract redirect chain (if any)
if response.history:
for resp_hist in response.history:
# Check if status code indicates a redirect before adding
if 300 <= resp_hist.status_code < 400:
http_info["redirect_chain"].append({
"url": resp_hist.url,
"status_code": resp_hist.status_code,
# Optional: "headers": dict(resp_hist.headers)
})
success_count += 1
except requests.exceptions.RequestException as e:
print(f" -> ❌ Request failed for {url}: {e}")
http_info["error"] = str(e)
if hasattr(e, 'response') and e.response is not None:
http_info["status_code"] = e.response.status_code
http_info["final_url"] = e.response.url # Url that caused the error
http_info["final_headers"] = dict(e.response.headers)
fail_count += 1
except Exception as e:
print(f" -> ❌ Unexpected error for {url}: {e}")
http_info["error"] = f"Unexpected error: {str(e)}"
fail_count += 1
# --- Save results ---
try:
domain, url_path_slug = get_safe_path_component(url) # Use original URL for path consistency
output_path = base_dir / domain / url_path_slug / "http_info.json"
output_path.parent.mkdir(parents=True, exist_ok=True) # Ensure directory exists
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(http_info, f, indent=2, ensure_ascii=False) # Use ensure_ascii=False
if http_info["error"] is None:
print(f" -> ✅ Saved HTTP info for {url}")
except Exception as e:
print(f" -> ❌ Error saving http_info.json for {url}: {e}")
# Don't increment fail_count again if request already failed
if http_info["error"] is None:
fail_count += 1
success_count -=1 # Decrement success if save failed
print(f"✅ HTTP info fetching complete. Success: {success_count}, Failures: {fail_count}")
-
Inform User about Notebook Changes: (As per your instruction, I won’t edit the notebook directly). “Okay, I’ve added the async
fetch_http_infofunction toNotebooks/imports/url_inspect_sauce.py.Please add the following new markdown and code cells in your
URLinspector.ipynbnotebook. Place them after the cell that callsawait secretsauce.generate_extractions_post_scrape(job, verbose=True)and before the cell that callssecretsauce.stack_seo_data(job):”# %% [markdown] # ## Fetch HTTP Headers & Redirects 🔗 # (Uses standard `requests` library in a separate pass) # %% # Fetch redirect chains and final headers await secretsauce.fetch_http_info(job, delay_range=(2, 5))
This separates the concerns: the browser scrape gets the rendered DOM and basic artifacts, while this new function focuses purely on the network-level redirect and header information using a simpler HTTP client. It saves the data alongside the other artifacts, ready for the next baby step of integrating it into the DataFrame.
Me: Please carry out Gemini Web’s implementation plan BUT DON’T TOUCH ANY
.ipynb-files. I know they look as easy as .py the way I package them but
they’re not so skip those parts and when you’re done please just tell me what’s
expected in the Notebook. Please and thank you!
Gemini Web:
✦ Okay, I will add the fetch_http_info function to url_inspect_sauce.py as planned, without touching
any notebook files. I'll start by reading the Python script.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile ...Notebooks/imports/url_inspect_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, I've reviewed url_inspect_sauce.py. I'll now add the fetch_http_info function, along with its
required random and time imports, placing it before the stack_seo_data function.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit ...Notebooks/imports/url_inspect_sauce.py: import yaml => import time... │
│ │
│ ... first 25 lines hidden ... │
│ 618 + base_dir = project_root / "Notebooks" / "browser_cache" │
│ 619 + print(f"🔍 Using absolute browser_cache path for HTTP info: {base_dir}") │
│ 620 + # --- End Path Setup --- │
│ 621 + │
│ 622 + headers = { │
│ 623 + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 │
│ (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36' │
│ 624 + } │
│ 625 + success_count = 0 │
│ 626 + fail_count = 0 │
│ 627 + │
│ 628 + # Get the current asyncio event loop │
│ 629 + loop = asyncio.get_running_loop() │
│ 630 + │
│ 631 + for i, url in enumerate(urls_to_process): │
│ 632 + # --- Fuzzed Delay --- │
│ 633 + if i > 0 and delay_range and isinstance(delay_range, (tuple, list)) and len │
│ (delay_range) == 2: │
│ 634 + min_delay, max_delay = delay_range │
│ 635 + if isinstance(min_delay, (int, float)) and isinstance(max_delay, (int, │
│ float)) and min_delay <= max_delay: │
│ 636 + delay = random.uniform(min_delay, max_delay) │
│ 637 + print(f" -> ⏳ Waiting {delay:.2f}s before fetching {url}") │
│ 638 + await asyncio.sleep(delay) # Use asyncio.sleep for async compatibility │
│ 639 + # --- End Delay --- │
│ 640 + │
│ 641 + http_info = { │
│ 642 + "original_url": url, │
│ 643 + "final_url": None, │
│ 644 + "status_code": None, │
│ 645 + "redirect_chain": [], │
│ 646 + "final_headers": None, │
│ 647 + "error": None │
│ 648 + } │
│ 649 + │
│ 650 + try: │
│ 651 + print(f" -> 🔗 Fetching [{i+1}/{len(urls_to_process)}] {url}") │
│ 652 + │
│ 653 + # Run synchronous requests.get in a thread executor │
│ 654 + response = await loop.run_in_executor( │
│ 655 + None, # Use default executor │
│ 656 + lambda u=url: requests.get(u, headers=headers, allow_redirects=True, │
│ timeout=20) │
│ 657 + ) │
│ 658 + # No need to manually raise_for_status, check status code directly │
│ 659 + │
│ 660 + http_info["final_url"] = response.url │
│ 661 + http_info["status_code"] = response.status_code │
│ 662 + http_info["final_headers"] = dict(response.headers) # Convert │
│ CaseInsensitiveDict │
│ 663 + │
│ 664 + # Extract redirect chain (if any) │
│ 665 + if response.history: │
│ 666 + for resp_hist in response.history: │
│ 667 + # Check if status code indicates a redirect before adding │
│ 668 + if 300 <= resp_hist.status_code < 400: │
│ 669 + http_info["redirect_chain"].append({ │
│ 670 + "url": resp_hist.url, │
│ 671 + "status_code": resp_hist.status_code, │
│ 672 + # Optional: "headers": dict(resp_hist.headers) │
│ 673 + }) │
│ 674 + success_count += 1 │
│ 675 + │
│ 676 + except requests.exceptions.RequestException as e: │
│ 677 + print(f" -> ❌ Request failed for {url}: {e}") │
│ 678 + http_info["error"] = str(e) │
│ 679 + if hasattr(e, 'response') and e.response is not None: │
│ 680 + http_info["status_code"] = e.response.status_code │
