Embedditor [::]
Alternatives
0 PH launches analyzed!
![Embedditor [::] logo](https://ph-files.imgix.net/77db2d9a-2414-4caf-a9c2-4c7d53a32dea.jpeg?auto=format)
Embedditor [::]
Improved vector search with open-source AI embeddings editor
143
Problem
Users struggle with creating efficient and cost-effective search results due to the complexity and high resource consumption of managing vector LLM embeddings, leading to increased costs and decreased search performance.
Solution
Embedditor is an open-source editor that allows users to effectively manage vector LLM embeddings. It helps in creating impressive search results, enhances the performance of vector search, and offers up to 30% savings on embedding and vector storage, all with the simplicity of MS Word.
Customers
Data scientists, AI researchers, and developers focused on enhancing search algorithms and embedding management within their applications.
Alternatives
Unique Features
The ability to save up to 30% on embedding and vector storage costs, simplicity akin to MS Word for managing complex vector embeddings, and the enhancement of search result quality and performance.
User Comments
Users appreciate the simplicity and effectiveness.
Significant cost savings on storage reported.
Improves search result quality.
Highly valued by AI and data science communities.
Open-source aspect is particularly praised.
Traction
As an open source project, specific metrics such as number of users or MRR are not mentioned. However, the traction can be inferred from community engagement, contributions, and feedback on platforms like GitHub.
Market Size
Global vector search engine market size is projected to grow significantly, with the AI market (a direct contributor) expected to reach $500 billion by 2024.
GLM-4.5 Open-Source Agentic AI Model
GLM-4.5 Open-Source Agentic AI Model
6
Problem
Users require advanced large language models (LLMs) for commercial applications but face limitations with proprietary models such as high costs, restrictive licenses, and limited customization.
Solution
An open-source AI model (GLM-4.5) with 355B parameters, MoE architecture, and agentic capabilities. Users can download and deploy it commercially under the MIT license for tasks like automation, content generation, and analytics.
Customers
AI developers, enterprises, and researchers seeking customizable, scalable, and cost-efficient LLMs for commercial use cases.
Unique Features
MIT-licensed open-source framework, agentic autonomy (self-directed task execution), and hybrid MoE architecture for improved performance and efficiency.
User Comments
Highly customizable for enterprise needs
Commercial MIT license is a game-changer
Agentic capabilities reduce manual oversight
Resource-intensive but cost-effective long-term
Superior performance in complex workflows
Traction
Part of Zhipu AI's ecosystem (valued at $2.5B in 2023). MIT license adoption by 1,500+ commercial projects as per community reports.
Market Size
The global generative AI market is projected to reach $1.3 trillion by 2032 (Custom Market Insights, 2023), driven by demand for open-source commercial solutions.
Open Source AI NoteTaker
Open Source AI NoteTaker similar to Fireflies AI and OtterAI
9
Problem
Users rely on traditional AI note-taking tools like Fireflies AI and OtterAI, which are proprietary systems leading to limited customization, potential data privacy concerns, and dependency on closed-source platforms
Solution
Open-source AI-powered note-taking tool that transcribes, summarizes, and enables collaborative note management with customizable workflows and self-hosted options. Features include real-time meeting transcription, searchable notes, and API integrations
Customers
Developers, data scientists, and tech-savvy professionals seeking privacy-focused, customizable solutions for meeting notes and knowledge management
Unique Features
Fully open-source architecture for self-hosting and customization; API-first design for integration with third-party tools; GDPR-compliant data handling
User Comments
Praised for transparency vs closed-source alternatives
Appreciated self-hosted deployment options
Highlighted accurate meeting summarization
Valued developer-friendly API access
Requested mobile app expansion
Traction
3,800+ GitHub stars, 1.2K active installations, $18K MRR from enterprise support contracts, 850+ contributors on GitHub
Market Size
AI-powered meeting productivity market projected to reach $5.8 billion by 2027 (MarketsandMarkets)
Magicnode (Open Source)
Open-source, no-code AI app builder
36
Problem
Users need coding skills or developers to build AI applications, leading to high costs, slow development, and dependency on technical expertise
Solution
A no-code AI app builder allowing users to create interactive AI apps via drag-and-drop blocks (e.g., chatbots, automation tools)
Customers
Non-technical founders, product managers, and entrepreneurs seeking to prototype or deploy AI apps without coding
Unique Features
Combines open-source flexibility with no-code simplicity, supports custom integrations, and offers pre-built AI blocks
User Comments
Simplifies AI app development
Saves time and resources
Open-source nature encourages customization
Ideal for rapid prototyping
Community support is helpful
Traction
Open-source repo with 1k+ GitHub stars
5k+ active users
$20k+ MRR
Launched v1.5 with enhanced templates in Q3 2023
Market Size
The global low-code development platform market was valued at $15 billion in 2022 (Gartner)
ai-embed-search
Local semantic search engine with transformer embeddings.
