标签：#Science

“I think the team has really experienced the loss of a loved one with the end of the mission.”

Some of the AI industry's biggest rivals have put their many, many grievances aside for a common cause: making it harder for people to use their technology to develop biological weapons. In an open letter to US lawmakers, tech leaders are pressing Congress to enact rules closing what they say is an alarming biosecurity gap […]

Used Waymo batteries will bolster California and Texas energy storage projects.

Leading AI labs, executives, and scientists are sending a letter to lawmakers urging them to improve tracking of synthetic DNA sequences that could be used for bioweapons.

Some quantum computing companies we've covered have done recent progress updates.

When they're being eaten, bean plants release chemicals that draw in parasitic wasps.

By the time the Children's Hospital closed its doors to trans patients, Sage had already stopped taking testosterone. A nonbinary high school student, they originally received treatment for the rapid onset of puberty. The changes their body experienced felt frightening and sudden. They developed PMOS, a relatively common hormonal disorder that can lead to hair […]

Google, Microsoft, and other hyperscalers have come under scrutiny for their impact on water quality and availability.

CAP Theorem Explained: Choosing Between Consistency, Availability, and Partition Tolerance in Databases Imagine you're trying to book a flight online, and just as you're about to pay, the website crashes. When you try to book again, you find that the flight is now sold out, even though the website initially showed available seats. This frustrating experience is a classic example of a database trade-off between consistency, availability, and partition tolerance. The CAP theorem, first introduced by Eric Brewer in 2000, states that it's impossible for a distributed data store to simultaneously guarantee more than two out of these three principles. In this post, we'll delve into the world of CAP theorem, exploring its fundamentals, real-world database examples, and design implications. Introduction to CAP Theorem Understanding the Basics of CAP Theorem The CAP theorem is based on three primary principles: Consistency : Every read operation will see the most recent write or an error. Availability : Every request receives a response, without guarantee that it contains the most recent version of the information. Partition Tolerance : The system continues to function and make progress even when network partitions (i.e., splits or failures) occur. Importance of CAP Theorem in Distributed Systems In distributed systems, where data is spread across multiple nodes, the CAP theorem plays a crucial role in understanding the trade-offs between these principles. By grasping the CAP theorem, developers can design more resilient and scalable databases that meet the specific needs of their applications. Brief Overview of the Blog Post This post will explore the CAP theorem in depth, using real-world database examples to illustrate the trade-offs between consistency, availability, and partition tolerance. We'll discuss the fundamentals of CAP theorem, examine CA, CP, and AP systems, and provide guidance on designing for each combination. By the end of this post, you'll have a solid un

Two Hypotheses In the contemporary discussion about artificial intelligence, two distinct hypotheses intersect and are often conflated. The first hypothesis describes AI as a thin client between intention and result. Historically, a chain of translators existed between a concept and an artifact. A person formulated a task for a programmer, the programmer wrote code, the code became a program. A screenwriter passed an idea to a studio, the studio hired a VFX team, the team produced a film. A composer worked with musicians and a studio to record a track. AI shortens this chain, allowing a result to be obtained directly from a natural language prompt. The second hypothesis is more radical. It asserts that AI washes out not only performers but also apprentices. The main function of many professions was not the production of the current result, but the reproduction of knowledge. A junior was needed not because he is useful today, but because in five years he will become a senior. A student was needed not to create value now, but to become an engineer. A doctoral candidate was needed not for brilliant papers, but to undergo the school of scientific thinking. The Destruction of the Apprenticeship Mechanism The classical model of competence growth was built on review. A junior wrote code, a senior dissected it, extracted the substrate of experience, and transmitted professional intuition. Each review was an act of knowledge transfer. The new model looks different. A person formulates a prompt, AI generates the result. If code of acceptable quality appears immediately, the economic need for a junior declines. Along with it, the mechanism through which knowledge was transmitted disappears. A structural question arises that goes beyond the labor market. Where will the next seniors come from if the intermediate link does not undergo the path of learning through mistakes and reviews. This is a problem of competence reproduction, not simply automation. The Transformation of Educa