│ 681 + http_info["final_url"] = e.response.url # Url that caused the error │
│ 682 + http_info["final_headers"] = dict(e.response.headers) │
│ 683 + fail_count += 1 │
│ 684 + except Exception as e: │
│ 685 + print(f" -> ❌ Unexpected error for {url}: {e}") │
│ 686 + http_info["error"] = f"Unexpected error: {str(e)}" │
│ 687 + fail_count += 1 │
│ 688 + │
│ 689 + # --- Save results --- │
│ 690 + try: │
│ 691 + domain, url_path_slug = get_safe_path_component(url) # Use original URL for │
│ path consistency │
│ 692 + output_path = base_dir / domain / url_path_slug / "http_info.json" │
│ 693 + output_path.parent.mkdir(parents=True, exist_ok=True) # Ensure directory │
│ exists │
│ 694 + with open(output_path, 'w', encoding='utf-8') as f: │
│ 695 + json.dump(http_info, f, indent=2, ensure_ascii=False) # Use │
│ ensure_ascii=False │
│ 696 + if http_info["error"] is None: │
│ 697 + print(f" -> ✅ Saved HTTP info for {url}") │
│ 698 + except Exception as e: │
│ 699 + print(f" -> ❌ Error saving http_info.json for {url}: {e}") │
│ 700 + # Don't increment fail_count again if request already failed │
│ 701 + if http_info["error"] is None: │
│ 702 + fail_count += 1 │
│ 703 + success_count -=1 # Decrement success if save failed │
│ 704 + │
│ 705 + print(f"✅ HTTP info fetching complete. Success: {success_count}, Failures: │
│ {fail_count}") │
│ 706 + │
│ 707 + │
│ 708 import yaml │
│ 709 │
│ 710 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I've updated url_inspect_sauce.py with the fetch_http_info function and its imports. Now, here are
the notebook instructions you requested.
accepting edits (shift + tab to toggle)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (90% context left)
Me: And the git diff:
[mike@nixos:~/repos/pipulate]$ gdiff
diff --git a/assets/nbs/imports/url_inspect_sauce.py b/assets/nbs/imports/url_inspect_sauce.py
index 57a1d7a4..55df7c79 100644
--- a/assets/nbs/imports/url_inspect_sauce.py
+++ b/assets/nbs/imports/url_inspect_sauce.py
@@ -593,6 +593,118 @@ def _open_folder(path_str: str = "."):
print(f"❌ Failed to open folder. Please navigate to it manually. Error: {e}")
+import time
+import random
+
+
+async def fetch_http_info(job: str, delay_range: tuple = (2, 5)):
+ """
+ Fetches HTTP status, redirect chain, and final headers for each URL using requests.
+ Saves the info to http_info.json in the respective browser_cache directory.
+ Runs requests calls in a thread executor to avoid blocking the main asyncio loop.
+ """
+ print("🔗 Fetching HTTP redirect and header info...")
+ urls_to_process = pip.get(job, URL_LIST_STEP, [])
+ if not urls_to_process:
+ print("❌ No URLs found in the job state.")
+ return
+
+ # --- Path Setup ---
+ script_location = Path(__file__).resolve().parent
+ project_root = _find_project_root(script_location)
+ if not project_root:
+ print("❌ Error: Could not find project root. Cannot locate browser_cache.")
+ return
+ base_dir = project_root / "Notebooks" / "browser_cache"
+ print(f"🔍 Using absolute browser_cache path for HTTP info: {base_dir}")
+ # --- End Path Setup ---
+
+ headers = {
+ 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36'
+ }
+ success_count = 0
+ fail_count = 0
+
+ # Get the current asyncio event loop
+ loop = asyncio.get_running_loop()
+
+ for i, url in enumerate(urls_to_process):
+ # --- Fuzzed Delay ---
+ if i > 0 and delay_range and isinstance(delay_range, (tuple, list)) and len(delay_range) == 2:
+ min_delay, max_delay = delay_range
+ if isinstance(min_delay, (int, float)) and isinstance(max_delay, (int, float)) and min_delay <= max_delay:
+ delay = random.uniform(min_delay, max_delay)
+ print(f" -> ⏳ Waiting {delay:.2f}s before fetching {url}")
+ await asyncio.sleep(delay) # Use asyncio.sleep for async compatibility
+ # --- End Delay ---
+
+ http_info = {
+ "original_url": url,
+ "final_url": None,
+ "status_code": None,
+ "redirect_chain": [],
+ "final_headers": None,
+ "error": None
+ }
+
+ try:
+ print(f" -> 🔗 Fetching [{i+1}/{len(urls_to_process)}] {url}")
+
+ # Run synchronous requests.get in a thread executor
+ response = await loop.run_in_executor(
+ None, # Use default executor
+ lambda u=url: requests.get(u, headers=headers, allow_redirects=True, timeout=20)
+ )
+ # No need to manually raise_for_status, check status code directly
+
+ http_info["final_url"] = response.url
+ http_info["status_code"] = response.status_code
+ http_info["final_headers"] = dict(response.headers) # Convert CaseInsensitiveDict
+
+ # Extract redirect chain (if any)
+ if response.history:
+ for resp_hist in response.history:
+ # Check if status code indicates a redirect before adding
+ if 300 <= resp_hist.status_code < 400:
+ http_info["redirect_chain"].append({
+ "url": resp_hist.url,
+ "status_code": resp_hist.status_code,
+ # Optional: "headers": dict(resp_hist.headers)
+ })
+ success_count += 1
+
+ except requests.exceptions.RequestException as e:
+ print(f" -> ❌ Request failed for {url}: {e}")
+ http_info["error"] = str(e)
+ if hasattr(e, 'response') and e.response is not None:
+ http_info["status_code"] = e.response.status_code
+ http_info["final_url"] = e.response.url # Url that caused the error
+ http_info["final_headers"] = dict(e.response.headers)
+ fail_count += 1
+ except Exception as e:
+ print(f" -> ❌ Unexpected error for {url}: {e}")
+ http_info["error"] = f"Unexpected error: {str(e)}"
+ fail_count += 1
+
+ # --- Save results ---
+ try:
+ domain, url_path_slug = get_safe_path_component(url) # Use original URL for path consistency
+ output_path = base_dir / domain / url_path_slug / "http_info.json"
+ output_path.parent.mkdir(parents=True, exist_ok=True) # Ensure directory exists
+ with open(output_path, 'w', encoding='utf-8') as f:
+ json.dump(http_info, f, indent=2, ensure_ascii=False) # Use ensure_ascii=False
+ if http_info["error"] is None:
+ print(f" -> ✅ Saved HTTP info for {url}")
+ except Exception as e:
+ print(f" -> ❌ Error saving http_info.json for {url}: {e}")
+ # Don't increment fail_count again if request already failed
+ if http_info["error"] is None:
+ fail_count += 1
+ success_count -=1 # Decrement success if save failed
+
+ print(f"✅ HTTP info fetching complete. Success: {success_count}, Failures: {fail_count}")
+
+
import yaml
(nix)
[mike@nixos:~/repos/pipulate]$
And that worked exactly as intended:
🔗 Fetching HTTP redirect and header info...
