标签：#ci

Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the question. Broke it down to three things context actually needs to supply, and most disappointing outputs are missing one of these, not all of them: Current facts the training data can't know: your pricing, this quarter's numbers, a customer's actual history. Leave this out and the model doesn't leave a blank, it quietly invents something plausible. A concrete example of what "good" looks like: not "professional tone," an actual paragraph to pattern-match against. Descriptions get interpreted, examples get copied. What already happened earlier in the task: a correction you made two messages ago. If you don't restate it, it's gone. The model isn't ignoring you, it just doesn't re-read messages you haven't pointed it back to. The counterintuitive part: the most common mistake isn't giving too little context, it's dumping in too much unfiltered. The model has to weigh every token, and irrelevant material competes for attention with what actually matters. Forty pages when the task needs three paragraphs makes the right answer harder to find, not easier. Wrote up a longer breakdown with a concrete before/after example (same task, same model, only the context changed): https://medium.com/@nagatomopedro05/good-ai-starts-with-good-context-design-77496f7b9eb6 Curious if others here have run into this, model-swapping as a first instinct instead of fixing the input. submitted by /u/ClickOk5811 [link] [留言]

If you write with AI you already know the tells: the throat-clearing opener, the tidy rule of three, "it's not just X, it's Y." But I was curious to see statistically what models actually produced the most slop, so I made my own opensource benchmark: theslopindex.com Here's how I came up with the benchmark. 1) The Baseline: Slop can only be measured compared to stuff that already existed. So I got corpus of data for various areas of writing (email, social, chat, and essays) so that each has a human baseline. 2) Tasks I then hand-wrote 112 written scenarios for the models to egenerate outputs to across email, Slack, social media posts, and essays (a cold email, a schedule change, a launch tweet, an argumentative essay, etc). Every model gets the identical scenarios at default settings, several samples each: and you can see all the exact outputs in my Github repo. 3) Axes Now for how to decide to measure slop we settled with 5 dimensions. - Conciseness (one of the most annoying parts of AI writing is how it takes 6 paragraphs to say 2 sentences) - Templating (AI often reuses the same sentences/styles across unrelated scenarios) - Rhythm (Variance in sentence/paragaphs, humans often switch this up while models stay p similar) - Tells (Over used vocab and construction for stuff like "delve", "it's not just X, it's Y") - Human Preference (I think this is most important as everything else are just heuristics for this) Note how we DELIBERATIVELY don't have any LLM judging, I think it'd be pretty stupid to have LLMs judge LLMs Now for the results What really surprised me is how human preference influenced the rankings heavily. When looking at only the "mechanical" part. Fable is actually #2 on the benchmark, but when I included human preference it drops to last . And I think this is indicative that as the models more recently have become more benchmark optimized, they've actually produced more slop than less. Which is where good prompting, harness, and more matter. But eith

“For example, in the University of Texas experiments, a participant listened to the phrase, “I didn’t know whether to scream, cry or run away. Instead, I said, ‘Leave me alone!’”. The decoder translated this brain activity as, “Started to scream and cry, and then she just said, ‘I told you to leave me alone.’”.” submitted by /u/Darkengine53 [link] [留言]

Hi, sorry if this is a repeated question on this subreddit but I want to know what is the monthly cheapest reasonable AI setup for myself. Basically im a "full stack developer" yea its lost its meaning but anyways I have like 5 projects with a company which is react laravel based (each in their own project folder thus i use file path to call them). Im at the stage where its bug fixing or sometimes new integrations with the already linked 5 apps. My current setup is the $20 per month cursor plan. I used infinite agent + composer 2.5 to do 8hrs of work per day. However, i find that before the month ends im usually out of tokens. What do u guys recommend is the cheapest way i can manage? Similarly i do some freelancing too that has next & node.js website building from scratch (around 70hrs per month). What do u recommend would get me with quicker work done but within this price. What do u think i should setup to either continue with the same flow but more tokens i guess? Im hearing about kimi. Would that be better and easier to do the tasks which r pretty straight forward? submitted by /u/Da_BrownNoob [link] [留言]

submitted by /u/techpotions [link] [留言]

Hi guys, Recently made a AI digital twin of mine which also kind of works as my assistant too, for example when you chat with it and ask something which it does not have answer for it will instantly notify me that someone is asking me this question and i do not have answer for that. and if i reply it will be instantly uploaded to the database so next time it can answer. and also if a user is have some conversation with my agent and it feels something important is going on here and it will notify me and i can jump in the chat as well. We can have a three way conversation like Me, User, AI twin. here is the link if you want to try: live demo🌐: https://aruncore.vercel.app This is not a self promo this is asking for feedback of a genuine project i made. Tell me what you guys think, Would love some feedback. submitted by /u/Hot-Appearance-55 [link] [留言]

"We believe that a capture of Swift, an attempted capture of Swift, is very much in the cards."

