Llmvm. Using GPT3.5 to cooperatively execute user tasks on a Python VM

Hacker News

Hi there HN. I've hacked a prototype together that enables GPT to break down user tasks into manageable sub-tasks, and then schedule and oversee their execution on a Python VM while collaboratively addressing syntax and semantic errors through a back-and-forth message dialogue. Example: "Go to the https://ten13.vc/team website, extract the list of names, and then get me a summary of their LinkedIn profiles." Will turn into GPT generated a-normal form Starlark code: var1 = download(" https://ten13.vc/team ") # Step 1: Download the webpage var2 = llm_call([var1], "extract list of names") # Step 2: Extract the list of names from the webpage answers = [] # Initialize an empty list to store the summaries for list_item in llm_loop_bind(var2, "list of names"): # Step 3: Loop over the list of names var3 = llm_bind(list_item, "WebHelpers.search_linkedin_profile(first_name, last_name company_name)") # Step 4: Search and get the LinkedIn profile of each person var4 = llm_call([var3], "summarize career profile") # Step 5: Summarize the career profile of each person answers.append(var4) # Step 6: Add the summary to the list of answers answer(answers) # Step 7: Show the summaries of the LinkedIn profiles to the user Which will then be executed statement-by-statement by the Python runtime. When syntax errors, exceptions or semantic issues occur, there's a error correction loop where GPT will get involved to identify the issue, regenerate code, and try again. Lots of fun little programming language/compiler challenges in here. Happy to answer questions if you have them.

Visit product View on Hacker NewsAug 29, 2023

Share card

Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks, using · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Shalchemy – Conveniently execute binaries from Python70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Shalchemy – Conveniently execute binaries from Python

Hacker News3

Solid IMAP and SMTP in Go Using Python52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Solid IMAP and SMTP in Go Using Python

Hacker News2

Using functions to construct Regex in Python54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Using functions to construct Regex in Python

Hacker News105

WebRTC implementation for Python using Asyncio64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

WebRTC implementation for Python using Asyncio

Hacker News216

Manipulate NumPy arrays in Python using Uiua44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Manipulate NumPy arrays in Python using Uiua

Hacker News2

FlowCode – Visual Flowcharts That Generate and Execute Python46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FlowCode – Visual Flowcharts That Generate and Execute Python

Hacker News1

Microfox58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Microfox can access & execute tasks across any too

Indie Hackerscommitment-full-time

Select Sections from Images of Newspaper Clippings Using OpenCV, Python47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Select Sections from Images of Newspaper Clippings Using OpenCV, Python

Hacker News38

Python WebApp where Python runs within the user's browser with Pyodide53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Python WebApp where Python runs within the user's browser with Pyodide

Hacker News3

A Python UI Module Using Websockets, Bootstrap and D349%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Python UI Module Using Websockets, Bootstrap and D3

Hacker News4

Browse all products Analyze your launch