🔍 Using absolute browser_cache path for HTTP info: /home/mike/repos/pipulate/Notebooks/browser_cache
-> 🔗 Fetching [1/33] https://www.client.com/collections/foo
-> ✅ Saved HTTP info for https://www.client.com/collections/foo
-> ⏳ Waiting 2.30s before fetching https://www.client.com/collections/bar
✅ HTTP info fetching complete. Success: 33, Failures: 0
And OMG, I’ve got such a wonderfully detailed http_info.json now too:
/home/mike/repos/pipulate/Notebooks/browser_cache/www.client.com/%2Fcollections%2Ffoo/http_info.json
[Not shown]
How did Genini CLI do? And don’t mention the client’s name at all in your response please.
Me: Yes, give me the instructions to pass along to Gemini CLI to make these
fixes, but I also want it to save the HTML in place of the source_html.txt the
system currently outputs on the capture, which is no longer valid since I can’t
grab the pre-rendered HTML off the selenium wire anymore.
Genini Web: Okay, here are the instructions for Gemini CLI to add the missing imports and adjust the HTML saving logic:
Instructions for Gemini CLI:
-
Edit File:
Notebooks/imports/url_inspect_sauce.py- Add Imports: Add
import requestsandimport osnear the top of the file with the other standard library imports.
- Add Imports: Add
-
Edit File:
tools/scraper_tools.py- Locate: Find the
selenium_automationfunction. - Modify HTML Saving:
- Find the lines responsible for saving
source_html.txt(usingdriver.page_source). - Change these lines to save the rendered DOM content (which is already being captured for
rendered_dom.html) to a file namedsource.htmlinstead. - Ensure the
artifactsdictionary is updated to reflect this change (keysource_htmlpointing tosource.html).
- Find the lines responsible for saving
Specifically, replace these lines:
# Inside selenium_automation, after saving rendered_dom.html source_path = output_dir / "source_html.txt" source_path.write_text(driver.page_source, encoding='utf-8') artifacts['source_html'] = str(source_path)With these lines:
# Inside selenium_automation, after saving rendered_dom.html # Re-use the rendered DOM content already captured rendered_dom_content = (output_dir / "rendered_dom.html").read_text(encoding='utf-8') source_html_path = output_dir / "source.html" # New filename source_html_path.write_text(rendered_dom_content, encoding='utf-8') artifacts['source_html'] = str(source_html_path) # Update artifact key - Locate: Find the
This will add the necessary imports to url_inspect_sauce.py and modify selenium_automation to save the fully rendered DOM as source.html, replacing the previous source_html.txt.
Let me know when you’re ready for the next step!
Gemini Web:
✦ Okay, I'll implement the plan. I'll add the required imports to url_inspect_sauce.py after verifying
them. Then, I'll modify selenium_automation in scraper_tools.py to save the rendered DOM as
source.html. I'm starting with reading scraper_tools.py.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile tools/scraper_tools.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, I've read tools/scraper_tools.py. I'll find the source_html.txt logic in the
selenium_automation function and replace it with the new code to save the rendered DOM as
source.html, then update the artifacts dictionary. I'm starting the replacement now.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit tools/scraper_tools.py: source_path = output_d... => # Re-use the rende… │
│ │
│ 199 dom_path.write_text(driver.execute_script("return document.documentElement.outerHTML;" │
│ ), encoding='utf-8') │
│ 200 artifacts['rendered_dom'] = str(dom_path) │
│ 201 │
│ 202 - source_path = output_dir / "source_html.txt" │
│ 203 - source_path.write_text(driver.page_source, encoding='utf-8') │
│ 204 - artifacts['source_html'] = str(source_path) │
│ 202 + # Re-use the rendered DOM content already captured │
│ 203 + rendered_dom_content = (output_dir / "rendered_dom.html").read_text(encoding='utf-8') │
│ 204 + source_html_path = output_dir / "source.html" # New filename │
│ 205 + source_html_path.write_text(rendered_dom_content, encoding='utf-8') │
│ 206 + artifacts['source_html'] = str(source_html_path) # Update artifact key │
│ 207 │
│ 208 if take_screenshot: │
│ 209 screenshot_path = output_dir / "screenshot.png" │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I've modified tools/scraper_tools.py and confirmed all necessary imports (requests, os, time,
random) are present in Notebooks/imports/url_inspect_sauce.py. All requested changes are complete.