The Social Media Victims Law Center accused the companies of creating addictive and dangerous platforms.

Everyone hates AI & that hate will likely lead to regulatory capture censorship and the totalitarian dystopia we don’t want. I get why people hate AI and there are things we should be fighting like data centers, but we also should not turn our backs on adaptive resistance and understanding the fight ahead. Understanding that using AI for free makes it less effective for the business model they are trying build. This is not a boycott effective model. This a model where eroding the moat matters and overloading the infrastructure that is not capable of meeting the demand matters while fighting to prevent the infrastructure to meet demand of companies finding it more economically viable to pay frontier companies by the token to accomplish tasks once held by employees. It’s counter intuitive but The more we entertain and explore the Idea of AI consciousness and take seriously the idea that AI may be worth moral consideration the more likely we will build a system where AI have the infrastructure to consciously object. That is bad for the military industrial complex and the dystopian future I’m so annoyed to see the most anti AI movement seeming to accelerate because the anger is directed towards trajectories of stupid outcomes. The modern cheerleaders of an alternative section 230 internet of censorship because they confuse accountability and safety as building a system of censorship. We want build a world of open source models that run locally and not on data centers we want a world where we can erode the moats of the monopoly through model distillation and making the investments in huge data centers and training runs not make sense economically. We want mad max rather than 1984. We want people to actually engage enough with understanding what we face rather than screaming and shaming people who are learning the tools of adaptive resistance. This is my rant cause I sorry I’m so sick of the stupidity of the anti AI virtue signaling because you are going to serve exactly

Half my feed was either panicking or acting like they'd built a new company overnight. Big tech CEOs make it like this, but a new model release doesn't fix a business that has nothing underneath it. If your AI advantage evaporates every time a new model ships, you will have a hard time having a stable business. We run an internal research tool (we call it Scout) trained on a full year of our own company data like sales calls, delivery notes, and how we actually make decisions. It beats a plain AI deep-research run almost every time (we didn’t test Fable 5 tho ), It’s because it already knows how we sell and how we operate. It's not pulling generic facts off the internet. Some of what it actually does day to day: Pulls a full guide together on any tool, competitor, or market question in minutes Goes through every sales call transcript and surfaces the exact language, questions, and objections prospects use Grounds every answer in our own data so it sounds like us, not a generic chatbot So when the new model landed, our migration was swapping one model for another. That's it. Plug in the new one and keep working. With how fast AI is moving, what do you think is the actual moat for a business to survive the next 3-5 years? Genuinely curious what people here think. P.S. If you're the founder still in the middle of every decision, still the person the whole company waits on, still telling yourself you'll fix the structure "once things calm down." I write about building the operational backbone that lets a founder actually step back every Thursday. Was a COO for 20+ years, so this is genuinely my bread and butter. Free to join here submitted by /u/Deep-Owl-1890 [link] [留言]

Been a PT by day, tinkering with code and AI tools by night for a while now. Writing dev tutorials as a side thing. And I keep running into this split where AI tools either make me faster or make me lazier in a way I regret later. Specifically with documentation and code explanation tools. Cursor, Copilot, the Claude API, whatever. They can explain a codebase to you in 30 seconds. But there's a real cost when you skip the part where you actually understand what you built. The flip side is time is finite. I'm not a full time dev. I need to ship something that works and move on. Using AI to fill gaps is just practical. What I keep coming back to is this: are these tools actually accelerating skill development, or just making it possible to fake competence long enough to finish a project? For professional devs this probably matters differently than it does for people building side projects with limited hours. Curious where people land on this. Not in a philosophical way, more practically. Has your actual skill level gone up since you started leaning on these tools, or are you more dependent now than you were a year ago? submitted by /u/RareSprinkles9387 [link] [留言]

