Building AI-native products in public · 5 projects tracked
Finding meaningful relationships is difficult, especially for developers whose interests, passions, and personalities are often reflected more accurately in the projects they build than in traditional dating profiles.
Merge is a dating platform built specifically for developers, where GitHub becomes the foundation of compatibility. Instead of relying solely on photos and bios, Merge analyzes repositories, open-source contributions, coding habits, technology preferences, and project interests to create authentic compatibility between people.
The goal is to help developers form meaningful relationships based on shared passions, complementary skills, and genuine compatibility rather than superficial swipes.
Merge is powered by a collection of AI agents orchestrated through long-running workflows.
When a user joins the platform, agents collaborate to:
Temporal workflows orchestrate the entire pipeline, allowing GitHub analysis, AI inference, compatibility scoring, and match generation to execute reliably while providing transparency into every stage of the matching process.
Outbound sales teams spend significant time identifying prospects, researching companies, understanding pain points, and personalizing outreach before a conversation even happens.
ProspectFlow automates this entire workflow. Users define their ideal customer profile using filters such as industry, company size, geography, and other targeting criteria. The platform then discovers potential prospects, researches them, builds personalized outreach strategies, and initiates outbound calls through AI voice agents.
The goal is to help businesses scale highly personalized outbound sales without scaling headcount.
ProspectFlow is built around a collection of specialized agents orchestrated through long-running workflows.
Once a campaign is created, agents collaborate to:
Discover companies matching the target audience.
Research each prospect using public information.
Analyze company context, challenges, and opportunities.
Generate personalized sales angles and messaging.
Create AI voice agents tailored to the campaign.
Execute outbound calls and qualify leads.
Capture conversation outcomes and insights.
Temporal workflows coordinate the entire process, enabling reliable execution across thousands of prospects while maintaining visibility into every stage of the pipeline.
ICP-based lead discovery
Geographic and demographic targeting
Automated prospect research
Multi-agent workflow orchestration
Personalized sales messaging generation
AI-powered outbound calling
Lead qualification and scoring
Conversation summaries and insights
Campaign monitoring and analytics
Human review and intervention points
Next.js
TypeScript
FastAPI
Pydantic
PostgreSQL
Vapi
Twilio
Long-running agent workflows managed through Temporal
Multi-stage lead research and qualification pipeline
Personalized outreach generated per prospect rather than template-based campaigns
AI voice agents dynamically configured from campaign context
Reliable execution with retries, checkpoints, and workflow observability
End-to-end outbound pipeline from lead discovery to qualified conversation
Most document Q&A tools search through chunks of text and generate answers from retrieved passages. While effective, this approach often loses document structure and makes citation verification difficult.
AskDoc explores a different approach: understanding a document through its structure first.
The goal was to build a document assistant that can answer questions while maintaining a direct connection to the original pages, making every response easy to verify.
Instead of embedding every chunk of a document into a vector database, AskDoc first generates a structured Table of Contents representing the document's topics and page ranges.
When a user asks a question, the AI uses this high-level document map to identify the most relevant sections and pages before loading detailed context. This significantly reduces the amount of document content that needs to be processed while preserving the document's original structure.
Because retrieval happens at the page level, AskDoc can directly link answers back to the source pages and highlight the supporting text in the document viewer.
AI-generated Table of Contents for every document
Page-level retrieval instead of chunk-level search
Citation-backed answers
Direct navigation to source pages
PDF and DOCX support
Source passage highlighting
Multi-model support through Anthropic, OpenAI, and Google models
Context-efficient querying for large documents
Next.js 15
React 19
TypeScript
Tailwind CSS
react-pdf
FastAPI
Pydantic AI
Anthropic
OpenAI
pdfplumber
MarkItDown
PyMuPDF
LibreOffice
Vercel
Railway
Most background removal tools require users to upload images to third-party servers, pay per image, or create an account before they can use the product. For quick edits, these requirements add unnecessary friction and raise privacy concerns.
ClearCut was built to prove that modern AI image processing can run entirely inside the browser. Users can remove backgrounds from images instantly without uploads, accounts, subscriptions, or server-side processing.
ClearCut runs a complete background removal pipeline on the client using the BRIA RMBG-1.4 segmentation model. The model is downloaded once, cached locally, and reused across sessions.
When an image is uploaded, inference runs inside a dedicated Web Worker to keep the UI responsive. The application automatically detects WebGPU availability and falls back to WASM when necessary, ensuring compatibility across browsers and devices.
The generated segmentation mask is applied directly to the image using the Canvas API, producing a transparent PNG without sending any data outside the user's device.
Fully client-side background removal
No image uploads or server processing
Privacy-first architecture
WebGPU-accelerated inference
Automatic WASM fallback
Local model caching with IndexedDB
Responsive processing through Web Workers
Transparent PNG export
No accounts, subscriptions, or watermarks
Next.js 16
React 19
TypeScript
Tailwind CSS
Transformers.js
ONNX Runtime Web
BRIA RMBG-1.4
WebGPU
WebAssembly (WASM)
Web Workers
IndexedDB
Canvas API
Entire AI inference pipeline runs in-browser
44MB model cached locally after first visit
Zero-copy processing using Transferable Objects
Automatic backend selection between WebGPU and WASM
Static deployment with no backend infrastructure
Purpose
Hiring teams spend countless hours reviewing resumes, comparing candidates, and manually evaluating applications against job requirements. Traditional ATS platforms rely heavily on keyword matching, making it difficult to identify strong candidates with unconventional backgrounds or transferable experience.
Clect was built to automate candidate evaluation using AI. Instead of filtering resumes through rigid rules, it analyzes a candidate's profile holistically, scores them against role requirements, and provides recruiters with a structured breakdown of strengths, weaknesses, and overall fit.
The goal is to reduce screening time while helping teams make more consistent hiring decisions.
Recruiters create a role by defining job requirements, evaluation criteria, and weighting preferences. Candidates can apply through a generated application form and upload resumes in any format.
Once an application is received, Clect extracts and normalizes candidate data, evaluates it against the job requirements, and generates a detailed scorecard. Rather than producing a single score, the platform explains the reasoning behind every evaluation category, giving recruiters visibility into how recommendations are made.
The evaluation framework is configurable, allowing organizations to prioritize different attributes such as technical expertise, project quality, communication skills, leadership experience, or domain knowledge.
AI-powered resume evaluation
Custom hiring scorecards
Configurable evaluation criteria and weightages
Automated candidate ranking
Detailed candidate fit analysis
Resume parsing and normalization
Application form builder
Recruiter dashboard for candidate management
Explainable scoring with evaluation breakdowns
Candidate comparison across applications
Next.js
React
TypeScript
Tailwind CSS
Node.js
Express
MongoDB
Converts unstructured resumes into structured candidate profiles
Evaluates candidates against custom role-specific criteria
Generates explainable hiring recommendations instead of keyword-based filtering
Supports configurable scoring frameworks for different hiring workflows
Designed to reduce manual screening effort while maintaining evaluation consistency