This is definitely very useful, and something i've been thinking of to build myself. A personal CRM. Issue is when I see something like this - where it will take me around half hour to figure out - while i can vibe code something in an hour which will do much more and personalize it for me, I hold back on trying it out.
This is a 5 day - only code , no maths -introduction to data science covering all critical topics and suitable for beginners. As a practicing data scientist, this covers 80% of my toolkit. All datasets are created synthetically. I created this course as almost all the existing courses cover too much material, too much theory and maths and don't emphasize the practical aspect of using existing algorithms to achieve impactful output. I hope this helps beginners and I wish a course like this existed when I started years back to not dissuade me from learning this approachable field
Very impressed. I am not a scientist but am building a product for intent-based discounting in Shopify. Typically Google scholar gives me very generic results using LSTM etc however this search gave me some interesting results with focus on real world implementation. The clarifying questions are also quite impressive as it gives the impression that it is understanding the query really well. Good stuff. I think it might be useful for end-users and not just company/research folks as well
India's new data privacy law is quite simple to understand. Allows cross border data sharing with few countries. Stiff penalties for personal data breach or failure to report them
As an entrepreneur, I empathise with fellow startups losing customers due to mandatory check for recurring payments. However as a customer, this has been a godsend as I had almost 15 subscriptions totalling $300 monthly, quite a large amount in India.
The constant reminder of how many of those subscriptions are useless has allowed me to cut my expenses. Case in point, was subscribed to linkedin premium for last 2 years, while I make use of it only once in 3-4 months. Now I simply dont recharge my credit card and only do so once its required.
Not sure if its the ideal solution but definitely am thankful to it!
Wondering how can one build something massively successful which has GPT as the core engine. Will be ridiculously easy for anyone else to copy, if its trained on similar data as the use case
Surprised how this is in the front page. I had a similar feeling of bewilderment that I had when reading Das capital- fancy in theory but after several decades of wasting your/others life, discover it’s unimplementable
Really sad , I remember when I was setting up Myntra (Indian zappos) customer experience department, Tony and his book were a huge influence and everything that we did was measured up with the zappos way of customer experience. We were one of the few large companies having in house call centre with a five day week and training period of one month to all customer cate executives, plus a mandatory call listening session once a quarter for all senior leadership.
There are still some question marks on the ROI of trying to ensure customer delight especially via call center since discounts are such a huge factor in buying shoes and clothes online that even an NPS of 60 will not help if you don’t price lower especially for the value conscious Indian buyer however he definitely brought a new dimension of thinking for a lot of customer care folks
Honestly haven't seen a clear answer till now. We had tried multiple ways to link high satisfaction scores with purchase behavior - correlation/regression of NPS with frequency/value of purchase.
At least in myntra case, the detractors turned out to be much more valuable than the entire universe, we put it down to customers who love us enough to hate us!
Have always marvelled at Amazon’s ability to do local Innovation at scale. Here is a behemoth for whom India is 10 percent of global revenue, yet launch a service which has nothing to do with their core e-commerce and cloud functions. Is it to get users, hiring tool or make more money. Will be interesting to learn the culture which facilities such decision making - any other large company would have shot down such a crazy idea long ago
They have a very flat / horizontal management structure for such a large company. Usually for an IC it goes:
You <- Manager/PMs <- VP <- SVP <- Bezos or C-level.
Usually a manager runs a 2-10 person team who they advocate for, and a few PMs interact with the teams that work on the projects they oversee. The PMs are sort of like sales in a traditional company, customer-facing roles who prioritize and occasionally promise features.
There are usually enough VPs that you can meet with one if you have an idea that your peers like, and the support of a VP seems to be enough to let a small team work on a tangent for a year or two.
It sounds a lot like how Google develops new products, because it's surprisingly easy to turn a working proof-of-concept into a public-facing product or service. So why don't they suffer from the product-killing disease that plagues Google?
I don't know, but I have a couple ideas:
* Amazon throws people and money at things that work. Products like Lambda and Alexa used to be small upstarts with modest expectations, but when they sold well, the company invested heavily in them.
* Amazon promotes people based on peer feedback and manager recommendations. It's an easy system to game, and there's a bit of graft as teams defend likeable underperformers since they stopped leaning so heavily on "rank-and-yank" performance reviews. But it also means that you can get promoted for maintenance, like "X prepared our retail service for holiday traffic, and we didn't lose millions in sales when it didn't crash."
I'm not sure how applicable their management structure is to most organizations, though. They can tolerate massive loss-leaders because they have a couple of money printers, and they re-invest most of that money in the company rather than sitting on it or paying it out in dividends. People tend to like working there if they can cope with a bit of stress, and I saw as many people leave the company as I saw take internal transfers during my time there, so "tribal knowledge" also fades more slowly despite the churn.
My experience at Amazon paints a very different picture.
There's layers and layers of management. There were 12 people between me and Bezos.
Unlike Google, peer feedback is a lot less important at Amazon. Promotions and PIPs are solely based on your manager. If you have a great relationship with your manager, you're fine.
Google Colab has a very very strong Engineering team. Part of Google Research they definetly changed the game when they offer a free product which 1) increased collaboration across ML research 2) offer everybody access to GPUs first K80 and now T4 which is great among researchers and students. Now many other notebooks products now want to be the Enterprise colab version
Very good question. They target different audience,
Google has many notebooks solutions: colab (free) colab enterprise (monthly) kaggle, ai platform notebooks and datalab. Colab is targeted to students/researchers which are just experimenting, as there is no guarantee that kernel will run more than 24hrs. The paid version removes this restriction. AI notebooks is targeted for enterprise data scientists (Jupyterlab) which require patches/VPC-SC/IAM integration, security, etc. Datalab is the very first version of the notebooks and that's going away. Kaggle is mainly for competitions. In the end Google will have only 2 Notebooks colab and AIP notebooks
I dunno, I believe Microsoft's Azure Notebooks predates Colab and perhaps gave Google the kick to make it public, and Colab was a spinout of an internal tool with a rather rocky relationship with open source that has still been a pain point I run into using it for teaching ("what is the difference between Colab and Jupyter?" is a question without an especially clear answer). Also they stole the name from GE's old internal social network.
That latter one, at least, was not entirely serious criticism.
I'm not sure that even Colab is an unmitigated win for the company, it does definitely have "that AWS feeling" of an internal tool that was made a product without really retooling it for that purpose thoroughly. This has kind of become the norm in cloud platforms, though.
