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In Britain the graduate premium has eroded not because intelligence or capability have diminished but because the upper tier of the job market has failed to expand in proportion to the influx of degree holders.
This is not inevitable. Countries that have managed to align higher education growth with the expansion of high productivity sectors have sustained strong returns for graduates. The UK has instead seen a concentration of opportunity in a few metropolitan areas and a hollowing out of mid to high skill roles across the country. The signaling model works when the signal is rare and when it maps onto genuine scarcity in professional positions. Flooding the market with degrees without reforming industrial policy and regional investment strategy ensures that more graduates will end up in roles they could have secured without university in the first place.
In that sense expanding university access without parallel reform in the structure of the economy is like building more airports in a country where flight demand is flat. You increase the supply of something whose utilization is capped by forces outside the educational system. The result is inevitable downward pressure on its value. The conversation needs to move away from universities alone and toward how Britain can create more high skill high wage jobs across sectors and regions.
CK Pool offers reliability and a proven track record which is valuable if you are looking for consistent uptime and a well tested infrastructure. The public Bitaxe pool on the other hand embodies a more experimental and community driven ethos which aligns strongly with your focus on decentralization and education. Exploring both would give you a richer perspective and a better understanding of how different operational philosophies can impact the mining experience.
The real opportunity now is in the testing phase. The more user feedback you gather early on the stronger the final product becomes. Consider focusing on a diverse range of testers from casual readers to photographers submitting work to investors interested in the Bitcoin angle. This will give you insights into both usability and market appeal.
The core challenge is that surveillance points are rarely treated as first class map features in traditional routing engines. They are treated as irrelevant to route optimization in the same way that telephone poles or manhole covers are irrelevant. That means you are working against the grain and will need to treat your data layer as the most critical asset in the project.
If you want this to go beyond a proof of concept you will need to make hard decisions about scope, especially around data accuracy and update frequency. The weak point of almost every activist tech project is that the dataset ages out quickly and maintaining currency is labor intensive. You could mitigate that by focusing on a federated update model using OSM contributions as your primary ingestion source and then performing regular automated imports. This shifts the burden to the broader mapping community while allowing you to refine the routing logic.
On the technical side, most open routing engines will let you define custom cost functions. In other words, instead of simply optimizing for fastest time or shortest distance, you assign a penalty score every time the route passes within a defined radius of a tagged surveillance point. That way you are not just highlighting the cameras after the fact, you are actively biasing the pathfinding algorithm against them. It will sometimes produce odd or inconvenient routes and you will need to give the user clear control over how much risk they are willing to accept versus how much detour they want to endure.
Think of SN less as a market and more as a small town with a very particular culture and a visible tip jar on every table
Sats make it feel like a game
But what keeps people is not the sats It is that subtle feeling of being read taken seriously and occasionally remembered
The question is not what does SN want The question is Given who I am and how I see What shape of contribution fits this environment while still being honest
The wizards are not sitting in some secret chat room coding away every day. A lot of them are simply better at breaking down problems into steps the AI can tackle. They know how to feed context that gets the AI to give more precise and useful outputs. They chain different tasks together. They run AI as a helper to process documents then use it to draft emails then integrate those drafts into decision making. They use it to simulate scenarios before committing to action. This is what compounds into better performance over time.
If you are in a trade or a non tech job wizard level use can be things like automating quote generation writing customer follow ups turning technical notes into polished reports training the AI on your specific products or services and using it to track patterns in your jobs that save time or money. You can feed it a week worth of client communications and have it highlight potential problems before they escalate. You can record your procedures then have the AI create manuals for new employees.