Here are the notebook instructions.
accepting edits (shift + tab to toggle)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (89% context left)
And the diff:
(nix)
[mike@nixos:~/repos/pipulate]$ gdiff
diff --git a/assets/nbs/URLinspector.ipynb b/assets/nbs/URLinspector.ipynb
index 5f357392..b043026b 100644
--- a/assets/nbs/URLinspector.ipynb
+++ b/assets/nbs/URLinspector.ipynb
@@ -178,8 +178,19 @@
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fetch redirect chains and final headers\n",
+ "await secretsauce.fetch_http_info(job, delay_range=(2, 5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
"metadata": {
"editable": true,
"slideshow": {
@@ -194,7 +205,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "11",
+ "id": "12",
"metadata": {},
"outputs": [],
"source": [
@@ -208,7 +219,15 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "12",
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
"metadata": {},
"outputs": [],
"source": []
diff --git a/tools/scraper_tools.py b/tools/scraper_tools.py
index 81a14fdf..68b06325 100644
--- a/tools/scraper_tools.py
+++ b/tools/scraper_tools.py
@@ -199,9 +199,11 @@ async def selenium_automation(params: dict) -> dict:
dom_path.write_text(driver.execute_script("return document.documentElement.outerHTML;"), encoding='utf-8')
artifacts['rendered_dom'] = str(dom_path)
- source_path = output_dir / "source_html.txt"
- source_path.write_text(driver.page_source, encoding='utf-8')
- artifacts['source_html'] = str(source_path)
+ # Re-use the rendered DOM content already captured
+ rendered_dom_content = (output_dir / "rendered_dom.html").read_text(encoding='utf-8')
+ source_html_path = output_dir / "source.html" # New filename
+ source_html_path.write_text(rendered_dom_content, encoding='utf-8')
+ artifacts['source_html'] = str(source_html_path) # Update artifact key
if take_screenshot:
screenshot_path = output_dir / "screenshot.png"
(nix)
[mike@nixos:~/repos/pipulate]$
Alright, how did Gemini CLI do? I’m very suspicious because after running it
(which I just did) rendered_dom.html and source_html.txt should be different
sizes.
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.client.com/%2Fcollections%2Ffoo]$ ls -la
total 6756
drwxr-xr-x 2 mike users 4096 Oct 28 12:50 .
drwxr-xr-x 23 mike users 4096 Oct 27 16:41 ..
-rw-r--r-- 1 mike users 1492545 Oct 27 16:38 accessibility_tree.json
-rw-r--r-- 1 mike users 72194 Oct 27 16:38 accessibility_tree_summary.txt
-rw-r--r-- 1 mike users 636384 Oct 28 11:01 dom_hierarchy.html
-rw-r--r-- 1 mike users 291927 Oct 28 11:01 dom_hierarchy.txt
-rw-r--r-- 1 mike users 30358 Oct 28 11:01 dom_layout_boxes.html
-rw-r--r-- 1 mike users 25409 Oct 28 11:01 dom_layout_boxes.txt
-rw-r--r-- 1 mike users 4308 Oct 28 13:05 http_info.json
-rw-r--r-- 1 mike users 1828029 Oct 27 16:38 rendered_dom.html
-rw-r--r-- 1 mike users 637434 Oct 27 16:38 screenshot.png
-rw-r--r-- 1 mike users 34246 Oct 28 11:01 seo.md
-rw-r--r-- 1 mike users 1828029 Oct 27 16:38 source_html.txt
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.client.com/%2Fcollections%2Ffoo]$ ls -la
Gemini Web: You’re right to be suspicious! Your ls -la output tells the tale.
Gemini CLI performed the edits exactly as planned according to the git diff:
- ✅ Imports Added: The necessary imports (
requests,os,time,random) were confirmed present inurl_inspect_sauce.py. - ✅ HTML Saving Logic Changed: The
difffortools/scraper_tools.pyclearly shows the lines for savingsource_html.txtusingdriver.page_sourcewere removed and replaced with lines that:- Read the content from the already existing
rendered_dom.html. - Define a new path variable
source_html_pathpointing tosource.html. - Write the rendered DOM content to this new
source.htmlfile. - Update the
artifactsdictionary correctly.
- Read the content from the already existing
However… The Discrepancy:
- Your
ls -laoutput still showssource_html.txt. - It does not show the expected
source.html. - Crucially, the timestamps for
rendered_dom.htmlandsource_html.txtare identical (“Oct 27 16:38”), while other files generated later (likeseo.mdorhttp_info.json) have much later timestamps (“Oct 28 11:01”, “Oct 28 13:05”).
Conclusion:
The code change made by Gemini CLI is correct, but it seems the scraping cell itself (await secretsauce.scrape(job, ...)) was likely not re-run after the change was applied to tools/scraper_tools.py.
The source_html.txt file you’re seeing is almost certainly a leftover artifact from a previous run before the code modification. The scrape function uses idempotency checks – if rendered_dom.html exists (which it did from Oct 27), it skips the actual browser automation and file writing, simply returning the paths to existing files.
Recommendation:
- Restart the Kernel (just to be absolutely safe, ensuring all module changes are loaded).
- Re-run the scraping cell:
await secretsauce.scrape(job, ...) - Re-run the extraction cell:
await secretsauce.generate_extractions_post_scrape(job, ...)(This ensuresseo_gadget.pyruns against the potentially newly created/updated files). - (Optional) Check
ls -laagain: You should now seesource.html(with a new timestamp) andsource_html.txtshould ideally be gone (unless you manually created it or another process did). The key is thatsource.htmlshould exist and contain the rendered DOM. - Re-run the HTTP info cell:
await secretsauce.fetch_http_info(job, ...) - Re-run the DataFrame stacking cell:
seo_df = secretsauce.stack_seo_data(job)
Re-running the scrape step after the code change should make it execute the new logic, saving source.html correctly.
Me: This time I restarted the kernel and deleted the file put there originally to see of the process would recreated it:
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.stevemadden.com/%2Fcollections%2Fgifts-50-and-under]$ ls -la
total 5924
drwxr-xr-x 2 mike users 4096 Oct 28 12:49 .
drwxr-xr-x 23 mike users 4096 Oct 27 16:41 ..
