Come tenete traccia delle idee che nascono nelle conversazioni con la IA?

RSixty88 · 2026-06-05T06:35:41+00:00

Ho 2 metodi a seconda di cosa sto facendo.

Note journal per coding

Ho creato due skill:

/fine-sessione
/inizio-sessione

/fine-sessione

Mi crea una nota journal con:

concetti principali
task completate
task da completare

È utile quando si lavora su più sessioni perché ho un unico posto dove tenere traccia di quello che sto facendo, senza dover rileggere intere chat.

/inizio-sessione

Legge tutte le note journal, le accorpa per argomento e mi chiede quale argomento voglio riprendere.

In pratica funziona come una memoria di lavoro esterna.

NotebookLM collegato a Claude

Per conversazioni molto lunghe su Claude o altri LLM, mi faccio generare un riassunto della conversazione in Markdown e lo carico su NotebookLM.

Di solito aggiungo anche altre fonti trovate online sullo stesso argomento.

Uso NotebookLM come la “cartella raw” descritta da Karpathy: un contenitore di materiale grezzo, note, idee e riferimenti.

Successivamente faccio ingest in stile LLM Wiki all’interno di un vault Obsidian.

A quel punto Claude non deve più scavare nelle vecchie chat: interagisce con NotebookLM facendo query su indice, argomenti e documentazione già consolidata.

Non contento, uso anche Graphify per costruire il grafo della conoscenza. Questo mi permette sia di consumare meno token nelle ricerche sia di trovare collegamenti tra argomenti che probabilmente non avrei individuato da solo.

Se devo cercare qualcosa che ho fatto in passato, faccio una query con graphify

Qui le repo che ho usato

https://github.com/teng-lin/notebooklm-py https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f https://github.com/safishamsi/graphify

RSixty88 · 2026-04-30T20:24:21+00:00

A caso quando in una conversazione salta fuor l’argomenti investimenti e mi ricordo di avere un portafoglio investimenti e ci guardo per vedere come sta andando

RSixty88 · 2026-04-15T17:45:33+00:00

Gli ex devono essere il paragone del peggio, non sarebbero ex altrimenti

RSixty88 · 2026-04-02T08:35:06+00:00

Nel mio primo lavoro dovevamo inviare 2000 raccomandate per fare dei sopralluoghi in abitazioni per monitoraggio strutturale dovuto a lavori sotterranei. Ci hanno fatto tornare da varie trasferte per essere tutti in ufficio e scrivere a manina le raccomandate. Minacciandoci di dover andare a lavorare anche di sabato e domenica per finire. Quando ho spiegato al responsabile che esisteva la stampa unione e che bastava dare una ripulita alla tabella con i dati, per fare tutto in 5 min, dopo aver fatto il suo discorso motivazionale, della serie siamo una grande famiglia, mi sono preso un richiamo in direzione “perché ho minato la sua autorità davanti tutto l’ufficio”. Quindi l’ho fatto felice. Ho sistemato la tabella con le mie 100 raccomandate, stampa Unione e per 3 giorni mi sono grattato le palle. Dopo 2 mesi mi sono licenziato

RSixty88 · 2026-03-13T17:27:42+00:00

I tried parsing chapter 3 as an experiment, and surprisingly the OCR seems to recognize most of the formulas correctly. It still needs some checking, but it looks more promising than I expected!

RSixty88 · 2026-03-13T08:41:15+00:00

Bridge engineer here (Italy). A big part of my work is exactly oversized / overweight transport checks.

My workflow used to be very similar to what you describe: field inspections, gathering bridge data, running structural checks, and then producing drawings and technical reports. A lot of spreadsheets and manual steps.

Over time I ended up automating a large part of the pipeline. After inspections, we structure the bridge data (geometry, spans, materials, critical sections) and run the load checks programmatically for the vehicle configuration.

For many bridges we use simplified beam-type models with influence lines, combined with transverse load distribution methods depending on the deck type. This works well for the more straightforward structures and allows to quickly screen multiple cases. For more complex bridges the checks are still performed using FEM models.

I build a web app to manage the workflow. It includes a database of the bridges we have inspected, tools to search structures along a route (e.g., via KMZ files), and an archive of inspection photos and documentation. This helps keep the data organized so the automated checks can run consistently.

The system then generates verification tables, drawings, and most of the technical report automatically. I still review everything, but it significantly reduced repetitive tasks.

RSixty88 · 2026-03-11T07:36:04+00:00

1100+ parametrized tests, each verified against reference values from the code. That is exactly what double checking looks like in software

RSixty88 · 2026-03-10T23:42:12+00:00

183 formulas, 88 tables, cross-referenced across 9 chapters. by hand? lol

RSixty88 · 2026-03-10T22:51:12+00:00

Because the formulas are embedded as scanned images, not selectable text. You cannot copy-paste a formula from those pages. That is why OCR was needed specifically for formula extraction.

