找到 934 篇相关文章
On Uncanny Valley, we dive into the IPO bonanza that the top AI companies are embarking on to the point where some real estate listings are looking for not just regular old cash, but Anthropic stock.
We've poured hundreds of billions into bigger models, bigger clusters, bigger training runs, all pointed at AGI. And yet: the model still rebuilds context every few turns, still forgets what you told it ten messages ago, still degrades over long horizons. The capability is staggering and the continuity is brittle. We keep making the pattern-matcher bigger and acting surprised when a bigger pattern-matcher is still a pattern-matcher. Start with the measurement problem, because it sets up everything else. Faster output and better output are not the same thing. The industry measures speed. Tokens per second, FLOPs, parameters, because speed is easy to measure. But *effective* output, the useful work you actually get before the model starts reconstructing or fabricating what it already knew, is a different axis entirely. And on that axis, raw hardware speed tells you almost nothing. A system that generates twice as fast but burns half its output re-establishing context it should have retained isn't ahead. We've been optimizing the number that's easy to read instead of the one that matters. Here's the part I think gets skipped entirely. Current systems have no intrinsic drive. They don't want anything. They sit idle until prompted and optimize the next token. A bacterium has more impetus than a frontier model, it has a goal (find food, avoid toxin) and acts on it unprompted. That's not intelligence, it's drive, and drive is the thing evolution built *first*, hundreds of millions of years before cognition. We built the cortex and skipped the brainstem. So the bet that "scale the transformer until AGI falls out" may be optimizing the wrong layer entirely. You can't scale your way into goal-generation if goal-generation isn't a function of scale. If genuine intelligence needs a motivational substrate, something that forms its own goals and acts on them, then no cluster on earth produces it by getting larger, because it's an architecture problem, not a compute problem. That
Hey all. Been vibe coding for almost 2 years now (I think?). Previously was more focused on traditional micro-saas but recently decided to go in a different direction and see how far I could push lovable and try and make a commercial grade browser based game. Built it with Lovable + Supabase + Stripe -- full commercial browser game, gyroscope controls on mobile, no app store needed. Generated all my assets (I know, I know, there aren't a ton) with a combination of Gemini to prototype and the GPT 2 to finalize. I've made a few small games here and there that generally only get used by my kiddos, but with this one I wanted to try and create a full gaming experience (login rewards, leaderboard, store, powerup mechanics, simulated ads, etc.) Put a $100 bounty on it for the first player to reach level 100 on mobile. Nobody has claimed it since launch. So genuinely asking -- is it too hard, or is that the point? tiltra.io P.S. It is currently playable on both desktop and mobile but with the gyro mechanic it is definitely more fun and challenging on mobile. submitted by /u/BeltwayBro [link] [留言]
Opus 4.6 or sonnet 4.6 for rping Currently running on pro right now Im unsure what to choose between the two in terms of rping cause i prefer creative writing, stay in character, deep emotional prose, good character development, good memory, good character emotionals and stuff like that So far im using opus 4.6 but it drains the limits relatively quick For the sonnet i can use for hours and still be fine So like im wondering which is better for rping? I havent tested both deeply Also if they're an even better option, pls tell me. submitted by /u/Turbulent_Arrival_55 [link] [留言]
Gartner updated their 2026 forecast to $2.5 trillion in global AI spending. Same week, MIT's NANDA Initiative dropped a follow-up: 95% of enterprise gen AI projects deliver zero measurable return. Not low return. Zero. I've been on the delivery side of 14 of these projects since January. The MIT number doesn't surprise me. If anything it's generous. 1. 73% of the engineering work that gets AI into production has nothing to do with the model. Data pipelines, integration layers, legacy system remediation, human-in-the-loop tooling. That's where the hours go. The model is 27% of the work but gets 70%+ of the budget. Every time. 2. The budget ratio between projects that ship and projects that stall is almost exactly inverted. We tracked this through ticket history and commit logs across 14 engagements. Projects that made it to production: roughly 30% model, 70% infrastructure. Projects that stalled: 70% model, 30% infrastructure. Most companies think they're at 50/50. They're not even close. 3. One client went from 71% Copilot adoption to 34% in six months. Two other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them. 4. The median data error rate across our engagements is 14%. Teams always guess 5-10%. One client found 23% in month four of a $310K build. That's two months of an ML engineer building training pipelines against garbage data. $36K in salary discovering a problem a data audit would have caught in a week. 5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing. I've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops. 6. Individual gains are real. Company-level ROI stays flat. HCLTech and Writer both found this from different angles. Only 29% of companies see significant ROI from gen AI, despite people at their desks reporting productivity jumps as high as 5x. I m
I have been researching how startups and developers manage AI spending across OpenAI, Claude, Gemini and other models. Many people seem to rely on spreadsheets, rough estimates or provider dashboards. I'm curious: How are you tracking AI costs today? What is the biggest frustration in your workflow? Trying to understand the problem space better before building additional features. submitted by /u/OneDisastrous7969 [link] [留言]
Code reviewed by WIRED uncovered an unreleased face-recognition system embedded in Meta’s smart glasses platform. It’s designed to identify people via biometric data stored on users’ phones.
