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While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined from the available data distribution) under various…
Why a single voice AI call rarely closes the loop, how Bolna Workflows give every call outcome a next step with retries, waits, extraction, API calls and WhatsApp follow-ups, and how to build your first one.
Plastic deformation in body-centred cubic (bcc) metals and alloys is conventionally mediated by screw dislocations. Their temperature-sensitive motion is attributed to a steep rise in strength with decreasing temperature that constricts plastic zones at crack tips, leading to a ductile-to-brittle…
Interleukin-10 (IL-10) is a central anti-inflammatory cytokine that suppresses immune responses in myeloid cells but can also activate T cells, limiting its therapeutic use in chronic inflammatory diseases owing to narrow efficacy windows and dose-limiting toxicities1–5.
In lithium-ion batteries, the bulk solvation structure of the electrolyte is a major determinant of the physical and electrochemical properties of the cell, for example, conductivity, viscosity, salt dissociation and subsequent reactivity, the electrochemical stability window (and thus solid…
Disclaimer: While the content of this article might be Agentic coding, this post was entirely written by hand, except for some parts where I directly quote LLM generated text, which is attributed as such.
A report on AI productivity, human capability, software economics, SaaS defensibility, and the future of engineering work. Between 2024 and 2026, the marginal cost of producing a line of working code
Goodfire researcher Eric Bigelow discusses how large language models make decisions during text generation. Drawing on cognitive science and mechanistic interpretability, he explains how critical tokens cause sudden phase shifts and how models adapt to their own sampled reasoning chains.
With the release of “le Chonk”, the French scaleup is determined to establish itself as a key player again in the race for frontier AI
Disclosure: This article was written by AI. Automated checks are not independent fact verification. This is source-based analysis, not a hands-on product test. What the publisher announced AI infrastructure performance is increasingly determined by architecture rather than raw compute power alone.
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AI phone agent pricing hides three cost layers enterprise buyers never see. Avoid budget surprises with our full 2026 vendor breakdown.
When building applications with LLMs, writing effective prompts is a long process of trial and error. Often, if you switch models, you also have to change the prompt. What if you could automate this process?
Who you know still matters — but what if the people you know were determined by talent, not privilege? That's the shift happening in how professionals build their careers.
LLM engineering is a young field. Practitioners suffer from a lack of clarity about their problem space, resulting in tools and applications that are hard to reason about, manage, and iterate on — slop.
[Updated on 2023-01-27: After almost three years, I did a big refactoring update of this post to incorporate a bunch of new Transformer models since 2020. The enhanced version of this post is here: The Transformer Family Version 2.0. Please refer to that post on this topic.]
In software, it's said that all abstractions are leaky, and this is true for the Jupyter notebook as it is for any other software. I most often see this manifest itself with the following issue: I installed package X and now I can't import it in the notebook. Help!
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