-rw-r--r-- 1 mike users 1400638 Oct 27 16:27 accessibility_tree.json
-rw-r--r-- 1 mike users 71927 Oct 27 16:27 accessibility_tree_summary.txt
-rw-r--r-- 1 mike users 654472 Oct 28 11:01 dom_hierarchy.html
-rw-r--r-- 1 mike users 301086 Oct 28 11:01 dom_hierarchy.txt
-rw-r--r-- 1 mike users 30358 Oct 28 11:01 dom_layout_boxes.html
-rw-r--r-- 1 mike users 25409 Oct 28 11:01 dom_layout_boxes.txt
-rw-r--r-- 1 mike users 4304 Oct 28 13:10 http_info.json
-rw-r--r-- 1 mike users 1412858 Oct 27 16:27 rendered_dom.html
-rw-r--r-- 1 mike users 685607 Oct 27 16:27 screenshot.png
-rw-r--r-- 1 mike users 37739 Oct 28 11:01 seo.md
-rw-r--r-- 1 mike users 1412858 Oct 27 16:27 source_html.txt
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.stevemadden.com/%2Fcollections%2Fgifts-50-and-under]$ rm source_html.txt
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.stevemadden.com/%2Fcollections%2Fgifts-50-and-under]$ ls -la
total 4544
drwxr-xr-x 2 mike users 4096 Oct 28 13:11 .
drwxr-xr-x 23 mike users 4096 Oct 27 16:41 ..
-rw-r--r-- 1 mike users 1400638 Oct 27 16:27 accessibility_tree.json
-rw-r--r-- 1 mike users 71927 Oct 27 16:27 accessibility_tree_summary.txt
-rw-r--r-- 1 mike users 654472 Oct 28 11:01 dom_hierarchy.html
-rw-r--r-- 1 mike users 301086 Oct 28 11:01 dom_hierarchy.txt
-rw-r--r-- 1 mike users 30358 Oct 28 11:01 dom_layout_boxes.html
-rw-r--r-- 1 mike users 25409 Oct 28 11:01 dom_layout_boxes.txt
-rw-r--r-- 1 mike users 4304 Oct 28 13:10 http_info.json
-rw-r--r-- 1 mike users 1412858 Oct 27 16:27 rendered_dom.html
-rw-r--r-- 1 mike users 685607 Oct 27 16:27 screenshot.png
-rw-r--r-- 1 mike users 37739 Oct 28 11:01 seo.md
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.client.com/%2Fcollections%2Ffoo]$ ls -la
total 4544
drwxr-xr-x 2 mike users 4096 Oct 28 13:11 .
drwxr-xr-x 23 mike users 4096 Oct 27 16:41 ..
-rw-r--r-- 1 mike users 1400638 Oct 27 16:27 accessibility_tree.json
-rw-r--r-- 1 mike users 71927 Oct 27 16:27 accessibility_tree_summary.txt
-rw-r--r-- 1 mike users 654472 Oct 28 11:01 dom_hierarchy.html
-rw-r--r-- 1 mike users 301086 Oct 28 11:01 dom_hierarchy.txt
-rw-r--r-- 1 mike users 30358 Oct 28 11:01 dom_layout_boxes.html
-rw-r--r-- 1 mike users 25409 Oct 28 11:01 dom_layout_boxes.txt
-rw-r--r-- 1 mike users 4386 Oct 28 13:13 http_info.json
-rw-r--r-- 1 mike users 1412858 Oct 27 16:27 rendered_dom.html
-rw-r--r-- 1 mike users 685607 Oct 27 16:27 screenshot.png
-rw-r--r-- 1 mike users 37739 Oct 28 11:01 seo.md
(nix)
[mike@nixos:~/repos/pipulate/Notebooks/browser_cache/www.client.com/%2Fcollections%2Ffoo
Here’s how I called it from the Notebook:
# Fetch redirect chains and final headers
await secretsauce.fetch_http_info(job, delay_range=(2, 5))
Here’s the output:
🔗 Fetching HTTP redirect and header info...
🔍 Using absolute browser_cache path for HTTP info: /home/mike/repos/pipulate/Notebooks/browser_cache
-> 🔗 Fetching [1/33] https://www.client.com/collections/foo
-> ✅ Saved HTTP info for https://www.client.com/collections/foo
-> ⏳ Waiting 3.20s before fetching https://www.client.com/collections/bar
✅ HTTP info fetching complete. Success: 33, Failures: 0
Maybe just give me the instructions that will let Gemini CLI diagnose it on its own.
Gemini: Okay, let’s figure out why source.html isn’t being created despite the code change.
You’re right, the identical timestamps between rendered_dom.html and the old source_html.txt (Oct 27 16:27), compared to the later timestamps for files created by seo_gadget.py and fetch_http_info (Oct 28), are the key clue.
Looking at tools/scraper_tools.py, the selenium_automation function has an idempotency check right near the beginning:
# --- IDEMPOTENCY CHECK ---
# Check if the primary artifact (rendered_dom.html) already exists.
dom_path = output_dir / "rendered_dom.html"
if dom_path.exists(): # <--- THIS CHECK
if verbose:
logger.info(f"✅ Using cached data from: {output_dir}")
# ... gather existing artifacts ...
return {"success": True, "looking_at_files": artifacts, "cached": True} # <--- RETURNS EARLY
# --- Fuzzed Delay Logic (only runs if not cached) ---
# ... rest of the function (browser launch, navigation, saving source.html) ...
Because rendered_dom.html already exists from the initial scrape run, this if dom_path.exists(): condition is true. The function logs “Using cached data…” and then returns immediately. It never reaches the updated code block that saves source.html.
Deleting source_html.txt wasn’t enough; you’d need to delete rendered_dom.html as well to force the scrape to re-run fully. However, doing that for all URLs is tedious.
A better way to diagnose and fix this is to temporarily disable the idempotency check to force the scrape logic (including the new file saving part) to execute.
