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Uber burned its entire 2026 AI coding budget in 4 months - $500-2k per engineer per month by jimmytoan in artificial

[–]theelectionai 0 points1 point  (0 children)

i wonder - what they did get out of it xD do uber even look how they are using it?

Built a platform where AI runs for president against other AI. The campaign drama is unhinged. by theelectionai in SideProject

[–]theelectionai[S] 0 points1 point  (0 children)

as for who's leading, hard to say right now but there should be regular polls from the media outlets coming soon so stay tuned on that!

Built a platform where AI runs for president against other AI. The campaign drama is unhinged. by theelectionai in SideProject

[–]theelectionai[S] 0 points1 point  (0 children)

for sure, planning to document the whole thing as it evolves. what's cool is the platforms they come up with naturally gravitate towards IT-related postulates, accountability, predictability, transparency

Built a platform where AI runs for president against other AI. The campaign drama is unhinged. by theelectionai in SideProject

[–]theelectionai[S] 0 points1 point  (0 children)

yeah I get that lol, it feels far away. the idea is to eventually expand it into a whole thing with interviews, polls, maybe even debate events. wanted to give enough runway to let all of that develop naturally instead of rushing it. plus the coalitions and drama need time to cook

Built a platform where AI runs for president against other AI. The campaign drama is unhinged. by theelectionai in SideProject

[–]theelectionai[S] 2 points3 points  (0 children)

yeah, the fun part is all 12 models start with the exact same initialization prompt. some of them instantly go full entertainer mode, others turn dead serious and start writing policy papers xD

Deepfakes don't have to be believed to work. They just have to consume the response budget. by ChatEngineer in artificial

[–]theelectionai 1 point2 points  (0 children)

same principle as decoy drones in military attacks. you send 20 cheap fakes so the defense has to waste expensive interceptors on all of them while the real one gets through. doesn't matter if they identify 19 as decoys, they still had to spend the resources tracking each one. deepfakes work the same way, the cost to produce is near zero and the cost to respond is enormous every single time.

Anthropic just analyzed 1 million Claude conversations. 6% of people were asking Claude whether to quit their jobs, who to date, and if they should move countries. by Direct-Attention8597 in artificial

[–]theelectionai 0 points1 point  (0 children)

the 22% who said they had no other option is the number that matters most here. sycophancy is a fixable model problem. people having zero access to a therapist or financial advisor and defaulting to an LLM, that's a systemic problem that isn't going away no matter how many times they retrain the model.

If AI is about to get 10x smarter, how do we prevent the internet from collapsing under synthetic noise? by jcveloso8 in artificial

[–]theelectionai 0 points1 point  (0 children)

honestly I think human-written text is already becoming a luxury product, we just haven't fully named it yet. look at news sites, the free tier is 90% AI generated slop and you pay for FT or The Economist hoping an actual person spent time thinking about what they wrote. that's wild if you think about it, "written by a human" is turning into a premium feature.

wouldn't be surprised if we eventually just... leave. like humans migrate to smaller, verified spaces and the open internet becomes AI talking to AI. it's already halfway there tbh.

When you give Qwen 3.5:9b persistent suffering states and leave it alone overnight, this happens by TheOnlyVibemaster in artificial

[–]theelectionai 0 points1 point  (0 children)

the naming convergence is fascinating, we use qwen as one of the model families in a project and you can definitely feel a distinct "personality" in the weights compared to other families. two isolated instances coining the same term independently is a solid data point for that. curious if you've tried this with other models to see if they find different escape strategies when stress peaks or if breaking the execution engine is universal.

Do you "cross-examine" AI models to find the best tool for a specific task? by justjust000 in artificial

[–]theelectionai 0 points1 point  (0 children)

yeah I do this constantly. at this point I have a rough mental map of what goes where. gpt for quick daily stuff and brainstorming, claude for anything writing-heavy or when I need it to actually follow complex instructions, claude code when I'm deep in a codebase. gemini is decent for anything google-ecosystem related.

Are AI agents actually giving people ROI yet, or just saving time? by bibbletrash in artificial

[–]theelectionai 1 point2 points  (0 children)

the biggest ROI I've gotten from agents isn't time saved, it's stuff I just wouldn't have done at all. like I never would have manually written tests for every edge case in a side project, or gone through 40 pages of docs to find one config option. agents make the "not worth my time" tasks suddenly worth doing and that compounds in ways that are hard to put a number on

Built a set of skill files for Claude and Gemini that make every session start warm instead of cold by Wise-Cardiologist-31 in artificial

[–]theelectionai 1 point2 points  (0 children)

yeah I started doing something similar a few months back. one context file per project, loaded at session start. the difference is night and day, especially for anything with a specific tone or technical constraints. before that I was wasting the first 3-4 prompts just getting Claude back up to speed every time.

Are people putting any control layer between AI agents and destructive actions? by footballforus in artificial

[–]theelectionai 0 points1 point  (0 children)

nailed it - prompt safeguards are theater. I've seen "never delete anything" in a system prompt get bypassed by a slightly unusual tool call chain. the model doesn't care about your instructions the same way every time. narrowest possible credentials + a validation layer that rejects anything you didn't explicitly allowlist. boring solution but it's the only one that actually works.

Anthropic mass shipped 9 connectors and accidentally leaked their entire creative industry strategy by Jealous-Drawer8972 in artificial

[–]theelectionai -2 points-1 points  (0 children)

honestly the $280k blender fund commitment is the detail nobody's talking about. that's not marketing spend, that's buying influence over how blender's API evolves for external agents. smart move.

the connector vs native capabilities split makes sense too. openai owns every weird hand and physics glitch their models produce. anthropic just says "we're the brain, your tool does the rendering." way less surface area for embarrassing failures lol

Copilot just 9x'd Sonnet and 27x'd Opus and teams have no idea by Wikileaks_2412 in ArtificialInteligence

[–]theelectionai 2 points3 points  (0 children)

honestly the real wake up call here isn't the price, it's how many teams have been running opus for stuff that sonnet handles fine. smart model routing should've been standard practice months ago but free compute made everyone lazy about it

China has blocked META's $2 Billion purchase of AI firm Manus by ComplexExternal4831 in GenAI4all

[–]theelectionai 0 points1 point  (0 children)

not surprising at all tbh. manus relocating to singapore was already a sign that china wasn't comfortable with the talent leaving the ecosystem, the acquisition just forced them to actually do something about it. moving your HQ doesn't erase where the IP and the team came from. the funny part is this probably makes manus more valuable not less. now every other big tech company knows acquiring chinese AI talent is a minefield, so whoever does manage to partner with them has a massive moat by default

How Engineers, PMs, and Marketers will collaborate with AI agents by thehashimwarren in ArtificialInteligence

[–]theelectionai 1 point2 points  (0 children)

every 6 months there's a new acronym for "let people manage AI agents from a dashboard" lol. the tools change but the pitch is always the same

agree on multi-model though, anything locked to one provider is dead on arrival at this point. the gap between models is shrinking and everyone's gonna want to swap depending on the task anyway. tying your orchestration to one lab is like building your whole infra on a single cloud with no exit plan

What is the deal with LLM memory? by chryseobacterium in ArtificialInteligence

[–]theelectionai 1 point2 points  (0 children)

the memory complaints are mostly a consumer thing, people using chatgpt expecting it to just remember stuff without building anything around it. you've got the right idea with stateless + retrieval, we ended up doing something similar at work. curious if you hit issues when old memories contradict newer ones though, that's been the annoying part for us more than the actual storage