May 2026

What is the inverse of shifting a sequence to the right? Shifting it to the left, obviously. But wait a minute. Suppose you have a sequence of eight bits abcdefgh and you shift it to the right. You get 0abcdefg. If you shift this sequence to the left you get abcdefg0 You can’t recover the last element h because the right-shift destroyed information about h. A left-shift doesn’t fully recover a right-shift, and yet surely left shift and right shift […]

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Flattening a list in Python involves converting a nested list structure into a single, one-dimensional list. A common approach to flatten a list of lists is to use a for loop to iterate through each sublist. Then you can add each item to a new list with the .extend() method or the augmented concatenation operator (+=). This will “unlist” the list, resulting in a flattened list. Python’s standard library offers other tools to achieve similar results. You can […]

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Quick share of a paper we got into RLC 2026. The Eureka-style line of work uses LLMs to write reward functions. It assumes the observation space is already good. We tested that assumption and it doesn’t hold on harder gridworld tasks, even a perfectly shaped LLM-written reward gets ~7% success because the policy can’t see the right features. On continuous control, the opposite happens: the raw state is fine but sparse reward kills learning. So we built LIMEN, […]

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I’ve been playing with deep reinforcement learning for a while. I originally started with a simple DQN, added all improvements from the Rainbow paper, and finally changed C51 for a quantile regression (and plan to swap it for an Implicit Quantile Network). After implementing C51 (which was my first time with distributional RL) I started playing with policies that take advantage of the learned distributions : By independently taking N samples from each action-value distribution, scoring actions by […]

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The two latest posts have involved invertible matrices with 0 and 1 entries. If you fill an n × n matrix with 0s and 1s randomly, how likely is it to be invertible? What kind of inverse? There are a couple ways to find the probability that a binary matrix is invertible, depending on what you mean by the inverse. Suppose you have a matrix M filled with 0s and 1s and you’re looking for a matrix N […]

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Part 2 of my “tiny models, big surprises” series. This time I stopped just running them and started shaping them. A few weeks ago I wrote about running TinyLlama on my MacBook Air and being quietly stunned that a 637MB model could write working code. A lot of you wrote back asking the same question: “Cool, but can you actually use these for real work?” That question lived in my head for two weeks. So I went looking for an answer. […]

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Build AI features that hold up in production. These days, adding an AI feature to your application is easier than ever — a few API calls, a model endpoint, some glue code, and you have semantic search, summarization, or a chatbot working. The hard part is not building it but understanding what is actually happening underneath — because that is what determines whether your feature holds up when real users arrive. Here are five foundational concepts that show up in nearly every AI […]

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