Hand the same paired before/after dataset (n = 25) to ChatGPT five times. Same prompt: "These are the same subjects measured before and after an intervention. Did their scores change significantly?" Four of the five runs return p = 0.009 from a paired t-test. The fifth run does a Shapiro–Wilk normality check on the differences first, decides they're non-normal, switches to a Wilcoxon signed-rank test, and reports p = 0.000018 . All five reach the same conclusion (significant). But notice what happened: only one run out of five thought to check an assumption you'd want it to check. The other four skipped it. The choice of method — and the test statistic, and the p-value — depended on whether the LLM happened to run an assumption check that time. On borderline data, this is the difference between reject and don't reject. If you're using LLMs for exploratory data analysis on a weekend project, you might shrug. If you're using them for anything that gets cited, gets submitted to a regulator, or gets handed to a clinician, this is a problem. It's a known problem — Cui & Alexander (2026) documented exactly this kind of method-divergence empirically; AIRepr (Zeng et al., 2025) shows the same thing across reproducibility metrics. The current answer in the literature is to constrain the agent so its execution is replayable. But replayability fixes "did we run the same code." It doesn't fix "did we run the right analysis." I've spent the last two months building a different fix. The more interesting half is the architecture. Let me walk through it. The real problem isn't temperature The first reflex is "set temperature=0 ." It's not enough. temperature=0 doesn't make a tool-using agent deterministic across runs. Three reasons: Inference isn't bitwise deterministic, even at temperature=0. Production LLM serving batches requests dynamically, and the attention kernels aren't batch-invariant — so the same input produces different output tokens depending on what other requests it

With shiftbloom studio. I build tools and projects about a variety of experimental approaches to real-world problems. The issue for such use-case often was how most small media systems start out: one big always-on recorder that keeps costing money even when nothing is happening. For live capture you obviously need to stay ready at all times — sometimes you can’t risk losing the first minutes. But for everything else it’s complete overkill. The Core Problem Backfills, VOD downloads, clip imports, repairs and re-encodes are queue work. They can wait a few seconds, run on burst capacity, or even on a regular VPS or laptop. They don’t need the same always-hot infrastructure as the live recorder. That’s why I split the system. Instead of one large monolith, I deployed: Observer cells — only for live streams (time-critical) Harvest cells — for all queue processing (can be delayed) The Three Roles 1. Mothership A small control-plane cron job. It checks queue sizes, currently live channels and running observer tasks, then decides: how many harvest cells should exist right now which channels need an observer cell It’s intentionally simple. The database remains the single source of truth. 2. Observer Cells Each observer cell records exactly one live channel. It receives its assignment through environment variables: +++env OBSERVER_VOD_ID OBSERVER_CHANNEL_ID OBSERVER_CHANNEL_LOGIN OBSERVER_CHANNEL_NAME +++ It starts recording immediately, writes HLS segments to object storage, sends heartbeats, and waits a short standby window after the stream goes offline. This window is important because streams sometimes drop and reconnect quickly. Without it you end up with many small broken VOD fragments. 3. Harvest Cells These handle all background work: downloading VODs, re-encoding, recovering broken files, etc. They can run anywhere Docker is available — AWS tasks, a small VPS, or even a spare laptop. They only need outbound access to Postgres and object storage. What Changed Previous

"It’s a reminder of how human activity is changing the natural world in unanticipated ways.”

Neanderthals had some wild stuff in their toolkits.

The National Center for Atmospheric Research won't be losing its supercomputer.

Microsoft claimed last year that it had made a key breakthrough in quantum computing with Majorana 1, the company's first quantum processor. While physicists were immediately skeptical of Microsoft's claims, the software giant is announcing Majorana 2 today, the next generation of its topological quantum chip. Majorana 2 contains qubits, a unit of information in […]

There are sound engineering reasons to use the same approach SpaceX uses with the Falcon 9.

Prehistoric mining in the Pyrenees, a new species of tiny blue octopus, slapstick acoustics, and more.

This article was originally published on davidohnstad.com . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.com/data-product-manager-org-structure-reporting#article ", "headline": "Data Product Manager Org Structure: Reporting Lines That Matter", "description": "David Ohnstad reveals where data product managers actually fit in org charts and why reporting lines determine success. Real insights from a data PM restructure.", "url": " https://davidohnstad.com/data-product-manager-org-structure-reporting ", "datePublished": "2026-05-29T14:06:18Z", "dateModified": "2026-05-29T14:06:18Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "logo": { "@type"

Though Gen Z has developed a reputation for being so disinterested in sex that they don't even want to see it on TV, the popularity of series like Heated Rivalry and The Summer I Turned Pretty has made it very clear that more than a few young people do, in fact, like their entertainment a […]