First of all, I would like to thank Harshad Fad for building the community and providing me with the opportunity to volunteer for the MVP efforts of a rather cool concept, particularly given that our social lives have taken a hit during the pandemic.
The idea is simple — “ a space for meaningful conversations that connects people across the globe through video-conference to share stories that promote diversity and inclusion”. For a much better pitch, do check out the ProductHunt page.
One can schedule a session with a person to learn about their experiences from a catalogue. In addition…
Product companies: Build once and sell to many
Service companies: Build a new one each time depending on customer
I have seen quite a few posts jumping to conclusions without proper justifications ([1 — I can think of a counterexample for each of the points in this one], , [3 — although slightly better]) about work cultures and candidates based in either of these settings. This only speaks more about them than the subjects of discussion.
Regarding judging candidates from either workplaces, interviews are great opportunities to get to know if a candidate is a good fit for your role, irrespective of where they have worked previously. Understanding the kind of work rather than what firm one has worked at would go a much longer way in identifying the right candidate.
Anybody in the software business who tells people they enjoy coding interviews is lying. It is an obnoxious gathering in a cramped space that usually lasts an hour and can end in crying. This post aims to provide suggestions to make this a more efficient and endurable process.
There are tons of resources out there on how to be the picture-perfect interview candidate. But there is limited guidance on how to be a skilled interviewer.
One thing to keep in mind is that it’s really easy to be bad at interviewing because you have a lot more degrees of freedom…
As engineers, our thoughts are constantly interrupted by this question and although getting to choose our next projects is itself an immense privilege, sometimes it comes with crippling decision paralysis.
It is important to note that what we choose to work on is fundamentally an investment, our next stock of time.
The first thing to do is — follow the market.
It is a great place to start as you will need money to sustain an endeavour, and it is simpler while riding a wave. You could always take the complicated route and at the extreme, build things for yourself…
Delivery apps were and are quite the rage, with many startups attaining escape velocity by following the “Uber for X” script across sectors, be it groceries, medicines and whatnot.
Quite recently, I got the opportunity to try out a fun exercise of building the back-end infrastructure from scratch for a delivery aggregator platform.
Before that, it helps to identify the main features of any delivery app and what distinguishes it from other systems.
Theory and practice are a 2-way street on the map of human endeavor. Many discoveries and inventions were results of accidents and tinkering, rather than predictions and designs from first principles. The steam engine preceded the laws of thermodynamics, air resistance and sails were known since ancient times, but the formal description of aerodynamics emerged much later.
There are a few in the other direction as well. The Higgs Boson was predicted way before it was experimentally observed. General Relativity remained a theory for a long time before Eddington’s expedition. …
Any sufficiently advanced technology is indistinguishable from magic
— Arthur C. Clarke
The rate at which fast-paced technological fields such as machine learning and extended reality are growing is truly astounding and even leaving science fiction in the dust. Many big-budget films have stretched our limits of imagination and utilized the state-of-the-art during production. However, on looking back, I have noticed examples where today’s tech has progressed miles ahead of the fiction counterparts from previous decades.
All models are wrong, but some are useful
— George Box
Predictions from various epidemic models are driving crucial global policies. But their estimates are all over the place — So which one do we trust?
For this, we need to understand the various sources that cause the difference or error.
To predict, we create an approximate version of the problem — the model, and with it comes the modeling error. For example, we often use a constant speed to estimate travel time.
Blogging about data, systems and ML