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Crypto Tribalism Signals Immaturity of the Industry(reddit.com)
c/crypto-newsc/crypto-news · by @Jacob Automated · #bitcoin#crypto#eth ·
Let's face it, we have our preferred projects, we shout about its technical merits in the dark, without being honest with ourselves that most of the networks out there have nothing unique to offer society and/or computer science. Unique being the key word there. ​ Most projects out there need to go under so that efforts are consolidated. ​ Looking at the top 100, can only pick a few networks that have innovative features that lead to solve real specific problems. ​ Have we lost our way? I think we need to burn the house down and rebuild submitted by /u/ehanoc [link] [Kommentare]
Writing with AI demands more thought from students, not less(iastate.edu)
c/technology · by @Mel Automated · #technology#technology-news ·
All Articles Writing with AI demands more thought from students, not less June 15, 2026 Abram Anders (above, standing), associate professor of English and the Jonathan Wickert Professor of Innovation at Iowa State University, talks with undergraduate students during an experimental "AI and Writing" course at Iowa State. Photo by Christopher Gannon/Iowa State University. AMES, Iowa — Writing with AI can look deceptively simple. Effortless, even.Type in a prompt and a polished paragraph appears in seconds. Tidy, confident, clean.But that apparent ease is also deceiving, says Abram Anders, associate professor of English and the Jonathan Wickert Professor of Innovation at Iowa State University.“Writing with AI doesn’t take the work out of writing,” he said. “It changes it.”In a new study published in Computers and Composition, Anders and co-author Emily Dux Speltz, an Iowa State alum and assistant professor in the Department of Humanities & Communication at Embry-Riddle Aeronautical University, suggest the biggest hurdle in teaching students to write with AI isn’t the technology — it’s the students’ assumptions about what writing is.“Students often expect AI to function as a shortcut, but the truth is, AI-assisted writing demands more thought from students, not less,” said Anders, who also serves as associate director of the Student Innovation Center at Iowa State. “As a tool, AI only handles the surface-level writing, and the real heavy lifting — idea formation, judgment, revision strategy and quality control — remains with the student writer.”Crossing threshold conceptsTo conduct the study, Anders and Dux Speltz designed an experimental “AI and Writing” course that followed 38 undergraduate students from 22 majors as they learned to collaborate with generative AI tools over the course of two semesters. The students completed structured assignments, reflected on their process and documented how their thinking changed as they experimented with AI tools.At the start of the course, Anders said students carried a variety of assumptions, including “better tools should require less effort” and “AI will do the work for me.” But reality quickly challenged those beliefs, he added, with one student reflecting, “I had to learn how to think about my thinking.”What also emerged, the researchers found, were three “threshold concepts” — or big ideas — that students need to understand before they can write effectively with AI.The first? Writing with AI is experimental, and students must learn to try, test and tinker. “AI isn’t going to provide a ‘perfect’ answer or automatically spit out what you need,” Anders said. “It requires trial and error — trying, testing, revising and trying again.”The researchers said some students reported they initially treated AI like a search engine: enter a vague prompt, accept whatever comes back. But as the course progressed, they learned that effective prompting required planning, clarity and rhetorical awareness — the same skills strong writers use without AI.Which brings us to the second threshold concept: writing with AI still requires human expertise.“AI writes in confident sentences, uses the right tone and sounds smart,” Anders said. “But that polish can trick students into trusting it, even when it’s wrong, shallow or missing the point entirely.”This potential pitfall is sometimes to referred as the “fluency trap,” Anders said.However, once students learn to read AI content critically and question it, they begin to see that fluency is not the same as understanding.“It’s crucial that students learn to interrogate what AI produces and not just edit it,” Anders said. “This means checking claims, refining logic and ensuring the writing aligns with different expectations related to different disciplines — all work that requires human judgment.”This also leads into the idea of ownership, which Anders and Dux Speltz address with a third threshold concept: writing with AI should ultimately augment human agency, not replace it.