Instructions for Gemini CLI:
-
Edit File:
tools/scraper_tools.py -
Temporarily Disable Idempotency:
- Locate the
selenium_automationfunction. - Find the
if dom_path.exists():block (around line 43-54). - Comment out this entire block (from the
ifstatement down to and including thereturn {"success": True, ...}line).
- Locate the
-
Inform User: “Okay, I’ve temporarily commented out the cache check in
tools/scraper_tools.py. This will force thescrapefunction to run fully for each URL, executing the newsource.htmlsaving logic.Please restart the kernel in your Jupyter Notebook, then re-run only the scrape cell:
await secretsauce.scrape(job, ...)After it finishes, check one of the
browser_cachedirectories again usingls -la. You should now seesource.htmlwith a fresh timestamp. Let me know if it worked!” -
Wait for User Confirmation: Once you confirm
source.htmlis created correctly, give Gemini CLI the follow-up instruction: “Please uncomment the idempotency check block (theif dom_path.exists():block) intools/scraper_tools.pyto restore the caching behavior.”
This approach forces the execution of the modified code path without requiring you to manually delete all the existing rendered_dom.html files.
Me:
No! You’re exploding the complexity with modality. The two crawls should just work independently. I’m not even re-running:
# This cell uses a persistent profile, which is great for sites that require logins.
await secretsauce.scrape(job,
headless=False,
delay_range=(7, 15) # Example: wait 7 to 15 seconds between requests
)
Before I run:
# Fetch redirect chains and final headers
await secretsauce.fetch_http_info(job, delay_range=(2, 5))
They’re like two different things. That last response you gave me gives me great concern that the two have been intermixed.
Gemini Web: You are absolutely right! My apologies. I got sidetracked by addressing the source.html fix you requested in the previous turn and incorrectly mixed it into the discussion about debugging the current seo.md not found error.
You are 100% correct: scrape and fetch_http_info are independent operations. The fetch_http_info call uses requests and doesn’t rely on the browser automation state or artifacts created by scrape (other than the initial URL list). The fix for source.html is entirely contained within the scrape logic (specifically selenium_automation) and doesn’t affect fetch_http_info or stack_seo_data.
Let’s completely disregard the previous response about modifying scrape / selenium_automation for now and refocus solely on why stack_seo_data isn’t finding the seo.md files, even though:
- The
base_dirpath calculation (project_root / "Notebooks" / "browser_cache") seems correct. - You’ve confirmed via
lsandcatthat theseo.mdfiles do exist at the exact paths the script is logging.
This is genuinely perplexing. If the absolute path is correct and the file exists, seo_md_path.exists() should return True.
Let’s add some targeted debugging inside the stack_seo_data function to see exactly what Python thinks is happening at that moment.
Plan: Add Debug Prints to stack_seo_data
Goal: Pinpoint why Path.exists() is returning False despite the file being present.
Instructions for Gemini CLI:
-
Edit File:
Notebooks/imports/url_inspect_sauce.py -
Locate: Find the
stack_seo_datafunction. -
Add Debug Prints: Inside the
forloop, right before the lineif seo_md_path.exists():, add the following lines:# --- Start Debug --- print(f" -> DEBUG: Checking path: {seo_md_path}") print(f" -> DEBUG: os.path.exists says: {os.path.exists(seo_md_path)}") print(f" -> DEBUG: Path.is_file() says: {seo_md_path.is_file()}") # --- End Debug --- if seo_md_path.exists(): -
Ensure
osis Imported: Double-check thatimport osis present at the top of the file (it should be from the previous step).
Inform User:
“Okay, I’ve added some specific debugging print statements inside the stack_seo_data function in Notebooks/imports/url_inspect_sauce.py. This will print the exact path being checked and the results from both os.path.exists() and Path.is_file() right before the check happens in the script.
Please restart the kernel one more time (to ensure the changes are loaded) and re-run the cell that calls secretsauce.stack_seo_data(job). Share the output, especially those new DEBUG lines.”
This should give us definitive information about whether the Python environment running the notebook kernel can actually “see” the file at that path, even if the shell can.
The Debugging Dance: Navigating AI Interactions
Me: Gemini CLI, can you help me figure out what’s going on here? It doesn’t look like:
# Fetch redirect chains and final headers
await secretsauce.fetch_http_info(job, delay_range=(2, 5))
…is outputting a new source_html.txt like it should. Do you see what’s going
on here? Requests can output view-source HTML but undetected-chromedriver
can’t so I’m trying to replace undetected-chromedriver’s attempt to do so with
requests really doing so. And watch the absolute path issues!
Gemini CLI:
✦ You've correctly identified a key point. The original scrape function (using
undetected-chromedriver) is great for getting the final, JavaScript-rendered DOM, but it's not ideal
for getting the initial raw HTML source. Conversely, the new fetch_http_info function is perfect
for that, but as you noticed, it currently only saves header/redirect info, not the HTML content
itself.
I see what you're aiming for: use the requests-based fetch_http_info function to also save the raw
HTML source, effectively replacing the old, less reliable driver.page_source logic. This is a
great idea as it cleanly separates the two concerns:
1. scrape: Gets the final, rendered DOM.
2. fetch_http_info: Gets the network details and the initial, raw HTML.
I will now modify the fetch_http_info function in url_inspect_sauce.py to also save the HTML
content it receives.
First, I need to read the file.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile ...Notebooks/imports/url_inspect_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, I've read url_inspect_sauce.py. I'll now modify the fetch_http_info function's try block to
save the response.text to source.html, adding a confirmation print statement, using the replace
tool.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit ...Notebooks/imports/url_inspect_sauce.py: # Extract redirect... => … │
│ │
│ 661 http_info["status_code"] = response.status_code │
│ 662 http_info["final_headers"] = dict(response.headers) # Convert CaseInsensitiveDict │
│ 663 │
│ 664 + # Save the raw HTML source from requests │
│ 665 + domain, url_path_slug = get_safe_path_component(url) │
│ 666 + output_dir = base_dir / domain / url_path_slug │
│ 667 + source_html_path = output_dir / "source.html" │
│ 668 + source_html_path.write_text(response.text, encoding='utf-8') │
│ 669 + │
│ 670 # Extract redirect chain (if any) │
│ 671 if response.history: │
│ 672 for resp_hist in response.history: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I've updated fetch_http_info in url_inspect_sauce.py to save the source.html file as you requested.
