There’s a gap in the impact arena, limiting early stage startups seeking Venture Capital. This article is to help Founders and Investors align with positive impacts.
What is Venture Capital’s role in positively impacting the world? Can VC be used as a tool to create a Sustainable future, while not sacrificing outstanding returns that venture capital demands? Can we Break Sh** Responsibly?
I’ve been asking myself these questions for more than two years as an active Angel Investor, in over 25 US startups [see my personal portfolio here], each aligned with multiple Sustainable Development Goals (SDGs) factors, and it’s working!
We just passed over 3.5k members and listeners in our posi2ive club recently on Clubhouse (a sidecar to our podcast) on Venture Scale Positive Impacts. And decided to start two new clubs aligned also with the SDGs, following research on the VC Impact startup market landscape in the US, I did in 2020.
The purpose of this article, is to document and highlight what some of the underlying verticals are within VC Impact [*Social and Sustainable Tech], and to help educate both myself and our listeners / readers on this…
Finding ways to connect Investors to Entrepreneurs, is a challenge for both parties. It can be even more difficult to find opportunities based on mission alignments. The posi2ive podcast is one project I’ve been working on for the past year, to help connect the VC Impact Community.
Many Angels / VCs rely on private networks to find deals aligned with their thesis. Many use VC Scouts to find early deals before the crowd becomes aware. Free websites like AngelList list new companies prior to F&F or Angel Rounds, but many Founders simply don’t list their startups for Angels to find…
What a whirlwind this year has been for us all. Finding sanity in this experience is unique to each person. Whatever anyone can do to make sense of it all, is valid. For me it took the form of a goal to complete 52 books by the end of the year, and to track the scores.
The books that interested me personally are in the genre of true stories in leadership / startups / VC + impact / positive psychology / history / politics / adventure. For a complete list (each rated up to 10), please see the link here.
Fearless Web Scraping with Python in DataLab Notebooks
This post empowers the Pythonista, with a complete framework to explore the world of data on the internet — all behind randomized proxy servers in a fast parallelized sequence, while protecting your company’s immutable IP from curious eyes, and other potential trolls. With this new outlet, the reader is requested to take all measures, and to not abuse the privilege of their acquired ghost-ninja skills, to not tax any such services inappropriately, nor unethically. The user takes all responsibility for implementing (of course) and all risks associated with running the attached code.
With Private Bucket, IAM Permissions, and Safe Firewall Configs
The road to expertise in Cloud Computing is fraught with harrowingly extended afternoons, and countless under completed blog posts. When you want to quickly spin up a virtual machine, and start working in Python from a notebook in the browser, you’re often met with these frustrations. Why can’t I write to my bucket? What is preventing the notebook from working in my browser? Why can’t I see any files when I SSH into the machine? And maybe even “What is Docker?”
With over 2500+ active blockchain projects around the globe, each with it’s own unique statistical characteristic, we rarely see a top level analysis of the overall crypto market, because cleaning and collecting the time series is too time consuming.
And on the retail side, we don’t have a clear set of functions to collect, clean, and explore the critical data needed to customize portfolios from the blockchain ecosystem. The following blog post will start as a strong foundation for more in-depth quantitative approaches related to things like volatility, clustering, forecasting, and log return based portfolios using data science and quant…
Graphs (think Vertices and Edges here), where the relationship from metadata provided by social networks, for example, help uncover hidden relationships among n-grams / likes / demographic data / tags / links / names. Graph databases and applications which support them, help explore this metadata in a faster-to-compute distributed environment.
Many open source packages and graph platforms exist, with support from both Python and R (even Spark). …
A less known feature of Github are “gists” (code chunks, and micro-repos used for quick reuse ⚡️). These bite-sized bits are searchable in the open community or accessible in secret. They can be stored indefinitely for free. Beware, the secret gists can be scraped by 3rd parties and publicly accessible by their discovered links from bots. This pseudo-private feature makes gists great for sharing one-on-one, but they lack the deep privacy which the commercial repositories (“repos”) provide.
After completing Google’s excellent Data Engineering Certified Specialization on Coursera recently (*which I highly recommend), I thought it would be both practical and useful to get my own Google Compute Engine (“GCE” or #GCloud) instance working — with GPUs in Tensorflow, with R and Python on one a single GCE virtual machine. Like many other Data Scientists, I use both languages frequently and love working with Google Compute Platform (“GCP”). Hope you enjoy the simplicity of my approach, and get some mileage out of this walk-through!
Emerging Human | Host of @posi2ive