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President Trump’s new executive order creates a voluntary regime for pre-deployment AI evaluations. That is a meaningful step. The order gets the policy problem right, and frontier AI models with advanced cyber capabilities should not be released into the world without serious testing. Does it leave the legitimacy problem unresolved? Secrecy, voluntary participation, and industry proximity are a fragile combination. Link 🔗 here . submitted by /u/BubblyOption7980 [link] [留言]

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

Sepehr Khosravi discusses the evolution of developer productivity tools. Evaluating the strengths of tools like Cursor and Claude Code, he explains actionable techniques for senior engineers - including context engineering, custom rules, and Model Context Protocol (MCP) integrations. He shares real-world benchmarks and strategic frameworks for balancing AI adoption with clean code quality. By Sepehr Khosravi

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 […]

This is not about the money. It’s about the principle. ​We are constantly told that AI is here to "help" us, but multi-million dollar companies like Perplexity are weaponizing their own AI to steal from regular users, stonewall our complaints, and blatantly violate consumer rights. It is systemic corporate greed, and they are getting away with it because people are too exhausted to fight back against a machine. ​Well, I am fighting back, and you should too. Here is the absolute scam Perplexity is running right now. ​ How they steal your money: ​Living in Latvia, I pay for my Education Pro subscription in Euros (equivalent to $10/month). ​April 27: A payment was due, but my card declined. Fair enough. Perplexity froze my account immediately. I had ZERO access to Pro features. ​May 16: I manually paid for my subscription to reactivate it. The payment cleared. ​May 29: Barely 13 days later, my account was stripped of its Pro status and locked again. ​When I demanded an explanation, their billing system's "logic" was revealed: They took my May 16 payment and retroactively applied it to the "past due" period of April 27 - May 16. A period where my account was completely frozen and the service was actively withheld. ​They effectively charged me for a full month of service, gave me 13 days of access, and pocketed the rest. This isn’t a glitch; it’s unjust enrichment. It is theft. ​Enter "Sam" the AI ​If you try to get your money back, you don't get a human. You get "Sam, the AI Support Agent." ​I tried to explain that under European law, you cannot charge a customer for digital services you didn't provide. Sam’s response? A pre-programmed loop denying my refund, claiming I was "outside the 14-day EU refund window." ​Here is the most infuriating part: I did submit a ticket well within that window. But their automated system closed it without resolving it. When I pointed this out, the AI literally replied: "I don't have access to separate ticket histories." ​They use their o

Several real estate listings in the San Francisco Bay Area are offering to exchange a home for a piece of the AI startup.

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

Fusion startup Xcimer fired up the world's largest privately owned laser.

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

I put my family on a private social network, and all I got was this lousy group chat. At least it’s secure.

MiniMax released M3 today and the API is already live. Worth separating what comes from their own official model page versus what comes from the launch announcement, because some of the numbers are sourced differently. From the official model page: BrowseComp 83.5, ahead of Claude Opus 4.7 at 79.3. PostTrainBench 37.1, which ranks third behind Opus 4.7 at 42.4 and GPT-5.5 at 39.3. From the launch announcement: SWE-Bench Pro 59.0%, Terminal Bench 2.1 66.0%, MCP Atlas 74.2%. The headline "beats Opus" is BrowseComp-specific, not a general capability claim across all dimensions. The context window is up to 1M tokens, implemented through their in-house MiniMax Sparse Attention architecture. They state 512K as the guaranteed minimum with 1M as the ceiling. The model was trained on 100T+ tokens and is natively multimodal rather than vision being added after the fact. Open-weights release is coming to HuggingFace and GitHub but listed as "coming soon." API access is available now through several paths, including OpenAI-compatible endpoints, while the weights are still pending. The model also supports native MCP tooling, which is where the 74.2% MCP Atlas number comes from. The demo claims are the part worth being skeptical about. A 12-hour autonomous ICLR paper replication run and a CUDA kernel optimization loop reaching 9.4x speedup are impressive if real, but these are curated showcase demos that are hard to evaluate from a screenshot. Whether sparse attention holds up at 900K+ tokens in practice rather than in controlled benchmarks is an open question. submitted by /u/Drysetcat [link] [留言]

Every impressive agent demo skips the same three things: Auth. The demo target is open. The real one has a login and a 2FA prompt. Identity. The demo agent acts as the developer. The real one needs its own email, accounts, and a place to keep secrets. State. The demo is one clean run. The real one has to remember what it did last time and resume. These are not AI problems, which is exactly why they get skipped in AI demos. But they are most of the work to go from "cool clip" to "thing that runs unattended." The model is increasingly the easy part. The unglamorous identity-and-state layer around it is where products actually live or die. Curious whether people think this layer gets commoditized into the foundation models, or stays a separate thing you assemble. submitted by /u/kumard3 [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/n4r735 [link] [留言]

Operator perspective. I use AI daily across three companies and I am bullish on it, but the gap between what gets shouted on stage and what the data shows is enormous. Best measured number across hundreds of engineers is about 7.8%, and 66% of the people who hit a peak gain saw it fade the next quarter. At the same time, people are being pushed onto it under threat of their jobs while the return is not even proven to the people mandating it. My read is the anger is not really “AI is bad,” it is “my boss profits from me using it and I do not.” Where do you land - is the resistance cognitive (it erodes skill) or economic (the gain is not shared)? submitted by /u/Alternative_Letter72 [link] [留言]

I’ve been using AI pretty heavily for the past few months — quick research, rewriting emails, brainstorming ideas, even helping outline stuff I need to write. It saves so much time and the output is usually decent. But lately I’ve noticed something weird: I’m second-guessing myself way less. I’ll get an answer from it and just kind of roll with it instead of thinking it through like I used to. Yesterday I asked it about something I already had a rough opinion on, accepted its take, and only later realized I didn’t even challenge any part of it. It feels convenient as hell, but also a little unsettling. Like I’m outsourcing the actual thinking part. Is this normal? Or am I slowly losing the habit of thinking deeply on my own? Anyone else feeling this? submitted by /u/pen-pineapple-apple [link] [留言]

Online it feels like every company is fully embracing AI. In reality, most organizations I interact with are still trying to figure out where it fits into existing workflows, processes and software. The interesting conversations aren't usually about models anymore. They're about trust, reliability, permissions, governance and how AI fits into the way people already work. The gap between AI demos and real-world adoption still feels larger than most people realize. submitted by /u/Bladerunner_7_ [link] [留言]

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

Thoughts? Do you guys use models like Kimi or DeepSeek? Are you worried about data privacy, or not so much concern? submitted by /u/RutabagaTechnical822 [link] [留言]

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