标签：#Product

If you've ever tried converting a bank statement PDF to CSV and ended up with a jumbled mess of merged cells, missing rows, or split transaction descriptions — you're not alone. This is one of the most common data pain points for accountants, bookkeepers, and anyone who does their own finances. In this post, I'll walk through why this happens, what the right approach looks like, and how to get clean, analysis-ready CSV output from any bank statement. Why Bank Statement PDFs Are So Hard to Parse Bank statements aren't structured documents — they're designed for printing, not data extraction. Here's what generic converters run into: Merged cells : PDF renderers often group date + description + amount into a single visual block. Naive converters pick one cell boundary and split it wrong. Multi-line transactions : A single transaction entry (especially with memos) can span 2–3 lines in the PDF, but gets split into separate rows in the spreadsheet. Negative vs. positive amounts : Debit/credit columns vary by bank.Chase uses a single "Amount" column with negatives for debits. BoA uses two separate columns. A generic converter treats them identically and produces wrong signs. Running balance drift : If even one row is misaligned, every balance figure below it is off. Method 1: Manual Copy-Paste (What You're Probably Doing Now) The baseline. Open the PDF, select all, paste into Excel, then spend 30 minutes fixing column alignment. Pros: Free, no tools required. Cons: Slow (20–40 minutes per statement), error-prone, completely unscalable if you have multiple accounts or months to process. Method 2: Python + pdfplumber For developers who want a scriptable solution: import pdfplumber import csv with pdfplumber . open ( " statement.pdf " ) as pdf : rows = [] for page in pdf . pages : table = page . extract_table () if table : rows . extend ( table ) with open ( " output.csv " , " w " , newline = "" ) as f : writer = csv . writer ( f ) writer . writerows ( rows ) This works reas

I have released the Sovereign package on Github. It deploys Lemmings into up to 3 cloud regions for your choice in configuration flavor with cost estimations through dry-runs and aggregating the report into one final report as lemmings come and go in the result of the load test. I built it for organizations that plan on using AI to build something, not hire somebody like me who helped write The Library to do it for them. You can hire me in consulting if you need help, but it's available now. But, before you spend $50,000 on a television ad driving people to your new app that you just built after spending $50,000 on tokens, why not run Lemmings and Sovereign first? It's 100% free and does not involve me at all in order for you to read through the extensive README.md files and comments in the code for you to understand what to do to run it and adapt to its results. Enjoy using it! There - that is the post. Now - 🙌🏻 - Ask me anything 🙇🏻 👇🏻

The 1st Firewall for AI Agents Discussion | Link

Run GPT-5.6 on OpenClaw & Hermes safely on your Mac Discussion | Link

Two lines of code. Same variable. Same operator. Completely different behavior. a = [ 1 , 2 , 3 ] b = a b += [ 4 ] # line A print ( a ) # [1, 2, 3, 4] x = 1 y = x y += 1 # line B print ( x ) # 1 Line A changes a . Line B does not change x . The only difference is whether the variable holds a mutable or immutable object. To understand why this happens, you need to understand how Python actually represents variables internally. Variables Are Not Boxes The box metaphor is how most introductory programming courses explain variables: a variable is a box that holds a value. You put 5 into the box called x . Later you can replace it with 10. In Python this metaphor is accurate for immutable types and dangerously misleading for mutable types. The more accurate model: a Python variable is a name that is bound to an object. The object exists independently of the name. Multiple names can be bound to the same object. Binding a name to a new object does not affect the old object or other names that reference it. You can inspect this directly: a = [ 1 , 2 , 3 ] b = a print ( id ( a ) == id ( b )) # True -- same object, two names x = 5 y = x print ( id ( x ) == id ( y )) # True -- both point to integer object 5 Both cases start the same: two names pointing to the same object. What happens next depends on whether you mutate the object or rebind the name. Two Fundamental Operations Every operation on a Python object falls into one of two categories. Mutation : the object at a given memory address is modified. All names pointing to that address see the change. Rebinding : a name is pointed at a different memory address. Other names pointing to the original address are unaffected. List methods like .append() , .extend() , .sort() , and item assignment lst[i] = x are mutations. Assignment with = is rebinding. The += operator is either mutation or rebinding depending on whether __iadd__ is implemented and the object is mutable. Tracing Through the Object Graph a = [[ 1 , 2 ], [ 3 , 4 ]]

Health in ChatGPT now lets eligible U.S. users securely connect medical records and Apple Health to get more personalized insights and better understand their health.

