Alternatives
Products that do what Memory Graph – Interactive Python execution and memory visualizer does
Hello everybody, I built Memory Graph to help students (and myself) build a correct mental model of Python references, mutability, and copying, and to make debugging data structures less “print-driven”. It’s inspired by Python Tutor, but focuses on clearer graphs and on running locally in many different environments and debuggers. The Memory Graph Web Debugger quickly turns your web browser into a Python debugger where the whole program state is visualized in each step, clearly showing aliasing and the structure of the data, giving insight that is hard to get with just printing. Some…
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2019 · github.com
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2021 · github.com
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2014 · github.com
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Hi HN! During the last year, I've been building python.cards: a site to learn Python using spaced repetition. My goal is to make the most of spaced repetition by making it extremely simple to use and by providing high quality flash cards. The site has been live for a month, with a few daily users who had joined the waitlist. The feedback has been quite positive, with most of the users using the site every day. Currently, we have a free deck (A Tour of the Stdlib) and a paid one (Pathlib in depth, for $9.99). I have other decks in the works, covering topics such as f-strings, collections,…
2024 · python.cards
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Hey HN. I've just launched my very first webappp and would love your feedback. http://www.7bks.com It's a book list sharing website - kind of like playlists for bookworms. The site is built on pyton/appengine - and I learned to code in under 4 weeks to build the site. There's more details on the background of the site here: http://www.7bks.com/blog/179001 I've also published the full Python code for the site here: https://gist.github.com/670034 Feedback is really important to me and I want to work hard to make the site better so any and all suggestions are greatly appreciated. Contact…
2010
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2020 · github.com
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Hi HN, I built ast-visualizer.com because I wanted a way to visualize the architecture/structure of a Python repo before dived into the code. Most tools tell you what the code does; I wanted to see how it's built. The Problem: Onboarding onto a large codebase is a nightmare. LLMs help with single functions, but they struggle to show you the "God Objects," circular dependencies, or high-complexity hotspots across 50+ files. What it does: Dependency Graph: Visualizes imports and file complexity to find architectural bottlenecks. Radial AST Heatmaps: Maps individual files and color-codes…
Feb 2026 · ast-visualizer.com
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I've made another Python cheat sheet tutorial. Yeah yeah, nothing new, I know. But here's the thing: The main idea was not just to write a wall of text telling about everything, but to make it interactive. So that everything would have its own example code snippet, which you could change, run, and see how it worked. And not somewhere in a web version, but on your own computer, in your own environment. Fortunately, Python has the perfect tool for this - the Jupyter Notebook. That's why all chapters are written as separate notebooks and there is an example for each point (well, almost). I…
2024 · github.com
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Hey there HN! We’re Vasilije, Boris, and Laszlo, and we’re excited to introduce cognee, an open-source Python library that approaches building evolving semantic memory using knowledge graphs + data pipelines Before we built cognee, Vasilije(B Economics and Clinical Psychology) worked at a few unicorns (Omio, Zalando, Taxfix), while Boris managed large-scale applications in production at Pera and StuDocu. Laszlo joined after getting his PhD in Graph Theory at the University of Szeged. Using LLMs to connect to large datasets (RAG) has been popularized and has shown great promise.…
2025 · github.com
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Hello, I have several years of experience as a Python developer, and during that time, I've worked on intricate applications dealing with large volumes of data. One frequent challenge I faced was benchmarking the application and identifying performance bottlenecks. While there are some excellent Python profiling tools available, they can be quite daunting for beginners. The utility I have developed simplifies this process, making it as straightforward as possible to transition from slow code to a detailed flame chart. I would greatly appreciate your feedback!
2023 · github.com
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Hi all, I'm Ivan, and together with Alex, we're building a diagram visualization tool for codebases. Alex and I are devs, and we've noticed that recently we've been super productive at writing code (prompting :D). But when it comes to understanding big systems, prompting doesn't work that well — for that, diagrams are best imo. Most tools out there don't scale to big projects (e.g. PyTorch), so we're building CodeBoarding — a recursive visualizer for codebases. It starts from the highest level of abstractions and lets you dive deeper. We use static analysis and LLM agents. The control-flow…
2025 · github.com
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Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…
Feb 2026
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2018 · github.com
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Hello everyone! My friend Ori Kabeli and I have been playing around with GPT-4 Vision as a way to control websites and complete complicated tasks online. We created a small proof of concept python package called pywebagent. pywebagent allows performing high level tasks in websites, accessible to developers as simple python functions. It is highly experimental, and only sometimes works, but we think it still might be useful to all you tinkerers out there. Feel free to contribute! Example Use Case: You can order things from amazon with one line of code. pywebagent.act(…
2023 · github.com
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2021 · github.com
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Hi HN, I am one of the cofounders of http://turingdb.ai. We built TuringDB while working on large biological knowledge graphs and graph-based digital twins with pharma & hospitals, where existing graph databases were unusable for deep graph traversals with hundreds or thousands of hops on (crappy) machines you can find in a hospital. https://github.com/turing-db/turingdb TuringDB is a new in-memory, column-oriented graph database optimised for read-heavy analytical workloads: - Milliseconds (1) for multi-hop queries on graphs with 10M+ nodes/edges -…
Jan 2026 · github.com
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Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
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I was frustrated that memory is usually tied to a specific tool. They’re useful inside one session but I have to re-explain the same things when I switch tools or sessions. Furthermore, most agents' memory systems just append to a markdown file and dump the whole thing into context. Eventually, it's full of irrelevant information that wastes tokens. So I built this local memory layer that unifies memory across agents. Instead of a flat file, it builds a structured knowledge graph of "memory notes" inspired by the paper "A-MEM: Agentic Memory for LLM Agents"…
Apr 2026 · github.com
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IDEA Imagine how infuriating it would be if you had to write code in a text editor: no tools to diff changes; no organized way to merge changes from a new branch; no way to even see what's running on master. This is how we design, deploy and run most processes in every tech company: we describe them in text docs. Human memory and sense-making is visual and sequential. Stories and maps are sticky because they put information in context. We do sometimes use diagrams to show important processes because–unlike a text summary (especially LLM-generated summaries)--a diagram makes it easy to see…
2025 · splotch.ink
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Hi Hacker News, As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself). This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator. Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline…
2024 · github.com
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2015 · nbviewer.ipython.org
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I've created an in-browser Python/Pandas/Git practice environment for my online learning platform and also for my corporate training classes. I'd be happy to discuss how I went about designing this, how I'm using it in my classes, and the architectural decisions I've made. Most interesting, to me, is how much is running in the browser. Thanks to Svelte, Pyodide, isomorphic-git, LightningFS, and CodeMirror I'm able to provide a full environment for Python, Pandas, and Git. I built much of this with Claude Code, and I'm happy to discuss how that went — what worked well and where I…
Jun 2026 · practice.lernerpython.com
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