2,041 karma · joined March 28, 2013
First, the whole systems of chips architecture that everyone is talking about will solve for increasing overall SRAM available to keep more model state on super fast memory and avoid going to slow memory.
Secondly, anyone serious about their data (enterprises) won't be okay with making API calls to Groq. Anyone serious about their data and have a lot scale (consumer internet) won't also be okay with making expensive API calls to Groq at scale.
Their cloud is attractive only if I can use their API for experimentation toy apps to continue developing in this direction while the rest of the major industry players systems of chip architecture catches up and solves for SRAM size bottleneck and manufacturing process bottleneck, and once that's solved, I get more powerful compute for cheaper $$ to deploy on-prem.
So, this cloud strategy is short-lived. I see another pivot on the horizon.
Passwords+OTP plus a whole bunch of heuristics based account lockdown mechanisms improve this quite a bit. But still OTP is vulnerable for sophisticated targeted attacks.
Passkeys improve on this while actually improving the usability as well.
Passkeys come in two flavors – independent-hardware-bound (example: yubikey) and platform-authenticator-bound (example: Passkeys by Apple).
The former is bound to a single hardware token but can be used on many devices (your phones, laptops etc) to login to your accounts.
The latter is primarily tied to your Apple ID account (where Apple is the platform provider, for example) and also to your Apple devices like iPhones and MacBooks where your Apple ID is already logged in and iCloud Keychain syncing is turned on. Apple automatically and securely syncs your passkeys to those devices.
Obviously hardware-bound is a bit more secure than platform-authenticator bound, because platform authenticator can have a SPOF within the platform (a compromise anywhere within that platform stack - client-side, server-side etc. large surface area) where such a compromise is also completely hidden from the user. In contrast, the hardware-bound has a much smaller surface area. Only possibility of compromise are the hardware, the client (browser or the app), and the particular app backend server-side. Like all software stack, it is very likely this stack also has bugs that can be exploited. But this stack is relatively lot simpler to audit and secure.
Instead, Apple should have disaggregated their fees. IMO, they can charge the following fees fairly:
1. Developer Ecosystem Tools – compilers, libraries, entitlements – can be one time or annual fees, not coupled with number of installs or revenue. (mandatory, entitlement fees could be per entitlement purchased by the app).
2. App Store registration fees – can be one time or annual fees, not coupled with number of installs or revenue. (optional – can choose to not use App Store)
2.1 App Store technical fees for app binary release certification – charged per app release/update.
2.2 App Store bandwidth fees – per app install delivered via App Store – charged per total bytes delivered.
2.3 App Store in-app purchase payment fees – percent of payment processed (currently 0.25%).
2.4 App Store ads fees – developers can choose to pay for Apple Ads for boosted discovery of their app within App Store.
They could provide free quotas for each of the above. That way free apps can still exist.
Apple can also provide iOS level capability via System Preferences or managed device profiles to lock App stores (or alternate app stores).
IMHO, this is likely the path that would be well-received and will be followed by alternate app stores as well.
At minimum, I want a why-5 analysis. Let me start with the first question:
1. Why did ChatGPT generate gibberish?
A: the model chose slightly wrong numbers.
2. Why did the model choose slightly wrong numbers?
A: ??
In big data AI space, this is exactly what's happening with the top 20th to 100th companies in the world right now.
And they also had bio-neural circuits. And photonic chips.
So, hardware was already way ahead of software.
All this goes to show that in real world, the actual science (and fiction) around material sciences was already quite advanced compared to software.
By one tradition that's thousands of years old – Yoga+Pranayama+Meditation (linking physical/mental_movement+breadth) and fasting + eating moderately (eat only if you are hungry and eat enough to quench hunger, and eat 'satvik' food) and cultivating positive thought patterns (practicing gratitude, humility, curiosity) is also known to solve for sleep and anxiety.
Coming to coffee, all coffee is not made the same way and everyone don't drink the same quantity per day either. And it doesn't have the same impact on everyone's body. So, it is best to avoid jumping to broad generalized conclusions based on a few anecdotal stories.
But, it is good for people to share their experiences and for others to consider them.
Your risk tolerance: Be honest about how much uncertainty you can handle, both financially and emotionally.
Support system: Do you have people who can offer support (even practical help) on either path?
Before you decide to go full-time: Analyze your current startup's potential, your financial runway, and have a fallback plan for the worst-case scenario.
Before you decide to go part-time: Be realistic about limitations as a part-time entrepreneur.
This was clearly true for Google with its mission. There are so many other successful examples.
Looks like author is burned by bad startups with inauthentic mission statements.
Well, that's not true due to two key design points:
- Size of the read or write quorum can be chosen on a per transaction basis depending on what level of availability and consistency you want. The design goal is to lower the likelyhood of the minimum number of quorum members you need for your transaction will not be available.
- Given the transaction processing nodes are ephemeral and independent from storage nodes, if a transaction processing node which is designated as a member of a quorum dies, it can be quickly and instantly replaced with a new one and all in-flight operations will be retried by the smart clients.
Layer-3 IP Clos networks are pretty much a standard within a datacenter, making multiple redundant paths available. Across datacenters, optical circuit switched networks with lots of redundant paths are very common.
Modern network fabrics also have end-to-end mechanisms for bandwidth engineering based on per message QoS/priority set by applications as well as very accurate time-keeping.
Observability mechanisms and operational practices have also tremendously improved. A single node becoming unreachable from all other nodes is quite possible. But a single node being reachable to some subset of other nodes in the cluster and not others is nearly impossible. Only way a network partition can happen is due to misconfiguration. Purely layer-3 IP clos networks with BGP advertised multiple ECMP routes are a lot simpler than layer-2 networks of the past. With modern switch operating systems, modern devops practices, Infrastructure-as-Code etc. misconfiguration is nearly impossible.
At a higher layer, disaggregated storage and compute architectures are standard for any distributed database system. In this model, storage fabric is quite simple and much more distributed and redundant. Today, it is a lot easier to guarantee a consistent and highly available transactional write to a quorum of simple storage nodes spread across multiple datacenters in a region. On top of that, building higher-level isolation guaranteeing database transactions in a tier of ephemeral compute nodes is relatively much simpler.
This kind of system is extremely resilient to failures at all levels – node failures are detected and lost processing capacity recovered in seconds; failed transactions are retried in milliseconds; loss of redundancy due to failed storage nodes are rebuilt in seconds; loss of network capacity due to failed circuits or intermediate switches are routed around in microseconds.
So, in today's context, we build databases that provide flexible transaction isolation guarantees that is selected appropriately on a per transaction basis by the application. Application also specifies the latency budget it has available per transaction. With all these improvements, thankfully, application developers don't have worry about CAP theorem like they had to in the heydays of NoSQL databases in 2012.
In companies that had to do aggressive cost cutting, the recruiters were the first to be impacted. These roles have had high churn and sufficient training and experience quality monitoring may have suffered during this period. That could be the reason why the certain steps in the process involving recruiters (like communicating back to the rejected candidates) may have suffered w.r.t quality of interactions.