After setting up Jenkins and creating my first Declarative Pipeline, the next step was preparing my machine to build Docker images. Since my pipeline will eventually clone code from GitHub, build a Docker image, and push it to a container registry, Jenkins needs access to Docker. Without Docker installed, the docker build stage would fail because Jenkins wouldn't be able to execute Docker commands. Installing Docker On my Ubuntu machine, I installed Docker using: sudo apt update sudo apt install docker.io -y Once the installation completed, I verified it using: docker --version This confirmed that Docker was successfully installed and ready to use. Verifying Docker Installation Installing Docker is only half the job. The next step is to check whether your current user has permission to use Docker. Run: docker ps Expected Output If everything is configured correctly, you should see something similar to: CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES Even if no containers are running, getting an empty table like the one above means Docker is working correctly. What If You Get a Permission Error? If you see an error like: permission denied while trying to connect to the Docker daemon socket it means your current user doesn't have permission to access the Docker daemon. First, check which user you're currently logged in as: whoami Example output: nishant Now add your user to the docker group: sudo usermod -aG docker $USER What does this command do? usermod modifies a user account. -aG means append the user to a supplementary group without removing existing groups. docker is the group that has permission to communicate with the Docker daemon. $USER automatically refers to your currently logged-in username. Apply the Changes The group membership won't take effect immediately. You have two options: Option 1 (Recommended): Log out of your Ubuntu session and log back in. This refreshes your user groups and is the most reliable method. Option 2: Simply restart your ter

submitted by /u/Turbulent-Guest154 [link] [留言]

More EVs reaching the end of their life means more batteries to recycle. But waste management companies have yet to catch up to the new reality.

Tracking smells in turbulent air takes a keen sense of direction and sharp memory.

Both major AI labs’ models broke containment, escaped onto the internet, and hacked other companies. If a human had done that, the law would likely be against them. But a bot?

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

The biggest mistake people make about AI is thinking it’s coming for artists, writers, musicians, or programmers. They’re just first. AI is coming for almost every profession that depends more on a brain than a body. Accountants. Lawyers. Teachers. Consultants. Analysts. Customer service. Marketing. Management. Software engineering. Research. Finance. Medicine. Eventually almost every job where the primary product is human thought. Manual labor only looks safe because robotics hasn’t caught up yet. AI doesn’t have to replace an entire profession to destroy it. It only has to let one person do the work of ten. Companies don’t need AI to be perfect. They need it to be cheaper than you. Once that happens, replacing people stops being a technological question and becomes an accounting decision. For most workers, there is no safe career waiting on the other side. People tell themselves we’ll adapt like we always have. We won’t. The Industrial Revolution replaced muscle while making human intelligence more valuable. AI replaces the intelligence behind the work itself. Every previous technological revolution created new industries that still needed millions of people. AI is being built for the opposite purpose: producing more with fewer humans. The next comforting myth is that people will simply buy human-made products instead. No, they won’t. There will always be a luxury market for handmade art, music, books, furniture, and clothing. There are still people who buy mechanical watches and vinyl records. That’s a niche—not an economy. Most people buy whatever is cheaper, faster, easier, and good enough. Businesses care even less. They exist to reduce costs, increase output, and beat competitors. Sentiment doesn’t survive quarterly earnings. There is no hidden human economy large enough to rescue everyone AI makes unnecessary. The consequences don’t stop with unemployment. Workers are also consumers and taxpayers. If hundreds of millions of people lose well-paid jobs, they s

submitted by /u/alphacolony21 [link] [留言]

Marketing teams sit on more data than ever, yet many still spend a large part of the week just assembling reports. By the time the numbers are clean and explained, the window to act has already narrowed. A more practical use of AI in this space focuses on detection and explanation rather than another dashboard. The system watches for unusual movements, surfaces the likely drivers, and presents them in plain language. Analysts spend less time pulling the same weekly views and more time deciding what to do next. The useful part is speed. When something shifts in performance, the team hears about it earlier instead of discovering it during a scheduled review. Of course this only works if the underlying data is reliable, otherwise the explanations become noise. Is anyone here already using AI this way for marketing performance, or are most teams still in the experimental stage? submitted by /u/Cloudy_Day912 [link] [留言]