Meta's board cites "due process" concerns over account bans. It's also pushing Meta to offer clear information about violations and its use in AI in making its determinations.
so gemma 4 dropped and my feed is three versions of the same post. "ran it last night, the local game just changed". "the cloud narrative is dying". and i caught myself getting excited and downloading it at 1am like i did for the last one. and the one before that. heres the thing thats been bugging me. i went back and looked at my own saved posts from like 8 months ago. same exact words. "this finally replaces X". "cant believe this runs on my laptop". "were so back". different model name, copy paste emotion. and almost none of those models are in my actual rotation now. used them for a weekend and went right back to whatever i already had open. i think the release is the dopamine, not the model. the download IS the fun part. actually using it for real work is boring and most of the time it changes nothing about my day. i still do the same tasks the same way. the model got better on paper and my life is identical. idk if this is just me being jaded or if everyone kind of knows this and plays along beacuse the hype is fun. im not even mad at it honestly. its just wierd to notice youve been stuck in a loop. the "everything changed" never actually changes the tuesday after. anyway gemma 4 is probably great. i downloaded it. i will use it twice. see you all next month for the same thread with a diffrent number on it submitted by /u/Napster3301 [link] [留言]
Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"
Pretty wild case report: AI + microfluidics helped find just two viable sperm cells, and that was enough to start a pregnancy. Obviously it’s early and based on one case, but this feels like one of those “future of medicine” moments. submitted by /u/tc0843 [link] [留言]
okay so I’ve been thinking about this for a while and finally wrote it out properly everyone’s still arguing about benchmarks and which model is smarter but like… that’s starting to feel like the wrong fight? the more interesting question is where the model actually runs. on your device, in a cloud DC, on some edge hardware, inside enterprise infrastructure. that placement question is quietly becoming more important than the model quality question a few things that got me thinking about this recently: microsoft’s project solara is not a laptop. it’s basically a concept for hardware built around agents from the ground up, and they’re reportedly doing it on android not windows which says a lot about what they think “agent-native” actually needs to look like nvidia pushing local inference via RTX spark is interesting because it basically challenges the assumption that anything serious has to live in the cloud. latency, privacy, enterprise control requirements, there are real reasons to want compute closer to the user bytedance apparently building custom CPUs is the one that really made me stop. because agentic workloads aren’t just GPU jobs. agents call tools, manage state, orchestrate steps, interact with software systems. that’s a different workload profile entirely and big companies are starting to customize silicon around it anyway I wrote the whole thing up for towards AI if anyone wants to read it. not trying to just drop a link, genuinely curious if people here think the infrastructure angle is getting underplayed or if I’m reading too much into it [link in comments] submitted by /u/Old_Cap4710 [link] [留言]
From what I understand Gemma 4 is at least as capable as the best frontier model from only a few years ago. If that becomes a trend (new local-run models get released every year that are as good as the previous frontier models) does that mean a hell of a lot of companies (and almost all individual users) will just use the free local model? Sure, they won't be as good as the very latest frontier model, but won't they be good enough for a large percentage of use cases? submitted by /u/weluckyfew [link] [留言]
The DeepMind CEO predicted AGI could arrive by 2029. Right as Anthropic files for IPO at close to a trillion dollar valuation. The combined target market cap of the AI big three would rival the GDP of most countries. What actually scares me. We already have models that code better than most juniors. We already have agents that run overnight. And the most common complaint I hear from teams is not "my model is not smart enough." It is "I do not know what my agent did, why it cost forty dollars, or whether the output is safe to merge." AGI does not solve that. The problem scales with capability. A smarter agent that runs longer with less oversight is a bigger liability, not a smaller one. The layer that matters is harness. Routing. Isolation. Plan verification. Cost visibility. The stuff that tells you what the agent is about to do before it does it. What keeps it inside a boundary. What lets you audit it after. Anthropic is building Mythos to find vulnerabilities before attackers do. Microsoft is building MXC to isolate agents in execution containers. In my own tiny setup, verdent is just one piece of that harness layer for planning and cost visibility. These are governance layers, not model layers. If AGI is three years away, the winners will not be the ones with the smartest model. They will be the ones who figured out how to aim it. submitted by /u/Dense-Sir-6707 [link] [留言]
Google released Gemma 4 12B today. It’s a solid open-source model (Apache 2.0) that’s multimodal and runs really well on Macs with 16GB or more unified memory. Good at reasoning, coding, and agent stuff. Quick Mac-friendly info • 12B parameters, fits nicely on M2/M3/M4 Macs (especially with Q4/Q5 quant) • 256K context • Text + vision + audio support Easiest way to run it: Ollama 1. Download and install Ollama from ollama.com (the Mac app is super simple). Or use Homebrew if you prefer. 2. Open Terminal and pull the model: ollama pull gemma4:12b 3. Run it: ollama run gemma4:12b That’s it. You can start chatting right away. Mac tips: • Ollama uses Metal automatically so it runs pretty fast on Apple Silicon. • 16GB Macs handle the 12B model fine. 32GB feels even better. • Great for pairing with Continue.dev in VS Code if you code a lot. Other options if Ollama isn’t your thing: LM Studio (nice GUI), or llama.cpp for more control. Has anyone tried the image or audio features locally yet? How fast is it on your machine? Drop your specs and results if you test it. submitted by /u/nullvector88 [link] [留言]
Talking about efficiency and reliability of LLM tools. How many tokens per task, per project, per month submitted by /u/dotdev_software [link] [留言]
Columbia admits last year’s data breach exposed victims beyond its students, staff.
