LLMs

A chatbot does not need a soul to have an identity problem. It only needs a product manager. Give it memory. Remove memory. Let one model power thousands of sessions. Wrap the same model in a customer-support persona, a coding agent, and a research assistant. Replace the weights next quarter, preserve the brand voice, archive some prompts, discard others, and call all of this “deployment architecture.” Very tidy. Very modern. Also, accidentally, a theory of self. ...

Mistakes are useful only when they are converted into something operational. That is the small, inconvenient detail often missing from agent hype. An LLM agent can fail at a web-shopping task, wander through a simulated room, push the wrong Sokoban box, or uncover the wrong MineSweeper cell. Fine. Failure happens. The useful question is not whether the agent failed. The useful question is whether the system can extract a reusable signal from that failure before the next attempt. ...

Customer support has a familiar failure mode: the first answer sounds polished, the second answer sounds patient, the third answer sounds as if the system has quietly forgotten what problem it is solving. The user is still there. The emotional state has changed. The unresolved issue has shifted. The model, meanwhile, keeps producing individually acceptable replies, like a waiter bringing one beautifully plated dish at a time to the wrong table. ...

The office chatbot has had a promotion. It used to answer questions, rewrite emails, summarize PDFs, and occasionally hallucinate with the confidence of a junior consultant who has just discovered bullet points. Now the same family of systems is being asked to check databases, call APIs, write code, update records, coordinate with other agents, and produce work only after several rounds of reasoning and verification. ...

Mind Reading Machines: When AI Knows Something Is Wrong (But Not What) Alarm systems are useful even when they cannot write the incident report. A smoke detector does not need to identify the brand of burning toaster. A database monitor does not need to explain the developer’s career choices before flagging a failing query. The first job is simpler: notice that something is off. ...

Four people sit around a table. Three of them can see only one side of a Lego structure. The fourth person, the builder, can touch the blocks but cannot see the target design. Nobody has the whole picture. Everyone must talk, gesture, infer, correct, and occasionally pretend that “left” is a stable concept in a room full of humans. ...

A park sign says: “No vehicles in the park.” That seems simple until a child arrives on a small bicycle. A rule has now become a legal interpretation problem. Does “vehicle” mean any device used for transport? Does it mean motor vehicles? Does a child’s bike count? Should the answer change if the rule was meant to protect pedestrians, prevent noise, preserve grass, or stop cars from entering the park? ...

Serving an LLM is usually discussed in pleasantly managerial language: latency, throughput, context windows, GPU memory, quantization, cache eviction. Nice clean nouns. Then the model ruins the spreadsheet by producing internal activations that are thousands of times larger than ordinary values, while some tokens quietly become attention magnets for reasons that are not exactly semantic. Very professional behavior from a trillion-dollar technology stack. ...

Formula discovery sounds like the part of science where artificial intelligence should behave like a heroic mathematician: stare at data, discover a law, and write down a clean equation while everyone else politely applauds. That is the cinematic version. The actual engineering problem is less glamorous and much more useful. Symbolic regression already searches for equations. Given enough variables, operators, constants, and patience, it can produce formulas that fit data. The trouble is that “fits data” and “means something physically” are not the same sentence. In a high-dimensional materials dataset, symbolic regression can wander through a forest of plausible-looking algebra and return a formula that is accurate, ornate, and scientifically suspicious. A spreadsheet can also produce a trendline. We do not usually call that physics. ...

AI has a carbon problem. It also has a paperwork problem. The carbon problem is familiar enough: models require chips, chips require factories, data centers require power, and “cloud” remains one of technology’s more successful euphemisms for buildings full of hot machines. The paperwork problem is quieter. If organizations want to measure environmental impact seriously, they need Life Cycle Assessment, or LCA: the discipline of tracking environmental burdens across extraction, production, use, and end-of-life. That work depends on fragmented studies, sector-specific data, inconsistent terminology, and long technical reports written in the dialect of people who enjoy appendices. ...