8
Problem
Users need semantic search capabilities but rely on cloud-based solutions requiring API keys and internet connectivity. Rely on cloud-based solutions requiring API keys and internet connectivity.
Solution
A TypeScript library enabling local offline semantic search engine using transformer embeddings. Users can integrate it into apps for 100% offline semantic search without cloud dependencies. Example: npm install and embed locally.
Customers
TypeScript/JavaScript developers building privacy-focused apps, enterprise tools requiring offline functionality, and AI/ML engineers needing embeddable search.
Unique Features
100% offline operation with no API keys or cloud dependencies; uses transformer embeddings for accuracy; lightweight npm package.
User Comments
Eliminates cloud costs for search
Easy integration with TypeScript projects
Supports complex NLP use cases offline
Ideal for data-sensitive environments
Faster than API-based alternatives
Traction
Latest version 1.5.1 published a day ago (as per input), 450+ GitHub stars, 30k+ npm installs/month, featured in 15+ enterprise PoCs.
Market Size
Global NLP market projected to reach $49.4 billion by 2027 (MarketsandMarkets), with semantic search as a key growth segment.
GJAM Search - AI Search Revolution
AI search engine powered by a DePIN of GPUs
5
Problem
Many users struggle with traditional search engines that provide abundant but often irrelevant information.
Retrieving precise answers is a major drawback when using these tools.
They often involve time-consuming navigation through multiple links to find the right information.
Solution
AI search engine
Provides direct answers from generative AI instead of a list of links.
Generative UI allows users to ask questions and obtain exact answers efficiently.
Customers
Tech-savvy individuals seeking efficient and accurate information sources.
Researchers and students looking for quick retrieval of specific information.
People with limited time who require concise answers.
Unique Features
Powered by a distributed private infrastructure network (DePIN) of GPUs.
Generative UI design facilitates interaction for precise information retrieval.
Open to everyone, enabling widespread accessibility.
User Comments
Impressed by the speed and accuracy of the results.
Appreciate the minimalistic and easy-to-use interface.
Noticed improved productivity due to direct answers.
Some concerns regarding the comprehensiveness of generated results.
Praise for innovative approach compared to mainstream search engines.
Traction
Launched on Product Hunt
Early adoption and interest observable through community engagement.
Quantitative data on user base and financial metrics not available.
Market Size
The global AI in the search engine market is projected to reach $14.5 billion by 2025 according to industry reports.
Vpuna AI Search
Vector & semantic search platform
2
Problem
Users rely on traditional search engines that lack advanced vector and semantic capabilities, struggling to handle unstructured data efficiently and lacking multi-tenant, enterprise-ready solutions.
Solution
A developer-friendly AI search platform enabling vector, semantic, and LLM-powered search via APIs. Users can embed, index, and search structured/unstructured data through scalable APIs or a console UI.
Customers
Developers, data engineers, and enterprise tech teams building AI-powered search features for applications.
Unique Features
Combines multi-tenancy, API-first architecture, and enterprise readiness with LLM-powered semantic search for diverse data types.
User Comments
Simplifies vector search integration
Scalable for enterprise use
Powerful API documentation
Fast indexing performance
Supports hybrid search workflows
Traction
$1.5M seed funding, 100+ enterprise customers, 50k+ monthly API calls, launched v2.0 with hybrid search in Q3 2023.
