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Wanna Oneㅣ타이틀곡 선정 마감 D-DAY 오늘 밤 11시 59분 타이틀곡 선정이 마감됩니다! TITLE SONG #1# 활활(Burn It Up) TITLE SONG #2# 에너제틱(Energetic) ▶️

Fast Poker is not just permissionless poker. It is a live operator economy. Dealer License holders run the settlement layer that keeps games moving, rake routing, rewards updating, and protocol revenue flowing. Play to Mint. Burn to Earn. Deal to Earn.

An Econ PhD student at the 20th ranked program who is working on stuff they are passionate about will have a better job market than one at MIT who's been doing nothing but phd-app-maxxing since undergrad. People get confused by this because they don't observe *how* successful people came about their insane knowledge bases. It wasn't by relentlessly grinding away at stuff because they had to. They look at Scott Kominers and say "if i grind and learn as much math as he did, i will be successful." You can't! *You* can't learn as much math as Kominers because he gets energized by configuration results for type ii lattices. You will burn out if you try to do it this way. You cannot, through grind alone, learn more about the economics of cities than Glaeser, or about how to maximize a value function than Acemoglu. Research careers are long. Most people give up and stop working on research (graph is share of elite PhD graduates with at least one publication in year X after graduation). If you're starting a PhD, you're presumably doing it to have a successful 40-year research career. The number one factor in whether that happens is not which program you get into, it's whether you find a research angle that energizes you enough to push through the endless barriers an academic career throws in your path. This is why a lot of the received wisdom around PhD applications is wrong. If you're 100% consumed by the predoc rat race already, it's going to be a long, hard road ahead. Obv you still have to do admissions, you should study a lot for the GRE, sigh it seems like taking real analysis is probably worth it. But spending time on the things that energize you about economics is a no-brainer, whether it's policy, or blogging, or whatever, you gotta do the things that light your fire and make you want to be on this road.

Claude Cowork just got 10x more powerful! Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer. The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude calls each one separately. That's 5-10 tool calls per query. Each source returns results with different quality and ranking. Claude over-fetches to compensate for weak search. Then it filters and synthesizes everything with LLM reasoning. Often needs retry loops when results miss. Burns 50-80k tokens per query. The centralized approach: All data from every source gets indexed into one unified layer. Knowledge graph connects entities across sources. Claude makes one MCP call. Gets back the top ranked results. No over-fetching, minimal filtering needed. Uses 42-44k tokens consistently. The results: Centralized indexing preferred 2.5x more often. Federated consumed 30% more tokens on average. When federated finally got correct answers, it burned 83k tokens vs 43k for centralized. The gap widened as tasks got more complex. Simple tasks: centralized won 66% of the time. Complex tasks: 73%. Why centralized wins: Over-fetching doesn't just cost tokens. It dilutes the context window with noise and contradictory information. Models have finite attention. Cramming 50-100 items hoping the right ones are in there doesn't work as well as getting the right 5-10 upfront. Federated search also loses cross-application signals. Things like document relationships, who authored what, and how content is used across the enterprise. These signals improve ranking but they only exist when data is indexed together in one layer. The compounding problem: In multi-step tasks, each missed or incorrect retrieval compounds. By the time you reach the final output, you're working with flawed data. More tool calls and reasoning loops don't fix this. They just burn more tokens trying to recover. You can't brute-force around bad search. More tool calls, more data fetching, more reasoning loops don't fix poor context quality. They just burn more tokens. Why this matters: Token costs are surging. Reasoning models cost more. Companies are burning through AI budgets faster. Federated search compounds the problem. Better search architecture beats more compute. I've shared the link in the replies!