Artificial Intelligence Reddit

Firm is one of a number of companies trying to foster the critical thinking, judgment and other traits needed to better work alongside AI submitted by /u/SpiritRealistic8174 [link] [comments]

Most teams that catch bad AI output catch it one instance at a time, someone notices something’s off, fixes that specific case, moves on. Works fine at low volume. Falls apart once output volume grows past what any person can actually look at, because you’re now sampling a tiny fraction of what’s happening and treating each catch as an isolated event instead of a symptom. What actually scales is logging failures with enough structure to look for shape, not just fixing them one by one. Which failures cluster around a specific input type. Which cluster around a specific length or complexity threshold. Which show up more after a certain kind of edge case. That turns individual bad outputs into a pattern you can actually design around, instead of an endless stream of one-off patches that never converges on anything. The resistance to doing this is understandable, it’s slower up front and doesn’t feel productive the way fixing a specific bad output does. But fixing instance by instance means the same failure mode keeps recurring in slightly different clothes, and nobody notices it’s the same underlying gap because each occurrence looks new in isolation. submitted by /u/ClickOk5811 [link] [comments]

submitted by /u/coolbern [link] [comments]

Been thinking about this all week and I keep landing on the same uncomfortable place. Hugging Face is where you go when you want AI without strings attached. Open weights, no vendor, no lock-in. That was kind of the whole point. NVIDIA reportedly just agreed to buy it for $12.9 billion. The company that manufactures the chips that run every major AI system now owns the platform where developers go to escape depending on any single company. I’m not saying NVIDIA will do something bad with it. Their track record with developer communities is actually pretty decent. But the architecture of the open source AI ecosystem just changed and most people haven’t registered it yet. Meanwhile ChatGPT launched ads across 31 European countries last Sunday. Free and Go tier only, paid users are untouched. OpenAI says the ads don’t influence answers. Probably true today. The question worth asking is what happens to that promise when ad revenue becomes load-bearing for the business. Sam Altman called advertising a “last resort” in 2024. It’s now the strategy in 40 countries. And then there’s the McKinsey number that got buried under the other two stories. 32% of companies skipped a software purchase this year because an AI agent built the solution internally instead. If you sell software that’s a direct hit to your pipeline. If you buy software that’s a budget line your finance team hasn’t optimized yet. Three different moves. All pointing the same direction. Which one actually changes something for how your organization operates? submitted by /u/Dapper-Tale-4021 [link] [comments]

submitted by /u/motherjonesmag [link] [comments]

Well, its named MoM, and it means Mixture of Models. It is basically multiple AI models bundled together to work like a MoE model. Here’s the link: https://github.com/nanoOperator/MoM-AI Check it out, it is not promotional, fully open-source and for the community. submitted by /u/Few_Dragonfruit_6729 [link] [comments]

When an agent can make decisions and execute actions across multiple systems, should every step be traceable, or is the final outcome enough? I am curious how people see this. submitted by /u/Bahog_veesong [link] [comments]

I’m currently paying for Claude and using it to help me make some fairly complex macros in MacroDroid. I’m not a programmer, but Claude has been really good at understanding what I’m trying to do and turning it into the right MacroDroid setup and JSON. I’m using Opus 5 rather than Fable 5 because Fable 5 seems to use up my limit much quicker. The problem is that I keep hitting the usage limit with Claude and then I’m told I have to wait around three hours before I can carry on. So I’m thinking about trying something else. My two main questions are really simple: Which AI is best for coding? And which one lets you use it for the longest without hitting a limit? I’m looking at the normal paid plans around £18–£20 a month. I’m not paying £100 or £200 a month for an AI. I know Claude is meant to be one of the best for coding, which is why I chose it, but I’d be happy using something that’s slightly worse at coding if it means I can actually keep using it throughout the day without constantly getting locked out. For anyone who uses AI a lot for coding, what would you recommend? Which one is actually good at coding AND lets you use it for hours without constantly hitting a limit? That’s probably my biggest issue with Claude at the moment. submitted by /u/quat1e [link] [comments]

Don’t get me wrong I’m not against AI in any shape or form but lately I feel like people around me just can’t stop talking about it and it is driving me crazy. I can’t escape it. Work, friends, family everybody keep telling me about AI, how it’s dangerous, how it’s great, how it works, how society is gonna collapse, how it’s gonna cure cancer, how Chat jipiti is better than this guy Claude… Anyone feel the same? I know I can’t be the only one. submitted by /u/Low-Faithlessness140 [link] [comments]