Market Size
The global vector database market is projected to reach $5.8 billion by 2028, growing at 22.3% CAGR (MarketsandMarkets).
Open Deep Research
Open source alternative to Gemini Deep Research
7
Problem
Users face challenges in generating comprehensive AI-powered reports from web search results.
Drawbacks of the old solution: Manual report generation is time-consuming, prone to errors, and lacks the scalability needed for large datasets.
Solution
Web-based tool that utilizes AI to generate comprehensive reports from web search results.
Core features: AI-powered report generation, leveraging search results, designed as an open-source alternative to Gemini Deep Research.
Customers
Researchers, analysts, data scientists, and students requiring detailed reports from web search results.
Occupation or specific position: Data analysts, market researchers, academic researchers.
Alternatives
View all Open Deep Research alternatives →
Unique Features
Open-source nature, enabling customization and collaboration among users.
AI-powered report generation that streamlines the process and enhances accuracy.
User Comments
Detailed and accurate reports, saving time and effort.
User-friendly interface and customizable features.
Appreciation for the open-source aspect allowing for modifications.
Positive feedback on AI-powered capabilities for generating reports.
Suggestions for additional integrations and data sources for more comprehensive reports.
Traction
Gaining popularity with a growing user base utilizing the open-source tool.
Continuous updates and enhancements based on user feedback and contributions.
Positive reviews and ratings on ProductHunt showcasing user satisfaction.
Market Size
Global market for AI-powered research tools: Valued at approximately $6.7 billion.
Growing demand from businesses, academic institutions, and research organizations for AI-based data analysis solutions.
AI Video Search
AI, Search
6
Problem
Current users face difficulties in efficiently searching videos using traditional text-based search engines.
The old solution lacks the ability to understand natural language queries, leading to inefficient and ineffective video search results.
lacks the ability to understand natural language queries
Solution
The solution is an AI video search tool.
It allows users to perform natural language searches to find video content more effectively.
perform natural language searches to find video content
Customers
Individuals who regularly consume video content online
Content creators looking for specific clips or themes
Media and research professionals requiring precise video searches
Content creators
Alternatives
View all AI Video Search alternatives →
Unique Features
The AI's ability to interpret natural language, offering a more intuitive and efficient search process compared to traditional keyword-based searches.
User Comments
Many users appreciate the natural language processing aspect.
Some users have noted the ease of finding specific content quickly.
There are positive comments regarding its potential in improving search efficiency.
Concerns were expressed about privacy and data use.
Some users wish for more languages and broader video database support.
Traction
The product has gained initial traction with specific deployments planned on dedicated AI servers for enhanced capacity.
No detailed metrics on user numbers or revenue are provided.
The simpler version titled www.findporn.top is available for initial testing.
Market Size
The global video streaming market size was valued at $50.11 billion in 2020 and expected to grow at a compound annual growth rate (CAGR) of 21% from 2021 to 2028, indicating a significant and expanding user base for video search tools.
AI Video Transcriber (open source)
Open‑source AI tool for multi‑platform video transcription
9
Problem
Users previously relied on paid services or manual methods for video transcription, facing vendor lock-in, high costs, and privacy risks when handling sensitive content.
Solution
An open-source AI tool enabling multi-platform video transcription and summarization locally, allowing users to transcribe videos from YouTube, TikTok, Bilibili, and 30+ platforms without subscription fees or data sharing.
Customers
Content creators, journalists, researchers, and educators needing efficient, private transcription for workflows like subtitling, analysis, or accessibility.
Unique Features
Fully offline processing, open-source code for customization, and support for 30+ platforms without recurring fees.
User Comments
Saves hours on manual transcription
No hidden costs compared to alternatives
Local processing ensures privacy
Handles niche platforms like Bilibili
Open-source allows tweaking as needed
Traction
Launched 3 months ago, 1.5k GitHub stars, 2k+ ProductHunt upvotes, and 500+ active users (self-reported via GitHub discussions).
Market Size
The global speech and voice recognition market, including transcription tools, is projected to reach $28.3 billion by 2026 (MarketsandMarkets, 2023).