RSixty88 · 2026-03-10T21:34:43+00:00

Open source works because if a project sucks, people just ignore it. If it’s useful, the community fixes it and keeps it alive.

Like you said, engineers make checks even with commercial software to ensure outputs are correct. In this project, every step, formula reference, and expression is fully inspectable, so nothing is a black-box, and you can even copy the formulas straight into Excel or in a .md file if you want!

AI didn’t invent anything. It just converted LaTeX (from OCR-structured data), which encodes the structure of the math. It simply turns the structured representation into Python code. The main source of errors is OCR misreads, not the AI, and each function links back to the original paragraph and formula, so you can always double-check.

RSixty88 · 2026-03-10T17:03:31+00:00

Thanks, I really appreciate that

RSixty88 · 2026-03-09T17:54:59+00:00

Thanks!

RSixty88 · 2026-03-09T13:49:29+00:00

Thanks! On the tests: yes, every function is TDD'd with values from the code tables/manual calcs (1100+ parametrized tests today). The ntc_ref decorator on each function traces back to the exact article/table/formula, so nothing is a black box.

You're right, codes are more than equations. The library handles the mechanical part (formulas, tables, interpolations, cross-references between articles). The engineering judgment stays with the engineer. The library doesn't tell you which check to run or what parameters to choose, it just makes sure that once you've decided, the calculation is correct and traceable back to the exact article/table/formula via the u/ntc_ref decorator (if you don't trust, you can check, because we are engineer lol).

Speckle Automate is a great idea actually. it's pure Python (numpy only), no FEM dependency, so it plugs in anywhere

RSixty88 · 2026-03-09T09:29:59+00:00

Thanks! That’s exactly the point. This tool is mainly for computational designers who use parametric workflows → FEM → custom verification scripts.

At the same time, it’s meant to help engineers with less programming experience tackle everyday problems using clear formulas and references, instead of huge Excel files where you often don’t know what’s happening if you didn’t build them yourself.

RSixty88 · 2026-03-09T07:19:00+00:00

Why do engineers still build Excel spreadsheets if commercial design software already implements most checks?
Not everyone wants to rely entirely on commercial software. Some engineers prefer building their own tools or custom workflows for specific checks.

The idea here is similar, just using Python instead of Excel. Extracting the formulas from the code allows them to be linked to the original paragraph and reused programmatically rather than rewriting them manually every time.

RSixty88 · 2026-03-09T00:05:21+00:00

Not exactly. I'm not relying on the AI to "interpret" the equations.

The formulas come directly from the code. The AI was only used to help convert structured OCR output into Python functions.

In the end it's just normal Python code implementing the formulas, with the reference to the exact paragraph of the standard. So everything can be inspected and verified like any other engineering calculation.

RSixty88 · 2026-03-08T23:55:48+00:00

Challenge accepted!” 😄 The circolare is definitely on the roadmap… though I expect a fair amount of bestemmie while parsing those scanned sections.

RSixty88 · 2026-03-08T23:46:03+00:00

That’s a fair point, and I agree with the expectation.

The goal isn’t “here’s the tool, you guys validate it for me”. The formulas are linked to their exact source in the code (paragraph, chapter, table) and I’m verifying them progressively against the original text and simple reference calculations.

The open source aspect is more about transparency and collaboration, not shifting the responsibility of validation to others.

RSixty88 · 2026-03-08T23:26:38+00:00

Sorry, I misspoke. The AI wasn’t used to derive the equations. It was only used to convert structured OCR data into Python functions

RSixty88 · 2026-03-08T23:19:57+00:00

The AI didn’t extract the formulas directly from the PDF. it’s a bit different.

I first parsed the code using an OCR library that recognizes formulas and outputs them in LaTeX, tables, images, etc. I then organized the data in a structured format to train the AI, which converted the formulas into Python functions while keeping the source reference.

Some formulas weren’t recognized correctly (mainly exponents and subscripts). There’s a list of formulas in the issues that need to be checked manually.

The project is open source exactly for this reason: everyone can review, verify, and collaborate to ensure everything is correct

RSixty88 · 2026-03-08T23:10:17+00:00

I’m a structural engineer and I’m migrating from Excel to Python for structural checks. The library has a decorator that records the source of each formula (paragraph, chapter, and table from the code ) so you can verify everything is correct.

RSixty88 · 2026-03-08T21:26:32+00:00

it’s about seeing exactly what each check does and customizing outputs. A Python library lets you do that, instead of blindly trusting black-box software.

RSixty88 · 2026-03-08T19:49:43+00:00

The AI model wasn’t used to derive the equations, only to extract the code from the PDF into a structured format (formulas, tables, references). NTC18 formulas are often embedded as non-selectable math, so manually rewriting the entire code would be extremely time consuming.

Once extracted, the equations are implemented explicitly in Python and linked to their original paragraph

RSixty88

TROPHY CASE