Au

Auto-Suggest LLM Prompts, with Graphlit and Azure OpenAI

Hacker News

Auto-Suggest LLM Prompts, with Graphlit and Azure OpenAI

Share card

Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · Missing: mac, agents, macos
75%75% 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.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
69%69% predicted probability of success on BetaList, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Re
Recursive LLM Prompts53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Recursive LLM Prompts

Hacker News97
Ca
Cataloging the NYRB and LRB with OpenAI Embeddings46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cataloging the NYRB and LRB with OpenAI Embeddings

Hacker News3
Li
Litellm – Simple library to standardize OpenAI, Cohere, Azure LLM I/O44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Litellm – Simple library to standardize OpenAI, Cohere, Azure LLM I/O

Hacker News62
Pr
PrePrompt – rewrites vague prompts before they reach the LLM44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PrePrompt – rewrites vague prompts before they reach the LLM

Hacker News23
Coralflavor
Coralflavor33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Unfiltered LLM That Doesn't Reject Prompts

Indie Hackerscommitment-side-project
Op
OpenAI O1 for Free55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

OpenAI O1 for Free

Hacker News4
Vi
Vim-like undo history for OpenAI API prompts48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Vim-like undo history for OpenAI API prompts

Hacker News1
Se
Serve LLM prompts via CDN (free and no account allowed)56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Serve LLM prompts via CDN (free and no account allowed)

Hacker News1
Re
Redis-LLM – Redis module integrates LLM with Redis45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Redis-LLM – Redis module integrates LLM with Redis

Hacker News2
LLM Hotkey
LLM Hotkey39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TrustMRROther