Surprised not to see any mention of the global memory supply shock. Would love to learn more about how that economic is shifting software priorities toward memory allocation for the first time in my (relatively young) career
I have felt somewhat frustrated with what I perceive as a broad tendency to malign "prompt engineering" as an antiquated approach for whatever new the industry technique is with regards to building a request body for a model API. Whether that's RAG years ago, nuance in a model request's schema beyond simple text (tool calls, structured outputs, etc), or concepts of agentic knowledge and memory more recently.
While models were less powerful a couple of years ago, there was nothing stopping you at that time from taking a highly dynamic approach to what you asked of them as a "prompt engineer"; you were just more vulnerable to indeterminism in the contract with the models at each step.
Context windows have grown larger; you can fit more in now, push out the need for fine-tuning, and get more ambitious with what you dump in to help guide the LLM. But I'm not immediately sure what skill requirements fundamentally change here. You just have more resources at your disposal, and can care less about counting tokens.
I think the point is more around the collective distraction of dressing to a formal (and for kids, unfamiliar) standard, rather than enjoying time with the people around you at a party.
I’m skeptical that established automakers even felt threatened. For one thing, manufacturers seemed to overestimate present demand for high-end, expensive EVs (no way Apple was going to build a lower-tier “economy” car). Also, companies just entering the automotive industry (Tesla, Rivian etc.) seem to face loads of challenges that long-lived ICE manufacturers have solved a long time ago.
Could someone explain exactly what it means to be "completely sequence" the human genome when all humans have distinct genetic makeup (ie, different sequences of nucleobases in their DNA/RNA)?
This sort of approach is only really viable in certain climates and with certain starting conditions, ideally away from extremes in temperature and drought where seedlings can compete with what's already established. I live in the (climate change pending) sun-baked former prairie of East Denver and a laissez faire approach to the vegetation in my yard is a virtual guarantee that invasives (kochia, bindweed, curly dock) will completely dominate.
I've found you don't have to be totally organized about it uprooting the worst of the noxious weeds, scattershot planting of desirables, and watering unpredictably around a busy life schedule, but this still takes a lot of time and energy. At that point, if you value your time and energy, you find yourself needing to make plans and genuinely care about the process.
Not sure I agree, on one hand you have actual, physical potential energy, on the other hand you have numbers on a computer that could become worthless depending on unpredictable economic factors.
The punctuation of the sentence is a little odd, but I take it to mean $1732 + $1200 = $2932.
Less $1100 of current rent, that’s $1832 per month to cover all other costs for two people. Few would elect to be traveling 6 days a week obtaining food assistance if there were easy expenditures to cut.
I don't understand this argument. If 8k employees were laying technical groundwork that can be transformed into product features more efficiently than greenfield work, how were they "doing nothing"? Twitter is a mature product used by millions, not every engineer is going to be shipping user-facing changes regularly.
Especially sad given that, in my opinion, this project is doomed to fail anyway— a 21st century Tower of Babel. The plans for a year-round ski resort seem especially ridiculous given the relatively mild climate in the Sarawat mountains.
> We conducted 5 studies involving 198 samples including 633,263 researchers, entertainers, politicians, and amateur and professional athletes.
It seems tough to take anything meaningful from that study, based on the job types it examined, and the tiny percentage of the workforce those jobs represent. Working in tech our job responsibilities are often quite fuzzy and performance is difficult to quantify.