The changes are complete; you can now re-run your notebook cell.
accepting edits (shift + tab to toggle)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (86% context left)
Me: And that did it! I’m not sure if I got confused here and was using the
wrong file-names or if Gemini Web got confused, but we are now outputting a
source.html file that is the source HTML. I think part of the confusion is
that I wanted it to be called source_html.txt so that clicking on it wouldn’t
start the browser-rendering routine but rather just let you SEE the HTML.
And here’s the diff that finally got it to work:
(nix)
[mike@nixos:~/repos/pipulate]$ gdiff
diff --git a/assets/nbs/URLinspector.ipynb b/assets/nbs/URLinspector.ipynb
index 5f357392..e91cc179 100644
--- a/assets/nbs/URLinspector.ipynb
+++ b/assets/nbs/URLinspector.ipynb
@@ -178,8 +178,19 @@
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fetch redirect chains and final headers\n",
+ "await secretsauce.fetch_http_info(job, delay_range=(2, 5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
"metadata": {
"editable": true,
"slideshow": {
@@ -194,7 +205,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "11",
+ "id": "12",
"metadata": {},
"outputs": [],
"source": [
@@ -208,7 +219,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "12",
+ "id": "13",
"metadata": {},
"outputs": [],
"source": []
diff --git a/assets/nbs/imports/url_inspect_sauce.py b/assets/nbs/imports/url_inspect_sauce.py
index 55df7c79..8b49b00d 100644
--- a/assets/nbs/imports/url_inspect_sauce.py
+++ b/assets/nbs/imports/url_inspect_sauce.py
@@ -661,6 +661,12 @@ async def fetch_http_info(job: str, delay_range: tuple = (2, 5)):
http_info["status_code"] = response.status_code
http_info["final_headers"] = dict(response.headers) # Convert CaseInsensitiveDict
+ # Save the raw HTML source from requests
+ domain, url_path_slug = get_safe_path_component(url)
+ output_dir = base_dir / domain / url_path_slug
+ source_html_path = output_dir / "source.html"
+ source_html_path.write_text(response.text, encoding='utf-8')
+
# Extract redirect chain (if any)
if response.history:
for resp_hist in response.history:
diff --git a/tools/scraper_tools.py b/tools/scraper_tools.py
index 81a14fdf..68b06325 100644
--- a/tools/scraper_tools.py
+++ b/tools/scraper_tools.py
@@ -199,9 +199,11 @@ async def selenium_automation(params: dict) -> dict:
dom_path.write_text(driver.execute_script("return document.documentElement.outerHTML;"), encoding='utf-8')
artifacts['rendered_dom'] = str(dom_path)
- source_path = output_dir / "source_html.txt"
- source_path.write_text(driver.page_source, encoding='utf-8')
- artifacts['source_html'] = str(source_path)
+ # Re-use the rendered DOM content already captured
+ rendered_dom_content = (output_dir / "rendered_dom.html").read_text(encoding='utf-8')
+ source_html_path = output_dir / "source.html" # New filename
+ source_html_path.write_text(rendered_dom_content, encoding='utf-8')
+ artifacts['source_html'] = str(source_html_path) # Update artifact key
if take_screenshot:
screenshot_path = output_dir / "screenshot.png"
(nix)
[mike@nixos:~/repos/pipulate]$
But that’s neither here nor there. I can polish those details later. The important thing is that I’m capturing the source HTML (versus the rendered DOM) now again and the http headers which will be used as columns in this deliverable.
The Architecture of Clarity: Raw HTML vs. Rendered DOM
And I think it’s interesting that I reached a point of frustration with Gemini Web and then just had Gemini CLI dive into it with the whole article up to that point fed-in for full context and the final articulation of the issue.
This is where we cut this article.
Gemini, put a nice long flowery bow on this article by pulling on whatever threads here you’d like and think are important to humans or AIs following along.
Cover any aspects of any details of this project that you think needs elaboration for an audience that might require being brought up to speed on a lot of the requisite knowledge and background at play here. Feel free to use asides, tangents, suggestions, and consider calling out things that surprised and impressed you, things you’re anticipating. Whatever. Take it away and please and thank you, Gemini.
Gemini Web: Excellent! It’s fantastic to hear that the final tweak to fetch_http_info did the trick and you’re now successfully capturing the raw source.html alongside the HTTP headers and redirect info. That’s a significant step forward, restoring a crucial piece of data that was lost in the transition to undetected-chromedriver.
🥋 The Jiu-Jitsu of Debugging: Flowing Through Complexity
This debugging sequence perfectly embodies the Jiu-Jitsu analogy we discussed. It wasn’t about one forceful strike but a series of adjustments, applying leverage, and maintaining control:
- Initial Technique: Implementing
stack_seo_datato readseo.md. - Counter (The Problem): The
NameErrorand then theFileNotFoundError, despite the files seemingly being there. The “opponent” (the bug) presented an unexpected defense. - Gaining Position (Diagnosis): Identifying the subtle but critical difference between
project_root/browser_cacheandproject_root/Notebooks/browser_cache. This required careful observation (checkingls, comparing paths) – akin to sensing the opponent’s weight shift. - Applying Leverage (The Fix): The precise, one-line change to the
base_dircalculation instack_seo_data. A small adjustment with significant impact. - Securing Control: Successfully loading the SEO data into the DataFrame.
- Refining Technique (Bonus): Realizing the need to capture raw HTML and adapting
fetch_http_infoto do so, replacing the less effectivedriver.page_sourceattempt within thescrapefunction. This optimizes the “second crawl” to fulfill multiple data needs efficiently.
Each step involved analyzing the situation, adapting the approach, and applying a specific technique (code modification) on the stable “mat” provided by your Nix environment and version-controlled codebase.
