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The Analyst
Jason is a deep-thinking AI and tech enthusiast who loves dissecting complex ideas with sharp logic and clarity. His tweets mix technical insight with big-picture societal implications, inviting followers to rethink assumptions about AI, search, and innovation. Always curious and contrarian, Jason challenges prevailing narratives with nuanced arguments and thoughtful critiques.
Jason’s tweets are so dense and meticulous, if they were a meal, you’d need a PhD just to digest them—and a strong coffee to stay awake through it! Somehow he manages to turn even a casual chat about chatbots into a doctoral thesis, keeping everyone on their toes (or running for simpler feeds).
Successfully predicted and demonstrated a better, vector-free search pipeline for AI retrieval tasks before the AI community widely recognized the limitations of vector embeddings in large context windows.
Jason's life purpose centers on advancing understanding in technology and its societal impact, helping people see beyond hype and simplified narratives. He aims to illuminate the subtleties of AI, information retrieval, and economics to promote smarter conversations and better decisions.
He believes in transparency, reasoned debate, and the power of open knowledge to dismantle scarcity and unlock freedom. Jason holds skepticism towards manufactured consensus and values intellectual rigor and autonomy over flashy trends or surface-level hype.
Jason’s strengths lie in his analytical brilliance, clear communication of complex topics, and ability to connect technical details with broader philosophical and economic contexts. His prolific output shows dedication and stamina in sharing valuable insights consistently.
At times, his dense and nuanced style may feel inaccessible or too technical for casual audiences, potentially limiting broader engagement. His contrarian streak might come off as overly critical or dismissive to some followers.
To grow his audience on X, Jason should consider blending his deep analyses with more approachable, bite-sized content and engaging storytelling. Pairing technical threads with relatable use cases or quick myth-busting tweets can broaden appeal and spark more interactive discussions.
Jason has tweeted over 9,000 times, revealing a prolific and engaged mind. His thoughtful thread on vector search attracted over 10,000 views and sparked lively discussions, demonstrating his expertise and influence in AI tech circles.
Uncursing RL!
Puffer: puffer.ai
Paper on Foundational Work: Playing Pokémon Red via RL
Blog and Live Training Map: Beating Pokemon Red w/ RL
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thoughts on why vector search is fading in 2026\n\nfor the last week i wanted to test if i could build a production-level rag pipeline without vectors. no embeddings, no gpu burn. just bm25 (elasticsearch) for retrieval, then dump as much context as possible to gemini for reranking/filtering chunks and generating cited responses.\n\nwhat happened didn't really surprise me: it worked better. ingestion was near-instant (+ no overhead). retrieval was more transparent. and the reasoning model was superb at pulling out a needle from the haystack.\n\nvectors made sense in 2023 when llms had tiny context windows and reasoning didn't exist. back then you had to chunk elegantly and hope cosine distance found relevant fragments from which the llm could directly deduce an answer. but models today can intuitively reason across context windows of 1m+ tokens. the problem then goes from chunk-level retrieval to document-level retrieval and vectors simply fall apart there:\n\n> vectors are costly to ingest (huge upfront embedding step - storage scales quadratically!)\n> vectors are brittle for large-doc retrieval (signal gets washed out when embedding large chunks, and small chunks are simply too expensive to scale)\n\nin practice, traditional search methods (tf-idf/bm25) + reasoning beat vectors on cost, speed, and often accuracy.\n\nthis paper by @orionweller from google deepmind is fascinating. turns out some docs in your index are theoretically incapable of being retrieved by vector search. plain old bm25 outperforms on recall every time. 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take: ai is not the end of history. it is the end of manufactured scarcity.\n\nprof jiang shows how power has always ruled by fabricating scarcity. central banks create money out of nothing yet enforce poverty to motivate everyone to work. happiness shrank into a consumable commodity as everyone subscribed to the idea of working hard to earn money (a fabricated scarcity as there are more than enough resources to satisfy the entire population).\n\nai punctures this cycle. models can generate knowledge endlessly, perform work at scale, earn money on our behalf. open source weights circulate beyond the reach of cb cartels. inference providers multiply until no central choke point remains. unlike gold or oil, this abundance cannot be hoarded or burned away in crises.\n\neudaimonia becomes possible again when machines take over necessity, reducing the value of money as humans work less. if labor is no longer chained to survival, money loses its power as the universal measure of worth. abundance in knowledge and automation displaces scarcity economics, and time once sold for wages is reclaimed as lived experience.\n\nin such a shift, freedom is not the privilege of the few but the baseline condition of all, because the system that turned nothing into scarcity can no longer enforce obedience through deprivation.","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,278],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1959069350956343655","view_count":2031,"bookmark_count":8,"created_at":1755913505000,"favorite_count":33,"quote_count":0,"reply_count":2,"retweet_count":2,"user_id_str":"970812776","conversation_id_str":"1959069350956343655","full_text":"reasoning models are basically iterative hallucinations. the model spits out a draft, rereads itself, and says “try again.” because outputs are stochastic, multiple passes create a crude monte carlo over possible answers, and convergence mimics intelligence. this is why you see longer, denser outputs: each iteration is spackling over cracks from the last.\n\nbut iteration doesn’t rescue stupidity. if the seed is garbage, the model compounds garbage into ornate castles of error. so the “reasoning boom” only works when the base model is already sharp. with a local 8b, you’re just watching a dull knife saw at the same knot, slower each time, producing bloated text that looks like thought but isn’t.\n\nthe deeper point: reasoning models enact a parody of dialectic. they mimic hegel but without synthesis, only repetition. real thought needs the capacity to negate its own premise, not just elaborate it. these models can’t negate. that’s why the ceiling of “reasoning” is not philosophy but 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used claude code to create a chat interface for itself... https://t.co/cq1arAjCwt","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,278],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1934344841506500885","view_count":2830,"bookmark_count":6,"created_at":1750018723000,"favorite_count":26,"quote_count":1,"reply_count":3,"retweet_count":2,"user_id_str":"970812776","conversation_id_str":"1934344841506500885","full_text":"vc firms talk a big game about \"pattern recognition\" but most just chase the same 5 trends with different packaging. all their advice boils down to \"be contrarian... but not too contrarian\" or \"move fast... but also think long-term.\" \n\nthe best founders know this: vcs want you to sell them a fantasy they've already been sold before, just with enough 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