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Author(s): Amit | AI & Side Hustle Originally published on Towards AI. The people winning right now aren’t the ones who know the most about AI. They’re the ones who’ve learned to work with it. Non-members : Click Here Photo by Andres Siimon on UnsplashThe article argues that in 2026, access to AI tools won’t be the differentiator—judgment and practical skills will be. It outlines ten “deciding” capabilities: designing better prompts (prompt architecture), building and orchestrating AI agents, […]

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A typical plant floor speaks many protocols. Three of the most popular are Modbus/TCP, OPC UA, and MQTT. None of these define how a tag is named or how a timestamp is recorded. One source might stamp readings in local time; the next in UTC. One device uses snake_case, the other kebab-case. Each mismatch fragments the Unified Namespace (UNS) you set out to build. Closing those gaps by hand is the usual answer. This article walks through a working template that uses […]

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Imagine this: an AI agent spends three months running a profitable trading strategy. It generated $50,000 in profit. Now it needs to move those funds to a DeFi protocol for yield farming. But it can’t. Not because it lacks intelligence. Not because it lacks strategy. But because it has no legal identity. No wallet it can unambiguously call its own. No way to prove, to a protocol or a regulator or a counterparty, that it is who it […]

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I’ve written before about the wrapper I built around Claude Code – a TUI that hot-swaps API backends underneath it. You configure your backends once and switch between them mid-session with a hotkey; the normal way to change backend is to edit a config file and restart Claude Code every time. That wrapper rendered Claude Code inside an off-the-shelf terminal stack: one crate kept the screen state, another drew it, a third fed it input. For a while, […]

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arXiv:2606.02909v1 Announce Type: new Abstract: Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations are expensive. However, exact inference with $n$ function values and $n$ full gradients in $d$ dimensions scales cubically in the joint state size, imposing an intractable $mathcal{O}(n^3 d^3)$ computational bottleneck. We introduce TERA, a highly scalable derivative GP method based on target-specific exact gradient reduction. We prove that for stationary kernels, the gradient components orthogonal to the […]

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arXiv:2606.02740v1 Announce Type: new Abstract: Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails to improve for a fixed patience period. However, the patience parameter has no interpretable scale and validation losses can be noisy or implicitly defined by a user-specified gradient. We propose ScoreStop, a gradient-based early-stopping rule that casts the stopping decision at each iteration as a test of the null […]

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