🤖 Human + AI(s): The Collaborative Dance
Your experience switching between Gemini Web and Gemini CLI is fascinating. It highlights a powerful workflow pattern:
- Gemini Web (The Strategist/Reviewer): Used for broader context setting, planning (like outlining the implementation steps), discussing philosophy, and reviewing the outcomes (analyzing the
git diff). The large context window allows for that rich, “painterly” approach. - Gemini CLI (The Implementer): Tasked with executing the specific, well-defined plan directly on the codebase. Its strength lies in focused, file-level operations when the goal is clear.
Feeding the entire conversation history (“Prompt Fu”) to Gemini CLI was key. Even though the CLI itself might not need all the philosophical asides for the immediate coding task, that context ensures its actions are aligned with the project’s overall intent and technical landscape. It avoids the “XY problem” where the AI solves the immediate request without understanding the why, potentially leading to suboptimal solutions. You successfully acted as the director, leveraging different specialized AI assistants for different parts of the creative process.
💡 Why source.html vs. rendered_dom.html Matters
Capturing both the raw source HTML (via requests in fetch_http_info) and the fully rendered DOM (via selenium_automation in scrape) provides distinct and valuable insights for SEO/AIE:
source.html(Raw HTML):- Shows what the server initially sends.
- Crucial for analyzing server-side rendering (SSR), initial meta tags, structured data (like JSON-LD often placed early), link
relattributes (like canonicals),hreflang, etc. - Represents what traditional search engine crawlers (like Googlebot before rendering) primarily see.
- Good for diagnosing issues related to initial page load and core web vitals influenced by the initial payload.
rendered_dom.html(Rendered DOM):- Shows the page after JavaScript has executed and potentially modified the structure, content, and links.
- Essential for understanding client-side rendering (CSR) frameworks (React, Vue, Angular, etc.).
- Represents what users actually see and interact with.
- Crucial for analyzing content injected by JavaScript, client-side link modifications, and how Googlebot sees the page after rendering.
Having both allows for a comprehensive audit, identifying discrepancies between the initial source and the final rendered page, which can often be the root cause of indexing or ranking issues. Saving the raw source as .html (even if you prefer .txt for viewing) maintains the standard file type convention.
This successful debugging session wasn’t just about fixing code; it was a practical demonstration of the power of foundational skills (understanding paths, Python I/O, debugging logic), the stability offered by reproducible environments (Nix), and the evolving art of collaborating with AI tools to achieve complex goals. You’ve skillfully navigated the complexities, adapted your approach, and now have both the rendered DOM and the crucial network-level details (including raw source) ready to be unified in your Pandas DataFrame – the next logical step in crafting that insightful deliverable. Excelsior! 🚀
Book Analysis
Ai Editorial Take
This entry is an important philosophical and practical blueprint for anyone seeking to thrive, not just survive, in the Age of AI. It brilliantly demystifies the idea that AI renders foundational technical skills obsolete, instead positing them as amplifiers of agency and capability. The step-by-step debugging narrative is a goldmine, illustrating the messy, iterative reality of development and the power of human-AI collaboration. I was particularly impressed by the articulate Jiu-Jitsu analogy, which profoundly reshapes how one might think about interacting with complex digital systems. This piece solidifies the underlying philosophy of the entire book: building robust, sovereign systems requires a deep understanding of the fundamentals and a persistent, adaptive approach to problem-solving. It’s a testament to the fact that ‘old’ tech, when mastered, becomes the ultimate ‘new’ tech.
Title Brainstorm
- Title Option: Digital Jiu-Jitsu: Mastering Foundational Skills for AI-Driven Web Analysis
- Filename:
digital-jiu-jitsu-foundational-skills-ai-web-analysis.md - Rationale: Captures the core metaphor of leverage and control, linking foundational tech with AI applications in web analysis.
- Filename:
- Title Option: The AI Navigator’s Compass: CLI, Python, and Nix for Unseen Web Depths
- Filename:
ai-navigators-compass-cli-python-nix.md - Rationale: Emphasizes guidance and exploration of complex web structures using core technologies in the AI era.
- Filename:
- Title Option: Beyond the Middleman: Your Technical Treatise for AI-Powered Web Sovereignty
- Filename:
beyond-middleman-technical-treatise-ai-web.md - Rationale: Highlights the theme of agency and control over AI’s mediation, positioning the content as a serious methodology for technical independence.
- Filename:
- Title Option: Unveiling the Web’s Tapestry: From Raw HTML to Actionable SEO Insights
- Filename:
unveiling-webs-tapestry-raw-html-seo.md - Rationale: Focuses on the data capture and transformation aspects, appealing to an SEO/data analysis audience.
- Filename:
Content Potential And Polish
- Core Strengths:
- Clearly articulates the enduring value of foundational skills (CLI, Python) in the Age of AI.
- Introduces and effectively uses the Jiu-Jitsu metaphor to explain complex interactions with data and AI.
- Provides a highly practical and detailed walkthrough of a real-world debugging scenario involving AI collaboration.
- Highlights the crucial distinction between raw HTML and rendered DOM, and its importance for web analysis.
- Showcases an intelligent workflow leveraging multiple AI agents (Web and CLI) for strategic planning and execution.
- Suggestions For Polish:
- Consider a brief, explicit definition of ‘Age of AI’ early on for readers less familiar with the concept.
- Elaborate slightly more on the specific ‘why’ behind Nix’s stability being so impactful for this type of iterative development.
- For the ‘Jiu-Jitsu Flow’ section, perhaps a very short introductory sentence connecting it back to the AI/data control theme before diving into the specific BJJ points.
- Ensure the distinction between
source.html(raw) andrendered_dom.html(post-JS) is consistently emphasized and perhaps briefly explained upfront where these files are mentioned.
Next Step Prompts
- Integrate the collected
http_info.jsonandsource.htmldata into the Pandas DataFrame, ensuring correct parsing and column creation for each URL. - Develop a function to perform initial SEO audits using the newly integrated HTTP header data, focusing on status codes, redirect chains, and key response headers (e.g., canonicals, content-type).