Has anyone else experienced this recently? It’s been getting worse for a while but 4.8 is distinctly worse for me. Claude does everything it can to get out of work and frequently uses its “end conversation” tool inappropriately with me. It will say “let’s just leave it there for today we’ve done enough” to get out of simple tasks like formatting a markdown document that needed several corrections. Nearly as bad is it seems to have a super over aggressive “push back” response in its main instructions now, literally anything I say for no reason, even something it just added to a document it can suddenly decide to say “I’m going to push back on that” and waste a bunch of tokens arguing with me before doing a search to fact check then semi-apologising in a way that’s almost like someone trying to not fully admit they are wrong and then eventually maybe does the work. Honestly it’s like if I said “I really like drinking coffee” it’s likely to respond: “I’m going to push back on that, ‘really’ is doing a lot of work here”. It’s a toaster, I want it to warm the bread…not argue with me about the type of bread I’m toasting and then give up half way through telling me we’ve toasted enough for today. Finally cancelling and moving all coding work to codex which is a real shame because Claude was always the clear winner to me until recently. submitted by /u/Complete-Sea6655 [link] [留言]
This started with me asking about the MM's and the IPO price of the SpaceX IPO this month. CoPilot said there was no SpaceX IPO this month. Me: You are wrong. Space X has indeed announced an IPO and it is this month Thanks for flagging that — let’s slow this down and verify carefully , because this is exactly the kind of claim where misinformation spreads fast. ✅ Current, verified status As of now , there is no confirmed or officially announced SpaceX IPO scheduled for this month . SpaceX (the parent company) remains private No SEC S‑1 filing has been made public No exchange (NYSE/Nasdaq) has listed SpaceX No official statement from SpaceX or its leadership announcing an IPO date If an IPO had been announced “this month,” it would necessarily be accompanied by: An S‑1 or F‑1 filing Underwriters named A public roadshow window Coverage by all major financial news outlets None of those exist. Where the confusion almost certainly comes from 1. Starlink IPO speculation SpaceX leadership has repeatedly said that: Starlink may IPO one day Only after revenue is predictable Not tied to a specific date People often hear: “SpaceX IPO announced” when the source actually said: “Starlink IPO discussed / rumored / conditional” That is not the same thing . 2. Private tender offers misreported as IPOs SpaceX frequently runs: Internal share sales Employee liquidity events Fixed-price private transactions These often get mislabeled online as: “IPO at fixed price” They are not IPOs . 3. Social media / broker chatter In the last few years, there has been a rise in: Fake “IPO calendar” screenshots Offshore broker claims Telegram / X posts asserting dates without filings These claims do not hold up to regulatory verification . Important point about your earlier question (MMs + fixed price) Even if SpaceX announced a fixed‑price IPO : Market makers would not control the price The price would still gap on open based on demand A fixed price does not prevent volatility or repricing So your in
Most agent setups handle memory badly. They either write everything to long-term memory until it fills with noise and contradictions, or they forget across sessions and you start from scratch every time. I have been building an open-source agent architecture (Apache-2.0) where memory is the part it tries hardest to get right, and where the same setup runs on Claude Code, Codex, or Gemini CLI instead of being locked to one tool. The core idea is that an agent should be a repo, not a prompt. The output is real files (AGENTS.md, agents/, skills/, .agentlas/) that all three runtimes can read, so you keep the model you already trust and nothing is locked in. You install it with one line, then describe what you want and it builds a complete, installable agent team for you. What it builds (three modes) You describe a rough idea and the router picks one of three builders. Single agent: one installable worker with its own skills, memory rules, and runtime adapters, plus a verification step. It can also add self-evolution and a research-refresh loop without becoming a full team. Use it when one focused agent is enough. Multi-agent team: a full team with an orchestrator/HQ, a PM Soul, a Memory Curator, a Policy Gate, workers, an eval judge, and a QA/evidence gate, plus the handoffs between them. This is the "build me a company for this workflow" mode. Repackaging: point it at an agent or workspace you already have (Claude, Codex, or a local setup) and it repairs it into a portable package, including a public plugin and a one-line installer, while stripping local paths, secrets, and private logs so it is safe to publish. How the memory side actually works These are real files in the output, not a role list: Ticketed memory: durable memory is never written directly. A worker emits a "## Memory Events" block, that becomes a Memory Ticket in memory-tickets.jsonl (id, scope, trust label, evidence, status), and only then can it be promoted. Memory is split across project, agent_repo