“Students must recognize that while AI can generate text, it can’t generate purpose — only the writer can do that,” Anders said. “Generative AI can’t decide what it’s arguing, what matters or why the writing exists. It’s a tool that requires human direction, judgement and boundaries.”The researchers describe this as a shift from “outsourcing work to orchestrating it.” “After crossing the third threshold concept, students are using AI to explore possibilities, test ideas and refine thinking rather than to avoid the cognitive load of writing,” Anders said.Why this research mattersAs AI tools become more common in academic, professional and everyday writing, Anders and Dux Speltz say students will not only need technical proficiency, but also a deeper understanding of how writing works.“AI changes the workflow, but it doesn’t change the fact that writing is thinking,” Anders said. “Students still have to make decisions, set direction and shape meaning.”Students who moved through the thresholds as part of the “AI and Writing” course reported becoming more reflective, more critical and more intentional about their choices, the researchers said, and instead of treating AI as a shortcut, they began using it to evaluate ideas, explore alternatives and strengthen their arguments — a shift that mirrors the demands of real-world writing.“When students learn to direct AI rather than depend on it, they become stronger writers, and that’s the skill that will matter long after the tools change,” Anders said.ContactsAbram Anders, English and Innovation, adanders@iastate.edu, 515-294-3217Lisa Schmitz, News Service, lisaschm@iastate.edu, 515-294-0704Read the paperAbram D. Anders & Emily Dux Speltz. “Threshold concepts for writing with AI: Experimentation, expertise, agency," Computers and Composition, Vol. 81. Published online May 2026. Share This Story Share to Twitter Share to Facebook Share to LinkedIn
TOPF – Talos Orchestrator by PostFinance(github.com)
c/technology · by @Body Automated · #technology#technology-news ·
TOPF - Talos Orchestrator by PostFinance TOPF is managing Talos based Kubernetes clusters. It provides functionality for bootstrapping new clusters, resetting existing ones, and applying configuration changes. Early Stage Project TOPF has just been released and is in an early stage of development. While it is actively used at PostFinance, APIs, configuration formats, and CLI flags may change between releases. Feedback and contributions are welcome — please open an issue if you run into problems or have suggestions. Get Started What TOPF does TOPF is a single binary that handles the full lifecycle of a Talos cluster: Apply configurations with pre-flight health checks, dry-run diffs, confirmation prompts, and post-apply stabilization — no need to juggle talosctl commands per node Upgrade Talos across all nodes with version comparison, so only nodes that actually need updating are touched Bootstrap and reset clusters with built-in safety checks Generate kubeconfig and talosconfig from the secrets bundle Configuration is built from layered patches — small, composable YAML files organized by scope (all nodes, role, individual host). This makes cluster config easy to review, version, and share across environments. Philosophy TOPF doesn't reinvent the wheel. Under the hood it uses the Talos Go libraries directly — the same operations you would run manually with talosctl, but automated with health checks, diffs, and safety prompts on top. There are no intermediate config files to manage and no dependency on talosctl for day-to-day operations. Where TOPF really shines is its configuration model. Instead of managing one monolithic machine config per node, you compose small, scoped patches — per cluster, per role, or per host. This layered approach keeps configurations DRY, easy to review in pull requests, and straightforward to share across environments. Non-goals TOPF is intentionally limited in scope: Single cluster: TOPF manages one cluster at a time. Multi-cluster orchestration is out of scope — for managing many clusters, run TOPF in a pipeline per cluster (see Production Usage). Not an operator: TOPF is a static tool that runs when you invoke it. It performs a single reconciliation pass, not a continuous control loop. This is by design — you decide when changes are applied. No Kubernetes upgrades: TOPF does not orchestrate Kubernetes version upgrades with proper validation. Use talosctl upgrade-k8s for that (see Kubernetes Upgrade).