Tag: Reddit

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

bro google just casually released a 12 billion parameter multimodal model that runs on 16gb of ram like… your macbook pro can run this. no cloud. no api calls. no monthly bill. it’s encoder-free, handles images and text, apache 2.0 license so you can do whatever with it commercially the “cloud is the only way” narrative is dying fast. on-device AI is not a gimmick anymore, it’s where the serious money is going submitted by /u/NewMuffin3926 [link] [comments]

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 manu...

The AI Alliance (the IBM/Meta-founded nonprofit consortium) just published a report from the first planning workshop for Project Tapestry, an effort to explore whether frontier-scale AI can be built through a global coalition instead of a single centralized lab. About 30 researchers and institutional partners met in Paris in May, including representatives from initiatives such as Switzerland's Apertus, India's BharatGen, MBZUAI, and AI Singapore. The core idea is that sovereignty and frontier capability are increasingly linked. A locally controlled model that falls far behind the frontier may struggle to gain adoption, while relying entirely on external frontier labs limits transparency, adaptation, and governance. Tapestry is exploring a model where participants contribute data, compute, ...

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

The tooling is abstracting faster than people's mental models are updating. Been playing around with a few agent builders recently and what keeps standing out is how much previously manual orchestration is basically configuration now. Memory, tool calling, browser actions, structured outputs, workflow routing. You used to build this stuff manually. Now you're mostly wiring it together. Which makes "can this be built?" a much less interesting question for a lot of use cases. The harder problems now feel operational. Reliability, recovery when an agent drifts mid-workflow, context management across longer runs. Controlling behavior without supervising every step. Capability honestly isn't the bottleneck anymore imo. It's trust. Can these systems actually become reliable enough that people st...

If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated. But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it. Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can pr...

There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another "AI cracked X" headline, my first thought is...

Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by "silent bias." Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate's experience wasn't relevant despite praising that exact same experience on the baseline resume. We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved...