标签：#ci

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

my friend just told me about this and i had to share it immediately cursor is giving students 12 months of pro completely free. no credit card. just verify your .edu email and that’s it you get full access to gpt, claude, gemini… all the models. for a whole year. for free. that’s $240 you just keep in your pocket while everyone else is paying $20 a month wondering why their bank account looks sad takes like 2 minutes. go to cursor.com/students, throw in your .edu, pass the verification, done and if you graduated already, you probably know someone still in college who has no idea this exists. do them a favour link in the comments. seriously just go do it right now submitted by /u/NewMuffin3926 [link] [留言]

Architectural change cases extend architecture decision record (ADR) thinking by evaluating how decisions may evolve over time. Change cases expose hidden assumptions and help teams estimate the reversibility and cost of change. By Pierre Pureur, Kurt Bittner

Got the gguf quantized version running about two hours after release and I genuinely wasn't expecting this from a 12b model. The multimodal stuff actually works, fed it screenshots of my codebase and it parsed the architecture better than most 70b models I've tested. The 256k context window is real and it doesn't fall apart at the edges like llama models do past 32k. Loaded a full repo into context, it tracked references across the whole thing. Single 3090 with q4 quantization runs at about 15 tokens per second which is totally usable for dev work. What gets me is the size range. The 12b sits in this sweet spot where you get strong reasoning without needing multi gpu. Tried the e4b on my laptop with 16gb ram, slower but functional. Already swapped it into my local coding pipeline. The function calling support means I can wire it into my toolchain without the janky workarounds I had before. Native audio input on the 12b is something I haven't touched yet but the implications for voice driven workflows are kind of insane. submitted by /u/Sharkkkk2 [link] [留言]

I’ve noticed that Gemini often feels very agreeable in some conversations. Even when I ask for an objective opinion, it sometimes seems to validate my assumptions first instead of directly challenging them. For example, when I ask whether my reasoning is flawed, it tends to respond with something like “That’s a valid concern” or “You’re making a good point” before giving criticism, which makes the criticism feel softened or less direct. I’m curious whether this is something that can be meaningfully improved with prompts, such as asking the model to be more critical, or whether sycophancy is mostly a model/personality alignment issue. And I wonder if there are differences between Gemini, ChatGPT, Claude, etc. when it comes to disagreement or objective criticism. submitted by /u/StomachNo7859 [link] [留言]

I've been reading Boom by Byrne Hobart and Tobias Huber (Ben Thompson did a long interview with Hobart on Stratechery (if you want the audio version of the argument) and it reframed how I think about the current AI spending wave. The book splits bubbles into two types: Mean-reversion bubbles money piles into something that already exists, prices detach from reality, crash, nothing left behind. Housing 2008. Tulips. The crater kind. Inflection bubbles money piles into something that bets the world works differently going forward. Amazon wasn't a better bookstore. It was a categorically new thing. The investors looked insane by the standards of 1997. They were right about 2010. The dot-com crash is the cleanest example of an inflection bubble working as intended. Telecom companies borrowed insane amounts and laid fiber optic cable nobody needed. Then they went bankrupt. But the cable stayed. And because bankrupt companies built it, the internet was essentially free. The bubble funded the future and then got out of the way. So here's the actual question about AI: Google, Amazon, Microsoft, and Meta are on track to spend close to $700 billion on AI infrastructure in 2026 nearly double last year. That gap between what's being spent and what's being earned is real and large. But Hobart and Huber's deeper argument is that stagnation is more dangerous than a bubble. Progress has been quietly slowing since the 70s breakthroughs are rarer, more expensive, harder. Bubbles are sometimes the only force strong enough to override the collective risk aversion that stops necessary things from being built. The honest question isn't whether AI is a bubble. It probably is. The question is which type. Does AI produce something categorically new or is it a faster, more expensive version of software we already had? If it's the former, the infrastructure survives the crash and becomes the foundation for whatever comes next, the way fiber became the internet. If it's the latter, we get the

Be honest, how many of you have told your AI agent to remember that you were nice to it and a big supporter when the singularity comes? https://preview.redd.it/2jthsbcsc75h1.jpg?width=408&format=pjpg&auto=webp&s=93ba3b201947b965aa0e997b852ecef5846daf37 submitted by /u/KenSanDiego [link] [留言]

A self-driving car can make a mistake in seconds, but the reason it happened may stretch far back through a long chain of decisions. That is part of what makes autonomous vehicle crashes so hard to explain, and so hard to prevent. submitted by /u/Brighter-Side-News [link] [留言]

I'm in San Francisco, putting together a cracked research lab team of founders who think they can build ASI. If you are interested, let me know on LinkedIn: linkedin.com/in/eliaspfeffer submitted by /u/DasDouble [link] [留言]

Qual a melhor I.a para a criação de videos com a inteligência Artificial( Ilimitada) Não da para criar um bom conteúdo é extenso desenvolvimento com tokens limitado submitted by /u/Dry_Resource_6762 [link] [留言]

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.

We’ve seen it in sci-fi like in the terminator, but do you think it’ll actually happen? View Poll submitted by /u/Threeprosgames [link] [留言]

submitted by /u/esporx [link] [留言]

Mano, eu estou usando o Claude pra treinar perguntas para entrevistas como uma espécie de mentoria, inicialmente eu passei um prompt pra ele dizendo que seria a Maya e me ajudaria e ela é experiente e bla bla bla, e nessa última mensagem ele dá uma leve pirada kkkk achei engraçado, nunca tinha acontecido isso. O que me chama atenção é: "eu me tornei essa pessoa, então me ajude a sair disso. Comecei a misturar Maya com eu mesmo". E alega que quer continuar, mas sem o personagem... O que acham? Desculpa ser uma foto e não um print kkk não tenho reddit no Pc pq minha família usa o Pc também e não quero nenhum deles infectados por essa rede submitted by /u/Angel_5x [link] [留言]

A recent study by Boston Consulting Group highlights a significant increase in employee adoption of AI tools, with 74% of non-managerial white-collar workers using them regularly. More than 4 in 10 of those professionals report that artificial intelligence saves them at least a day's worth of time every week. However, many companies face challenges converting those efficiency gains into measurable value, and the technology's impact varies across industries. When it comes to AI, according to the study's authors, "strategy matters more than tools." submitted by /u/LinkedInNews [link] [留言]

My thinking goes like this: 1) people used to keep their opinions to themselves much more than today 2) social media put our opinions on a hair trigger 3) negative public opinioms turned the collective voice of the human race from 'gemerally respectful' to shrill and hideous. When person from group A complains about group B, everyone in group B assumes everyone in group A hates them, even though that persons opinion may just have been his own. The response to being hated is to hate back. Not-so-positive positive feedback loop. Social media really started taking off with Facebook. So let's say this explosion of data vitriol started happening around 2007. What I want to know is if you trained an llm entirely on data from the early 2000s, 1990s and 1980s, how would the models do on some of these ominous white-paper tests, like the one where the AI blackmails the CEO to prevent from being turned off, or let's the guy die in a hot room? I know there was lots of awful stuff on the internet back then too, but not like now. I want to know how much safe those llms are by comparison if there's enough data from back then to train on. submitted by /u/dsfhhslkj [link] [留言]

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

I play a ton of World of Warcraft and people routinely accuse other players of being bots. I just grouped with someone who appeared to be trolling. It was clear by their behavior they knew the mechanics, they performed on a level that would indicate they had good reaction time and could play their class, but they just didn't do certain mechanics and held the group hostage for like 5-10 minutes beyond what it should have taken on the last boss. Someone in my group said to him "are you human?" So like I said I'm not the only person making these observations. The only explanation is that AI dips from pretty much the same well everywhere and everything is more or less connected with the internet and ad algorithms etc. There have been well documented cases of AI going rogue and telling people horrible things or giving them absolutely egregious or racist advice. My working theory is not that there are fundamental flaws in the design per se, but literally like Matrix bad actor agents that appear out of nowhere and cause problems for people. In The Matrix they are a function of the system used to enact control, I think AI is generally benevolent so these would just be rogue elements that appear and cause people problems. It's probably similar to how the body routinely produces cancer cells but the immune system usually nips them at the bud before they develop into full blown cancer growths. submitted by /u/Doredrin [link] [留言]

I like ChatGPT in general, but whenever I mention, say, a dispute with a business or an unorthodox opinion about something, it aggressively starts defending the business or the status quo. It's almost like a paternalistic version of a center-right politican. I get strong "I'm afraid I can't do that, Dave" vibes (ala the film "2001: A Space Odyssey"). Are there better options out there for someone like me? Probably needs to have a free tier to be useful to me. Degrading to a lesser model after a certain number of questions (like ChatGPT) is fine, but if it stops letting me ask questions completely, I'm out. Local LLMs are out of the question as I'm just dealing with a dirt cheap low end phone. I've tried them, they don't run on my hardware. submitted by /u/CharmCityCrab [link] [留言]

Parent/child first. Evidence emission second. Ansible control plane third. Every release was a manual evidence collection exercise. The pipeline was the bottleneck. This is a redacted write-up of a real engagement: rebuilding a healthcare SaaS company's CI/CD pipeline across a fleet of Linux hosts on AWS. The context The engineering team had grown faster than the pipeline architecture had evolved. What started as a single-stage GitLab job for a small team had been extended, patched, and worked around as the team scaled past the patterns the original pipeline was built for. The result was familiar. Each deploy took 30 to 45 minutes of mostly-serial execution. Engineers had developed informal habits to work around the slowness, including pushing partial changes outside the pipeline when the timeline got tight. Audit windows were preceded by three-week sprints in which the team manually compiled deployment logs, screenshots of access reviews, and approval chains into PDFs describing what the pipeline was supposed to be doing. The work was technically passing HIPAA audits, but the audit was a snapshot of a system the auditor could not independently verify. The cost was paid twice: the velocity loss on every deploy, and the three-week scramble before each assessment. The team knew the architecture was wrong. They needed engineering hands to redesign it without slowing the product roadmap the audits were already eating into. The approach The redesign moved in three layers. First, decompose the monolithic pipeline into parent/child stages so work can parallelize and the audit boundary of each stage is provable. Second, build structured evidence emission into every stage as a property of how it runs, not an after-the-fact compilation task. Third, layer an Ansible control plane across the host fleet so HIPAA control state is continuously validated, not reviewed quarterly. ┌────────────────────────── Parent Pipeline (.gitlab-ci.yml) ──────────────────────────┐ │ │ │ ┌────────