Au
Autodev-codebase – Local MCP service for code embedding indexing
Hacker NewsAutodev-codebase – Local MCP service for code embedding indexing
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, code · Missing: mac, agents, macos83%83% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto59%59% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% 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, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting15%15% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Quaere33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Embedding as a Service
Product Hunt+7Em
Embedding visualizations for bloggers and journalists – VizFiddle52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Embedding visualizations for bloggers and journalists – VizFiddle
Hacker News1Vi
Visualizing and Comparing Embedding Vectors as Heatmaps56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Visualizing and Comparing Embedding Vectors as Heatmaps
Hacker News3Im
Implementing Embedding Gemma in PyTorch28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Implementing Embedding Gemma in PyTorch
Hacker News3Em
EmbedFlow –> Upgrade embedding models without re-embedding your corpus56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
EmbedFlow –> Upgrade embedding models without re-embedding your corpus
Hacker News6An
An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More
Hacker News7Aq
Aquila a small code embedding tool50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Aquila a small code embedding tool
Hacker News3Vi
Visualizing a Codebase48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Visualizing a Codebase
Hacker News283I
I made a dataset for finetuning embedding models61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I made a dataset for finetuning embedding models
Hacker News1Bi
Binarized Attributed Network Embedding (ICDM 2018)50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Binarized Attributed Network Embedding (ICDM 2018)
Hacker News3