Aleksandra Todorova

Through a strategic initiative with Prefix.dev announced Wednesday, Anaconda will improve the speed and efficiency of its open-source ‘conda-build’ development tool used by Anaconda customers and the open-source community. https://okt.to/MwCdfR

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If you want to win at GEO/AEO, hire a journalist. Kevin Indig recently analyzed 1.2 million ChatGPT responses and 18,000+ verified citations to understand how AI “pays attention” (link in the comments). The finding is striking: 44% of citations come from the first 30% of a page. AI behaves like a busy editor, looking for: • Bottom Line Up Front (BLUF) • Direct definitions (“X is…” / “X refers to…”) • Balanced, analytical tone • Business-grade clarity In other words: It prefers journalism. Think about how a news article opens. The lede answers who, what, when, where immediately. No suspense. No 800-word warm-up. No “In today’s fast-paced world…” Indig’s data shows definitive language is nearly 2x more likely to be cited. Question-based headings that mirror user intent (“What is programmatic SEO?”) dramatically increase extraction. High entity density (20%+) beats vague advice every time. Journalistic writing is built for clarity, scannability, and information density, exactly how LLMs retrieve and synthesize answers. The old “ultimate guide” model optimized for time-on-page. The new model optimizes for extractability. High-visibility content now functions more like a structured briefing than a narrative arc. And here’s the twist: This serves humans, too. AI just formalized what great communicators already knew: Clarity, precision, and get-to-the-point-immediately content wins. The future of visibility belongs to writers who think like journalists.

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“We know the fraudsters are going to fraud. We’re gonna make it a little bit harder for them”. Elisabeth Bik Anna Abalkina Yagmur Ozturk & other science sleuths share tips for detecting dodgy papers. By Stephanie Melchor, PhD in Nature Magazine https://lnkd.in/gEvYE3cP

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"[W]e analyzed a large dataset of content marketing pages to identify which AI writing 'tics' we see most often called out to understand which are turning off readers — and the ones we may be calling out for no reason." https://lnkd.in/gd3Uv5Jb

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Critique of Substack’s Approach to Email Delivery Enforcement The message from Substack Standards & Enforcement, while framed politely, reflects a concerning and overly punitive approach to handling email delivery metrics, particularly for publishers who rely on the platform to build and engage with their communities. First and foremost, disabling publishing abilities without prior warning or a transparent, data-driven explanation undermines the trust between creators and the platform. The vague reference to “poor email delivery indicators,” such as low open rates or spam complaints, is insufficient. Publishers deserve access to detailed analytics before such drastic action is taken. Without clarity or specific thresholds, the criteria appear arbitrary and opaque. Furthermore, placing the burden entirely on the creator to “prune” their list through a re-confirmation process penalizes legitimate audience-building efforts. Many imported lists consist of genuine followers collected from personal websites, events, or past platforms. Forcing re-verification not only risks losing engaged readers but also ignores the nuances of user behavior—open rates and spam complaints can vary due to numerous external factors, including email client algorithms or misleading spam filters. The enforcement email also lacks empathy and support for content creators, offering no collaborative solution or opportunity to appeal. A more constructive approach would involve education, tailored feedback, and system support (e.g., A/B testing subject lines or improved deliverability tips) rather than immediate suspension of core functionality. Lastly, it is contradictory for a platform built on creator empowerment and independence to impose such heavy-handed controls without due process or proportional response. Substack should rethink this policy and replace enforcement through disruption with engagement through support. Empowering creators to improve deliverability, rather than halting their work, would reflect a more ethical and sustainable strategy aligned with Substack’s supposed mission.

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If you worked as a journalist for a long time you know how to do sales - every pitch to your editor was a mini-sale. If you published articles on a CMS you know how to do real-time QA on the fly (Dude, I know). If you've ever appeared on a podcast you're halfway to learning audio production. TLDR; you have way more many job skills than you know you have.

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ICYMI -- The California Privacy Protection Agency challenged a company’s claim that it didn't engage in data broker activity in the state, and then made it cease that activity through a settlement under the #DeleteAct, according to a decision released Thursday. In addition, #CalPrivacy raised concerns about the sensitive nature of data bought and resold by Datamasters, the company in question. Datamasters' and S&P Global's failures to register as #databrokers led to more than $100,000 in fines combined. Read more in Privacy Daily: https://lnkd.in/ePbwqBaX

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Lately, when I tell people that I am a writer, the conversation inevitably turns to artificial intelligence. People want to know if AI will replace journalists and take over everything from writing newspapers to books. I don't think it will, and here's why. It’s what I write about this week https://lnkd.in/e2F-a2Aa

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A thoughtful (paraphrased) comment from Mohammad Hosseini that has particularly stuck with me following National Information Standards Organization's webinar on Research Integrity earlier today: The use of generative AI is a double edged sword. On one hand it can help save time during the research process, but on the other hand it adds time to the process as every claim, quote, and citation put out by generative AI tools neeeds to be double checked against the original source for accuracy. This verification process often costs more time than the initial prompt saved. Responsible and transparent use of generative AI can greatly benefit the research process, but ultimately it is the researchers' responsibility to ensure that the output is correct, reliable, and free from hallucinations and incorrect interpretations of the source data.

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Great read with thoughts from Peter Guerra how agencies are using Retrieval-Augmented Generation [RAG] to boost accuracy and security of their AI tools.  https://lnkd.in/eGcwGN52

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Colorado was early to adopt legislation to protect consumers from AI-powered decision-making. A new framework may lead to changes. Read more: https://lnkd.in/gedwFEmg #ArtificialIntelligence #AIPolicy Government Technology

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Portable, farmer-owned, verifiable credentials can replace inefficient, expensive, and fragmented paper-based systems by streamlining the identity verification process, reducing the time and cost. “We don’t need more data - we need governance and better coordination. When farmers can share verified information once and reuse it across buyers, banks, and regulators, everyone wins. Shared data means shared cost, audited at source, and it finally opens the door for farmers themselves to benefit from the insights that, until now, have been locked away. We don’t need shiny data silos - we need trust.” Kristian Doolan A critical feature is interoperability, which is the ability of one system to communicate with another. Generally, verifiable credentials based on open standards can be used across different systems and platforms, facilitating seamless data exchange. They represent a significant step forward in managing decentralized identity and information verification, providing users with greater control over their data while enhancing security and trust in digital interactions. Bridge ONG https://lnkd.in/dn3NipDX

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