Full-Stack Platform for Training Small Language Models Discussion | Link

Stop typing, start talking, get perfectly written text Discussion | Link

Describe your problem, get the YC startup that solves it Discussion | Link

Undetectable AI copilot for live meetings Discussion | Link

Build better color palettes. Ship complete color systems. Discussion | Link

I'm a scrum master. I was a developer ten years ago. I have enough background to discuss design and trade-offs with an LLM — but three months ago I made a deliberate bet on my solo project: I would never read the code. The specs define the tests. The tests control the code. The code is a black box. I'm not claiming this is what everyone should do. But it's my bet, and it forced a system into existence: when nobody reads the code, the process has to carry the trust that a code-reading human normally provides. I've just published that system as a reference implementation: backlog-as-data — the full writeup, the Claude Code skills translated to English, and the CLI source, verbatim from my daily setup. Here's the short version. The backlog is git data, not a document Most agent task-management tools store tasks in a dedicated place — a tasks.json , a database, a backlog/ folder. My bet is different: the backlog is the YAML frontmatter of my spec files. One file per ticket, and the ticket's status is a field — never a location in a document. --- id : PARSE-07 title : Tolerate CRLF in decklist import type : ticket status : todo priority : should exec : model : sonnet effort : think review : light matured : 2026-07-22 --- # PARSE-07 — Tolerate CRLF in decklist import The spec body: design, contracts, test cases. The ticket file IS the spec. Everything below the frontmatter is the spec — written by the LLM, after it has challenged the need I expressed in conversation. The frontmatter is data — owned by a small CLI, mutated only through it. Same file, so they can never drift apart. Why it matters: "move it to Done" is not an operation. LLMs (and humans) mangle documents when a state change means relocating text. Making status a field makes every transition a one-line, idempotent, testable mutation. The board I look at (a small web page on my server, with GitHub deep links to each spec) and the readable markdown view are generated projections , locked by a do-not-edit sentin

LLM-written bibliographies do not stop at arXiv preprints. Law review drafts invent reporter cites; multilingual papers mangle Chinese titles. citesure 0.2 extends the integrity gate into those failure modes. US case law via CourtListener References that look like court cases — @jurisdiction entries, Plaintiff v. Defendant titles, or reporter strings such as 347 U.S. 483 — are resolved against Free Law Project CourtListener. Ranking prefers an exact reporter cite over companion orders, so Brown lands on 347 U.S. 483 rather than a later procedural listing. @jurisdiction { brown1954 , title = {Brown v. Board of Education} , year = {1954} , howpublished = {347 U.S. 483} , } citesure check examples/packs/us-case-law.bib citesure warm-cache cases.bib Optional COURTLISTENER_TOKEN for higher rate limits. Law-review CI: templates/journal/law-review.yml . CJK-aware matching NFKC + fullwidth folding; character-level similarity for CJK-heavy titles; CJK bigrams in claim scoring so Chinese claims are not silently empty. Evidence Integrity bench 242/242 (US cases + Chinese titles + multi-domain set) Claims mini-bench 29/29 Eight domain packs including us-case-law Install pip install "git+https://github.com/SybilGambleyyu/citesure.git[pdf]" Source: github.com/SybilGambleyyu/citesure · Demo: workers.dev

Save on WIRED-tested LED therapy devices from TheraFace, HigherDose, and more.

San Francisco co-working dataset visualized, as a game! Discussion | Link

A game development tutor now on iOS Discussion | Link

Turns hours of team video and notes into searchable memory Discussion | Link

The notch on a MacBook is strange real estate. It is always there. It sits at the top of the screen, close to the menu bar, close to system controls, close to whatever you are doing. But most of the time it is treated like a cutout to design around instead of a place software can use. That felt like a missed opportunity. So I built SwiftNotch , a macOS menu bar app that turns the notch area into an expandable productivity dashboard. Hover near the notch or press Option + Space , and the quiet black shape becomes a small command center for widgets, files, media, shortcuts, developer tools, and window actions. Website: swiftnotch.xyz Demo video: Launch note: SwiftNotch 1.x is free during beta . I want early Mac users to try the full experience, share feedback, and help shape the app before SwiftNotch 2.0 introduces paid plans. The idea I did not want to build another large dashboard that asks you to leave your current app. The goal was the opposite: make useful tools available in the smallest possible space, without breaking flow. The notch is perfect for this because it already behaves like a visual anchor. You know where it is without thinking. If it can expand only when needed, it becomes a temporary interface layer instead of another permanent panel. SwiftNotch starts collapsed. When activated, it opens into a compact dashboard with the widgets and actions you choose. What SwiftNotch does The current app includes 31 built-in widgets across productivity, system utilities, media, automation, and developer workflows. Some examples: Media Control for Spotify, Apple Music, VLC, YouTube, and browser players Shelf for drag-and-drop file staging, quick sharing, paths, iCloud actions, and zip workflows Clipboard History for snippets, links, and quick paste Calendar Events , Reminders , Notes , Weather , World Clock , and Pomodoro Quick Toggles for Wi-Fi, Bluetooth, Dark Mode, and volume Window Snapping with layouts for halves, thirds, quarters, and custom grids Developer H

Out of Dyson’s two new affordable stick vacuums, this one is easily my favorite.

A platform that teaches how to navigate real-world code. Discussion | Link