Tag: Reddit

if so, what would these jobs look like? submitted by /u/AppropriateHamster [link] [comments]

Shazeer to openai, then John Jumper (the alphaFold nobel guy) to anthropic, plus Adler and Pritzler out the same door within a week. Every time one of these drops the framing is google is bleeding. I think people are reading it backwards. If the people who actually trained the thing can leave and instantly matter at a competitor, the weights were never the asset. The judgment about how to steer a model, what to eval it on, where it breaks, that stuff lives in heads not in checkpoints. Hardware you can buy. That you cannot. What it means for the rest of us is simpler than the talent drama. If capability is going to keep walking between labs every few months, betting your whole stack on one provider's model is a bet on that lab keeping its people, which is the one thing you cannot control. I...

Anthropic dropped their June 2026 Economic Index today and buried inside the survey data is something that should be making headlines: Over a third of respondents (9,700 actual Claude users, linked to real usage data) believe AI will be capable of handling most or nearly all of their work tasks within the next year. Not “some tasks.” Not “help me write emails.” MOST of their work. And here’s the part nobody wants to talk about: the people who delegate the most to AI are the MOST optimistic about their job prospects. Meanwhile entry-level workers are the ones most worried about displacement. Senior devs and managers? Thriving. Junior colleagues? Everyone in the survey is more worried about them than themselves. The data also shows AI autonomy is measurably higher on Claude Code than o...

Most of the open vs closed talk here is about whether you can run the thing on your own hardware. fair, that's the obvious draw. but the part i think gets slept on is that open weights mean you can actually post train on top of the base, not just run inference. With a closed api you're renting intelligence. you can prompt it, you can rag around it, but you can never make it yours. you cant fine tune the actual weights for your domain, you cant distill it down, you cant freeze a version and own it forever. You're permanently downstream of whatever the provider decides. I saw some post about people post training their own models on top of glm-5.2 now that its open weight, and that framing stuck with me more than the benchmark numbers did. a frontier-ish base you can legally build on changes ...

Solo project, sharing it here for the AI angle rather than the subject matter. I fine-tuned Llama 3.1 8B (QLoRA, single T4) on the complete works of a 19th-century author whose corpus is fully public domain. The interesting problem wasn't the domain — it was trying to get a small model to cite its source (book, chapter, item) on every answer instead of just asserting things confidently. What I learned, which might be useful to others doing domain fine-tunes: - Teaching the *format* of citation is easy. Teaching *correct* citation is hard. The model reliably produces "Source: [Book], chapter X, item Y" — and the concept is usually right, but the exact number is often wrong. It learned the shape of grounding without the precision. - That gap is exactly why I run the production version as RAG...

The last few days, I've been getting obviously AI robocallers trying to sell me Medicare plans. (I'm not old enough for Medicare for another 20 years.) Sometimes it's a male voice, sometimes female. Always a different name. They've added a little trick where they start their speech then cough or sneeze, then say "Sorry about that," or a similar apology then continue. But if you try to interrupt them, they just keep talking, so you know it's AI. And they do the cough/apology in EVERY call, male or female voice, in just about the same spot. It's really annoying, and borderline offensive that they are trying so hard to pretend to be human. submitted by /u/PleasantCandidate785 [link] [comments]

For me, it would probably be search, writing assistance, and productivity tools. I'm curious-what Al-powered tool do you use most often without even thinking about it? submitted by /u/Sandesh_jagtap [link] [comments]

Here’s the full timeline: -June 9: Anthropic releases Claude Fable 5, their most powerful public model ever (Mythos-class with safeguards) -June 12: US government issues an export control directive at 5:21 PM, ordering Anthropic to cut off access to ALL foreign nationals. Model goes offline worldwide within 90 minutes -The reason? Amazon engineers reportedly found a narrow jailbreak that could bypass Fable’s cybersecurity classifiers -Anthropic complied but publicly pushed back, calling the action unfair -Trump met Dario Amodei at the G7 and softened his stance, but the directive was never officially lifted -June 26 (today): Congressional deadline for Commerce Secretary Lutnick to respond in writing about the export controls Prediction markets are pricing ~57% odds of restoration be...

I have worked with most all of Anthropics LLM's for development, but hands down Opus 4.8 has caused me more grief, aggravation, and it lies in every thing it does - especially near context mid-load and if you're doing deterministic work with no heuristics constraints you can't trust a thing out of it. So I stopped using it a while back, but today I had to do a container rebuild and in VS it slipped back into Opus 4.8 from Sonnet. And without even realizing the switch happen I could tell about a 1/3 of the way in into developing complex code it started arguing with me - I was about to loose it when I remembered the crap from the past and sure enough when I check the model... well you get the picture.... I was wondering if anyone else had similar experience with Opus 4.8 too? submitted b...

Some of our customers noticed Inter-1 (our omni-modal social-signal model) would occasionally "hear" a quote that didn't exist. Feed it a video with zero audio and ask what was said, and it would sometimes report: "Yeah, Friday at five." Verbatim. Same line, every time. We assumed it had to be baked into the training data somewhere, so we went looking everywhere: 30,960 training records with datetime mentions → zero hits on the phrase 4,603 video transcripts → zero hits ~800 inference probes, 584 storage objects → zero hits Turns out the phrase was sitting in our own system prompt — a worked example we'd written to show the model the expected output format, buried in a version our GEPA prompt-optimizer had shipped. But that only explained where the words came from, not why the model woul...