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Sony Music Publishing and Warner Chappell filed suit against Anthropic, Dario Amodei, and co-founder Benjamin Mann on August 28. What’s unusual is that the underlying facts aren’t in dispute anymore. Last September, in the Bartz case, a federal judge ruled that training an AI model on copyrighted text was legal, but downloading the training copies via piracy was not. Anthropic settled that case for $1.5 billion after admitting Mann personally torrented over five million books from Library Genesis in 2021, and staff pulled two million more from Pirate Library Mirror in 2022. Sony and Warner’s complaint cites those exact same downloads, now tied to MusixMatch and LyricFind lyric datasets. They’re not asking a court to rule on anything new, they’re applying a ruling that already exists to a different set of copyrighted works. Statutory damages run $150,000 per work, so the number could dwarf the book settlement depending on how many songs are in scope. What I don’t have a good answer for: once a company settles one IP class action over a specific data-acquisition method, does that admission become effectively permanent exposure for every other rightsholder whose work touched the same pirated corpus? Is there a legal mechanism that closes that door, or is Anthropic just going to keep getting sued by whoever’s catalog turns up in the same torrent logs? submitted by /u/Servola-Journal [link] [comments]

submitted by /u/002Chris [link] [comments]

The fastest-growing products today are open source. Tools like PostHog, Supabase, n8n, Postiz, or Resend have supercharged their growth by being extremely transparent. Their growth is coming from agents like Claude, ChatGPT, and Hermes, as they can discover, use, recommend and even contribute back. I took some time to review how these tools manage their open source and cme up with 5 best practices followed by these companies to make your open source agentic ready… Some are existing standards that became even more important, and others are specific for AI agents. Keep It Simple: Use clear naming and simple repo structures so agents can quickly understand what the product does and where things live. Write Docs for Agents: Use README, AGENTS.md, CLAUDE.md, skills, robots.txt, and llms.txt to give agents clear instructions and context. Give Agents a Way to Use the Product: APIs, MCPs, CLIs, SDKs, examples, and templates so agents can interact with the product directly. Make It Easy to Run: Make setup simple, support self-hosting when relevant, document required keys, and make licensing and product boundaries clear. Make Contributing Easy: Define contribution rules, testing, reviews, and AI-assisted contribution policies so agents can make valid changes. Main Takeaways: Monorepo is the most optimal configuration Agentic docs (Agents.md, Claude.md, llms.txt, robots.txt, skills) should be part of the repo A setup designed for machines removes friction Interfaces (APIs, MCPs, CLI, SDKs) turn every product actionable quickly and into infrastructure. You don’t need to open-source everything, just define the boundaries perfectly Examples and templates are distribution not only on boarding Read the full article here. submitted by /u/santanah8 [link] [comments]

I have been trying to think seriously about this for the last 5 months or so. Before this, my biggest worries about AI were the future of work, carbon footprint of rapidly scaling infrastructure, and financial speculation. But the Mythos incident, the Hugging Face incident, and spending a lot of time around AI safety/Rationality/EA spaces has led me to rethink my beliefs considerably. I’m 24 and I’m highly concerned about whether I and my civilisation is still around on my 40th birthday, or even my 30th. (Though I’m much more concerned about near-term effects of bad human actors using powerful non-superintelligent frontier AI to do dangerous things.) At the same time, I acknowledge that I’m a very anxious, neurotic person, and that these AI safety spaces seem to attract people with pessimistic worldviews. I am not the intellectual peer of the foremost AI safety thinkers in the world, not by a longshot, but I do recognise unfalsifiability when I see it. Whatever argument you give a doomer as to why doom might not happen, they can always come up with a reason why your argument doesn’t work, often boiling down to “Big-S ASI is omniscient and basically omnipotent”. But hey… they’re possibly not wrong. I want to calibrate my timelines and doom probabilities holistically, which means I need to exit those spheres for a moment and ask other AI spaces (like r/artificial) what they think. What do you think? submitted by /u/PhiliDips [link] [comments]

Been reading Ghost Work (Gray & Suri) and it got me wondering about a few things, not an expert on this, just what came up while reading: Has AI’s “human-in-the-loop last mile” actually shrunk since the book came out in 2019, or did it just move to new tasks (like sorting AI-generated content from human-made) in 2026? What is human judgment doing now in these last-mile tasks that current AI models genuinely can’t, and is that durable, or just a capability gap that’s closing? Ghost Work is a 2019 book by anthropologist Mary L. Gray and computer scientist Siddharth Suri about the hidden human labor propping up things we assume are “automated”, content moderation, search ranking, data labeling, that kind of thing. Their term for it is “ghost work,” work a customer thinks an algorithm is doing, but a real person is actually doing it, usually for very little pay and no real protections. submitted by /u/Realistic-Drag-8025 [link] [comments]

Working on repetitive ‘AI Training’ made me reflect on the future of work I am blown away by what we are training some of the specialized models to do. For example, as a consultant, a good percent of my time was spent in creating ‘presentation ready’ PPTs – essentially eye-candy that was formatted neatly using template. The models I am training can do this and more in minutes! Taking detailed prompts, these models can create 3-6 versions of decks that we humans can simply mix-match and reuse The money on these gigs are decent – $50-100 to review and sort through output from these models to help ‘train’ them to get better I was focused on my specialization (technology) and on the projects we had Lawyers, Medical professionals and other specialists too These ‘AI training’ gigs are not a regular source of income since the projects start and end rather abruptly and one has to be diligent while working on such project tasks or get offboarded since their ‘AI Agents’ will be watching your screen. I can clearly see these models taking away the job of ‘junior’ consultants and entry level specialists submitted by /u/Mo_h [link] [comments]

A Public Research Problem — 2026-08-31 TL;DR — we can build 10,000–30,000 weak classifiers in minutes, but we cannot reliably find the small subset whose majority vote is best. This is an NP-hard combinatorial problem. We publish the score corpora, the reference search tool and the reference results. We invite the community to supply better subset-search algorithms. ​ 1. The Problem — Exact Formulation 1.1 Input A library ℒ = {m₁, …, mN} of N trained classifiers (“members”). In our corpora N ∈ {1,152; 5,760; 10,080; 30,240}. For every member mi and every test sample xt (t = 1 … 10,000 on Fashion-MNIST) a score vector si(xt) ∈ ℝC over the C classes. The prediction is argmaxc si(xt)c. The true labels yt of the test set. 1.2 Ensemble decision rule (majority vote) For a subset S ⊆ ℒ, the ensemble prediction is the sum of the per-member argmax votes: pred_S(x_t) = argmax_c sum_{m_i in S} 1[ argmax_c’ s_i(x_t)_{c’} == c ] 1.3 Objective and constraints Objective: maximize ensemble accuracy on the test set acc(S) = (1/T) · Σt=1..T 1[predS(xt) = yt] Constraint: |S| ≤ K with K typically 12–100. Every member costs DRAM bit-mass on the target chip; the accuracy-per-member efficiency acc/|S| matters. Generalization: the search must not overfit the test set — in practice the metric is evaluated on a hillclimbing set and checked on held-out data (see §4.4). 1.4 Complexity Brute force over all C(N,K) subsets is infeasible: C(30,240; 12) ≈ 1040. The problem is a special case of ensemble/feature subset selection, which is NP-hard (selecting the best subset of an ensemble; see §4.5). All practical approaches are heuristics: greedy forward selection, beam search, 2-opt exchange, bagged selection, pruning. ​ … read more at: https://forward-prop.nhi1.de/papers/ensemble-selection-challenge.html submitted by /u/aotto1968_2 [link] [comments]

According to him, the technology does not change character geometry or redraw their appearance. Instead, it uses existing data to enhance lighting and detail especially on faces and hero models. Among the most noticeable improvements: — significantly more detailed skin and faces; — more realistic skin lighting; — enhanced ambient occlusion; — shadows from hats, helmets, hoods, and small details like buckles and bags; — more pronounced and darker shadows within hair; — minor improvements to shadows and textures of the environment and vegetation. Vavra says that as a result, characters look much closer to how the developers originally intended them. And, in his opinion, this is by no means “AI-slop.” submitted by /u/ImpressiveJicama7141 [link] [comments]

submitted by /u/Angman_Dutt [link] [comments]

