Git Deal Flow
Harmonic.ai
DealRoom
Forager
Crunchbase
Bolt to GitHub (Pro)
Equity Flow
Flowbo
Scikit-learn
Pandas
NumPy
OpenCV
Dataiku
Exploratory
WEKA
htm.java
VC Deal Flow Signal monitors GitHub engineering activity across thousands of startups and surfaces the ones showing unusual acceleration โ weeks before they hit your inbox.
We track commit velocity, contributor growth, and repository expansion to rank startups by engineering momentum. This is a leading indicator for seed and Series A investors.
What you get: - Weekly ranked reports of breakout startups across 20 sectors - Real GitHub acceleration data (not vanity metrics) - Filter by sector, stage, and geography - Live dashboard with 100+ startups tracked
Who it's for: Angel investors, VCs, and fund analysts looking for deal flow signals that aren't in everyone else's pipeline.
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Git Deal Flow's answer
We use GitHub engineering activity as a leading indicator for investors. While competitors like Harmonic, Dealroom, and Crunchbase rely on funding announcements, job postings, and web traffic, we track commit velocity, contributor growth, and repository expansion - signals that appear weeks before a startup shows up on anyone's radar. The data is public but nobody else packages it for investors.
Git Deal Flow's answer
Most deal flow tools show you what already happened - a round closed, a hire was made. We show you what's happening right now in the codebase. Engineering acceleration has historically preceded fundraise announcements by 3-6 weeks. That's the difference between setting terms and chasing a deal everyone already knows about.
Git Deal Flow's answer
Angel investors, seed and Series A VCs, fund analysts, and scout networks looking for data-driven deal sourcing. Anyone who wants to find breakout startups before consensus forms around them.
Git Deal Flow's answer
I watched a company's commit graph spike and three weeks later they announced a Series A. The signal was right there - public, free, updating in real time. Nobody was reading it. Quant funds have known for years that public data read correctly is the best leading indicator. The problem was that nobody built the lens for investors. So I did.
Git Deal Flow's answer
GitHub API for data collection, Next.js for the dashboard, Vercel for hosting, and custom algorithms for detecting acceleration patterns across thousands of startup GitHub organizations.
Git Deal Flow's answer
Based on our record, Scikit-learn seems to be a lot more popular than Git Deal Flow. While we know about 40 links to Scikit-learn, we've tracked only 2 mentions of Git Deal Flow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Quick context: I run GitDealFlow, an MCP server + dataset that tracks GitHub commit-velocity signals across ~100 venture-backed startups. Six free read-only tools, ~700 npm downloads in the first three weeks, listed on Glama and the official MCP registry. - Source: dev.to / 3 months ago
VC Deal Flow Signal monitors GitHub engineering activity across startup organizations and surfaces the ones showing unusual acceleration. The hypothesis: engineering acceleration (measured as the rate of change in commit velocity) is a leading indicator for fundraise announcements, usually by 6 to 12 weeks. - Source: dev.to / 3 months ago
Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
In practice, youโll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
Harmonic.ai - Harmonic's data engine keeps 20M+ companies & 150M+ professional profiles fresh, so you can always be in the loop when a company just raised a round, just hired a CTO, or just crossed the 1M follower mark on Twitter.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
DealRoom - M&A Lifecycle Management Software
NumPy - NumPy is the fundamental package for scientific computing with Python
Forager - Fashion discounts gathered in your size
OpenCV - OpenCV is the world's biggest computer vision library