Google says its 2026 Australia and New Zealand Google for Startups Accelerator has selected 15 startups. The 10-week, equity-free hybrid program is aimed at Seed and Series A companies using AI and machine learning, and is meant to help them scale rapidly and responsibly. The list includes Blunge, an AI design agent; Mentana, an autonomous supply chain manager; and Unseen, a trust layer for commercial real estate. The listed startups are framed around specific workflows such as design, research, supply chain, inventory, manufacturing, and commercial real estate. My read is that the interesting unit here is the domain workflow, not a general chatbot. Source: https://blog.google/intl/en-au/company-news/google-for-startups-accelerator-introducing-our-2026-australia-new-zealand-ai-cohort/ submitted by /u/Codeblix_Ltd [link] [comments]

I’ve been noticing a strange paradox with AI-assisted coding. A few years ago, if you had an idea for a software service, building it was the hard part. You needed months of development, a decent team, money, infrastructure, and a lot of specialized knowledge. Today, one competent developer using AI can build in days or weeks what might have taken a small team months. You would think this would lead to an explosion of new software products. But I’m starting to wonder if the opposite is happening. When something is difficult to build, building it creates value. There’s a barrier to entry. You can spend six months creating something and reasonably believe that thousands of other people aren’t going to reproduce it next weekend. Now imagine you have a great SaaS idea. You spend two weeks building it with AI. Great. But so can everyone else. And if the idea succeeds, competitors can inspect what you did and build something similar incredibly quickly. The technical moat is disappearing. That changes the psychological equation. Why spend months polishing a product when you know the implementation itself has almost no scarcity? AI may have dramatically reduced the cost of building software, while simultaneously reducing the incentive to build software. Maybe the scarce thing is no longer the ability to create the product. Maybe it’s distribution, brand, proprietary data, network effects, domain expertise, or simply having customers before you start. In other words, we may be entering a world where software becomes almost free to create, but increasingly difficult to turn into a business. Does anyone else feel this? Are you building more side projects because of AI, or have you actually become less motivated because everything now feels trivially reproducible? submitted by /u/niosurfer [link] [comments]

Amazon announced this week that Mechanical Turk closes on September 30 after 21 years. Bezos originally called it “artificial artificial intelligence”. the joke being that it handed humans the tasks computers couldn’t do yet. 500 000 people at peak, a few cents a task, labelling images and transcribing audio. Those labels trained the models. the models got good enough to do the labelling. the platform is now closing lol. But the part that got me is a 2023 EPFL study finding somewhere between a third and half of MTurk workers were already using LLMs to complete their tasks. so at the end you had humans pretending to be machines, on a platform designed to make humans look like machines, quietly using machines to do it. and the companies buying that work thought they were paying for human judgment. Which raises something I can’t resolve about my own work. I produce video with avatars instead of filming using different AI models such Argil and Seedance among many others and clients get a talking head that never existed. the honest version of what I sell is “this used to cost a filming day and now it doesn’t,” and everyone in the chain knows. MTurk’s version was the same trade with the disclosure removed at every layer. Amazon selling human judgment as an API, workers selling model output as human judgment. For me the tech was never the dishonest part, however the layer where someone stops saying what it is, that’s the part. Anyway, 500 000 people had accessible flexible income and on September 30 they don’t. that deserves more than a shrug about progress. submitted by /u/dettol99perc [link] [comments]

submitted by /u/coolbern [link] [comments]

Sony Music Publishing and Warner Chappell allege that Anthropic used mass torrenting, scraping, and downloading to train Claude. Anthropic disputes the claims and says it will defend itself. A fine could simply become the cost of doing business. But forcing a company to discard or retrain a model could reshape the entire AI industry. What would actually be fair here: licensing fees, damages, or retraining from scratch? submitted by /u/Content-Cheetah-6958 [link] [comments]

The idea: Agents keep the conversation history as part of their input while they reason. SKILL.state proposes to replace that with a structured representation of the current state, and the latest observation. While the agent reasons through the problem, it writes information it deems useful for future steps into the state. Then it discards the conversation history. So the input size remains roughly the same as the session goes. They ran a 100-step benchmark with Gemini-3-Flash: SKILL.state: 0.94 accuracy using 65k tokens LangGraph-style stateful baseline: 0.91 accuracy using 1.1m tokens Caveat: This works best if the agent can understand what it will need in the future steps, otherwise that information will not be written, so it’ll have to retrieve it again. Link to the paper: https://arxiv.org/abs/2608.26263 submitted by /u/hakansan [link] [comments]